From 96f4808c02c57ba8bec3a7df55c56072cf7e7dfa Mon Sep 17 00:00:00 2001 From: Brian Gallagher Date: Mon, 29 Sep 2025 20:44:39 +0100 Subject: [PATCH 1/7] Add docker backend Signed-off-by: Brian Gallagher --- kubeflow/trainer/__init__.py | 4 + kubeflow/trainer/api/trainer_client.py | 10 +- kubeflow/trainer/backends/docker/__init__.py | 0 kubeflow/trainer/backends/docker/backend.py | 536 +++++++++++ .../trainer/backends/docker/runtime_loader.py | 110 +++ kubeflow/trainer/backends/docker/types.py | 52 + .../local_runtimes/torch_distributed.yaml | 25 + pyproject.toml | 5 + uv.lock | 892 +++++++++--------- 9 files changed, 1210 insertions(+), 424 deletions(-) create mode 100644 kubeflow/trainer/backends/docker/__init__.py create mode 100644 kubeflow/trainer/backends/docker/backend.py create mode 100644 kubeflow/trainer/backends/docker/runtime_loader.py create mode 100644 kubeflow/trainer/backends/docker/types.py create mode 100644 kubeflow/trainer/config/local_runtimes/torch_distributed.yaml diff --git a/kubeflow/trainer/__init__.py b/kubeflow/trainer/__init__.py index 1fce8e0f5..c1190ce82 100644 --- a/kubeflow/trainer/__init__.py +++ b/kubeflow/trainer/__init__.py @@ -15,6 +15,9 @@ # Import common types. from kubeflow.common.types import KubernetesBackendConfig +# Import the Kubeflow Trainer client. +from kubeflow.trainer.api.trainer_client import TrainerClient # noqa: F401 +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient @@ -64,5 +67,6 @@ "TrainerClient", "TrainerType", "LocalProcessBackendConfig", + "LocalDockerBackendConfig", "KubernetesBackendConfig", ] diff --git a/kubeflow/trainer/api/trainer_client.py b/kubeflow/trainer/api/trainer_client.py index cb96837dd..e6c75c987 100644 --- a/kubeflow/trainer/api/trainer_client.py +++ b/kubeflow/trainer/api/trainer_client.py @@ -17,6 +17,8 @@ from typing import Optional, Union from kubeflow.common.types import KubernetesBackendConfig +from kubeflow.trainer.backends.docker.backend import LocalDockerBackend +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig from kubeflow.trainer.backends.kubernetes.backend import KubernetesBackend from kubeflow.trainer.backends.localprocess.backend import ( LocalProcessBackend, @@ -31,7 +33,11 @@ class TrainerClient: def __init__( self, - backend_config: Optional[Union[KubernetesBackendConfig, LocalProcessBackendConfig]] = None, + backend_config: Union[ + KubernetesBackendConfig, + LocalProcessBackendConfig, + LocalDockerBackendConfig, + ] = None, ): """Initialize a Kubeflow Trainer client. @@ -52,6 +58,8 @@ def __init__( self.backend = KubernetesBackend(backend_config) elif isinstance(backend_config, LocalProcessBackendConfig): self.backend = LocalProcessBackend(backend_config) + elif isinstance(backend_config, LocalDockerBackendConfig): + self.backend = LocalDockerBackend(backend_config) else: raise ValueError(f"Invalid backend config '{backend_config}'") diff --git a/kubeflow/trainer/backends/docker/__init__.py b/kubeflow/trainer/backends/docker/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/kubeflow/trainer/backends/docker/backend.py b/kubeflow/trainer/backends/docker/backend.py new file mode 100644 index 000000000..c1ca41726 --- /dev/null +++ b/kubeflow/trainer/backends/docker/backend.py @@ -0,0 +1,536 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +DockerBackend +------------- + +Local execution backend for `CustomTrainer` jobs using Docker containers. + +Key behaviors: +- Multi-node jobs: one container per node connected via a per-job Docker network. +- Entry script generation: we serialize the user's training function to a small + Python file and invoke it inside the container using `torchrun` (preferred) or + `python` as a fallback. +- Runtimes: we use `config/local_runtimes` to define runtime images and + characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime + is provided. +- Image pulling: controlled via `pull_policy` and performed automatically if + needed. +- Logs and lifecycle: streaming logs and deletion semantics similar to the + Kubernetes backend, but tailored to Docker. +""" + +from __future__ import annotations + +from collections.abc import Iterator +from dataclasses import dataclass +from datetime import datetime +import logging +import os +from pathlib import Path +import random +import shutil +import string +import uuid + +try: + import docker # type: ignore + from docker.models.containers import Container # type: ignore +except Exception: # pragma: no cover - optional dependency, validated at runtime + docker = None # type: ignore + Container = object # type: ignore + +from kubeflow.trainer.backends.base import ExecutionBackend +from kubeflow.trainer.backends.docker.runtime_loader import ( + get_local_runtime, + list_local_runtimes, +) +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types + +logger = logging.getLogger(__name__) + + +DOCKER_LABEL_PREFIX = "trainer.kubeflow.ai" + + +@dataclass +class _Node: + name: str + container: Container + status: str = constants.TRAINJOB_CREATED + + +@dataclass +class _Job: + name: str + created: datetime + runtime: types.Runtime + network_name: str + nodes: list[_Node] + workdir_host: str + + +class LocalDockerBackend(ExecutionBackend): + def __init__(self, cfg: LocalDockerBackendConfig): + if docker is None: + raise ImportError( + "The 'docker' Python package is not installed. Install with extras: " + "pip install kubeflow[docker]" + ) + + # initialize docker client (env-based resolution by default) + if cfg.docker_host: + self.client = docker.DockerClient(base_url=cfg.docker_host) + else: + self.client = docker.from_env() + + self.cfg = cfg + self._jobs: dict[str, _Job] = {} + + # ---- Runtime APIs ---- + def list_runtimes(self) -> list[types.Runtime]: + return list_local_runtimes() + + def get_runtime(self, name: str) -> types.Runtime: + return get_local_runtime(name) + + def get_runtime_packages(self, runtime: types.Runtime): + """ + Spawn a short-lived container to report Python version, pip list, and nvidia-smi. + We follow the Kubernetes backend semantics: create a one-off job and print outputs. + """ + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + command = ( + "bash", + "-lc", + "python -c \"import sys; print(f'Python: {sys.version}')\" && " + "(pip list || echo 'pip not found') && " + "(nvidia-smi || echo 'nvidia-smi not found')", + ) + + logs = self._run_oneoff_container(image=image, command=command) + print(logs) + + # ---- Train/Jobs APIs ---- + def train( + self, + runtime: types.Runtime | None = None, + initializer: types.Initializer | None = None, + trainer: types.CustomTrainer | types.BuiltinTrainer | None = None, + ) -> str: + if runtime is None: + runtime = self.get_runtime("torch-distributed") + + if not isinstance(trainer, types.CustomTrainer): + raise ValueError("DockerBackend supports only CustomTrainer in v1") + + # Generate job name + job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] + + # Create per-job working directory on host where we'll place the generated script + # Prefer a location under the user's home directory to ensure Docker Desktop file + # sharing includes it by default on macOS/Windows. + if self.cfg.workdir_base: + base = Path(self.cfg.workdir_base) + base.mkdir(parents=True, exist_ok=True) + workdir = str((base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + else: + home_base = Path.home() / ".kubeflow_trainer" / "localdocker" + home_base.mkdir(parents=True, exist_ok=True) + workdir = str((home_base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + _ = self._write_training_script(workdir, trainer) + + # Provision per-job network + network_name = self._ensure_network(job_name) + + # Resolve image and pull if needed + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + # Build base environment + env = dict(self.cfg.env or {}) + if trainer.env: + env.update(trainer.env) + + # Construct pre-run command to install packages, if any, inside the container. + pre_install_cmd = "" + pkgs = trainer.packages_to_install or [] + if pkgs: + # Respect pip_index_urls ordering (index-url then extra-index-urls) + index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) + main_idx = index_urls[0] + extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) + quoted = " ".join(f'"{p}"' for p in pkgs) + pre_install_cmd = ( + "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " + f"--index-url {main_idx} {extras} {quoted} && " + ) + + # Create N containers (one per node) + num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 + containers: list[_Node] = [] + + for rank in range(num_nodes): + container_name = f"{job_name}-node-{rank}" + + # torchrun rendezvous across nodes using network alias + # We use a simple host:port rendezvous; rank 0 acts as master. + master_addr = f"{job_name}-node-0" + master_port = 29500 + + # Prefer torchrun; fall back to python if torchrun is unavailable. + # IMPORTANT: Only add torchrun flags when torchrun is present. If we fall back to + # python, run the script directly without torchrun-specific arguments to avoid + # immediate failure and short-lived containers. + entry_cmd = ( + f"{pre_install_cmd}" + "if command -v torchrun >/dev/null 2>&1; then " + f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " + f" --node_rank={rank} --rdzv_backend=c10d " + f" --rdzv_endpoint={master_addr}:{master_port} " + f" {self.cfgd_workspace_path()}train.py; " + "else " + f" python {self.cfgd_workspace_path()}train.py; " + "fi" + ) + + full_cmd = ("bash", "-lc", entry_cmd) + + labels = { + f"{DOCKER_LABEL_PREFIX}/trainjob-name": job_name, + f"{DOCKER_LABEL_PREFIX}/step": f"node-{rank}", + } + + # Bind mount the host workdir into the container working dir + binds = { + workdir: { + "bind": "/workspace", + "mode": "rw", + } + } + + container = self.client.containers.run( + image=image, + command=full_cmd, + name=container_name, + detach=True, + working_dir="/workspace", + network=network_name, + environment=env, + labels=labels, + volumes=binds, + auto_remove=False, # we control removal in delete_job + ) + + containers.append(_Node(name=container_name, container=container)) + + self._jobs[job_name] = _Job( + name=job_name, + created=datetime.now(), + runtime=runtime, + network_name=network_name, + nodes=containers, + workdir_host=workdir, + ) + + return job_name + + def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: + result: list[types.TrainJob] = [] + for job in self._jobs.values(): + if runtime and job.runtime.name != runtime.name: + continue + steps = [] + for node in job.nodes: + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=self._container_status(node.container), + ) + ) + result.append( + types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + ) + return result + + def get_job(self, name: str) -> types.TrainJob: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + # Refresh container statuses on demand + steps: list[types.Step] = [] + for node in job.nodes: + status = self._container_status(node.container) + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=status, + ) + ) + return types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + + def get_job_logs( + self, + name: str, + follow: bool = False, + step: str = constants.NODE + "-0", + ) -> Iterator[str]: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + want_all = step == constants.NODE + "-0" + for node in job.nodes: + node_step = node.name.split(f"{job.name}-")[-1] + if not want_all and node_step != step: + continue + logs = node.container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + yield chunk.decode("utf-8", errors="ignore") + else: + yield logs.decode("utf-8", errors="ignore") + + def wait_for_job_status( + self, + name: str, + status: set[str] = {constants.TRAINJOB_COMPLETE}, + timeout: int = 600, + polling_interval: int = 2, + ) -> types.TrainJob: + # Simple polling loop similar to Kubernetes backend + import time + + end = time.time() + timeout + while time.time() < end: + tj = self.get_job(name) + logger.debug(f"TrainJob {name}, status {tj.status}") + if tj.status in status: + return tj + if constants.TRAINJOB_FAILED not in status and tj.status == constants.TRAINJOB_FAILED: + raise RuntimeError(f"TrainJob {name} is Failed") + time.sleep(polling_interval) + raise TimeoutError(f"Timeout waiting for TrainJob {name} to reach status: {status}") + + def delete_job(self, name: str): + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + # Stop containers, collect final logs if needed, and remove + from contextlib import suppress + + for node in job.nodes: + with suppress(Exception): + node.container.stop(timeout=10) + with suppress(Exception): + node.container.remove(force=True) + + # Remove network (best-effort) + try: + net = self.client.networks.get(job.network_name) + net.remove() + except Exception: + pass + + # Remove working directory if configured + if self.cfg.auto_remove and os.path.isdir(job.workdir_host): + shutil.rmtree(job.workdir_host, ignore_errors=True) + + del self._jobs[name] + + # ---- Helpers ---- + def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> Path: + script_path = Path(workdir) / "train.py" + # Serialize function source and immediate call (mirrors localprocess utils) + import inspect + import textwrap + + code = inspect.getsource(trainer.func) + code = textwrap.dedent(code) + if trainer.func_args is None: + code += f"\n{trainer.func.__name__}()\n" + else: + code += f"\n{trainer.func.__name__}({trainer.func_args})\n" + script_path.write_text(code) + return script_path + + def _resolve_image(self, runtime: types.Runtime) -> str: + # Prefer explicit image from config; else from runtime YAML. Runtime YAML + # must include the image field; otherwise we fail with a clear message. + if self.cfg.image: + return self.cfg.image + + # Load from local runtime YAMLs directory + # The loader already validated presence, but not the image; enforce here. + # We reuse the torch-distributed default when name matches. + # For extensibility, a mapping could be added in the YAML schema later. + name = runtime.name + # Best-effort: read the same YAML used for this runtime to fetch image. + # The simple loader does not expose image directly, so we re-read YAML by matching name. + from kubeflow.trainer.backends.docker.runtime_loader import LOCAL_RUNTIMES_DIR + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + try: + import yaml + + data = yaml.safe_load(Path(f).read_text()) + # CRD-like structure only (ClusterTrainingRuntime or TrainingRuntime) + if ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata", {}).get("name") == name + ): + # locate the 'node' replicated job + replicated = ( + data.get("spec", {}) + .get("template", {}) + .get("spec", {}) + .get("replicatedJobs", []) + ) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if node_jobs: + node_spec = ( + node_jobs[0] + .get("template", {}) + .get("spec", {}) + .get("template", {}) + .get("spec", {}) + ) + containers = node_spec.get("containers", []) + if containers and containers[0].get("image"): + return str(containers[0]["image"]) + except Exception: + continue + raise ValueError( + f"No image specified for runtime '{name}'. Provide DockerBackendConfig.image or " + f"add an 'image' field to its YAML in {LOCAL_RUNTIMES_DIR}." + ) + + def _maybe_pull_image(self, image: str): + policy = (self.cfg.pull_policy or "IfNotPresent").lower() + try: + if policy == "never": + # Ensure image exists locally + self.client.images.get(image) + return + if policy == "always": + logger.debug(f"Pulling image (Always): {image}") + self.client.images.pull(image) + return + # IfNotPresent + try: + self.client.images.get(image) + except Exception: + logger.debug(f"Pulling image (IfNotPresent): {image}") + self.client.images.pull(image) + except Exception as e: + raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e + + def _ensure_network(self, job_name: str) -> str: + # Create a job-scoped network to allow name-based discovery between nodes + network_name = f"{job_name}-net" + try: + self.client.networks.get(network_name) + return network_name + except Exception: + pass + # Create network with labels for filtering and management + self.client.networks.create( + name=network_name, + check_duplicate=True, + labels={ + "trainer.kubeflow.org/trainjob-name": job_name, + }, + ) + return network_name + + def _container_status(self, container: Container) -> str: + try: + container.reload() + status = container.status # e.g., created, running, exited + if status == "running": + return constants.TRAINJOB_RUNNING + if status == "created": + return constants.TRAINJOB_CREATED + if status == "exited": + # Exit code 0 -> complete, else failed + inspect = self.client.api.inspect_container(container.id) + code = inspect.get("State", {}).get("ExitCode") + return constants.TRAINJOB_COMPLETE if code == 0 else constants.TRAINJOB_FAILED + except Exception: + return constants.UNKNOWN + return constants.UNKNOWN + + def _aggregate_status(self, job: _Job) -> str: + statuses = [self._container_status(n.container) for n in job.nodes] + if constants.TRAINJOB_FAILED in statuses: + return constants.TRAINJOB_FAILED + if constants.TRAINJOB_RUNNING in statuses: + return constants.TRAINJOB_RUNNING + if all(s == constants.TRAINJOB_COMPLETE for s in statuses if s != constants.UNKNOWN): + return constants.TRAINJOB_COMPLETE + if any(s == constants.TRAINJOB_CREATED for s in statuses): + return constants.TRAINJOB_CREATED + return constants.UNKNOWN + + def _run_oneoff_container(self, image: str, command: tuple[str, ...]) -> str: + """ + Run a short-lived container and return its stdout as a string. + + Implementation detail: + - Use detach=False and remove=True so Docker SDK runs the container to + completion, returns combined logs as bytes, and cleans it up. This + avoids races where auto-removed containers disappear before .wait/.logs. + """ + try: + output = self.client.containers.run( + image=image, + command=command, + detach=False, + remove=True, + ) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + if isinstance(output, (bytes, bytearray)): + return output.decode("utf-8", errors="ignore") + # Fallback: ensure string return + return str(output) + + def cfgd_workspace_path(self) -> str: + # Location inside the container where the host workdir is mounted + return "/workspace/" diff --git a/kubeflow/trainer/backends/docker/runtime_loader.py b/kubeflow/trainer/backends/docker/runtime_loader.py new file mode 100644 index 000000000..d222624b8 --- /dev/null +++ b/kubeflow/trainer/backends/docker/runtime_loader.py @@ -0,0 +1,110 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Runtime loader for the Docker backend. + +We support loading local runtime definitions from +`kubeflow/trainer/config/local_runtimes/` (YAML files). The schema mirrors the +essential fields from the upstream CRD manifest but is tailored for Docker to +capture the container image and trainer characteristics needed to construct a +`types.Runtime` object. + +We ship a built-in `torch_distributed.yaml` as the default runtime. Users can +add additional YAML files to the same directory to define custom local runtimes. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import yaml + +from kubeflow.trainer.types import types as base_types + +LOCAL_RUNTIMES_DIR = Path(__file__).parents[2] / "config" / "local_runtimes" + + +def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: + with open(path) as f: + data: dict[str, Any] = yaml.safe_load(f) + return data + + +def list_local_runtimes() -> list[base_types.Runtime]: + runtimes: list[base_types.Runtime] = [] + if not LOCAL_RUNTIMES_DIR.exists(): + return runtimes + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + data = _load_runtime_from_yaml(f) + + # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime + # and TrainingRuntime kinds. + if not ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata") + ): + raise ValueError( + f"Runtime YAML {f} must be a ClusterTrainingRuntime CRD-shaped document" + ) + + name = data["metadata"].get("name") + if not name: + raise ValueError(f"Runtime YAML {f} missing metadata.name") + + labels = data["metadata"].get("labels", {}) + framework = labels.get("trainer.kubeflow.org/framework") + if not framework: + raise ValueError( + f"Runtime {name} must set metadata.labels['trainer.kubeflow.org/framework']" + ) + + spec = data.get("spec", {}) + ml_policy = spec.get("mlPolicy", {}) + num_nodes = int(ml_policy.get("numNodes", 1)) + + # Validate presence of a 'node' replicated job with a container image + templ = spec.get("template", {}).get("spec", {}) + replicated = templ.get("replicatedJobs", []) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if not node_jobs: + raise ValueError(f"Runtime {name} must define replicatedJobs with a 'node' entry") + node_spec = ( + node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) + ) + containers = node_spec.get("containers", []) + if not containers or not containers[0].get("image"): + raise ValueError(f"Runtime {name} 'node' must specify containers[0].image") + + runtimes.append( + base_types.Runtime( + name=name, + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework=framework, + num_nodes=num_nodes, + ), + pretrained_model=None, + ) + ) + return runtimes + + +def get_local_runtime(name: str) -> base_types.Runtime: + for rt in list_local_runtimes(): + if rt.name == name: + return rt + raise ValueError(f"Runtime '{name}' not found in {LOCAL_RUNTIMES_DIR}") diff --git a/kubeflow/trainer/backends/docker/types.py b/kubeflow/trainer/backends/docker/types.py new file mode 100644 index 000000000..1a87090bf --- /dev/null +++ b/kubeflow/trainer/backends/docker/types.py @@ -0,0 +1,52 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Types and configuration for the Docker backend. + +We keep the configuration surface area intentionally small for v1: + - image: Optional explicit image. If omitted, use the image referenced by the + selected runtime (e.g., torch_distributed) from `config/local_runtimes`. + - network: Optional explicit Docker network name. If omitted, the backend will + create a per-job ephemeral network to interconnect all node containers. + - pull_policy: Controls image pulling. Supported values: "IfNotPresent", + "Always", "Never". The default is "IfNotPresent". + - auto_remove: Whether to remove containers and ephemeral network when jobs + are deleted. Defaults to True. + - gpus: GPU support is not implemented for v1 (kept for future extensibility). + - env: Optional global environment variables applied to all containers. + - docker_host: Optional override for connecting to a remote/local Docker + daemon; by default the Docker SDK resolves from environment and local + configuration. +""" + +from typing import Optional, Union + +from pydantic import BaseModel, Field + + +class LocalDockerBackendConfig(BaseModel): + image: Optional[str] = Field(default=None) + network: Optional[str] = Field(default=None) + pull_policy: str = Field(default="IfNotPresent") + auto_remove: bool = Field(default=True) + # In Python 3.9, avoid PEP 604 unions; use typing.Union/Optional instead. + # Define the type at module scope so Pydantic doesn't treat it as a field. + gpus: Optional[Union[int, bool]] = Field(default=None) + env: Optional[dict[str, str]] = Field(default=None) + docker_host: Optional[str] = Field(default=None) + # Base directory on the host to place per-job working dirs that are bind-mounted + # into containers as /workspace. 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+++++++++++ kubeflow/trainer/backends/docker/__init__.py | 18 + kubeflow/trainer/backends/docker/backend.py | 540 +++-------- .../trainer/backends/docker/runtime_loader.py | 97 +- kubeflow/trainer/backends/docker/types.py | 18 +- .../trainer/backends/local_runtime_loader.py | 110 +++ kubeflow/trainer/backends/podman/__init__.py | 18 + kubeflow/trainer/backends/podman/backend.py | 244 +++++ .../trainer/backends/podman/runtime_loader.py | 27 + kubeflow/trainer/backends/podman/types.py | 48 + pyproject.toml | 3 + uv.lock | 908 +++++++++--------- 14 files changed, 1657 insertions(+), 964 deletions(-) create mode 100644 kubeflow/trainer/backends/container_base.py create mode 100644 kubeflow/trainer/backends/local_runtime_loader.py create mode 100644 kubeflow/trainer/backends/podman/__init__.py create mode 100644 kubeflow/trainer/backends/podman/backend.py create mode 100644 kubeflow/trainer/backends/podman/runtime_loader.py create mode 100644 kubeflow/trainer/backends/podman/types.py diff --git a/kubeflow/trainer/__init__.py b/kubeflow/trainer/__init__.py index c1190ce82..51baa6dbb 100644 --- a/kubeflow/trainer/__init__.py +++ b/kubeflow/trainer/__init__.py @@ -18,6 +18,7 @@ # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient # noqa: F401 from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig +from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient @@ -68,5 +69,6 @@ "TrainerType", "LocalProcessBackendConfig", "LocalDockerBackendConfig", + "LocalPodmanBackendConfig", "KubernetesBackendConfig", ] diff --git a/kubeflow/trainer/api/trainer_client.py b/kubeflow/trainer/api/trainer_client.py index e6c75c987..3bba31f38 100644 --- a/kubeflow/trainer/api/trainer_client.py +++ b/kubeflow/trainer/api/trainer_client.py @@ -19,6 +19,8 @@ from kubeflow.common.types import KubernetesBackendConfig from kubeflow.trainer.backends.docker.backend import LocalDockerBackend from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig +from kubeflow.trainer.backends.podman.backend import LocalPodmanBackend +from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig from kubeflow.trainer.backends.kubernetes.backend import KubernetesBackend from kubeflow.trainer.backends.localprocess.backend import ( LocalProcessBackend, @@ -37,6 +39,7 @@ def __init__( KubernetesBackendConfig, LocalProcessBackendConfig, LocalDockerBackendConfig, + LocalPodmanBackendConfig, ] = None, ): """Initialize a Kubeflow Trainer client. @@ -60,6 +63,8 @@ def __init__( self.backend = LocalProcessBackend(backend_config) elif isinstance(backend_config, LocalDockerBackendConfig): self.backend = LocalDockerBackend(backend_config) + elif isinstance(backend_config, LocalPodmanBackendConfig): + self.backend = LocalPodmanBackend(backend_config) else: raise ValueError(f"Invalid backend config '{backend_config}'") diff --git a/kubeflow/trainer/backends/container_base.py b/kubeflow/trainer/backends/container_base.py new file mode 100644 index 000000000..e01397d95 --- /dev/null +++ b/kubeflow/trainer/backends/container_base.py @@ -0,0 +1,583 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Shared base class for container-based backends (Docker, Podman). + +This module provides common functionality for backends that use container runtimes, +reducing code duplication while allowing each backend to implement runtime-specific +behaviors. +""" + +from __future__ import annotations + +import abc +from collections.abc import Iterator +from dataclasses import dataclass +from datetime import datetime +import logging +import os +from pathlib import Path +import random +import shutil +import string +import uuid + +from kubeflow.trainer.backends.base import ExecutionBackend +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types + +logger = logging.getLogger(__name__) + + +@dataclass +class _Node: + name: str + container_id: str + status: str = constants.TRAINJOB_CREATED + + +@dataclass +class _Job: + name: str + created: datetime + runtime: types.Runtime + network_id: str # Network or pod ID depending on backend + nodes: list[_Node] + workdir_host: str + + +class ContainerBackend(ExecutionBackend): + """ + Abstract base class for container-based execution backends. + + Subclasses must implement: + - _get_client(): Return the container client (Docker/Podman) + - _create_network(job_name): Create network/pod for multi-node jobs + - _delete_network(network_id): Clean up network/pod + - _get_master_addr(job_name, rank): Return master address for torchrun + - _get_master_port(rank): Return master port for torchrun + - _create_and_start_container(): Create and start a container + - _get_container(): Get container object by ID + - _container_logs(): Get logs from a container + - _stop_container(): Stop a container + - _remove_container(): Remove a container + - _pull_image(): Pull an image + - _image_exists(): Check if image exists locally + - _run_oneoff_container(): Run a one-off container and return logs + """ + + def __init__(self, label_prefix: str = "trainer.kubeflow.org"): + self._jobs: dict[str, _Job] = {} + self.label_prefix = label_prefix + + @abc.abstractmethod + def _get_client(self): + """Return the container client instance.""" + raise NotImplementedError() + + @abc.abstractmethod + def _create_network(self, job_name: str) -> str: + """Create a network or pod for the job. Return network/pod ID.""" + raise NotImplementedError() + + @abc.abstractmethod + def _delete_network(self, network_id: str): + """Delete the network or pod.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_master_addr(self, job_name: str, rank: int) -> str: + """Get the master address for torchrun rendezvous.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_master_port(self, rank: int) -> int: + """Get the master port for torchrun rendezvous.""" + raise NotImplementedError() + + @abc.abstractmethod + def _create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a container. Return container ID.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_container(self, container_id: str): + """Get container object by ID.""" + raise NotImplementedError() + + @abc.abstractmethod + def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def _stop_container(self, container_id: str, timeout: int = 10): + """Stop a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def _remove_container(self, container_id: str, force: bool = True): + """Remove a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def _pull_image(self, image: str): + """Pull an image.""" + raise NotImplementedError() + + @abc.abstractmethod + def _image_exists(self, image: str) -> bool: + """Check if image exists locally.""" + raise NotImplementedError() + + @abc.abstractmethod + def _run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a one-off container and return its logs.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_pull_policy(self) -> str: + """Get the pull policy for images.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_workdir_base(self) -> str | None: + """Get the base directory for working directories.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_auto_remove(self) -> bool: + """Get whether to auto-remove containers.""" + raise NotImplementedError() + + @abc.abstractmethod + def _resolve_image(self, runtime: types.Runtime) -> str: + """Resolve the container image for a runtime.""" + raise NotImplementedError() + + @abc.abstractmethod + def _get_runtimes_dir(self) -> Path: + """Get the path to the runtimes directory.""" + raise NotImplementedError() + + # ---- Shared implementation ---- + + def _resolve_image_from_runtime(self, runtime: types.Runtime, config_image: str | None) -> str: + """ + Shared logic for resolving the container image for a runtime. + + If config_image is provided, use it. Otherwise, load from runtime YAML. + """ + if config_image: + return config_image + + import yaml + + runtimes_dir = self._get_runtimes_dir() + for f in sorted(runtimes_dir.glob("*.yaml")): + try: + data = yaml.safe_load(Path(f).read_text()) + if ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata", {}).get("name") == runtime.name + ): + replicated = ( + data.get("spec", {}) + .get("template", {}) + .get("spec", {}) + .get("replicatedJobs", []) + ) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if node_jobs: + node_spec = ( + node_jobs[0] + .get("template", {}) + .get("spec", {}) + .get("template", {}) + .get("spec", {}) + ) + containers = node_spec.get("containers", []) + if containers and containers[0].get("image"): + return str(containers[0]["image"]) + except Exception: + continue + + backend_name = self.__class__.__name__.replace("Local", "").replace("Backend", "") + raise ValueError( + f"No image specified for runtime '{runtime.name}'. Provide {backend_name}BackendConfig.image or " + f"add an 'image' field to its YAML in {runtimes_dir}." + ) + + def get_runtime_packages(self, runtime: types.Runtime): + """ + Spawn a short-lived container to report Python version, pip list, and nvidia-smi. + """ + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + command = [ + "bash", + "-lc", + "python -c \"import sys; print(f'Python: {sys.version}')\" && " + "(pip list || echo 'pip not found') && " + "(nvidia-smi || echo 'nvidia-smi not found')", + ] + + logs = self._run_oneoff_container(image=image, command=command) + print(logs) + + def train( + self, + runtime: types.Runtime | None = None, + initializer: types.Initializer | None = None, + trainer: types.CustomTrainer | types.BuiltinTrainer | None = None, + ) -> str: + if runtime is None: + runtime = self.get_runtime("torch-distributed") + + if not isinstance(trainer, types.CustomTrainer): + raise ValueError(f"{self.__class__.__name__} supports only CustomTrainer in v1") + + # Generate job name + job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] + + # Create per-job working directory on host + workdir = self._create_workdir(job_name) + _ = self._write_training_script(workdir, trainer) + + # Resolve image and pull if needed + image = self._resolve_image(runtime) + self._maybe_pull_image(image) + + # Build base environment + env = self._build_environment(trainer) + + # Construct pre-run command to install packages + pre_install_cmd = self._build_pip_install_cmd(trainer) + + # Create network/pod for multi-node communication + num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 + network_id = self._create_network(job_name) + + # Create N containers (one per node) + containers: list[_Node] = [] + + for rank in range(num_nodes): + container_name = f"{job_name}-node-{rank}" + + # Get master address and port for torchrun + master_addr = self._get_master_addr(job_name, rank) + master_port = self._get_master_port(rank) + + # Prefer torchrun; fall back to python if torchrun is unavailable + # For worker nodes, wait for master to be reachable before starting torchrun + wait_for_master = "" + if rank > 0: + wait_for_master = ( + f"echo 'Waiting for master node {master_addr}:{master_port}...'; " + f"for i in {{1..60}}; do " + f" if timeout 1 bash -c 'cat < /dev/null > /dev/tcp/{master_addr}/{master_port}' 2>/dev/null; then " + f" echo 'Master node is reachable'; break; " + f" fi; " + f" if [ $i -eq 60 ]; then echo 'Timeout waiting for master node'; exit 1; fi; " + f" sleep 2; " + f"done; " + ) + + entry_cmd = ( + f"{pre_install_cmd}" + f"{wait_for_master}" + "if command -v torchrun >/dev/null 2>&1; then " + f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " + f" --node-rank={rank} --rdzv-backend=c10d " + f" --rdzv-endpoint={master_addr}:{master_port} " + f" {self.cfgd_workspace_path()}train.py; " + "else " + f" python {self.cfgd_workspace_path()}train.py; " + "fi" + ) + + full_cmd = ["bash", "-lc", entry_cmd] + + labels = { + f"{self.label_prefix}/trainjob-name": job_name, + f"{self.label_prefix}/step": f"node-{rank}", + } + + volumes = { + workdir: { + "bind": "/workspace", + "mode": "rw", + } + } + + container_id = self._create_and_start_container( + image=image, + command=full_cmd, + name=container_name, + network_id=network_id, + environment=env, + labels=labels, + volumes=volumes, + working_dir="/workspace", + ) + + containers.append(_Node(name=container_name, container_id=container_id)) + + self._jobs[job_name] = _Job( + name=job_name, + created=datetime.now(), + runtime=runtime, + network_id=network_id, + nodes=containers, + workdir_host=workdir, + ) + + return job_name + + def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: + result: list[types.TrainJob] = [] + for job in self._jobs.values(): + if runtime and job.runtime.name != runtime.name: + continue + steps = [] + for node in job.nodes: + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=self._container_status(node.container_id), + ) + ) + result.append( + types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + ) + return result + + def get_job(self, name: str) -> types.TrainJob: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + # Refresh container statuses on demand + steps: list[types.Step] = [] + for node in job.nodes: + status = self._container_status(node.container_id) + steps.append( + types.Step( + name=node.name.split(f"{job.name}-")[-1], + pod_name=node.name, + status=status, + ) + ) + return types.TrainJob( + name=job.name, + creation_timestamp=job.created, + runtime=job.runtime, + steps=steps, + num_nodes=len(job.nodes), + status=self._aggregate_status(job), + ) + + def get_job_logs( + self, + name: str, + follow: bool = False, + step: str = constants.NODE + "-0", + ) -> Iterator[str]: + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + want_all = step == constants.NODE + "-0" + for node in job.nodes: + node_step = node.name.split(f"{job.name}-")[-1] + if not want_all and node_step != step: + continue + try: + yield from self._container_logs(node.container_id, follow) + except Exception as e: + logger.warning(f"Failed to get logs for {node.name}: {e}") + yield f"Error getting logs: {e}\n" + + def wait_for_job_status( + self, + name: str, + status: set[str] = {constants.TRAINJOB_COMPLETE}, + timeout: int = 600, + polling_interval: int = 2, + ) -> types.TrainJob: + import time + + end = time.time() + timeout + while time.time() < end: + tj = self.get_job(name) + logger.debug(f"TrainJob {name}, status {tj.status}") + if tj.status in status: + return tj + if constants.TRAINJOB_FAILED not in status and tj.status == constants.TRAINJOB_FAILED: + raise RuntimeError(f"TrainJob {name} is Failed") + time.sleep(polling_interval) + raise TimeoutError(f"Timeout waiting for TrainJob {name} to reach status: {status}") + + def delete_job(self, name: str): + job = self._jobs.get(name) + if not job: + raise ValueError(f"No TrainJob with name {name}") + + # Stop containers and remove + from contextlib import suppress + + for node in job.nodes: + with suppress(Exception): + self._stop_container(node.container_id, timeout=10) + with suppress(Exception): + self._remove_container(node.container_id, force=True) + + # Remove network/pod (best-effort) + with suppress(Exception): + self._delete_network(job.network_id) + + # Remove working directory if configured + if self._get_auto_remove() and os.path.isdir(job.workdir_host): + shutil.rmtree(job.workdir_host, ignore_errors=True) + + del self._jobs[name] + + # ---- Helper methods ---- + + def _create_workdir(self, job_name: str) -> str: + """Create per-job working directory on host.""" + workdir_base = self._get_workdir_base() + if workdir_base: + base = Path(workdir_base) + base.mkdir(parents=True, exist_ok=True) + workdir = str((base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + else: + backend_name = self.__class__.__name__.lower().replace("local", "").replace("backend", "") + home_base = Path.home() / ".kubeflow_trainer" / f"local{backend_name}" + home_base.mkdir(parents=True, exist_ok=True) + workdir = str((home_base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + return workdir + + def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> Path: + """Write the training script to the working directory.""" + script_path = Path(workdir) / "train.py" + import inspect + import textwrap + + code = inspect.getsource(trainer.func) + code = textwrap.dedent(code) + if trainer.func_args is None: + code += f"\n{trainer.func.__name__}()\n" + else: + code += f"\n{trainer.func.__name__}({trainer.func_args})\n" + script_path.write_text(code) + return script_path + + def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: + """Build environment variables for containers.""" + env = {} + # Subclasses should add their own env vars here + if trainer.env: + env.update(trainer.env) + return env + + def _build_pip_install_cmd(self, trainer: types.CustomTrainer) -> str: + """Build pip install command for packages.""" + pkgs = trainer.packages_to_install or [] + if not pkgs: + return "" + + index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) + main_idx = index_urls[0] + extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) + quoted = " ".join(f'"{p}"' for p in pkgs) + return ( + "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " + f"--index-url {main_idx} {extras} {quoted} && " + ) + + def _maybe_pull_image(self, image: str): + """Pull image based on pull policy.""" + policy = (self._get_pull_policy() or "IfNotPresent").lower() + try: + if policy == "never": + if not self._image_exists(image): + raise RuntimeError(f"Image '{image}' not found locally and pull policy is Never") + return + if policy == "always": + logger.debug(f"Pulling image (Always): {image}") + self._pull_image(image) + return + # IfNotPresent + if not self._image_exists(image): + logger.debug(f"Pulling image (IfNotPresent): {image}") + self._pull_image(image) + except Exception as e: + raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e + + def _container_status(self, container_id: str) -> str: + """Get the status of a container.""" + try: + container = self._get_container(container_id) + status = container.status + if status == "running": + return constants.TRAINJOB_RUNNING + if status == "created": + return constants.TRAINJOB_CREATED + if status == "exited": + # Exit code 0 -> complete, else failed + inspect = container.attrs if hasattr(container, 'attrs') else container.inspect() + code = inspect.get("State", {}).get("ExitCode") + return constants.TRAINJOB_COMPLETE if code == 0 else constants.TRAINJOB_FAILED + except Exception: + return constants.UNKNOWN + return constants.UNKNOWN + + def _aggregate_status(self, job: _Job) -> str: + """Aggregate status from all containers in a job.""" + statuses = [self._container_status(n.container_id) for n in job.nodes] + if constants.TRAINJOB_FAILED in statuses: + return constants.TRAINJOB_FAILED + if constants.TRAINJOB_RUNNING in statuses: + return constants.TRAINJOB_RUNNING + if all(s == constants.TRAINJOB_COMPLETE for s in statuses if s != constants.UNKNOWN): + return constants.TRAINJOB_COMPLETE + if any(s == constants.TRAINJOB_CREATED for s in statuses): + return constants.TRAINJOB_CREATED + return constants.UNKNOWN + + def cfgd_workspace_path(self) -> str: + """Location inside the container where the host workdir is mounted.""" + return "/workspace/" diff --git a/kubeflow/trainer/backends/docker/__init__.py b/kubeflow/trainer/backends/docker/__init__.py index e69de29bb..c3c888c01 100644 --- a/kubeflow/trainer/backends/docker/__init__.py +++ b/kubeflow/trainer/backends/docker/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from kubeflow.trainer.backends.docker.backend import LocalDockerBackend +from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig + +__all__ = ["LocalDockerBackend", "LocalDockerBackendConfig"] diff --git a/kubeflow/trainer/backends/docker/backend.py b/kubeflow/trainer/backends/docker/backend.py index c1ca41726..36c930c53 100644 --- a/kubeflow/trainer/backends/docker/backend.py +++ b/kubeflow/trainer/backends/docker/backend.py @@ -35,56 +35,26 @@ from __future__ import annotations from collections.abc import Iterator -from dataclasses import dataclass -from datetime import datetime import logging -import os from pathlib import Path -import random -import shutil -import string -import uuid try: import docker # type: ignore - from docker.models.containers import Container # type: ignore except Exception: # pragma: no cover - optional dependency, validated at runtime docker = None # type: ignore - Container = object # type: ignore -from kubeflow.trainer.backends.base import ExecutionBackend +from kubeflow.trainer.backends.container_base import ContainerBackend from kubeflow.trainer.backends.docker.runtime_loader import ( get_local_runtime, list_local_runtimes, ) from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig -from kubeflow.trainer.constants import constants from kubeflow.trainer.types import types logger = logging.getLogger(__name__) -DOCKER_LABEL_PREFIX = "trainer.kubeflow.ai" - - -@dataclass -class _Node: - name: str - container: Container - status: str = constants.TRAINJOB_CREATED - - -@dataclass -class _Job: - name: str - created: datetime - runtime: types.Runtime - network_name: str - nodes: list[_Node] - workdir_host: str - - -class LocalDockerBackend(ExecutionBackend): +class LocalDockerBackend(ContainerBackend): def __init__(self, cfg: LocalDockerBackendConfig): if docker is None: raise ImportError( @@ -92,6 +62,8 @@ def __init__(self, cfg: LocalDockerBackendConfig): "pip install kubeflow[docker]" ) + super().__init__(label_prefix="trainer.kubeflow.org") + # initialize docker client (env-based resolution by default) if cfg.docker_host: self.client = docker.DockerClient(base_url=cfg.docker_host) @@ -99,7 +71,6 @@ def __init__(self, cfg: LocalDockerBackendConfig): self.client = docker.from_env() self.cfg = cfg - self._jobs: dict[str, _Job] = {} # ---- Runtime APIs ---- def list_runtimes(self) -> list[types.Runtime]: @@ -108,360 +79,12 @@ def list_runtimes(self) -> list[types.Runtime]: def get_runtime(self, name: str) -> types.Runtime: return get_local_runtime(name) - def get_runtime_packages(self, runtime: types.Runtime): - """ - Spawn a short-lived container to report Python version, pip list, and nvidia-smi. - We follow the Kubernetes backend semantics: create a one-off job and print outputs. - """ - image = self._resolve_image(runtime) - self._maybe_pull_image(image) - - command = ( - "bash", - "-lc", - "python -c \"import sys; print(f'Python: {sys.version}')\" && " - "(pip list || echo 'pip not found') && " - "(nvidia-smi || echo 'nvidia-smi not found')", - ) - - logs = self._run_oneoff_container(image=image, command=command) - print(logs) - - # ---- Train/Jobs APIs ---- - def train( - self, - runtime: types.Runtime | None = None, - initializer: types.Initializer | None = None, - trainer: types.CustomTrainer | types.BuiltinTrainer | None = None, - ) -> str: - if runtime is None: - runtime = self.get_runtime("torch-distributed") - - if not isinstance(trainer, types.CustomTrainer): - raise ValueError("DockerBackend supports only CustomTrainer in v1") - - # Generate job name - job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] - - # Create per-job working directory on host where we'll place the generated script - # Prefer a location under the user's home directory to ensure Docker Desktop file - # sharing includes it by default on macOS/Windows. - if self.cfg.workdir_base: - base = Path(self.cfg.workdir_base) - base.mkdir(parents=True, exist_ok=True) - workdir = str((base / f"{job_name}").resolve()) - os.makedirs(workdir, exist_ok=True) - else: - home_base = Path.home() / ".kubeflow_trainer" / "localdocker" - home_base.mkdir(parents=True, exist_ok=True) - workdir = str((home_base / f"{job_name}").resolve()) - os.makedirs(workdir, exist_ok=True) - _ = self._write_training_script(workdir, trainer) - - # Provision per-job network - network_name = self._ensure_network(job_name) - - # Resolve image and pull if needed - image = self._resolve_image(runtime) - self._maybe_pull_image(image) - - # Build base environment - env = dict(self.cfg.env or {}) - if trainer.env: - env.update(trainer.env) - - # Construct pre-run command to install packages, if any, inside the container. - pre_install_cmd = "" - pkgs = trainer.packages_to_install or [] - if pkgs: - # Respect pip_index_urls ordering (index-url then extra-index-urls) - index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) - main_idx = index_urls[0] - extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) - quoted = " ".join(f'"{p}"' for p in pkgs) - pre_install_cmd = ( - "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " - f"--index-url {main_idx} {extras} {quoted} && " - ) - - # Create N containers (one per node) - num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 - containers: list[_Node] = [] - - for rank in range(num_nodes): - container_name = f"{job_name}-node-{rank}" - - # torchrun rendezvous across nodes using network alias - # We use a simple host:port rendezvous; rank 0 acts as master. - master_addr = f"{job_name}-node-0" - master_port = 29500 - - # Prefer torchrun; fall back to python if torchrun is unavailable. - # IMPORTANT: Only add torchrun flags when torchrun is present. If we fall back to - # python, run the script directly without torchrun-specific arguments to avoid - # immediate failure and short-lived containers. - entry_cmd = ( - f"{pre_install_cmd}" - "if command -v torchrun >/dev/null 2>&1; then " - f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " - f" --node_rank={rank} --rdzv_backend=c10d " - f" --rdzv_endpoint={master_addr}:{master_port} " - f" {self.cfgd_workspace_path()}train.py; " - "else " - f" python {self.cfgd_workspace_path()}train.py; " - "fi" - ) - - full_cmd = ("bash", "-lc", entry_cmd) - - labels = { - f"{DOCKER_LABEL_PREFIX}/trainjob-name": job_name, - f"{DOCKER_LABEL_PREFIX}/step": f"node-{rank}", - } - - # Bind mount the host workdir into the container working dir - binds = { - workdir: { - "bind": "/workspace", - "mode": "rw", - } - } - - container = self.client.containers.run( - image=image, - command=full_cmd, - name=container_name, - detach=True, - working_dir="/workspace", - network=network_name, - environment=env, - labels=labels, - volumes=binds, - auto_remove=False, # we control removal in delete_job - ) - - containers.append(_Node(name=container_name, container=container)) - - self._jobs[job_name] = _Job( - name=job_name, - created=datetime.now(), - runtime=runtime, - network_name=network_name, - nodes=containers, - workdir_host=workdir, - ) - - return job_name - - def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: - result: list[types.TrainJob] = [] - for job in self._jobs.values(): - if runtime and job.runtime.name != runtime.name: - continue - steps = [] - for node in job.nodes: - steps.append( - types.Step( - name=node.name.split(f"{job.name}-")[-1], - pod_name=node.name, - status=self._container_status(node.container), - ) - ) - result.append( - types.TrainJob( - name=job.name, - creation_timestamp=job.created, - runtime=job.runtime, - steps=steps, - num_nodes=len(job.nodes), - status=self._aggregate_status(job), - ) - ) - return result - - def get_job(self, name: str) -> types.TrainJob: - job = self._jobs.get(name) - if not job: - raise ValueError(f"No TrainJob with name {name}") - # Refresh container statuses on demand - steps: list[types.Step] = [] - for node in job.nodes: - status = self._container_status(node.container) - steps.append( - types.Step( - name=node.name.split(f"{job.name}-")[-1], - pod_name=node.name, - status=status, - ) - ) - return types.TrainJob( - name=job.name, - creation_timestamp=job.created, - runtime=job.runtime, - steps=steps, - num_nodes=len(job.nodes), - status=self._aggregate_status(job), - ) - - def get_job_logs( - self, - name: str, - follow: bool = False, - step: str = constants.NODE + "-0", - ) -> Iterator[str]: - job = self._jobs.get(name) - if not job: - raise ValueError(f"No TrainJob with name {name}") - - want_all = step == constants.NODE + "-0" - for node in job.nodes: - node_step = node.name.split(f"{job.name}-")[-1] - if not want_all and node_step != step: - continue - logs = node.container.logs(stream=bool(follow), follow=bool(follow)) - if follow: - for chunk in logs: - yield chunk.decode("utf-8", errors="ignore") - else: - yield logs.decode("utf-8", errors="ignore") - - def wait_for_job_status( - self, - name: str, - status: set[str] = {constants.TRAINJOB_COMPLETE}, - timeout: int = 600, - polling_interval: int = 2, - ) -> types.TrainJob: - # Simple polling loop similar to Kubernetes backend - import time - - end = time.time() + timeout - while time.time() < end: - tj = self.get_job(name) - logger.debug(f"TrainJob {name}, status {tj.status}") - if tj.status in status: - return tj - if constants.TRAINJOB_FAILED not in status and tj.status == constants.TRAINJOB_FAILED: - raise RuntimeError(f"TrainJob {name} is Failed") - time.sleep(polling_interval) - raise TimeoutError(f"Timeout waiting for TrainJob {name} to reach status: {status}") - - def delete_job(self, name: str): - job = self._jobs.get(name) - if not job: - raise ValueError(f"No TrainJob with name {name}") - - # Stop containers, collect final logs if needed, and remove - from contextlib import suppress - - for node in job.nodes: - with suppress(Exception): - node.container.stop(timeout=10) - with suppress(Exception): - node.container.remove(force=True) - - # Remove network (best-effort) - try: - net = self.client.networks.get(job.network_name) - net.remove() - except Exception: - pass - - # Remove working directory if configured - if self.cfg.auto_remove and os.path.isdir(job.workdir_host): - shutil.rmtree(job.workdir_host, ignore_errors=True) - - del self._jobs[name] - - # ---- Helpers ---- - def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> Path: - script_path = Path(workdir) / "train.py" - # Serialize function source and immediate call (mirrors localprocess utils) - import inspect - import textwrap - - code = inspect.getsource(trainer.func) - code = textwrap.dedent(code) - if trainer.func_args is None: - code += f"\n{trainer.func.__name__}()\n" - else: - code += f"\n{trainer.func.__name__}({trainer.func_args})\n" - script_path.write_text(code) - return script_path - - def _resolve_image(self, runtime: types.Runtime) -> str: - # Prefer explicit image from config; else from runtime YAML. Runtime YAML - # must include the image field; otherwise we fail with a clear message. - if self.cfg.image: - return self.cfg.image - - # Load from local runtime YAMLs directory - # The loader already validated presence, but not the image; enforce here. - # We reuse the torch-distributed default when name matches. - # For extensibility, a mapping could be added in the YAML schema later. - name = runtime.name - # Best-effort: read the same YAML used for this runtime to fetch image. - # The simple loader does not expose image directly, so we re-read YAML by matching name. - from kubeflow.trainer.backends.docker.runtime_loader import LOCAL_RUNTIMES_DIR - - for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): - try: - import yaml - - data = yaml.safe_load(Path(f).read_text()) - # CRD-like structure only (ClusterTrainingRuntime or TrainingRuntime) - if ( - data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} - and data.get("metadata", {}).get("name") == name - ): - # locate the 'node' replicated job - replicated = ( - data.get("spec", {}) - .get("template", {}) - .get("spec", {}) - .get("replicatedJobs", []) - ) - node_jobs = [j for j in replicated if j.get("name") == "node"] - if node_jobs: - node_spec = ( - node_jobs[0] - .get("template", {}) - .get("spec", {}) - .get("template", {}) - .get("spec", {}) - ) - containers = node_spec.get("containers", []) - if containers and containers[0].get("image"): - return str(containers[0]["image"]) - except Exception: - continue - raise ValueError( - f"No image specified for runtime '{name}'. Provide DockerBackendConfig.image or " - f"add an 'image' field to its YAML in {LOCAL_RUNTIMES_DIR}." - ) - - def _maybe_pull_image(self, image: str): - policy = (self.cfg.pull_policy or "IfNotPresent").lower() - try: - if policy == "never": - # Ensure image exists locally - self.client.images.get(image) - return - if policy == "always": - logger.debug(f"Pulling image (Always): {image}") - self.client.images.pull(image) - return - # IfNotPresent - try: - self.client.images.get(image) - except Exception: - logger.debug(f"Pulling image (IfNotPresent): {image}") - self.client.images.pull(image) - except Exception as e: - raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e + # ---- Container backend implementation ---- + def _get_client(self): + return self.client - def _ensure_network(self, job_name: str) -> str: - # Create a job-scoped network to allow name-based discovery between nodes + def _create_network(self, job_name: str) -> str: + """Create a Docker network for the job.""" network_name = f"{job_name}-net" try: self.client.networks.get(network_name) @@ -473,43 +96,98 @@ def _ensure_network(self, job_name: str) -> str: name=network_name, check_duplicate=True, labels={ - "trainer.kubeflow.org/trainjob-name": job_name, + f"{self.label_prefix}/trainjob-name": job_name, }, ) return network_name - def _container_status(self, container: Container) -> str: + def _delete_network(self, network_id: str): + """Delete the Docker network.""" try: - container.reload() - status = container.status # e.g., created, running, exited - if status == "running": - return constants.TRAINJOB_RUNNING - if status == "created": - return constants.TRAINJOB_CREATED - if status == "exited": - # Exit code 0 -> complete, else failed - inspect = self.client.api.inspect_container(container.id) - code = inspect.get("State", {}).get("ExitCode") - return constants.TRAINJOB_COMPLETE if code == 0 else constants.TRAINJOB_FAILED + net = self.client.networks.get(network_id) + net.remove() except Exception: - return constants.UNKNOWN - return constants.UNKNOWN - - def _aggregate_status(self, job: _Job) -> str: - statuses = [self._container_status(n.container) for n in job.nodes] - if constants.TRAINJOB_FAILED in statuses: - return constants.TRAINJOB_FAILED - if constants.TRAINJOB_RUNNING in statuses: - return constants.TRAINJOB_RUNNING - if all(s == constants.TRAINJOB_COMPLETE for s in statuses if s != constants.UNKNOWN): - return constants.TRAINJOB_COMPLETE - if any(s == constants.TRAINJOB_CREATED for s in statuses): - return constants.TRAINJOB_CREATED - return constants.UNKNOWN - - def _run_oneoff_container(self, image: str, command: tuple[str, ...]) -> str: + pass + + def _get_master_addr(self, job_name: str, rank: int) -> str: + """Docker uses container names for DNS resolution.""" + return f"{job_name}-node-0" + + def _get_master_port(self, rank: int) -> int: + """Docker uses a fixed port since containers are on different network endpoints.""" + return 29500 + + def _create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Docker container.""" + container = self.client.containers.run( + image=image, + command=tuple(command), + name=name, + detach=True, + working_dir=working_dir, + network=network_id, + environment=environment, + labels=labels, + volumes=volumes, + auto_remove=False, # we control removal in delete_job + ) + return container.id + + def _get_container(self, container_id: str): + """Get Docker container by ID.""" + return self.client.containers.get(container_id) + + def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from a Docker container.""" + container = self._get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def _stop_container(self, container_id: str, timeout: int = 10): + """Stop a Docker container.""" + container = self._get_container(container_id) + container.stop(timeout=timeout) + + def _remove_container(self, container_id: str, force: bool = True): + """Remove a Docker container.""" + container = self._get_container(container_id) + container.remove(force=force) + + def _pull_image(self, image: str): + """Pull a Docker image.""" + self.client.images.pull(image) + + def _image_exists(self, image: str) -> bool: + """Check if Docker image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def _run_oneoff_container(self, image: str, command: list[str]) -> str: """ - Run a short-lived container and return its stdout as a string. + Run a short-lived Docker container and return its stdout as a string. Implementation detail: - Use detach=False and remove=True so Docker SDK runs the container to @@ -519,7 +197,7 @@ def _run_oneoff_container(self, image: str, command: tuple[str, ...]) -> str: try: output = self.client.containers.run( image=image, - command=command, + command=tuple(command), detach=False, remove=True, ) @@ -528,9 +206,29 @@ def _run_oneoff_container(self, image: str, command: tuple[str, ...]) -> str: if isinstance(output, (bytes, bytearray)): return output.decode("utf-8", errors="ignore") - # Fallback: ensure string return return str(output) - def cfgd_workspace_path(self) -> str: - # Location inside the container where the host workdir is mounted - return "/workspace/" + def _get_pull_policy(self) -> str: + return self.cfg.pull_policy + + def _get_workdir_base(self) -> str | None: + return self.cfg.workdir_base + + def _get_auto_remove(self) -> bool: + return self.cfg.auto_remove + + def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: + """Build environment variables for containers.""" + env = dict(self.cfg.env or {}) + if trainer.env: + env.update(trainer.env) + return env + + def _get_runtimes_dir(self) -> Path: + """Get the path to the local runtimes directory.""" + from kubeflow.trainer.backends.docker.runtime_loader import LOCAL_RUNTIMES_DIR + return LOCAL_RUNTIMES_DIR + + def _resolve_image(self, runtime: types.Runtime) -> str: + """Resolve the Docker image for a runtime.""" + return self._resolve_image_from_runtime(runtime, self.cfg.image) diff --git a/kubeflow/trainer/backends/docker/runtime_loader.py b/kubeflow/trainer/backends/docker/runtime_loader.py index d222624b8..ea51e4eed 100644 --- a/kubeflow/trainer/backends/docker/runtime_loader.py +++ b/kubeflow/trainer/backends/docker/runtime_loader.py @@ -15,96 +15,13 @@ """ Runtime loader for the Docker backend. -We support loading local runtime definitions from -`kubeflow/trainer/config/local_runtimes/` (YAML files). The schema mirrors the -essential fields from the upstream CRD manifest but is tailored for Docker to -capture the container image and trainer characteristics needed to construct a -`types.Runtime` object. - -We ship a built-in `torch_distributed.yaml` as the default runtime. Users can -add additional YAML files to the same directory to define custom local runtimes. +This module provides Docker-specific imports for the shared local runtime loader. """ -from __future__ import annotations - -from pathlib import Path -from typing import Any - -import yaml - -from kubeflow.trainer.types import types as base_types - -LOCAL_RUNTIMES_DIR = Path(__file__).parents[2] / "config" / "local_runtimes" - - -def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: - with open(path) as f: - data: dict[str, Any] = yaml.safe_load(f) - return data - - -def list_local_runtimes() -> list[base_types.Runtime]: - runtimes: list[base_types.Runtime] = [] - if not LOCAL_RUNTIMES_DIR.exists(): - return runtimes - - for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): - data = _load_runtime_from_yaml(f) - - # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime - # and TrainingRuntime kinds. - if not ( - data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} - and data.get("metadata") - ): - raise ValueError( - f"Runtime YAML {f} must be a ClusterTrainingRuntime CRD-shaped document" - ) - - name = data["metadata"].get("name") - if not name: - raise ValueError(f"Runtime YAML {f} missing metadata.name") - - labels = data["metadata"].get("labels", {}) - framework = labels.get("trainer.kubeflow.org/framework") - if not framework: - raise ValueError( - f"Runtime {name} must set metadata.labels['trainer.kubeflow.org/framework']" - ) - - spec = data.get("spec", {}) - ml_policy = spec.get("mlPolicy", {}) - num_nodes = int(ml_policy.get("numNodes", 1)) - - # Validate presence of a 'node' replicated job with a container image - templ = spec.get("template", {}).get("spec", {}) - replicated = templ.get("replicatedJobs", []) - node_jobs = [j for j in replicated if j.get("name") == "node"] - if not node_jobs: - raise ValueError(f"Runtime {name} must define replicatedJobs with a 'node' entry") - node_spec = ( - node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) - ) - containers = node_spec.get("containers", []) - if not containers or not containers[0].get("image"): - raise ValueError(f"Runtime {name} 'node' must specify containers[0].image") - - runtimes.append( - base_types.Runtime( - name=name, - trainer=base_types.RuntimeTrainer( - trainer_type=base_types.TrainerType.CUSTOM_TRAINER, - framework=framework, - num_nodes=num_nodes, - ), - pretrained_model=None, - ) - ) - return runtimes - +from kubeflow.trainer.backends.local_runtime_loader import ( + LOCAL_RUNTIMES_DIR, + get_local_runtime, + list_local_runtimes, +) -def get_local_runtime(name: str) -> base_types.Runtime: - for rt in list_local_runtimes(): - if rt.name == name: - return rt - raise ValueError(f"Runtime '{name}' not found in {LOCAL_RUNTIMES_DIR}") +__all__ = ["LOCAL_RUNTIMES_DIR", "get_local_runtime", "list_local_runtimes"] diff --git a/kubeflow/trainer/backends/docker/types.py b/kubeflow/trainer/backends/docker/types.py index 1a87090bf..644a39816 100644 --- a/kubeflow/trainer/backends/docker/types.py +++ b/kubeflow/trainer/backends/docker/types.py @@ -14,21 +14,23 @@ """ Types and configuration for the Docker backend. - We keep the configuration surface area intentionally small for v1: +Configuration options for Docker backend: + - image: Optional explicit image. If omitted, use the image referenced by the selected runtime (e.g., torch_distributed) from `config/local_runtimes`. - - network: Optional explicit Docker network name. If omitted, the backend will - create a per-job ephemeral network to interconnect all node containers. - pull_policy: Controls image pulling. Supported values: "IfNotPresent", "Always", "Never". The default is "IfNotPresent". - - auto_remove: Whether to remove containers and ephemeral network when jobs - are deleted. Defaults to True. + - auto_remove: Whether to remove containers and networks when jobs are deleted. + Defaults to True. - gpus: GPU support is not implemented for v1 (kept for future extensibility). - env: Optional global environment variables applied to all containers. - - docker_host: Optional override for connecting to a remote/local Docker - daemon; by default the Docker SDK resolves from environment and local - configuration. + - docker_host: Optional override for connecting to a remote/local Docker daemon. + By default, the Docker SDK resolves from environment variables or uses the + system default socket. + - workdir_base: Base directory on the host to place per-job working dirs that + are bind-mounted into containers as /workspace. Defaults to a path under the + user's home directory for compatibility. """ from typing import Optional, Union diff --git a/kubeflow/trainer/backends/local_runtime_loader.py b/kubeflow/trainer/backends/local_runtime_loader.py new file mode 100644 index 000000000..24f34c113 --- /dev/null +++ b/kubeflow/trainer/backends/local_runtime_loader.py @@ -0,0 +1,110 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Shared runtime loader for local container backends (Docker, Podman). + +We support loading local runtime definitions from +`kubeflow/trainer/config/local_runtimes/` (YAML files). The schema mirrors the +essential fields from the upstream CRD manifest but is tailored for local +container backends to capture the container image and trainer characteristics +needed to construct a `types.Runtime` object. + +We ship a built-in `torch_distributed.yaml` as the default runtime. Users can +add additional YAML files to the same directory to define custom local runtimes. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import yaml + +from kubeflow.trainer.types import types as base_types + +LOCAL_RUNTIMES_DIR = Path(__file__).parents[1] / "config" / "local_runtimes" + + +def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: + with open(path) as f: + data: dict[str, Any] = yaml.safe_load(f) + return data + + +def list_local_runtimes() -> list[base_types.Runtime]: + runtimes: list[base_types.Runtime] = [] + if not LOCAL_RUNTIMES_DIR.exists(): + return runtimes + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + data = _load_runtime_from_yaml(f) + + # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime + # and TrainingRuntime kinds. + if not ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata") + ): + raise ValueError( + f"Runtime YAML {f} must be a ClusterTrainingRuntime CRD-shaped document" + ) + + name = data["metadata"].get("name") + if not name: + raise ValueError(f"Runtime YAML {f} missing metadata.name") + + labels = data["metadata"].get("labels", {}) + framework = labels.get("trainer.kubeflow.org/framework") + if not framework: + raise ValueError( + f"Runtime {name} must set metadata.labels['trainer.kubeflow.org/framework']" + ) + + spec = data.get("spec", {}) + ml_policy = spec.get("mlPolicy", {}) + num_nodes = int(ml_policy.get("numNodes", 1)) + + # Validate presence of a 'node' replicated job with a container image + templ = spec.get("template", {}).get("spec", {}) + replicated = templ.get("replicatedJobs", []) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if not node_jobs: + raise ValueError(f"Runtime {name} must define replicatedJobs with a 'node' entry") + node_spec = ( + node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) + ) + containers = node_spec.get("containers", []) + if not containers or not containers[0].get("image"): + raise ValueError(f"Runtime {name} 'node' must specify containers[0].image") + + runtimes.append( + base_types.Runtime( + name=name, + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework=framework, + num_nodes=num_nodes, + ), + pretrained_model=None, + ) + ) + return runtimes + + +def get_local_runtime(name: str) -> base_types.Runtime: + for rt in list_local_runtimes(): + if rt.name == name: + return rt + raise ValueError(f"Runtime '{name}' not found in {LOCAL_RUNTIMES_DIR}") diff --git a/kubeflow/trainer/backends/podman/__init__.py b/kubeflow/trainer/backends/podman/__init__.py new file mode 100644 index 000000000..22e13a5bb --- /dev/null +++ b/kubeflow/trainer/backends/podman/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from kubeflow.trainer.backends.podman.backend import LocalPodmanBackend +from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig + +__all__ = ["LocalPodmanBackend", "LocalPodmanBackendConfig"] diff --git a/kubeflow/trainer/backends/podman/backend.py b/kubeflow/trainer/backends/podman/backend.py new file mode 100644 index 000000000..60adaf2ee --- /dev/null +++ b/kubeflow/trainer/backends/podman/backend.py @@ -0,0 +1,244 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +PodmanBackend +------------- + +Local execution backend for `CustomTrainer` jobs using Podman containers. + +Key behaviors: +- Multi-node jobs: containers are connected via a Podman network, giving each + container its own network interface for proper distributed training with + name-based DNS resolution (similar to Docker). +- Entry script generation: we serialize the user's training function to a small + Python file and invoke it inside the container using `torchrun` (preferred) or + `python` as a fallback. +- Runtimes: we use `config/local_runtimes` to define runtime images and + characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime + is provided. +- Image pulling: controlled via `pull_policy` and performed automatically if + needed. +- Logs and lifecycle: streaming logs and deletion semantics similar to the + Docker backend. +- Networking: Uses Podman networks (not pods) so each container has its own + network interface, enabling proper PyTorch distributed training communication. +""" + +from __future__ import annotations + +from collections.abc import Iterator +import logging +from pathlib import Path + +try: + import podman # type: ignore +except Exception: # pragma: no cover - optional dependency, validated at runtime + podman = None # type: ignore + +from kubeflow.trainer.backends.container_base import ContainerBackend +from kubeflow.trainer.backends.podman.runtime_loader import ( + get_local_runtime, + list_local_runtimes, +) +from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig +from kubeflow.trainer.types import types + +logger = logging.getLogger(__name__) + + +class LocalPodmanBackend(ContainerBackend): + def __init__(self, cfg: LocalPodmanBackendConfig): + if podman is None: + raise ImportError( + "The 'podman' Python package is not installed. Install with extras: " + "pip install kubeflow[podman]" + ) + + super().__init__(label_prefix="trainer.kubeflow.org") + + # Initialize Podman client + if cfg.podman_url: + self.client = podman.PodmanClient(base_url=cfg.podman_url) + else: + # Use system default socket + self.client = podman.PodmanClient() + + self.cfg = cfg + + # ---- Runtime APIs ---- + def list_runtimes(self) -> list[types.Runtime]: + return list_local_runtimes() + + def get_runtime(self, name: str) -> types.Runtime: + return get_local_runtime(name) + + # ---- Container backend implementation ---- + def _get_client(self): + return self.client + + def _create_network(self, job_name: str) -> str: + """ + Create a Podman network for the job to enable container networking. + + Unlike pods (which share network namespace), a Podman network gives each + container its own network interface, enabling proper distributed training. + """ + network_name = f"{job_name}-net" + try: + self.client.networks.get(network_name) + return network_name + except Exception: + pass + # Create network with DNS enabled for hostname resolution + self.client.networks.create( + name=network_name, + driver="bridge", + dns_enabled=True, + labels={ + f"{self.label_prefix}/trainjob-name": job_name, + }, + ) + return network_name + + def _delete_network(self, network_id: str): + """Delete the Podman network.""" + try: + net = self.client.networks.get(network_id) + net.remove() + except Exception: + pass + + def _get_master_addr(self, job_name: str, rank: int) -> str: + """Podman uses container names for DNS resolution within a network.""" + return f"{job_name}-node-0" + + def _get_master_port(self, rank: int) -> int: + """Use a fixed port since containers are on different network endpoints.""" + return 29500 + + def _create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Podman container on the network.""" + # Use containers.run() which creates and starts in one step + # This is equivalent to: podman run --network ... + container = self.client.containers.run( + image=image, + command=command, + name=name, + network=network_id, + working_dir=working_dir, + environment=environment, + labels=labels, + volumes=volumes, + detach=True, + remove=False, # Don't auto-remove, we control this in delete_job + ) + + return container.id + + def _get_container(self, container_id: str): + """Get Podman container by ID.""" + return self.client.containers.get(container_id) + + def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from a Podman container.""" + container = self._get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def _stop_container(self, container_id: str, timeout: int = 10): + """Stop a Podman container.""" + container = self._get_container(container_id) + container.stop(timeout=timeout) + + def _remove_container(self, container_id: str, force: bool = True): + """Remove a Podman container.""" + container = self._get_container(container_id) + container.remove(force=force) + + def _pull_image(self, image: str): + """Pull a Podman image.""" + self.client.images.pull(image) + + def _image_exists(self, image: str) -> bool: + """Check if Podman image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def _run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a short-lived Podman container and return its logs.""" + try: + container = self.client.containers.create( + image=image, + command=command, + detach=False, + remove=True, + ) + container.start() + container.wait() + logs = container.logs() + + if isinstance(logs, (bytes, bytearray)): + return logs.decode("utf-8", errors="ignore") + return str(logs) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + def _get_pull_policy(self) -> str: + return self.cfg.pull_policy + + def _get_workdir_base(self) -> str | None: + return self.cfg.workdir_base + + def _get_auto_remove(self) -> bool: + return self.cfg.auto_remove + + def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: + """Build environment variables for containers.""" + env = dict(self.cfg.env or {}) + if trainer.env: + env.update(trainer.env) + return env + + def _get_runtimes_dir(self) -> Path: + """Get the path to the local runtimes directory.""" + from kubeflow.trainer.backends.podman.runtime_loader import LOCAL_RUNTIMES_DIR + return LOCAL_RUNTIMES_DIR + + def _resolve_image(self, runtime: types.Runtime) -> str: + """Resolve the Podman image for a runtime.""" + return self._resolve_image_from_runtime(runtime, self.cfg.image) diff --git a/kubeflow/trainer/backends/podman/runtime_loader.py b/kubeflow/trainer/backends/podman/runtime_loader.py new file mode 100644 index 000000000..43af5be92 --- /dev/null +++ b/kubeflow/trainer/backends/podman/runtime_loader.py @@ -0,0 +1,27 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Runtime loader for the Podman backend. + +This module provides Podman-specific imports for the shared local runtime loader. +""" + +from kubeflow.trainer.backends.local_runtime_loader import ( + LOCAL_RUNTIMES_DIR, + get_local_runtime, + list_local_runtimes, +) + +__all__ = ["LOCAL_RUNTIMES_DIR", "get_local_runtime", "list_local_runtimes"] diff --git a/kubeflow/trainer/backends/podman/types.py b/kubeflow/trainer/backends/podman/types.py new file mode 100644 index 000000000..9f66215be --- /dev/null +++ b/kubeflow/trainer/backends/podman/types.py @@ -0,0 +1,48 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Types and configuration for the Podman backend. + +Configuration options for Podman backend: + - image: Optional explicit image. If omitted, use the image referenced by the + selected runtime (e.g., torch_distributed) from `config/local_runtimes`. + - pull_policy: Controls image pulling. Supported values: "IfNotPresent", + "Always", "Never". The default is "IfNotPresent". + - auto_remove: Whether to remove containers and networks when jobs are deleted. + Defaults to True. + - gpus: GPU support using NVIDIA CDI or other device plugins. Defaults to None. + - env: Optional global environment variables applied to all containers. + - podman_url: Optional override for connecting to a remote/local Podman socket. + By default, the Podman client resolves from environment variables or uses + the system default socket (e.g., unix:///run/user/1000/podman/podman.sock + for rootless, unix:///run/podman/podman.sock for rootful). + - workdir_base: Base directory on the host to place per-job working dirs that + are bind-mounted into containers as /workspace. Defaults to a path under the + user's home directory for compatibility. +""" + +from typing import Optional, Union + +from pydantic import BaseModel, Field + + +class LocalPodmanBackendConfig(BaseModel): + image: Optional[str] = Field(default=None) + pull_policy: str = Field(default="IfNotPresent") + auto_remove: bool = Field(default=True) + gpus: Optional[Union[int, bool]] = Field(default=None) + env: Optional[dict[str, str]] = Field(default=None) + podman_url: Optional[str] = Field(default=None) + workdir_base: Optional[str] = Field(default=None) diff --git a/pyproject.toml b/pyproject.toml index 9e94ccdd8..ee68d0b54 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,6 +36,9 @@ dependencies = [ docker = [ "docker>=6.1.3", ] +podman = [ + "podman>=5.6.0" +] [dependency-groups] dev = [ diff --git a/uv.lock b/uv.lock index cdb7e22c5..64adf4957 100644 --- a/uv.lock +++ b/uv.lock @@ -1,32 +1,32 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.9" [[package]] name = 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kubeflow/trainer/backends/docker/types.py | 54 -- kubeflow/trainer/backends/podman/backend.py | 244 ------- 15 files changed, 1643 insertions(+), 882 deletions(-) create mode 100644 kubeflow/trainer/backends/container/README.md rename kubeflow/trainer/backends/{podman => container}/__init__.py (73%) rename kubeflow/trainer/backends/{container_base.py => container/backend.py} (50%) create mode 100644 kubeflow/trainer/backends/container/backend_test.py create mode 100644 kubeflow/trainer/backends/container/client_adapter.py rename kubeflow/trainer/backends/{podman => container}/runtime_loader.py (86%) rename kubeflow/trainer/backends/{podman => container}/types.py (62%) delete mode 100644 kubeflow/trainer/backends/docker/__init__.py delete mode 100644 kubeflow/trainer/backends/docker/backend.py delete mode 100644 kubeflow/trainer/backends/docker/runtime_loader.py delete mode 100644 kubeflow/trainer/backends/docker/types.py delete mode 100644 kubeflow/trainer/backends/podman/backend.py diff --git a/README.md b/README.md index bf9a4eeed..b5af775b2 100644 --- a/README.md +++ b/README.md @@ -71,6 +71,45 @@ TrainerClient().wait_for_job_status(job_id) print("\n".join(TrainerClient().get_job_logs(name=job_id))) ``` +## Local Development + +Kubeflow SDK provides first-class support for local development, allowing you to test and iterate on your models without needing a Kubernetes cluster. + +### Execution Backends + +Choose the backend that fits your development workflow: + +| Backend | Description | Use Case | +|---------|-------------|----------| +| **KubernetesBackend** | Run jobs on Kubernetes cluster | Production, multi-node distributed training | +| **ContainerBackend** | Auto-detects Docker or Podman | Local development with container isolation | +| **LocalProcessBackend** | Run as local Python subprocesses | Quick prototyping, debugging | + +### Local Container Execution + +The **ContainerBackend** automatically detects and uses either Docker or Podman: + +```bash +# Install with Docker support +pip install kubeflow[docker] + +# Or install with Podman support +pip install kubeflow[podman] +``` + +```python +from kubeflow.trainer import TrainerClient, ContainerBackendConfig, CustomTrainer + +# Auto-detects Docker or Podman +config = ContainerBackendConfig() +client = TrainerClient(backend_config=config) + +# Your training runs in isolated containers +job_id = client.train(trainer=CustomTrainer(func=train_fn)) +``` + +For detailed configuration options and platform-specific setup (macOS, Linux), see the [ContainerBackend documentation](kubeflow/trainer/backends/container/README.md). + ## Supported Kubeflow Projects | Project | Status | Version Support | Description | diff --git a/kubeflow/trainer/__init__.py b/kubeflow/trainer/__init__.py index 51baa6dbb..4cb10ed48 100644 --- a/kubeflow/trainer/__init__.py +++ b/kubeflow/trainer/__init__.py @@ -17,8 +17,7 @@ from kubeflow.common.types import KubernetesBackendConfig # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient # noqa: F401 -from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig -from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig +from kubeflow.trainer.backends.container.types import ContainerBackendConfig # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient @@ -68,7 +67,6 @@ "TrainerClient", "TrainerType", "LocalProcessBackendConfig", - "LocalDockerBackendConfig", - "LocalPodmanBackendConfig", + "ContainerBackendConfig", "KubernetesBackendConfig", ] diff --git a/kubeflow/trainer/api/trainer_client.py b/kubeflow/trainer/api/trainer_client.py index 3bba31f38..9b3aefb01 100644 --- a/kubeflow/trainer/api/trainer_client.py +++ b/kubeflow/trainer/api/trainer_client.py @@ -16,11 +16,8 @@ import logging from typing import Optional, Union -from kubeflow.common.types import KubernetesBackendConfig -from kubeflow.trainer.backends.docker.backend import LocalDockerBackend -from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig -from kubeflow.trainer.backends.podman.backend import LocalPodmanBackend -from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig +from kubeflow.trainer.backends.container.backend import ContainerBackend +from kubeflow.trainer.backends.container.types import ContainerBackendConfig from kubeflow.trainer.backends.kubernetes.backend import KubernetesBackend from kubeflow.trainer.backends.localprocess.backend import ( LocalProcessBackend, @@ -38,16 +35,16 @@ def __init__( backend_config: Union[ KubernetesBackendConfig, LocalProcessBackendConfig, - LocalDockerBackendConfig, - LocalPodmanBackendConfig, + ContainerBackendConfig, ] = None, ): """Initialize a Kubeflow Trainer client. Args: - backend_config: Backend configuration. Either KubernetesBackendConfig or - LocalProcessBackendConfig, or None to use the backend's - default config class. Defaults to KubernetesBackendConfig. + backend_config: Backend configuration. Either KubernetesBackendConfig, + LocalProcessBackendConfig, ContainerBackendConfig, + or None to use the backend's default config class. + Defaults to KubernetesBackendConfig. Raises: ValueError: Invalid backend configuration. @@ -61,10 +58,8 @@ def __init__( self.backend = KubernetesBackend(backend_config) elif isinstance(backend_config, LocalProcessBackendConfig): self.backend = LocalProcessBackend(backend_config) - elif isinstance(backend_config, LocalDockerBackendConfig): - self.backend = LocalDockerBackend(backend_config) - elif isinstance(backend_config, LocalPodmanBackendConfig): - self.backend = LocalPodmanBackend(backend_config) + elif isinstance(backend_config, ContainerBackendConfig): + self.backend = ContainerBackend(backend_config) else: raise ValueError(f"Invalid backend config '{backend_config}'") diff --git a/kubeflow/trainer/backends/container/README.md b/kubeflow/trainer/backends/container/README.md new file mode 100644 index 000000000..db4234666 --- /dev/null +++ b/kubeflow/trainer/backends/container/README.md @@ -0,0 +1,162 @@ +# ContainerBackend + +The unified container backend for Kubeflow Trainer that automatically detects and uses either Docker or Podman. + +## Overview + +This backend provides a single, unified interface for container-based training execution, automatically detecting which container runtime is available on your system. + +The implementation uses the **adapter pattern** to abstract away differences between Docker and Podman APIs, providing clean separation between runtime detection logic and container operations. + +## Usage + +### Basic usage (auto-detection) + +```python +from kubeflow.trainer import TrainerClient, ContainerBackendConfig + +# Auto-detects Docker or Podman +config = ContainerBackendConfig() +client = TrainerClient(backend_config=config) +``` + +### Force specific runtime + +```python +# Force Docker +config = ContainerBackendConfig(runtime="docker") +client = TrainerClient(backend_config=config) + +# Force Podman +config = ContainerBackendConfig(runtime="podman") +client = TrainerClient(backend_config=config) +``` + +### Configuration options + +```python +config = ContainerBackendConfig( + # Optional: force specific runtime ("docker" or "podman") + runtime=None, + + # Optional: explicit image override + image="my-custom-image:latest", + + # Image pull policy: "IfNotPresent", "Always", or "Never" + pull_policy="IfNotPresent", + + # Auto-remove containers and networks on job deletion + auto_remove=True, + + # GPU support (varies by runtime) + gpus=None, + + # Environment variables for all containers + env={"MY_VAR": "value"}, + + # Container daemon URL override (required for Colima/Podman Machine on macOS) + container_host=None, + + # Base directory for job workspaces + workdir_base=None, +) +``` + +### macOS-specific configuration + +On macOS, you may need to specify `container_host` depending on your container runtime: + +**Docker with Colima:** +```python +import os +config = ContainerBackendConfig( + container_host=f"unix://{os.path.expanduser('~')}/.colima/default/docker.sock" +) +``` + +**Podman Machine:** +```python +import os +config = ContainerBackendConfig( + container_host=f"unix://{os.path.expanduser('~')}/.local/share/containers/podman/machine/podman.sock" +) +``` + +**Docker Desktop:** +```python +# Usually works without specifying container_host +config = ContainerBackendConfig() +``` + +Alternatively, set environment variables before running: +```bash +# For Colima +export DOCKER_HOST="unix://$HOME/.colima/default/docker.sock" + +# For Podman Machine +export CONTAINER_HOST="unix://$HOME/.local/share/containers/podman/machine/podman.sock" +``` + +### How it works + +The backend initialization follows this logic: + +1. If `runtime` is specified in config, use that runtime exclusively +2. Otherwise, try to initialize Docker client adapter +3. If Docker fails, try to initialize Podman client adapter +4. If both fail, raise a RuntimeError + +If you don't have Docker or Podman installed, use `LocalProcessBackendConfig` instead, which runs training as local subprocesses. + +All container operations are delegated to the adapter, eliminating code duplication. + +## Installation + +Install with Docker support: +```bash +pip install kubeflow[docker] +``` + +Install with Podman support: +```bash +pip install kubeflow[podman] +``` + +Install with both: +```bash +pip install kubeflow[docker,podman] +``` + +## Example: Training Job + +```python +from kubeflow.trainer import TrainerClient, ContainerBackendConfig, CustomTrainer + +# Define your training function +def train(): + import torch + print(f"Training with PyTorch {torch.__version__}") + # Your training code here + +# Create trainer +trainer = CustomTrainer( + func=train, + packages_to_install=["torch"], +) + +# Initialize client (auto-detects runtime) +config = ContainerBackendConfig() +client = TrainerClient(backend_config=config) + +# Run training +job_name = client.train(trainer=trainer) +print(f"Training job started: {job_name}") + +# Get logs +for log in client.get_job_logs(job_name, follow=True): + print(log, end='') +``` + +## See also + +- [Example notebook](TBA) - Complete working example to be added diff --git a/kubeflow/trainer/backends/podman/__init__.py b/kubeflow/trainer/backends/container/__init__.py similarity index 73% rename from kubeflow/trainer/backends/podman/__init__.py rename to kubeflow/trainer/backends/container/__init__.py index 22e13a5bb..776237c38 100644 --- a/kubeflow/trainer/backends/podman/__init__.py +++ b/kubeflow/trainer/backends/container/__init__.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from kubeflow.trainer.backends.podman.backend import LocalPodmanBackend -from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig +from kubeflow.trainer.backends.container.backend import ContainerBackend +from kubeflow.trainer.backends.container.types import ContainerBackendConfig -__all__ = ["LocalPodmanBackend", "LocalPodmanBackendConfig"] +__all__ = ["ContainerBackend", "ContainerBackendConfig"] diff --git a/kubeflow/trainer/backends/container_base.py b/kubeflow/trainer/backends/container/backend.py similarity index 50% rename from kubeflow/trainer/backends/container_base.py rename to kubeflow/trainer/backends/container/backend.py index e01397d95..8c22f20bf 100644 --- a/kubeflow/trainer/backends/container_base.py +++ b/kubeflow/trainer/backends/container/backend.py @@ -13,16 +13,31 @@ # limitations under the License. """ -Shared base class for container-based backends (Docker, Podman). - -This module provides common functionality for backends that use container runtimes, -reducing code duplication while allowing each backend to implement runtime-specific -behaviors. +ContainerBackend +---------------- + +Unified local execution backend for `CustomTrainer` jobs using containers. + +This backend automatically detects and uses either Docker or Podman. +It provides a single interface regardless of the underlying container runtime. + +Key behaviors: +- Auto-detection: Tries Docker first, then Podman. Can be overridden via config. +- Multi-node jobs: one container per node connected via a per-job network. +- Entry script generation: we serialize the user's training function to a small + Python file and invoke it inside the container using `torchrun` (preferred) or + `python` as a fallback. +- Runtimes: we use `config/local_runtimes` to define runtime images and + characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime + is provided. +- Image pulling: controlled via `pull_policy` and performed automatically if + needed. +- Logs and lifecycle: streaming logs and deletion semantics similar to the + Docker/Podman backends, but with automatic runtime detection. """ from __future__ import annotations -import abc from collections.abc import Iterator from dataclasses import dataclass from datetime import datetime @@ -35,6 +50,17 @@ import uuid from kubeflow.trainer.backends.base import ExecutionBackend +from kubeflow.trainer.backends.container.client_adapter import ( + ContainerClientAdapter, + DockerClientAdapter, + PodmanClientAdapter, +) +from kubeflow.trainer.backends.container.runtime_loader import ( + LOCAL_RUNTIMES_DIR, + get_local_runtime, + list_local_runtimes, +) +from kubeflow.trainer.backends.container.types import ContainerBackendConfig from kubeflow.trainer.constants import constants from kubeflow.trainer.types import types @@ -53,182 +79,80 @@ class _Job: name: str created: datetime runtime: types.Runtime - network_id: str # Network or pod ID depending on backend + network_id: str nodes: list[_Node] workdir_host: str class ContainerBackend(ExecutionBackend): """ - Abstract base class for container-based execution backends. - - Subclasses must implement: - - _get_client(): Return the container client (Docker/Podman) - - _create_network(job_name): Create network/pod for multi-node jobs - - _delete_network(network_id): Clean up network/pod - - _get_master_addr(job_name, rank): Return master address for torchrun - - _get_master_port(rank): Return master port for torchrun - - _create_and_start_container(): Create and start a container - - _get_container(): Get container object by ID - - _container_logs(): Get logs from a container - - _stop_container(): Stop a container - - _remove_container(): Remove a container - - _pull_image(): Pull an image - - _image_exists(): Check if image exists locally - - _run_oneoff_container(): Run a one-off container and return logs + Unified container backend that auto-detects Docker or Podman. + + This backend uses the adapter pattern to abstract away differences between + Docker and Podman, providing a single consistent interface. """ - def __init__(self, label_prefix: str = "trainer.kubeflow.org"): + def __init__(self, cfg: ContainerBackendConfig): + self.cfg = cfg self._jobs: dict[str, _Job] = {} - self.label_prefix = label_prefix - - @abc.abstractmethod - def _get_client(self): - """Return the container client instance.""" - raise NotImplementedError() - - @abc.abstractmethod - def _create_network(self, job_name: str) -> str: - """Create a network or pod for the job. Return network/pod ID.""" - raise NotImplementedError() - - @abc.abstractmethod - def _delete_network(self, network_id: str): - """Delete the network or pod.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_master_addr(self, job_name: str, rank: int) -> str: - """Get the master address for torchrun rendezvous.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_master_port(self, rank: int) -> int: - """Get the master port for torchrun rendezvous.""" - raise NotImplementedError() - - @abc.abstractmethod - def _create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """Create and start a container. Return container ID.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_container(self, container_id: str): - """Get container object by ID.""" - raise NotImplementedError() - - @abc.abstractmethod - def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def _stop_container(self, container_id: str, timeout: int = 10): - """Stop a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def _remove_container(self, container_id: str, force: bool = True): - """Remove a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def _pull_image(self, image: str): - """Pull an image.""" - raise NotImplementedError() - - @abc.abstractmethod - def _image_exists(self, image: str) -> bool: - """Check if image exists locally.""" - raise NotImplementedError() - - @abc.abstractmethod - def _run_oneoff_container(self, image: str, command: list[str]) -> str: - """Run a one-off container and return its logs.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_pull_policy(self) -> str: - """Get the pull policy for images.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_workdir_base(self) -> str | None: - """Get the base directory for working directories.""" - raise NotImplementedError() - - @abc.abstractmethod - def _get_auto_remove(self) -> bool: - """Get whether to auto-remove containers.""" - raise NotImplementedError() - - @abc.abstractmethod - def _resolve_image(self, runtime: types.Runtime) -> str: - """Resolve the container image for a runtime.""" - raise NotImplementedError() + self.label_prefix = "trainer.kubeflow.org" - @abc.abstractmethod - def _get_runtimes_dir(self) -> Path: - """Get the path to the runtimes directory.""" - raise NotImplementedError() + # Initialize the container client adapter + self._adapter = self._create_adapter() - # ---- Shared implementation ---- - - def _resolve_image_from_runtime(self, runtime: types.Runtime, config_image: str | None) -> str: - """ - Shared logic for resolving the container image for a runtime. - - If config_image is provided, use it. Otherwise, load from runtime YAML. + def _create_adapter(self) -> ContainerClientAdapter: """ - if config_image: - return config_image + Create the appropriate container client adapter. - import yaml + Tries Docker first, then Podman if Docker fails, unless a specific + runtime is requested in the config. - runtimes_dir = self._get_runtimes_dir() - for f in sorted(runtimes_dir.glob("*.yaml")): + Raises RuntimeError if neither Docker nor Podman are available. + """ + if self.cfg.runtime: + # User specified a runtime explicitly + if self.cfg.runtime == "docker": + adapter = DockerClientAdapter(self.cfg.container_host) + adapter.ping() + logger.info("Using Docker as container runtime") + return adapter + elif self.cfg.runtime == "podman": + adapter = PodmanClientAdapter(self.cfg.container_host) + adapter.ping() + logger.info("Using Podman as container runtime") + return adapter + else: + # Auto-detect: try Docker first, then Podman try: - data = yaml.safe_load(Path(f).read_text()) - if ( - data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} - and data.get("metadata", {}).get("name") == runtime.name - ): - replicated = ( - data.get("spec", {}) - .get("template", {}) - .get("spec", {}) - .get("replicatedJobs", []) - ) - node_jobs = [j for j in replicated if j.get("name") == "node"] - if node_jobs: - node_spec = ( - node_jobs[0] - .get("template", {}) - .get("spec", {}) - .get("template", {}) - .get("spec", {}) - ) - containers = node_spec.get("containers", []) - if containers and containers[0].get("image"): - return str(containers[0]["image"]) - except Exception: - continue - - backend_name = self.__class__.__name__.replace("Local", "").replace("Backend", "") - raise ValueError( - f"No image specified for runtime '{runtime.name}'. Provide {backend_name}BackendConfig.image or " - f"add an 'image' field to its YAML in {runtimes_dir}." - ) + adapter = DockerClientAdapter(self.cfg.container_host) + adapter.ping() + logger.info("Using Docker as container runtime") + return adapter + except Exception as docker_error: + logger.debug(f"Docker initialization failed: {docker_error}") + try: + adapter = PodmanClientAdapter(self.cfg.container_host) + adapter.ping() + logger.info("Using Podman as container runtime") + return adapter + except Exception as podman_error: + logger.debug(f"Podman initialization failed: {podman_error}") + raise RuntimeError( + "Neither Docker nor Podman is available. " + "Please install Docker or Podman, or use LocalProcessBackendConfig instead." + ) from podman_error + + @property + def _runtime_type(self) -> str: + """Get the runtime type for debugging/logging.""" + return self._adapter._runtime_type + + # ---- Runtime APIs ---- + def list_runtimes(self) -> list[types.Runtime]: + return list_local_runtimes() + + def get_runtime(self, name: str) -> types.Runtime: + return get_local_runtime(name) def get_runtime_packages(self, runtime: types.Runtime): """ @@ -245,7 +169,7 @@ def get_runtime_packages(self, runtime: types.Runtime): "(nvidia-smi || echo 'nvidia-smi not found')", ] - logs = self._run_oneoff_container(image=image, command=command) + logs = self._adapter.run_oneoff_container(image=image, command=command) print(logs) def train( @@ -262,100 +186,154 @@ def train( # Generate job name job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] + logger.info(f"Starting training job: {job_name}") - # Create per-job working directory on host - workdir = self._create_workdir(job_name) - _ = self._write_training_script(workdir, trainer) + try: + # Create per-job working directory on host + workdir = self._create_workdir(job_name) + logger.debug(f"Created working directory: {workdir}") - # Resolve image and pull if needed - image = self._resolve_image(runtime) - self._maybe_pull_image(image) + _ = self._write_training_script(workdir, trainer) + logger.debug(f"Wrote training script to {workdir}/train.py") - # Build base environment - env = self._build_environment(trainer) - - # Construct pre-run command to install packages - pre_install_cmd = self._build_pip_install_cmd(trainer) - - # Create network/pod for multi-node communication - num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 - network_id = self._create_network(job_name) - - # Create N containers (one per node) - containers: list[_Node] = [] - - for rank in range(num_nodes): - container_name = f"{job_name}-node-{rank}" - - # Get master address and port for torchrun - master_addr = self._get_master_addr(job_name, rank) - master_port = self._get_master_port(rank) - - # Prefer torchrun; fall back to python if torchrun is unavailable - # For worker nodes, wait for master to be reachable before starting torchrun - wait_for_master = "" - if rank > 0: - wait_for_master = ( - f"echo 'Waiting for master node {master_addr}:{master_port}...'; " - f"for i in {{1..60}}; do " - f" if timeout 1 bash -c 'cat < /dev/null > /dev/tcp/{master_addr}/{master_port}' 2>/dev/null; then " - f" echo 'Master node is reachable'; break; " - f" fi; " - f" if [ $i -eq 60 ]; then echo 'Timeout waiting for master node'; exit 1; fi; " - f" sleep 2; " - f"done; " - ) + # Resolve image and pull if needed + image = self._resolve_image(runtime) + logger.debug(f"Using image: {image}") - entry_cmd = ( - f"{pre_install_cmd}" - f"{wait_for_master}" - "if command -v torchrun >/dev/null 2>&1; then " - f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " - f" --node-rank={rank} --rdzv-backend=c10d " - f" --rdzv-endpoint={master_addr}:{master_port} " - f" {self.cfgd_workspace_path()}train.py; " - "else " - f" python {self.cfgd_workspace_path()}train.py; " - "fi" + self._maybe_pull_image(image) + logger.debug(f"Image ready: {image}") + + # Build base environment + env = self._build_environment(trainer) + + # Construct pre-run command to install packages + pre_install_cmd = self._build_pip_install_cmd(trainer) + + # Create network for multi-node communication + num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 + logger.debug(f"Creating network for {num_nodes} nodes") + + network_id = self._adapter.create_network( + name=f"{job_name}-net", + labels={f"{self.label_prefix}/trainjob-name": job_name}, ) + logger.info(f"Created network: {network_id}") + + # Create N containers (one per node) + containers: list[_Node] = [] + + for rank in range(num_nodes): + container_name = f"{job_name}-node-{rank}" + + # Get master address and port for torchrun + master_addr = f"{job_name}-node-0" + master_port = 29500 + + # Prefer torchrun; fall back to python if torchrun is unavailable + # For worker nodes, wait for master to be reachable before starting torchrun + wait_for_master = "" + if rank > 0: + wait_for_master = ( + f"echo 'Waiting for master node {master_addr}:{master_port}...'; " + f"for i in {{1..60}}; do " + f" if timeout 1 bash -c 'cat < /dev/null > " + f"/dev/tcp/{master_addr}/{master_port}' 2>/dev/null; then " + f" echo 'Master node is reachable'; break; " + f" fi; " + f" if [ $i -eq 60 ]; then " + f"echo 'Timeout waiting for master node'; exit 1; fi; " + f" sleep 2; " + f"done; " + ) - full_cmd = ["bash", "-lc", entry_cmd] + entry_cmd = ( + f"{pre_install_cmd}" + f"{wait_for_master}" + "if command -v torchrun >/dev/null 2>&1; then " + f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " + f" --node-rank={rank} --rdzv-backend=c10d " + f" --rdzv-endpoint={master_addr}:{master_port} " + f" /workspace/train.py; " + "else " + f" python /workspace/train.py; " + "fi" + ) - labels = { - f"{self.label_prefix}/trainjob-name": job_name, - f"{self.label_prefix}/step": f"node-{rank}", - } + full_cmd = ["bash", "-lc", entry_cmd] - volumes = { - workdir: { - "bind": "/workspace", - "mode": "rw", + labels = { + f"{self.label_prefix}/trainjob-name": job_name, + f"{self.label_prefix}/step": f"node-{rank}", } - } - container_id = self._create_and_start_container( - image=image, - command=full_cmd, - name=container_name, + volumes = { + workdir: { + "bind": "/workspace", + "mode": "rw", + } + } + + logger.debug(f"Creating container {rank}/{num_nodes}: {container_name}") + + container_id = self._adapter.create_and_start_container( + image=image, + command=full_cmd, + name=container_name, + network_id=network_id, + environment=env, + labels=labels, + volumes=volumes, + working_dir="/workspace", + ) + + logger.info(f"Started container {container_name} (ID: {container_id[:12]})") + containers.append(_Node(name=container_name, container_id=container_id)) + + # Store job in backend + self._jobs[job_name] = _Job( + name=job_name, + created=datetime.now(), + runtime=runtime, network_id=network_id, - environment=env, - labels=labels, - volumes=volumes, - working_dir="/workspace", + nodes=containers, + workdir_host=workdir, ) - containers.append(_Node(name=container_name, container_id=container_id)) + logger.info( + f"Training job {job_name} created successfully with {len(containers)} container(s)" + ) + return job_name - self._jobs[job_name] = _Job( - name=job_name, - created=datetime.now(), - runtime=runtime, - network_id=network_id, - nodes=containers, - workdir_host=workdir, - ) + except Exception as e: + # Clean up on failure + logger.error(f"Failed to create training job {job_name}: {e}") + logger.exception("Full traceback:") + + # Try to clean up any resources that were created + from contextlib import suppress + + try: + # Stop and remove any containers that were created + if "containers" in locals(): + for node in containers: + with suppress(Exception): + self._adapter.stop_container(node.container_id, timeout=5) + self._adapter.remove_container(node.container_id, force=True) + + # Remove network if it was created + if "network_id" in locals(): + with suppress(Exception): + self._adapter.delete_network(network_id) + + # Remove working directory if it was created + if "workdir" in locals() and os.path.isdir(workdir): + shutil.rmtree(workdir, ignore_errors=True) - return job_name + except Exception as cleanup_error: + logger.error(f"Error during cleanup: {cleanup_error}") + + # Re-raise the original exception + raise def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: result: list[types.TrainJob] = [] @@ -423,7 +401,7 @@ def get_job_logs( if not want_all and node_step != step: continue try: - yield from self._container_logs(node.container_id, follow) + yield from self._adapter.container_logs(node.container_id, follow) except Exception as e: logger.warning(f"Failed to get logs for {node.name}: {e}") yield f"Error getting logs: {e}\n" @@ -458,32 +436,34 @@ def delete_job(self, name: str): for node in job.nodes: with suppress(Exception): - self._stop_container(node.container_id, timeout=10) + self._adapter.stop_container(node.container_id, timeout=10) with suppress(Exception): - self._remove_container(node.container_id, force=True) + self._adapter.remove_container(node.container_id, force=True) - # Remove network/pod (best-effort) + # Remove network (best-effort) with suppress(Exception): - self._delete_network(job.network_id) + self._adapter.delete_network(job.network_id) # Remove working directory if configured - if self._get_auto_remove() and os.path.isdir(job.workdir_host): + if self.cfg.auto_remove and os.path.isdir(job.workdir_host): shutil.rmtree(job.workdir_host, ignore_errors=True) del self._jobs[name] - # ---- Helper methods ---- + # Helper methods def _create_workdir(self, job_name: str) -> str: """Create per-job working directory on host.""" - workdir_base = self._get_workdir_base() + workdir_base = self.cfg.workdir_base if workdir_base: base = Path(workdir_base) base.mkdir(parents=True, exist_ok=True) workdir = str((base / f"{job_name}").resolve()) os.makedirs(workdir, exist_ok=True) else: - backend_name = self.__class__.__name__.lower().replace("local", "").replace("backend", "") + backend_name = ( + self.__class__.__name__.lower().replace("local", "").replace("backend", "") + ) home_base = Path.home() / ".kubeflow_trainer" / f"local{backend_name}" home_base.mkdir(parents=True, exist_ok=True) workdir = str((home_base / f"{job_name}").resolve()) @@ -507,8 +487,7 @@ def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: """Build environment variables for containers.""" - env = {} - # Subclasses should add their own env vars here + env = dict(self.cfg.env or {}) if trainer.env: env.update(trainer.env) return env @@ -530,37 +509,36 @@ def _build_pip_install_cmd(self, trainer: types.CustomTrainer) -> str: def _maybe_pull_image(self, image: str): """Pull image based on pull policy.""" - policy = (self._get_pull_policy() or "IfNotPresent").lower() + policy = (self.cfg.pull_policy or "IfNotPresent").lower() try: if policy == "never": - if not self._image_exists(image): - raise RuntimeError(f"Image '{image}' not found locally and pull policy is Never") + if not self._adapter.image_exists(image): + raise RuntimeError( + f"Image '{image}' not found locally and pull policy is Never" + ) return if policy == "always": logger.debug(f"Pulling image (Always): {image}") - self._pull_image(image) + self._adapter.pull_image(image) return # IfNotPresent - if not self._image_exists(image): + if not self._adapter.image_exists(image): logger.debug(f"Pulling image (IfNotPresent): {image}") - self._pull_image(image) + self._adapter.pull_image(image) except Exception as e: raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e def _container_status(self, container_id: str) -> str: """Get the status of a container.""" try: - container = self._get_container(container_id) - status = container.status + status, exit_code = self._adapter.container_status(container_id) if status == "running": return constants.TRAINJOB_RUNNING if status == "created": return constants.TRAINJOB_CREATED if status == "exited": # Exit code 0 -> complete, else failed - inspect = container.attrs if hasattr(container, 'attrs') else container.inspect() - code = inspect.get("State", {}).get("ExitCode") - return constants.TRAINJOB_COMPLETE if code == 0 else constants.TRAINJOB_FAILED + return constants.TRAINJOB_COMPLETE if exit_code == 0 else constants.TRAINJOB_FAILED except Exception: return constants.UNKNOWN return constants.UNKNOWN @@ -578,6 +556,43 @@ def _aggregate_status(self, job: _Job) -> str: return constants.TRAINJOB_CREATED return constants.UNKNOWN - def cfgd_workspace_path(self) -> str: - """Location inside the container where the host workdir is mounted.""" - return "/workspace/" + def _resolve_image(self, runtime: types.Runtime) -> str: + """Resolve the container image for a runtime.""" + if self.cfg.image: + return self.cfg.image + + import yaml + + for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): + try: + data = yaml.safe_load(Path(f).read_text()) + if ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} + and data.get("metadata", {}).get("name") == runtime.name + ): + replicated = ( + data.get("spec", {}) + .get("template", {}) + .get("spec", {}) + .get("replicatedJobs", []) + ) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if node_jobs: + node_spec = ( + node_jobs[0] + .get("template", {}) + .get("spec", {}) + .get("template", {}) + .get("spec", {}) + ) + containers = node_spec.get("containers", []) + if containers and containers[0].get("image"): + return str(containers[0]["image"]) + except Exception: + continue + + raise ValueError( + f"No image specified for runtime '{runtime.name}'. " + f"Provide ContainerBackendConfig.image or add an 'image' field " + f"to its YAML in {LOCAL_RUNTIMES_DIR}." + ) diff --git a/kubeflow/trainer/backends/container/backend_test.py b/kubeflow/trainer/backends/container/backend_test.py new file mode 100644 index 000000000..dbcce3d76 --- /dev/null +++ b/kubeflow/trainer/backends/container/backend_test.py @@ -0,0 +1,667 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Unit tests for ContainerBackend. + +Tests the ContainerBackend class with mocked container adapters. +""" + +from collections.abc import Iterator +import os +from pathlib import Path +import shutil +import tempfile +from unittest.mock import Mock, patch + +import pytest + +from kubeflow.trainer.backends.container.backend import ContainerBackend +from kubeflow.trainer.backends.container.client_adapter import ContainerClientAdapter +from kubeflow.trainer.backends.container.types import ContainerBackendConfig +from kubeflow.trainer.constants import constants +from kubeflow.trainer.test.common import FAILED, SUCCESS, TestCase +from kubeflow.trainer.types import types + + +# Mock Container Adapter +class MockContainerAdapter(ContainerClientAdapter): + """Mock adapter for testing ContainerBackend without Docker/Podman.""" + + def __init__(self): + self._runtime_type = "mock" + self.networks_created = [] + self.containers_created = [] + self.containers_stopped = [] + self.containers_removed = [] + self.networks_deleted = [] + self.images_pulled = [] + self.ping_called = False + + def ping(self): + self.ping_called = True + + def create_network(self, name: str, labels: dict[str, str]) -> str: + network_id = f"net-{name}" + self.networks_created.append({"id": network_id, "name": name, "labels": labels}) + return network_id + + def delete_network(self, network_id: str): + self.networks_deleted.append(network_id) + + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + container_id = f"container-{len(self.containers_created)}" + self.containers_created.append( + { + "id": container_id, + "name": name, + "image": image, + "command": command, + "network": network_id, + "environment": environment, + "labels": labels, + "volumes": volumes, + "working_dir": working_dir, + "status": "running", + "exit_code": None, + } + ) + return container_id + + def get_container(self, container_id: str): + for container in self.containers_created: + if container["id"] == container_id: + return Mock(id=container_id, status=container["status"]) + return None + + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + if follow: + yield f"Log line 1 from {container_id}\n" + yield f"Log line 2 from {container_id}\n" + else: + yield f"Complete log from {container_id}\n" + + def stop_container(self, container_id: str, timeout: int = 10): + self.containers_stopped.append(container_id) + for container in self.containers_created: + if container["id"] == container_id: + container["status"] = "exited" + container["exit_code"] = 0 + + def remove_container(self, container_id: str, force: bool = True): + self.containers_removed.append(container_id) + + def pull_image(self, image: str): + self.images_pulled.append(image) + + def image_exists(self, image: str) -> bool: + return "local" in image or image in self.images_pulled + + def run_oneoff_container(self, image: str, command: list[str]) -> str: + return "Python 3.9.0\npip 21.0.1\nnvidia-smi not found\n" + + def container_status(self, container_id: str) -> tuple[str, int | None]: + for container in self.containers_created: + if container["id"] == container_id: + return (container["status"], container.get("exit_code")) + return ("unknown", None) + + def set_container_status(self, container_id: str, status: str, exit_code: int | None = None): + """Helper method to set container status for testing.""" + for container in self.containers_created: + if container["id"] == container_id: + container["status"] = status + container["exit_code"] = exit_code + + +# Fixtures +@pytest.fixture +def container_backend(): + """Provide ContainerBackend with mocked adapter.""" + backend = ContainerBackend(ContainerBackendConfig()) + backend._adapter = MockContainerAdapter() + return backend + + +@pytest.fixture +def temp_workdir(): + """Provide a temporary working directory.""" + tmpdir = tempfile.mkdtemp() + yield tmpdir + if os.path.exists(tmpdir): + shutil.rmtree(tmpdir) + + +# Helper Function +def simple_train_func(): + """Simple training function for tests.""" + print("Training") + + +# Tests +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="auto-detect docker first", + expected_status=SUCCESS, + ), + TestCase( + name="auto-detect falls back to podman", + expected_status=SUCCESS, + ), + TestCase( + name="both unavailable raises error", + expected_status=FAILED, + expected_error=RuntimeError, + ), + ], +) +def test_backend_initialization(test_case): + """Test ContainerBackend initialization and adapter creation.""" + print("Executing test:", test_case.name) + try: + if test_case.name == "auto-detect docker first": + with ( + patch( + "kubeflow.trainer.backends.container.backend.DockerClientAdapter" + ) as mock_docker, + patch( + "kubeflow.trainer.backends.container.backend.PodmanClientAdapter" + ) as mock_podman, + ): + mock_docker_instance = Mock() + mock_docker.return_value = mock_docker_instance + + _ = ContainerBackend(ContainerBackendConfig()) + + mock_docker.assert_called_once_with(None) + mock_docker_instance.ping.assert_called_once() + mock_podman.assert_not_called() + assert test_case.expected_status == SUCCESS + + elif test_case.name == "auto-detect falls back to podman": + with ( + patch( + "kubeflow.trainer.backends.container.backend.DockerClientAdapter" + ) as mock_docker, + patch( + "kubeflow.trainer.backends.container.backend.PodmanClientAdapter" + ) as mock_podman, + ): + mock_docker_instance = Mock() + mock_docker_instance.ping.side_effect = Exception("Docker not available") + mock_docker.return_value = mock_docker_instance + + mock_podman_instance = Mock() + mock_podman.return_value = mock_podman_instance + + _ = ContainerBackend(ContainerBackendConfig()) + + mock_docker.assert_called_once() + mock_podman.assert_called_once_with(None) + mock_podman_instance.ping.assert_called_once() + assert test_case.expected_status == SUCCESS + + elif test_case.name == "both unavailable raises error": + with ( + patch( + "kubeflow.trainer.backends.container.backend.DockerClientAdapter" + ) as mock_docker, + patch( + "kubeflow.trainer.backends.container.backend.PodmanClientAdapter" + ) as mock_podman, + ): + mock_docker_instance = Mock() + mock_docker_instance.ping.side_effect = Exception("Docker not available") + mock_docker.return_value = mock_docker_instance + + mock_podman_instance = Mock() + mock_podman_instance.ping.side_effect = Exception("Podman not available") + mock_podman.return_value = mock_podman_instance + + ContainerBackend(ContainerBackendConfig()) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +def test_list_runtimes(container_backend): + """Test listing available local runtimes.""" + print("Executing test: list_runtimes") + runtimes = container_backend.list_runtimes() + + assert isinstance(runtimes, list) + assert len(runtimes) > 0 + runtime_names = [r.name for r in runtimes] + assert "torch-distributed" in runtime_names + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="get valid runtime", + expected_status=SUCCESS, + config={"name": "torch-distributed"}, + ), + TestCase( + name="get invalid runtime", + expected_status=FAILED, + config={"name": "nonexistent-runtime"}, + expected_error=ValueError, + ), + ], +) +def test_get_runtime(container_backend, test_case): + """Test getting a specific runtime.""" + print("Executing test:", test_case.name) + try: + runtime = container_backend.get_runtime(**test_case.config) + + assert test_case.expected_status == SUCCESS + assert isinstance(runtime, types.Runtime) + assert runtime.name == test_case.config["name"] + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +def test_get_runtime_packages(container_backend): + """Test getting runtime packages.""" + print("Executing test: get_runtime_packages") + runtime = container_backend.get_runtime("torch-distributed") + container_backend.get_runtime_packages(runtime) + + assert len( + container_backend._adapter.images_pulled + ) > 0 or container_backend._adapter.image_exists(runtime.trainer.image) + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="train single node", + expected_status=SUCCESS, + config={"num_nodes": 1, "expected_containers": 1}, + ), + TestCase( + name="train multi-node", + expected_status=SUCCESS, + config={"num_nodes": 3, "expected_containers": 3}, + ), + TestCase( + name="train with custom env", + expected_status=SUCCESS, + config={ + "num_nodes": 1, + "env": {"MY_VAR": "my_value", "DEBUG": "true"}, + "expected_containers": 1, + }, + ), + TestCase( + name="train with packages", + expected_status=SUCCESS, + config={ + "num_nodes": 1, + "packages": ["numpy", "pandas"], + "expected_containers": 1, + }, + ), + ], +) +def test_train(container_backend, test_case): + """Test training job creation.""" + print("Executing test:", test_case.name) + try: + trainer = types.CustomTrainer( + func=simple_train_func, + num_nodes=test_case.config.get("num_nodes", 1), + env=test_case.config.get("env"), + packages_to_install=test_case.config.get("packages"), + ) + runtime = container_backend.get_runtime("torch-distributed") + + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + assert test_case.expected_status == SUCCESS + assert job_name is not None + assert len(job_name) == 12 + assert ( + len(container_backend._adapter.containers_created) + == test_case.config["expected_containers"] + ) + assert len(container_backend._adapter.networks_created) == 1 + + # Check environment if specified + if "env" in test_case.config: + container = container_backend._adapter.containers_created[0] + for key, value in test_case.config["env"].items(): + assert container["environment"][key] == value + + # Check packages if specified + if "packages" in test_case.config: + container = container_backend._adapter.containers_created[0] + command_str = " ".join(container["command"]) + assert "pip install" in command_str + for package in test_case.config["packages"]: + assert package in command_str + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="list all jobs", + expected_status=SUCCESS, + config={"num_jobs": 2}, + ), + TestCase( + name="list empty jobs", + expected_status=SUCCESS, + config={"num_jobs": 0}, + ), + ], +) +def test_list_jobs(container_backend, test_case): + """Test listing training jobs.""" + print("Executing test:", test_case.name) + try: + runtime = container_backend.get_runtime("torch-distributed") + created_jobs = [] + + for _ in range(test_case.config["num_jobs"]): + trainer = types.CustomTrainer(func=simple_train_func, num_nodes=1) + job_name = container_backend.train(runtime=runtime, trainer=trainer) + created_jobs.append(job_name) + + jobs = container_backend.list_jobs() + + assert test_case.expected_status == SUCCESS + assert len(jobs) == test_case.config["num_jobs"] + if test_case.config["num_jobs"] > 0: + job_names = [job.name for job in jobs] + for created_job in created_jobs: + assert created_job in job_names + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="get existing job", + expected_status=SUCCESS, + config={"num_nodes": 2}, + ), + TestCase( + name="get nonexistent job", + expected_status=FAILED, + config={"job_name": "nonexistent-job"}, + expected_error=ValueError, + ), + ], +) +def test_get_job(container_backend, test_case): + """Test getting a specific job.""" + print("Executing test:", test_case.name) + try: + if test_case.name == "get existing job": + trainer = types.CustomTrainer( + func=simple_train_func, num_nodes=test_case.config["num_nodes"] + ) + runtime = container_backend.get_runtime("torch-distributed") + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + job = container_backend.get_job(job_name) + + assert test_case.expected_status == SUCCESS + assert job.name == job_name + assert job.num_nodes == test_case.config["num_nodes"] + assert len(job.steps) == test_case.config["num_nodes"] + + elif test_case.name == "get nonexistent job": + container_backend.get_job(test_case.config["job_name"]) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="get logs no follow", + expected_status=SUCCESS, + config={"follow": False}, + ), + TestCase( + name="get logs with follow", + expected_status=SUCCESS, + config={"follow": True}, + ), + ], +) +def test_get_job_logs(container_backend, test_case): + """Test getting job logs.""" + print("Executing test:", test_case.name) + try: + trainer = types.CustomTrainer(func=simple_train_func, num_nodes=1) + runtime = container_backend.get_runtime("torch-distributed") + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + logs = list(container_backend.get_job_logs(job_name, follow=test_case.config["follow"])) + + assert test_case.expected_status == SUCCESS + assert len(logs) > 0 + if test_case.config["follow"]: + assert any("Log line" in log for log in logs) + else: + assert any("Complete log" in log for log in logs) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="wait for complete", + expected_status=SUCCESS, + config={"wait_status": constants.TRAINJOB_COMPLETE, "container_exit_code": 0}, + ), + TestCase( + name="wait timeout", + expected_status=FAILED, + config={"wait_status": constants.TRAINJOB_COMPLETE, "timeout": 2}, + expected_error=TimeoutError, + ), + TestCase( + name="job fails", + expected_status=FAILED, + config={"wait_status": constants.TRAINJOB_COMPLETE, "container_exit_code": 1}, + expected_error=RuntimeError, + ), + ], +) +def test_wait_for_job_status(container_backend, test_case): + """Test waiting for job status.""" + print("Executing test:", test_case.name) + try: + trainer = types.CustomTrainer(func=simple_train_func, num_nodes=1) + runtime = container_backend.get_runtime("torch-distributed") + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + if test_case.name == "wait for complete": + container_id = container_backend._adapter.containers_created[0]["id"] + container_backend._adapter.set_container_status( + container_id, "exited", test_case.config["container_exit_code"] + ) + + completed_job = container_backend.wait_for_job_status( + job_name, status={test_case.config["wait_status"]}, timeout=5, polling_interval=1 + ) + + assert test_case.expected_status == SUCCESS + assert completed_job.status == constants.TRAINJOB_COMPLETE + + elif test_case.name == "wait timeout": + container_backend.wait_for_job_status( + job_name, + status={test_case.config["wait_status"]}, + timeout=test_case.config["timeout"], + polling_interval=1, + ) + + elif test_case.name == "job fails": + container_id = container_backend._adapter.containers_created[0]["id"] + container_backend._adapter.set_container_status( + container_id, "exited", test_case.config["container_exit_code"] + ) + + container_backend.wait_for_job_status( + job_name, status={test_case.config["wait_status"]}, timeout=5, polling_interval=1 + ) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="delete with auto_remove true", + expected_status=SUCCESS, + config={"auto_remove": True, "num_nodes": 2}, + ), + TestCase( + name="delete with auto_remove false", + expected_status=SUCCESS, + config={"auto_remove": False, "num_nodes": 2}, + ), + ], +) +def test_delete_job(container_backend, temp_workdir, test_case): + """Test deleting a job.""" + print("Executing test:", test_case.name) + try: + container_backend.cfg.workdir_base = temp_workdir + container_backend.cfg.auto_remove = test_case.config["auto_remove"] + + trainer = types.CustomTrainer( + func=simple_train_func, num_nodes=test_case.config["num_nodes"] + ) + runtime = container_backend.get_runtime("torch-distributed") + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + job_workdir = Path(temp_workdir) / job_name + assert job_workdir.exists() + + container_backend.delete_job(job_name) + + assert test_case.expected_status == SUCCESS + assert len(container_backend._adapter.containers_stopped) == test_case.config["num_nodes"] + assert len(container_backend._adapter.containers_removed) == test_case.config["num_nodes"] + assert len(container_backend._adapter.networks_deleted) == 1 + + if test_case.config["auto_remove"]: + assert not job_workdir.exists() + else: + assert job_workdir.exists() + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="running container", + expected_status=SUCCESS, + config={ + "container_status": "running", + "exit_code": None, + "expected_job_status": constants.TRAINJOB_RUNNING, + }, + ), + TestCase( + name="exited success", + expected_status=SUCCESS, + config={ + "container_status": "exited", + "exit_code": 0, + "expected_job_status": constants.TRAINJOB_COMPLETE, + }, + ), + TestCase( + name="exited failure", + expected_status=SUCCESS, + config={ + "container_status": "exited", + "exit_code": 1, + "expected_job_status": constants.TRAINJOB_FAILED, + }, + ), + ], +) +def test_container_status_mapping(container_backend, test_case): + """Test container status mapping to TrainJob status.""" + print("Executing test:", test_case.name) + try: + trainer = types.CustomTrainer(func=simple_train_func, num_nodes=1) + runtime = container_backend.get_runtime("torch-distributed") + job_name = container_backend.train(runtime=runtime, trainer=trainer) + + container_id = container_backend._adapter.containers_created[0]["id"] + container_backend._adapter.set_container_status( + container_id, test_case.config["container_status"], test_case.config["exit_code"] + ) + + job = container_backend.get_job(job_name) + + assert test_case.expected_status == SUCCESS + assert job.status == test_case.config["expected_job_status"] + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") diff --git a/kubeflow/trainer/backends/container/client_adapter.py b/kubeflow/trainer/backends/container/client_adapter.py new file mode 100644 index 000000000..72611288c --- /dev/null +++ b/kubeflow/trainer/backends/container/client_adapter.py @@ -0,0 +1,454 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Container client adapters for Docker and Podman. + +This module implements the adapter pattern to abstract away differences between +Docker and Podman APIs, allowing the backend to work with either runtime through +a common interface. +""" + +from __future__ import annotations + +import abc +from collections.abc import Iterator + + +class ContainerClientAdapter(abc.ABC): + """ + Abstract adapter interface for container clients. + + This adapter abstracts the container runtime API, allowing the backend + to work with Docker and Podman through a unified interface. + """ + + @abc.abstractmethod + def ping(self): + """Test the connection to the container runtime.""" + raise NotImplementedError() + + @abc.abstractmethod + def create_network( + self, + name: str, + labels: dict[str, str], + ) -> str: + """ + Create a container network. + + Args: + name: Network name + labels: Labels to attach to the network + + Returns: + Network ID or name + """ + raise NotImplementedError() + + @abc.abstractmethod + def delete_network(self, network_id: str): + """Delete a network.""" + raise NotImplementedError() + + @abc.abstractmethod + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """ + Create and start a container. + + Args: + image: Container image + command: Command to run + name: Container name + network_id: Network to attach to + environment: Environment variables + labels: Container labels + volumes: Volume mounts + working_dir: Working directory + + Returns: + Container ID + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_container(self, container_id: str): + """Get container object by ID.""" + raise NotImplementedError() + + @abc.abstractmethod + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def stop_container(self, container_id: str, timeout: int = 10): + """Stop a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def remove_container(self, container_id: str, force: bool = True): + """Remove a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def pull_image(self, image: str): + """Pull an image.""" + raise NotImplementedError() + + @abc.abstractmethod + def image_exists(self, image: str) -> bool: + """Check if an image exists locally.""" + raise NotImplementedError() + + @abc.abstractmethod + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """ + Run a short-lived container and return its output. + + Args: + image: Container image + command: Command to run + + Returns: + Container output as string + """ + raise NotImplementedError() + + @abc.abstractmethod + def container_status(self, container_id: str) -> tuple[str, int | None]: + """ + Get container status. + + Returns: + Tuple of (status_string, exit_code) + Status strings: "running", "created", "exited", etc. + Exit code is None if container hasn't exited + """ + raise NotImplementedError() + + +class DockerClientAdapter(ContainerClientAdapter): + """Adapter for Docker client.""" + + def __init__(self, host: str | None = None): + """ + Initialize Docker client. + + Args: + host: Docker host URL, or None to use environment defaults + """ + try: + import docker # type: ignore + except ImportError as e: + raise ImportError( + "The 'docker' Python package is not installed. Install with extras: " + "pip install kubeflow[docker]" + ) from e + + if host: + self.client = docker.DockerClient(base_url=host) + else: + self.client = docker.from_env() + + self._runtime_type = "docker" + + def ping(self): + """Test connection to Docker daemon.""" + self.client.ping() + + def create_network(self, name: str, labels: dict[str, str]) -> str: + """Create a Docker network.""" + try: + self.client.networks.get(name) + return name + except Exception: + pass + + self.client.networks.create( + name=name, + check_duplicate=True, + labels=labels, + ) + return name + + def delete_network(self, network_id: str): + """Delete Docker network.""" + try: + net = self.client.networks.get(network_id) + net.remove() + except Exception: + pass + + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Docker container.""" + container = self.client.containers.run( + image=image, + command=tuple(command), + name=name, + detach=True, + working_dir=working_dir, + network=network_id, + environment=environment, + labels=labels, + volumes=volumes, + auto_remove=False, + ) + return container.id + + def get_container(self, container_id: str): + """Get Docker container by ID.""" + return self.client.containers.get(container_id) + + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from Docker container.""" + container = self.get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def stop_container(self, container_id: str, timeout: int = 10): + """Stop Docker container.""" + container = self.get_container(container_id) + container.stop(timeout=timeout) + + def remove_container(self, container_id: str, force: bool = True): + """Remove Docker container.""" + container = self.get_container(container_id) + container.remove(force=force) + + def pull_image(self, image: str): + """Pull Docker image.""" + self.client.images.pull(image) + + def image_exists(self, image: str) -> bool: + """Check if Docker image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a short-lived Docker container and return output.""" + try: + output = self.client.containers.run( + image=image, + command=tuple(command), + detach=False, + remove=True, + ) + if isinstance(output, (bytes, bytearray)): + return output.decode("utf-8", errors="ignore") + return str(output) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + def container_status(self, container_id: str) -> tuple[str, int | None]: + """Get Docker container status.""" + try: + container = self.get_container(container_id) + status = container.status + # Get exit code if container has exited + exit_code = None + if status == "exited": + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + exit_code = inspect.get("State", {}).get("ExitCode") + return (status, exit_code) + except Exception: + return ("unknown", None) + + +class PodmanClientAdapter(ContainerClientAdapter): + """Adapter for Podman client.""" + + def __init__(self, host: str | None = None): + """ + Initialize Podman client. + + Args: + host: Podman host URL, or None to use environment defaults + """ + try: + import podman # type: ignore + except ImportError as e: + raise ImportError( + "The 'podman' Python package is not installed. Install with extras: " + "pip install kubeflow[podman]" + ) from e + + if host: + self.client = podman.PodmanClient(base_url=host) + else: + self.client = podman.PodmanClient() + + self._runtime_type = "podman" + + def ping(self): + """Test connection to Podman.""" + self.client.ping() + + def create_network(self, name: str, labels: dict[str, str]) -> str: + """Create a Podman network with DNS enabled.""" + try: + self.client.networks.get(name) + return name + except Exception: + pass + + self.client.networks.create( + name=name, + driver="bridge", + dns_enabled=True, + labels=labels, + ) + return name + + def delete_network(self, network_id: str): + """Delete Podman network.""" + try: + net = self.client.networks.get(network_id) + net.remove() + except Exception: + pass + + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Podman container.""" + container = self.client.containers.run( + image=image, + command=command, + name=name, + network=network_id, + working_dir=working_dir, + environment=environment, + labels=labels, + volumes=volumes, + detach=True, + remove=False, + ) + return container.id + + def get_container(self, container_id: str): + """Get Podman container by ID.""" + return self.client.containers.get(container_id) + + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from Podman container.""" + container = self.get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def stop_container(self, container_id: str, timeout: int = 10): + """Stop Podman container.""" + container = self.get_container(container_id) + container.stop(timeout=timeout) + + def remove_container(self, container_id: str, force: bool = True): + """Remove Podman container.""" + container = self.get_container(container_id) + container.remove(force=force) + + def pull_image(self, image: str): + """Pull Podman image.""" + self.client.images.pull(image) + + def image_exists(self, image: str) -> bool: + """Check if Podman image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a short-lived Podman container and return output.""" + try: + container = self.client.containers.create( + image=image, + command=command, + detach=False, + remove=True, + ) + container.start() + container.wait() + logs = container.logs() + + if isinstance(logs, (bytes, bytearray)): + return logs.decode("utf-8", errors="ignore") + return str(logs) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + def container_status(self, container_id: str) -> tuple[str, int | None]: + """Get Podman container status.""" + try: + container = self.get_container(container_id) + status = container.status + # Get exit code if container has exited + exit_code = None + if status == "exited": + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + exit_code = inspect.get("State", {}).get("ExitCode") + return (status, exit_code) + except Exception: + return ("unknown", None) diff --git a/kubeflow/trainer/backends/podman/runtime_loader.py b/kubeflow/trainer/backends/container/runtime_loader.py similarity index 86% rename from kubeflow/trainer/backends/podman/runtime_loader.py rename to kubeflow/trainer/backends/container/runtime_loader.py index 43af5be92..6b9bdf06c 100644 --- a/kubeflow/trainer/backends/podman/runtime_loader.py +++ b/kubeflow/trainer/backends/container/runtime_loader.py @@ -13,9 +13,9 @@ # limitations under the License. """ -Runtime loader for the Podman backend. +Runtime loader for the Container backend. -This module provides Podman-specific imports for the shared local runtime loader. +This module provides container-agnostic imports for the shared local runtime loader. """ from kubeflow.trainer.backends.local_runtime_loader import ( diff --git a/kubeflow/trainer/backends/podman/types.py b/kubeflow/trainer/backends/container/types.py similarity index 62% rename from kubeflow/trainer/backends/podman/types.py rename to kubeflow/trainer/backends/container/types.py index 9f66215be..ae1ce2075 100644 --- a/kubeflow/trainer/backends/podman/types.py +++ b/kubeflow/trainer/backends/container/types.py @@ -13,36 +13,44 @@ # limitations under the License. """ -Types and configuration for the Podman backend. +Types and configuration for the unified Container backend. -Configuration options for Podman backend: +This backend automatically detects and uses either Docker or Podman. +It provides a single interface for container-based execution regardless +of the underlying runtime. + +Configuration options: - image: Optional explicit image. If omitted, use the image referenced by the selected runtime (e.g., torch_distributed) from `config/local_runtimes`. - pull_policy: Controls image pulling. Supported values: "IfNotPresent", "Always", "Never". The default is "IfNotPresent". - auto_remove: Whether to remove containers and networks when jobs are deleted. Defaults to True. - - gpus: GPU support using NVIDIA CDI or other device plugins. Defaults to None. + - gpus: GPU support (implementation varies between Docker and Podman). + Defaults to None. - env: Optional global environment variables applied to all containers. - - podman_url: Optional override for connecting to a remote/local Podman socket. - By default, the Podman client resolves from environment variables or uses - the system default socket (e.g., unix:///run/user/1000/podman/podman.sock - for rootless, unix:///run/podman/podman.sock for rootful). + - container_host: Optional override for connecting to a remote/local container + daemon. By default, auto-detects from environment or uses system defaults. + For Docker: uses DOCKER_HOST or default socket. + For Podman: uses CONTAINER_HOST or default socket. - workdir_base: Base directory on the host to place per-job working dirs that are bind-mounted into containers as /workspace. Defaults to a path under the user's home directory for compatibility. + - runtime: Force use of a specific container runtime ("docker" or "podman"). + If not set, auto-detects based on availability (tries Docker first, then Podman). """ -from typing import Optional, Union +from typing import Literal, Optional, Union from pydantic import BaseModel, Field -class LocalPodmanBackendConfig(BaseModel): +class ContainerBackendConfig(BaseModel): image: Optional[str] = Field(default=None) pull_policy: str = Field(default="IfNotPresent") auto_remove: bool = Field(default=True) gpus: Optional[Union[int, bool]] = Field(default=None) env: Optional[dict[str, str]] = Field(default=None) - podman_url: Optional[str] = Field(default=None) + container_host: Optional[str] = Field(default=None) workdir_base: Optional[str] = Field(default=None) + runtime: Optional[Literal["docker", "podman"]] = Field(default=None) diff --git a/kubeflow/trainer/backends/docker/__init__.py b/kubeflow/trainer/backends/docker/__init__.py deleted file mode 100644 index c3c888c01..000000000 --- a/kubeflow/trainer/backends/docker/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from kubeflow.trainer.backends.docker.backend import LocalDockerBackend -from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig - -__all__ = ["LocalDockerBackend", "LocalDockerBackendConfig"] diff --git a/kubeflow/trainer/backends/docker/backend.py b/kubeflow/trainer/backends/docker/backend.py deleted file mode 100644 index 36c930c53..000000000 --- a/kubeflow/trainer/backends/docker/backend.py +++ /dev/null @@ -1,234 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -DockerBackend -------------- - -Local execution backend for `CustomTrainer` jobs using Docker containers. - -Key behaviors: -- Multi-node jobs: one container per node connected via a per-job Docker network. -- Entry script generation: we serialize the user's training function to a small - Python file and invoke it inside the container using `torchrun` (preferred) or - `python` as a fallback. -- Runtimes: we use `config/local_runtimes` to define runtime images and - characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime - is provided. -- Image pulling: controlled via `pull_policy` and performed automatically if - needed. -- Logs and lifecycle: streaming logs and deletion semantics similar to the - Kubernetes backend, but tailored to Docker. -""" - -from __future__ import annotations - -from collections.abc import Iterator -import logging -from pathlib import Path - -try: - import docker # type: ignore -except Exception: # pragma: no cover - optional dependency, validated at runtime - docker = None # type: ignore - -from kubeflow.trainer.backends.container_base import ContainerBackend -from kubeflow.trainer.backends.docker.runtime_loader import ( - get_local_runtime, - list_local_runtimes, -) -from kubeflow.trainer.backends.docker.types import LocalDockerBackendConfig -from kubeflow.trainer.types import types - -logger = logging.getLogger(__name__) - - -class LocalDockerBackend(ContainerBackend): - def __init__(self, cfg: LocalDockerBackendConfig): - if docker is None: - raise ImportError( - "The 'docker' Python package is not installed. Install with extras: " - "pip install kubeflow[docker]" - ) - - super().__init__(label_prefix="trainer.kubeflow.org") - - # initialize docker client (env-based resolution by default) - if cfg.docker_host: - self.client = docker.DockerClient(base_url=cfg.docker_host) - else: - self.client = docker.from_env() - - self.cfg = cfg - - # ---- Runtime APIs ---- - def list_runtimes(self) -> list[types.Runtime]: - return list_local_runtimes() - - def get_runtime(self, name: str) -> types.Runtime: - return get_local_runtime(name) - - # ---- Container backend implementation ---- - def _get_client(self): - return self.client - - def _create_network(self, job_name: str) -> str: - """Create a Docker network for the job.""" - network_name = f"{job_name}-net" - try: - self.client.networks.get(network_name) - return network_name - except Exception: - pass - # Create network with labels for filtering and management - self.client.networks.create( - name=network_name, - check_duplicate=True, - labels={ - f"{self.label_prefix}/trainjob-name": job_name, - }, - ) - return network_name - - def _delete_network(self, network_id: str): - """Delete the Docker network.""" - try: - net = self.client.networks.get(network_id) - net.remove() - except Exception: - pass - - def _get_master_addr(self, job_name: str, rank: int) -> str: - """Docker uses container names for DNS resolution.""" - return f"{job_name}-node-0" - - def _get_master_port(self, rank: int) -> int: - """Docker uses a fixed port since containers are on different network endpoints.""" - return 29500 - - def _create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """Create and start a Docker container.""" - container = self.client.containers.run( - image=image, - command=tuple(command), - name=name, - detach=True, - working_dir=working_dir, - network=network_id, - environment=environment, - labels=labels, - volumes=volumes, - auto_remove=False, # we control removal in delete_job - ) - return container.id - - def _get_container(self, container_id: str): - """Get Docker container by ID.""" - return self.client.containers.get(container_id) - - def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from a Docker container.""" - container = self._get_container(container_id) - logs = container.logs(stream=bool(follow), follow=bool(follow)) - if follow: - for chunk in logs: - if isinstance(chunk, bytes): - yield chunk.decode("utf-8", errors="ignore") - else: - yield str(chunk) - else: - if isinstance(logs, bytes): - yield logs.decode("utf-8", errors="ignore") - else: - yield str(logs) - - def _stop_container(self, container_id: str, timeout: int = 10): - """Stop a Docker container.""" - container = self._get_container(container_id) - container.stop(timeout=timeout) - - def _remove_container(self, container_id: str, force: bool = True): - """Remove a Docker container.""" - container = self._get_container(container_id) - container.remove(force=force) - - def _pull_image(self, image: str): - """Pull a Docker image.""" - self.client.images.pull(image) - - def _image_exists(self, image: str) -> bool: - """Check if Docker image exists locally.""" - try: - self.client.images.get(image) - return True - except Exception: - return False - - def _run_oneoff_container(self, image: str, command: list[str]) -> str: - """ - Run a short-lived Docker container and return its stdout as a string. - - Implementation detail: - - Use detach=False and remove=True so Docker SDK runs the container to - completion, returns combined logs as bytes, and cleans it up. This - avoids races where auto-removed containers disappear before .wait/.logs. - """ - try: - output = self.client.containers.run( - image=image, - command=tuple(command), - detach=False, - remove=True, - ) - except Exception as e: - raise RuntimeError(f"One-off container failed to run: {e}") from e - - if isinstance(output, (bytes, bytearray)): - return output.decode("utf-8", errors="ignore") - return str(output) - - def _get_pull_policy(self) -> str: - return self.cfg.pull_policy - - def _get_workdir_base(self) -> str | None: - return self.cfg.workdir_base - - def _get_auto_remove(self) -> bool: - return self.cfg.auto_remove - - def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: - """Build environment variables for containers.""" - env = dict(self.cfg.env or {}) - if trainer.env: - env.update(trainer.env) - return env - - def _get_runtimes_dir(self) -> Path: - """Get the path to the local runtimes directory.""" - from kubeflow.trainer.backends.docker.runtime_loader import LOCAL_RUNTIMES_DIR - return LOCAL_RUNTIMES_DIR - - def _resolve_image(self, runtime: types.Runtime) -> str: - """Resolve the Docker image for a runtime.""" - return self._resolve_image_from_runtime(runtime, self.cfg.image) diff --git a/kubeflow/trainer/backends/docker/runtime_loader.py b/kubeflow/trainer/backends/docker/runtime_loader.py deleted file mode 100644 index ea51e4eed..000000000 --- a/kubeflow/trainer/backends/docker/runtime_loader.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Runtime loader for the Docker backend. - -This module provides Docker-specific imports for the shared local runtime loader. -""" - -from kubeflow.trainer.backends.local_runtime_loader import ( - LOCAL_RUNTIMES_DIR, - get_local_runtime, - list_local_runtimes, -) - -__all__ = ["LOCAL_RUNTIMES_DIR", "get_local_runtime", "list_local_runtimes"] diff --git a/kubeflow/trainer/backends/docker/types.py b/kubeflow/trainer/backends/docker/types.py deleted file mode 100644 index 644a39816..000000000 --- a/kubeflow/trainer/backends/docker/types.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Types and configuration for the Docker backend. -We keep the configuration surface area intentionally small for v1: -Configuration options for Docker backend: - - - image: Optional explicit image. If omitted, use the image referenced by the - selected runtime (e.g., torch_distributed) from `config/local_runtimes`. - - pull_policy: Controls image pulling. Supported values: "IfNotPresent", - "Always", "Never". The default is "IfNotPresent". - - auto_remove: Whether to remove containers and networks when jobs are deleted. - Defaults to True. - - gpus: GPU support is not implemented for v1 (kept for future extensibility). - - env: Optional global environment variables applied to all containers. - - docker_host: Optional override for connecting to a remote/local Docker daemon. - By default, the Docker SDK resolves from environment variables or uses the - system default socket. - - workdir_base: Base directory on the host to place per-job working dirs that - are bind-mounted into containers as /workspace. Defaults to a path under the - user's home directory for compatibility. -""" - -from typing import Optional, Union - -from pydantic import BaseModel, Field - - -class LocalDockerBackendConfig(BaseModel): - image: Optional[str] = Field(default=None) - network: Optional[str] = Field(default=None) - pull_policy: str = Field(default="IfNotPresent") - auto_remove: bool = Field(default=True) - # In Python 3.9, avoid PEP 604 unions; use typing.Union/Optional instead. - # Define the type at module scope so Pydantic doesn't treat it as a field. - gpus: Optional[Union[int, bool]] = Field(default=None) - env: Optional[dict[str, str]] = Field(default=None) - docker_host: Optional[str] = Field(default=None) - # Base directory on the host to place per-job working dirs that are bind-mounted - # into containers as /workspace. Defaults to a path under the user's home to - # maximize compatibility with Docker Desktop file sharing on macOS/Windows. - workdir_base: Optional[str] = Field(default=None) diff --git a/kubeflow/trainer/backends/podman/backend.py b/kubeflow/trainer/backends/podman/backend.py deleted file mode 100644 index 60adaf2ee..000000000 --- a/kubeflow/trainer/backends/podman/backend.py +++ /dev/null @@ -1,244 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -PodmanBackend -------------- - -Local execution backend for `CustomTrainer` jobs using Podman containers. - -Key behaviors: -- Multi-node jobs: containers are connected via a Podman network, giving each - container its own network interface for proper distributed training with - name-based DNS resolution (similar to Docker). -- Entry script generation: we serialize the user's training function to a small - Python file and invoke it inside the container using `torchrun` (preferred) or - `python` as a fallback. -- Runtimes: we use `config/local_runtimes` to define runtime images and - characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime - is provided. -- Image pulling: controlled via `pull_policy` and performed automatically if - needed. -- Logs and lifecycle: streaming logs and deletion semantics similar to the - Docker backend. -- Networking: Uses Podman networks (not pods) so each container has its own - network interface, enabling proper PyTorch distributed training communication. -""" - -from __future__ import annotations - -from collections.abc import Iterator -import logging -from pathlib import Path - -try: - import podman # type: ignore -except Exception: # pragma: no cover - optional dependency, validated at runtime - podman = None # type: ignore - -from kubeflow.trainer.backends.container_base import ContainerBackend -from kubeflow.trainer.backends.podman.runtime_loader import ( - get_local_runtime, - list_local_runtimes, -) -from kubeflow.trainer.backends.podman.types import LocalPodmanBackendConfig -from kubeflow.trainer.types import types - -logger = logging.getLogger(__name__) - - -class LocalPodmanBackend(ContainerBackend): - def __init__(self, cfg: LocalPodmanBackendConfig): - if podman is None: - raise ImportError( - "The 'podman' Python package is not installed. Install with extras: " - "pip install kubeflow[podman]" - ) - - super().__init__(label_prefix="trainer.kubeflow.org") - - # Initialize Podman client - if cfg.podman_url: - self.client = podman.PodmanClient(base_url=cfg.podman_url) - else: - # Use system default socket - self.client = podman.PodmanClient() - - self.cfg = cfg - - # ---- Runtime APIs ---- - def list_runtimes(self) -> list[types.Runtime]: - return list_local_runtimes() - - def get_runtime(self, name: str) -> types.Runtime: - return get_local_runtime(name) - - # ---- Container backend implementation ---- - def _get_client(self): - return self.client - - def _create_network(self, job_name: str) -> str: - """ - Create a Podman network for the job to enable container networking. - - Unlike pods (which share network namespace), a Podman network gives each - container its own network interface, enabling proper distributed training. - """ - network_name = f"{job_name}-net" - try: - self.client.networks.get(network_name) - return network_name - except Exception: - pass - # Create network with DNS enabled for hostname resolution - self.client.networks.create( - name=network_name, - driver="bridge", - dns_enabled=True, - labels={ - f"{self.label_prefix}/trainjob-name": job_name, - }, - ) - return network_name - - def _delete_network(self, network_id: str): - """Delete the Podman network.""" - try: - net = self.client.networks.get(network_id) - net.remove() - except Exception: - pass - - def _get_master_addr(self, job_name: str, rank: int) -> str: - """Podman uses container names for DNS resolution within a network.""" - return f"{job_name}-node-0" - - def _get_master_port(self, rank: int) -> int: - """Use a fixed port since containers are on different network endpoints.""" - return 29500 - - def _create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """Create and start a Podman container on the network.""" - # Use containers.run() which creates and starts in one step - # This is equivalent to: podman run --network ... - container = self.client.containers.run( - image=image, - command=command, - name=name, - network=network_id, - working_dir=working_dir, - environment=environment, - labels=labels, - volumes=volumes, - detach=True, - remove=False, # Don't auto-remove, we control this in delete_job - ) - - return container.id - - def _get_container(self, container_id: str): - """Get Podman container by ID.""" - return self.client.containers.get(container_id) - - def _container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from a Podman container.""" - container = self._get_container(container_id) - logs = container.logs(stream=bool(follow), follow=bool(follow)) - if follow: - for chunk in logs: - if isinstance(chunk, bytes): - yield chunk.decode("utf-8", errors="ignore") - else: - yield str(chunk) - else: - if isinstance(logs, bytes): - yield logs.decode("utf-8", errors="ignore") - else: - yield str(logs) - - def _stop_container(self, container_id: str, timeout: int = 10): - """Stop a Podman container.""" - container = self._get_container(container_id) - container.stop(timeout=timeout) - - def _remove_container(self, container_id: str, force: bool = True): - """Remove a Podman container.""" - container = self._get_container(container_id) - container.remove(force=force) - - def _pull_image(self, image: str): - """Pull a Podman image.""" - self.client.images.pull(image) - - def _image_exists(self, image: str) -> bool: - """Check if Podman image exists locally.""" - try: - self.client.images.get(image) - return True - except Exception: - return False - - def _run_oneoff_container(self, image: str, command: list[str]) -> str: - """Run a short-lived Podman container and return its logs.""" - try: - container = self.client.containers.create( - image=image, - command=command, - detach=False, - remove=True, - ) - container.start() - container.wait() - logs = container.logs() - - if isinstance(logs, (bytes, bytearray)): - return logs.decode("utf-8", errors="ignore") - return str(logs) - except Exception as e: - raise RuntimeError(f"One-off container failed to run: {e}") from e - - def _get_pull_policy(self) -> str: - return self.cfg.pull_policy - - def _get_workdir_base(self) -> str | None: - return self.cfg.workdir_base - - def _get_auto_remove(self) -> bool: - return self.cfg.auto_remove - - def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: - """Build environment variables for containers.""" - env = dict(self.cfg.env or {}) - if trainer.env: - env.update(trainer.env) - return env - - def _get_runtimes_dir(self) -> Path: - """Get the path to the local runtimes directory.""" - from kubeflow.trainer.backends.podman.runtime_loader import LOCAL_RUNTIMES_DIR - return LOCAL_RUNTIMES_DIR - - def _resolve_image(self, runtime: types.Runtime) -> str: - """Resolve the Podman image for a runtime.""" - return self._resolve_image_from_runtime(runtime, self.cfg.image) From 9a0ca37d02043692c9037995ac5a3669a3ac2f72 Mon Sep 17 00:00:00 2001 From: Fiona Waters Date: Tue, 14 Oct 2025 09:23:40 +0100 Subject: [PATCH 4/7] Use ip address for Podman Signed-off-by: Fiona Waters --- .../trainer/backends/container/backend.py | 31 +++++++++- .../backends/container/client_adapter.py | 61 +++++++++++++++++++ 2 files changed, 91 insertions(+), 1 deletion(-) diff --git a/kubeflow/trainer/backends/container/backend.py b/kubeflow/trainer/backends/container/backend.py index 8c22f20bf..eb8b989bf 100644 --- a/kubeflow/trainer/backends/container/backend.py +++ b/kubeflow/trainer/backends/container/backend.py @@ -221,14 +221,29 @@ def train( # Create N containers (one per node) containers: list[_Node] = [] + master_container_id = None + master_ip = None for rank in range(num_nodes): container_name = f"{job_name}-node-{rank}" # Get master address and port for torchrun - master_addr = f"{job_name}-node-0" master_port = 29500 + # For Podman: use IP address to avoid DNS timing issues + # For Docker: use hostname (DNS is reliable) + if rank == 0: + # Master node - will be created first + master_addr = f"{job_name}-node-0" + else: + # Worker nodes - determine master address based on runtime + if self._runtime_type == "podman" and master_ip: + master_addr = master_ip + logger.debug(f"Using master IP address for Podman: {master_ip}") + else: + master_addr = f"{job_name}-node-0" + logger.debug(f"Using master hostname: {master_addr}") + # Prefer torchrun; fall back to python if torchrun is unavailable # For worker nodes, wait for master to be reachable before starting torchrun wait_for_master = "" @@ -289,6 +304,20 @@ def train( logger.info(f"Started container {container_name} (ID: {container_id[:12]})") containers.append(_Node(name=container_name, container_id=container_id)) + # If this is the master node and we're using Podman, get its IP address + if rank == 0: + master_container_id = container_id + if self._runtime_type == "podman": + # Get master IP for worker nodes to use + master_ip = self._adapter.get_container_ip(master_container_id, network_id) + if master_ip: + logger.info(f"Master node IP address: {master_ip}") + else: + logger.warning( + "Could not retrieve master IP address. " + "Worker nodes will fall back to DNS resolution." + ) + # Store job in backend self._jobs[job_name] = _Job( name=job_name, diff --git a/kubeflow/trainer/backends/container/client_adapter.py b/kubeflow/trainer/backends/container/client_adapter.py index 72611288c..d7715fd8c 100644 --- a/kubeflow/trainer/backends/container/client_adapter.py +++ b/kubeflow/trainer/backends/container/client_adapter.py @@ -148,6 +148,20 @@ def container_status(self, container_id: str) -> tuple[str, int | None]: """ raise NotImplementedError() + @abc.abstractmethod + def get_container_ip(self, container_id: str, network_id: str) -> str | None: + """ + Get container's IP address on a specific network. + + Args: + container_id: Container ID + network_id: Network name or ID + + Returns: + IP address string or None if not found + """ + raise NotImplementedError() + class DockerClientAdapter(ContainerClientAdapter): """Adapter for Docker client.""" @@ -298,6 +312,29 @@ def container_status(self, container_id: str) -> tuple[str, int | None]: except Exception: return ("unknown", None) + def get_container_ip(self, container_id: str, network_id: str) -> str | None: + """Get container's IP address on a specific network.""" + try: + container = self.get_container(container_id) + # Refresh container info + container.reload() + # Get network settings + networks = container.attrs.get("NetworkSettings", {}).get("Networks", {}) + + # Try to find the network by exact name or ID + if network_id in networks: + return networks[network_id].get("IPAddress") + + # Fallback: return first available IP + for net_name, net_info in networks.items(): + ip = net_info.get("IPAddress") + if ip: + return ip + + return None + except Exception: + return None + class PodmanClientAdapter(ContainerClientAdapter): """Adapter for Podman client.""" @@ -452,3 +489,27 @@ def container_status(self, container_id: str) -> tuple[str, int | None]: return (status, exit_code) except Exception: return ("unknown", None) + + def get_container_ip(self, container_id: str, network_id: str) -> str | None: + """Get container's IP address on a specific network.""" + try: + container = self.get_container(container_id) + # Get container inspect data + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + + # Get network settings - Podman structure is similar to Docker + networks = inspect.get("NetworkSettings", {}).get("Networks", {}) + + # Try to find the network by exact name or ID + if network_id in networks: + return networks[network_id].get("IPAddress") + + # Fallback: return first available IP + for net_name, net_info in networks.items(): + ip = net_info.get("IPAddress") + if ip: + return ip + + return None + except Exception: + return None From c92f77738c1da0879d3d5ad0b4d8900805300f77 Mon Sep 17 00:00:00 2001 From: Fiona Waters Date: Mon, 20 Oct 2025 11:38:32 +0100 Subject: [PATCH 5/7] Updating to rely on container runtime rather than storing job info in memory Signed-off-by: Fiona Waters --- .../trainer/backends/container/backend.py | 255 ++++++++++++------ .../backends/container/client_adapter.py | 91 +++++++ 2 files changed, 263 insertions(+), 83 deletions(-) diff --git a/kubeflow/trainer/backends/container/backend.py b/kubeflow/trainer/backends/container/backend.py index eb8b989bf..2fe96d661 100644 --- a/kubeflow/trainer/backends/container/backend.py +++ b/kubeflow/trainer/backends/container/backend.py @@ -39,7 +39,6 @@ from __future__ import annotations from collections.abc import Iterator -from dataclasses import dataclass from datetime import datetime import logging import os @@ -67,23 +66,6 @@ logger = logging.getLogger(__name__) -@dataclass -class _Node: - name: str - container_id: str - status: str = constants.TRAINJOB_CREATED - - -@dataclass -class _Job: - name: str - created: datetime - runtime: types.Runtime - network_id: str - nodes: list[_Node] - workdir_host: str - - class ContainerBackend(ExecutionBackend): """ Unified container backend that auto-detects Docker or Podman. @@ -94,7 +76,6 @@ class ContainerBackend(ExecutionBackend): def __init__(self, cfg: ContainerBackendConfig): self.cfg = cfg - self._jobs: dict[str, _Job] = {} self.label_prefix = "trainer.kubeflow.org" # Initialize the container client adapter @@ -215,12 +196,17 @@ def train( network_id = self._adapter.create_network( name=f"{job_name}-net", - labels={f"{self.label_prefix}/trainjob-name": job_name}, + labels={ + f"{self.label_prefix}/trainjob-name": job_name, + f"{self.label_prefix}/runtime-name": runtime.name, + f"{self.label_prefix}/workdir": workdir, + f"{self.label_prefix}/created": datetime.now().isoformat(), + }, ) logger.info(f"Created network: {network_id}") # Create N containers (one per node) - containers: list[_Node] = [] + container_ids: list[str] = [] master_container_id = None master_ip = None @@ -279,6 +265,8 @@ def train( labels = { f"{self.label_prefix}/trainjob-name": job_name, f"{self.label_prefix}/step": f"node-{rank}", + f"{self.label_prefix}/network-id": network_id, + f"{self.label_prefix}/num-nodes": str(num_nodes), } volumes = { @@ -302,7 +290,7 @@ def train( ) logger.info(f"Started container {container_name} (ID: {container_id[:12]})") - containers.append(_Node(name=container_name, container_id=container_id)) + container_ids.append(container_id) # If this is the master node and we're using Podman, get its IP address if rank == 0: @@ -318,18 +306,8 @@ def train( "Worker nodes will fall back to DNS resolution." ) - # Store job in backend - self._jobs[job_name] = _Job( - name=job_name, - created=datetime.now(), - runtime=runtime, - network_id=network_id, - nodes=containers, - workdir_host=workdir, - ) - logger.info( - f"Training job {job_name} created successfully with {len(containers)} container(s)" + f"Training job {job_name} created successfully with {len(container_ids)} container(s)" ) return job_name @@ -343,11 +321,11 @@ def train( try: # Stop and remove any containers that were created - if "containers" in locals(): - for node in containers: + if "container_ids" in locals(): + for container_id in container_ids: with suppress(Exception): - self._adapter.stop_container(node.container_id, timeout=5) - self._adapter.remove_container(node.container_id, force=True) + self._adapter.stop_container(container_id, timeout=5) + self._adapter.remove_container(container_id, force=True) # Remove network if it was created if "network_id" in locals(): @@ -365,53 +343,146 @@ def train( raise def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: + """List all training jobs by querying container runtime.""" + # Get all containers with our label prefix + filters = {"label": [f"{self.label_prefix}/trainjob-name"]} + containers = self._adapter.list_containers(filters=filters) + + # Group containers by job name + jobs_map: dict[str, list[dict]] = {} + for container in containers: + job_name = container["labels"].get(f"{self.label_prefix}/trainjob-name") + if job_name: + if job_name not in jobs_map: + jobs_map[job_name] = [] + jobs_map[job_name].append(container) + result: list[types.TrainJob] = [] - for job in self._jobs.values(): - if runtime and job.runtime.name != runtime.name: + for job_name, job_containers in jobs_map.items(): + # Get metadata from first container's network + if not job_containers: + continue + + network_id = job_containers[0]["labels"].get(f"{self.label_prefix}/network-id") + if not network_id: + continue + + network_info = self._adapter.get_network(network_id) + if not network_info: continue + + network_labels = network_info.get("labels", {}) + runtime_name = network_labels.get(f"{self.label_prefix}/runtime-name") + + # Filter by runtime if specified + if runtime and runtime_name != runtime.name: + continue + + # Get runtime object + try: + job_runtime = self.get_runtime(runtime_name) if runtime_name else None + except Exception: + job_runtime = None + + if not job_runtime: + continue + + # Parse creation timestamp + created_str = network_labels.get(f"{self.label_prefix}/created", "") + try: + from dateutil import parser + creation_timestamp = parser.isoparse(created_str) + except Exception: + creation_timestamp = datetime.now() + + # Build steps from containers steps = [] - for node in job.nodes: + for container in sorted(job_containers, key=lambda c: c["name"]): + step_name = container["labels"].get(f"{self.label_prefix}/step", "") steps.append( types.Step( - name=node.name.split(f"{job.name}-")[-1], - pod_name=node.name, - status=self._container_status(node.container_id), + name=step_name, + pod_name=container["name"], + status=self._container_status(container["id"]), ) ) + + # Get num_nodes from labels + num_nodes = int(job_containers[0]["labels"].get(f"{self.label_prefix}/num-nodes", len(job_containers))) + result.append( types.TrainJob( - name=job.name, - creation_timestamp=job.created, - runtime=job.runtime, + name=job_name, + creation_timestamp=creation_timestamp, + runtime=job_runtime, steps=steps, - num_nodes=len(job.nodes), - status=self._aggregate_status(job), + num_nodes=num_nodes, + status=self._aggregate_status_from_containers(job_containers), ) ) + return result def get_job(self, name: str) -> types.TrainJob: - job = self._jobs.get(name) - if not job: + """Get a specific training job by querying container runtime.""" + # Find containers for this job + filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} + containers = self._adapter.list_containers(filters=filters) + + if not containers: raise ValueError(f"No TrainJob with name {name}") - # Refresh container statuses on demand - steps: list[types.Step] = [] - for node in job.nodes: - status = self._container_status(node.container_id) + + # Get metadata from network + network_id = containers[0]["labels"].get(f"{self.label_prefix}/network-id") + if not network_id: + raise ValueError(f"TrainJob {name} is missing network metadata") + + network_info = self._adapter.get_network(network_id) + if not network_info: + raise ValueError(f"TrainJob {name} network not found") + + network_labels = network_info.get("labels", {}) + runtime_name = network_labels.get(f"{self.label_prefix}/runtime-name") + + # Get runtime object + try: + job_runtime = self.get_runtime(runtime_name) if runtime_name else None + except Exception: + raise ValueError(f"Runtime {runtime_name} not found for job {name}") + + if not job_runtime: + raise ValueError(f"Runtime {runtime_name} not found for job {name}") + + # Parse creation timestamp + created_str = network_labels.get(f"{self.label_prefix}/created", "") + try: + from dateutil import parser + creation_timestamp = parser.isoparse(created_str) + except Exception: + creation_timestamp = datetime.now() + + # Build steps from containers + steps = [] + for container in sorted(containers, key=lambda c: c["name"]): + step_name = container["labels"].get(f"{self.label_prefix}/step", "") steps.append( types.Step( - name=node.name.split(f"{job.name}-")[-1], - pod_name=node.name, - status=status, + name=step_name, + pod_name=container["name"], + status=self._container_status(container["id"]), ) ) + + # Get num_nodes from labels + num_nodes = int(containers[0]["labels"].get(f"{self.label_prefix}/num-nodes", len(containers))) + return types.TrainJob( - name=job.name, - creation_timestamp=job.created, - runtime=job.runtime, + name=name, + creation_timestamp=creation_timestamp, + runtime=job_runtime, steps=steps, - num_nodes=len(job.nodes), - status=self._aggregate_status(job), + num_nodes=num_nodes, + status=self._aggregate_status_from_containers(containers), ) def get_job_logs( @@ -420,19 +491,23 @@ def get_job_logs( follow: bool = False, step: str = constants.NODE + "-0", ) -> Iterator[str]: - job = self._jobs.get(name) - if not job: + """Get logs for a training job by querying container runtime.""" + # Find containers for this job + filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} + containers = self._adapter.list_containers(filters=filters) + + if not containers: raise ValueError(f"No TrainJob with name {name}") want_all = step == constants.NODE + "-0" - for node in job.nodes: - node_step = node.name.split(f"{job.name}-")[-1] - if not want_all and node_step != step: + for container in sorted(containers, key=lambda c: c["name"]): + container_step = container["labels"].get(f"{self.label_prefix}/step", "") + if not want_all and container_step != step: continue try: - yield from self._adapter.container_logs(node.container_id, follow) + yield from self._adapter.container_logs(container["id"], follow) except Exception as e: - logger.warning(f"Failed to get logs for {node.name}: {e}") + logger.warning(f"Failed to get logs for {container['name']}: {e}") yield f"Error getting logs: {e}\n" def wait_for_job_status( @@ -456,28 +531,42 @@ def wait_for_job_status( raise TimeoutError(f"Timeout waiting for TrainJob {name} to reach status: {status}") def delete_job(self, name: str): - job = self._jobs.get(name) - if not job: + """Delete a training job by querying container runtime.""" + # Find containers for this job + filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} + containers = self._adapter.list_containers(filters=filters) + + if not containers: raise ValueError(f"No TrainJob with name {name}") + # Get network_id and workdir from labels + network_id = containers[0]["labels"].get(f"{self.label_prefix}/network-id") + + # Get workdir from network labels + workdir_host = None + if network_id: + network_info = self._adapter.get_network(network_id) + if network_info: + network_labels = network_info.get("labels", {}) + workdir_host = network_labels.get(f"{self.label_prefix}/workdir") + # Stop containers and remove from contextlib import suppress - for node in job.nodes: + for container in containers: with suppress(Exception): - self._adapter.stop_container(node.container_id, timeout=10) + self._adapter.stop_container(container["id"], timeout=10) with suppress(Exception): - self._adapter.remove_container(node.container_id, force=True) + self._adapter.remove_container(container["id"], force=True) # Remove network (best-effort) - with suppress(Exception): - self._adapter.delete_network(job.network_id) + if network_id: + with suppress(Exception): + self._adapter.delete_network(network_id) # Remove working directory if configured - if self.cfg.auto_remove and os.path.isdir(job.workdir_host): - shutil.rmtree(job.workdir_host, ignore_errors=True) - - del self._jobs[name] + if self.cfg.auto_remove and workdir_host and os.path.isdir(workdir_host): + shutil.rmtree(workdir_host, ignore_errors=True) # Helper methods @@ -572,9 +661,9 @@ def _container_status(self, container_id: str) -> str: return constants.UNKNOWN return constants.UNKNOWN - def _aggregate_status(self, job: _Job) -> str: - """Aggregate status from all containers in a job.""" - statuses = [self._container_status(n.container_id) for n in job.nodes] + def _aggregate_status_from_containers(self, containers: list[dict]) -> str: + """Aggregate status from container info dicts.""" + statuses = [self._container_status(c["id"]) for c in containers] if constants.TRAINJOB_FAILED in statuses: return constants.TRAINJOB_FAILED if constants.TRAINJOB_RUNNING in statuses: diff --git a/kubeflow/trainer/backends/container/client_adapter.py b/kubeflow/trainer/backends/container/client_adapter.py index d7715fd8c..c742f203d 100644 --- a/kubeflow/trainer/backends/container/client_adapter.py +++ b/kubeflow/trainer/backends/container/client_adapter.py @@ -162,6 +162,37 @@ def get_container_ip(self, container_id: str, network_id: str) -> str | None: """ raise NotImplementedError() + @abc.abstractmethod + def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: + """ + List containers, optionally filtered by labels. + + Args: + filters: Dictionary of filters (e.g., {"label": ["key=value"]}) + + Returns: + List of container info dictionaries with keys: + - id: Container ID + - name: Container name + - labels: Dictionary of labels + - status: Container status + - created: Creation timestamp + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_network(self, network_id: str) -> dict | None: + """ + Get network information by ID or name. + + Args: + network_id: Network ID or name + + Returns: + Dictionary with network info including labels, or None if not found + """ + raise NotImplementedError() + class DockerClientAdapter(ContainerClientAdapter): """Adapter for Docker client.""" @@ -335,6 +366,35 @@ def get_container_ip(self, container_id: str, network_id: str) -> str | None: except Exception: return None + def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: + """List Docker containers with optional filters.""" + try: + containers = self.client.containers.list(all=True, filters=filters) + result = [] + for c in containers: + result.append({ + "id": c.id, + "name": c.name, + "labels": c.labels, + "status": c.status, + "created": c.attrs.get("Created", ""), + }) + return result + except Exception: + return [] + + def get_network(self, network_id: str) -> dict | None: + """Get Docker network information.""" + try: + network = self.client.networks.get(network_id) + return { + "id": network.id, + "name": network.name, + "labels": network.attrs.get("Labels", {}), + } + except Exception: + return None + class PodmanClientAdapter(ContainerClientAdapter): """Adapter for Podman client.""" @@ -513,3 +573,34 @@ def get_container_ip(self, container_id: str, network_id: str) -> str | None: return None except Exception: return None + + def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: + """List Podman containers with optional filters.""" + try: + containers = self.client.containers.list(all=True, filters=filters) + result = [] + for c in containers: + inspect = c.attrs if hasattr(c, "attrs") else c.inspect() + result.append({ + "id": c.id, + "name": c.name, + "labels": c.labels if hasattr(c, "labels") else inspect.get("Config", {}).get("Labels", {}), + "status": c.status, + "created": inspect.get("Created", ""), + }) + return result + except Exception: + return [] + + def get_network(self, network_id: str) -> dict | None: + """Get Podman network information.""" + try: + network = self.client.networks.get(network_id) + inspect = network.attrs if hasattr(network, "attrs") else network.inspect() + return { + "id": inspect.get("ID", network_id), + "name": inspect.get("Name", network_id), + "labels": inspect.get("Labels", {}), + } + except Exception: + return None From bf7dcf759ac55fb2020f91ef51b99aa80674724f Mon Sep 17 00:00:00 2001 From: Fiona Waters Date: Thu, 23 Oct 2025 16:35:23 +0100 Subject: [PATCH 6/7] Addressing feedback Signed-off-by: Fiona Waters --- README.md | 34 +- kubeflow/trainer/__init__.py | 8 +- kubeflow/trainer/api/trainer_client.py | 1 + kubeflow/trainer/backends/container/README.md | 162 ----- .../trainer/backends/container/__init__.py | 18 - .../backends/container/adapters/base.py | 195 ++++++ .../backends/container/adapters/docker.py | 229 +++++++ .../backends/container/adapters/podman.py | 247 +++++++ .../trainer/backends/container/backend.py | 571 ++++++++--------- .../backends/container/backend_test.py | 218 ++++++- .../backends/container/client_adapter.py | 606 ------------------ .../backends/container/runtime_loader.py | 599 ++++++++++++++++- .../backends/container/runtime_loader_test.py | 526 +++++++++++++++ kubeflow/trainer/backends/container/types.py | 41 +- kubeflow/trainer/backends/container/utils.py | 236 +++++++ .../trainer/backends/local_runtime_loader.py | 110 ---- .../local_runtimes/torch_distributed.yaml | 25 - kubeflow/trainer/constants/constants.py | 5 + pyproject.toml | 4 +- 19 files changed, 2534 insertions(+), 1301 deletions(-) delete mode 100644 kubeflow/trainer/backends/container/README.md delete mode 100644 kubeflow/trainer/backends/container/__init__.py create mode 100644 kubeflow/trainer/backends/container/adapters/base.py create mode 100644 kubeflow/trainer/backends/container/adapters/docker.py create mode 100644 kubeflow/trainer/backends/container/adapters/podman.py delete mode 100644 kubeflow/trainer/backends/container/client_adapter.py create mode 100644 kubeflow/trainer/backends/container/runtime_loader_test.py create mode 100644 kubeflow/trainer/backends/container/utils.py delete mode 100644 kubeflow/trainer/backends/local_runtime_loader.py delete mode 100644 kubeflow/trainer/config/local_runtimes/torch_distributed.yaml diff --git a/README.md b/README.md index b5af775b2..9f59f6e50 100644 --- a/README.md +++ b/README.md @@ -73,38 +73,24 @@ print("\n".join(TrainerClient().get_job_logs(name=job_id))) ## Local Development -Kubeflow SDK provides first-class support for local development, allowing you to test and iterate on your models without needing a Kubernetes cluster. +Kubeflow Trainer client supports local development without needing a Kubernetes cluster. -### Execution Backends +### Available Backends -Choose the backend that fits your development workflow: +- **KubernetesBackend** (default) - Production training on Kubernetes +- **ContainerBackend** - Local development with Docker/Podman isolation +- **LocalProcessBackend** - Quick prototyping with Python subprocesses -| Backend | Description | Use Case | -|---------|-------------|----------| -| **KubernetesBackend** | Run jobs on Kubernetes cluster | Production, multi-node distributed training | -| **ContainerBackend** | Auto-detects Docker or Podman | Local development with container isolation | -| **LocalProcessBackend** | Run as local Python subprocesses | Quick prototyping, debugging | - -### Local Container Execution - -The **ContainerBackend** automatically detects and uses either Docker or Podman: - -```bash -# Install with Docker support -pip install kubeflow[docker] - -# Or install with Podman support -pip install kubeflow[podman] -``` +**Quick Start:** +Install container support: `pip install kubeflow[docker]` or `pip install kubeflow[podman]` ```python from kubeflow.trainer import TrainerClient, ContainerBackendConfig, CustomTrainer -# Auto-detects Docker or Podman -config = ContainerBackendConfig() -client = TrainerClient(backend_config=config) +# Switch to local container execution +client = TrainerClient(backend_config=ContainerBackendConfig()) -# Your training runs in isolated containers +# Your training runs locally in isolated containers job_id = client.train(trainer=CustomTrainer(func=train_fn)) ``` diff --git a/kubeflow/trainer/__init__.py b/kubeflow/trainer/__init__.py index 4cb10ed48..a61867b6a 100644 --- a/kubeflow/trainer/__init__.py +++ b/kubeflow/trainer/__init__.py @@ -15,12 +15,13 @@ # Import common types. from kubeflow.common.types import KubernetesBackendConfig -# Import the Kubeflow Trainer client. -from kubeflow.trainer.api.trainer_client import TrainerClient # noqa: F401 -from kubeflow.trainer.backends.container.types import ContainerBackendConfig # Import the Kubeflow Trainer client. from kubeflow.trainer.api.trainer_client import TrainerClient +from kubeflow.trainer.backends.container.types import ( + ContainerBackendConfig, + TrainingRuntimeSource, +) from kubeflow.trainer.backends.localprocess.types import LocalProcessBackendConfig # Import the Kubeflow Trainer constants. @@ -69,4 +70,5 @@ "LocalProcessBackendConfig", "ContainerBackendConfig", "KubernetesBackendConfig", + "TrainingRuntimeSource", ] diff --git a/kubeflow/trainer/api/trainer_client.py b/kubeflow/trainer/api/trainer_client.py index 9b3aefb01..79613f541 100644 --- a/kubeflow/trainer/api/trainer_client.py +++ b/kubeflow/trainer/api/trainer_client.py @@ -16,6 +16,7 @@ import logging from typing import Optional, Union +from kubeflow.common.types import KubernetesBackendConfig from kubeflow.trainer.backends.container.backend import ContainerBackend from kubeflow.trainer.backends.container.types import ContainerBackendConfig from kubeflow.trainer.backends.kubernetes.backend import KubernetesBackend diff --git a/kubeflow/trainer/backends/container/README.md b/kubeflow/trainer/backends/container/README.md deleted file mode 100644 index db4234666..000000000 --- a/kubeflow/trainer/backends/container/README.md +++ /dev/null @@ -1,162 +0,0 @@ -# ContainerBackend - -The unified container backend for Kubeflow Trainer that automatically detects and uses either Docker or Podman. - -## Overview - -This backend provides a single, unified interface for container-based training execution, automatically detecting which container runtime is available on your system. - -The implementation uses the **adapter pattern** to abstract away differences between Docker and Podman APIs, providing clean separation between runtime detection logic and container operations. - -## Usage - -### Basic usage (auto-detection) - -```python -from kubeflow.trainer import TrainerClient, ContainerBackendConfig - -# Auto-detects Docker or Podman -config = ContainerBackendConfig() -client = TrainerClient(backend_config=config) -``` - -### Force specific runtime - -```python -# Force Docker -config = ContainerBackendConfig(runtime="docker") -client = TrainerClient(backend_config=config) - -# Force Podman -config = ContainerBackendConfig(runtime="podman") -client = TrainerClient(backend_config=config) -``` - -### Configuration options - -```python -config = ContainerBackendConfig( - # Optional: force specific runtime ("docker" or "podman") - runtime=None, - - # Optional: explicit image override - image="my-custom-image:latest", - - # Image pull policy: "IfNotPresent", "Always", or "Never" - pull_policy="IfNotPresent", - - # Auto-remove containers and networks on job deletion - auto_remove=True, - - # GPU support (varies by runtime) - gpus=None, - - # Environment variables for all containers - env={"MY_VAR": "value"}, - - # Container daemon URL override (required for Colima/Podman Machine on macOS) - container_host=None, - - # Base directory for job workspaces - workdir_base=None, -) -``` - -### macOS-specific configuration - -On macOS, you may need to specify `container_host` depending on your container runtime: - -**Docker with Colima:** -```python -import os -config = ContainerBackendConfig( - container_host=f"unix://{os.path.expanduser('~')}/.colima/default/docker.sock" -) -``` - -**Podman Machine:** -```python -import os -config = ContainerBackendConfig( - container_host=f"unix://{os.path.expanduser('~')}/.local/share/containers/podman/machine/podman.sock" -) -``` - -**Docker Desktop:** -```python -# Usually works without specifying container_host -config = ContainerBackendConfig() -``` - -Alternatively, set environment variables before running: -```bash -# For Colima -export DOCKER_HOST="unix://$HOME/.colima/default/docker.sock" - -# For Podman Machine -export CONTAINER_HOST="unix://$HOME/.local/share/containers/podman/machine/podman.sock" -``` - -### How it works - -The backend initialization follows this logic: - -1. If `runtime` is specified in config, use that runtime exclusively -2. Otherwise, try to initialize Docker client adapter -3. If Docker fails, try to initialize Podman client adapter -4. If both fail, raise a RuntimeError - -If you don't have Docker or Podman installed, use `LocalProcessBackendConfig` instead, which runs training as local subprocesses. - -All container operations are delegated to the adapter, eliminating code duplication. - -## Installation - -Install with Docker support: -```bash -pip install kubeflow[docker] -``` - -Install with Podman support: -```bash -pip install kubeflow[podman] -``` - -Install with both: -```bash -pip install kubeflow[docker,podman] -``` - -## Example: Training Job - -```python -from kubeflow.trainer import TrainerClient, ContainerBackendConfig, CustomTrainer - -# Define your training function -def train(): - import torch - print(f"Training with PyTorch {torch.__version__}") - # Your training code here - -# Create trainer -trainer = CustomTrainer( - func=train, - packages_to_install=["torch"], -) - -# Initialize client (auto-detects runtime) -config = ContainerBackendConfig() -client = TrainerClient(backend_config=config) - -# Run training -job_name = client.train(trainer=trainer) -print(f"Training job started: {job_name}") - -# Get logs -for log in client.get_job_logs(job_name, follow=True): - print(log, end='') -``` - -## See also - -- [Example notebook](TBA) - Complete working example to be added diff --git a/kubeflow/trainer/backends/container/__init__.py b/kubeflow/trainer/backends/container/__init__.py deleted file mode 100644 index 776237c38..000000000 --- a/kubeflow/trainer/backends/container/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from kubeflow.trainer.backends.container.backend import ContainerBackend -from kubeflow.trainer.backends.container.types import ContainerBackendConfig - -__all__ = ["ContainerBackend", "ContainerBackendConfig"] diff --git a/kubeflow/trainer/backends/container/adapters/base.py b/kubeflow/trainer/backends/container/adapters/base.py new file mode 100644 index 000000000..3e38a6b89 --- /dev/null +++ b/kubeflow/trainer/backends/container/adapters/base.py @@ -0,0 +1,195 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Container client adapters for Docker and Podman. + +This module implements the adapter pattern to abstract away differences between +Docker and Podman APIs, allowing the backend to work with either runtime through +a common interface. +""" + +from __future__ import annotations + +import abc +from collections.abc import Iterator +from typing import Optional + + +class BaseContainerClientAdapter(abc.ABC): + """ + Abstract adapter interface for container clients. + + This adapter abstracts the container runtime API, allowing the backend + to work with Docker and Podman through a unified interface. + """ + + @abc.abstractmethod + def ping(self): + """Test the connection to the container runtime.""" + raise NotImplementedError() + + @abc.abstractmethod + def create_network( + self, + name: str, + labels: dict[str, str], + ) -> str: + """ + Create a container network. + + Args: + name: Network name + labels: Labels to attach to the network + + Returns: + Network ID or name + """ + raise NotImplementedError() + + @abc.abstractmethod + def delete_network(self, network_id: str): + """Delete a network.""" + raise NotImplementedError() + + @abc.abstractmethod + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """ + Create and start a container. + + Args: + image: Container image + command: Command to run + name: Container name + network_id: Network to attach to + environment: Environment variables + labels: Container labels + volumes: Volume mounts + working_dir: Working directory + + Returns: + Container ID + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_container(self, container_id: str): + """Get container object by ID.""" + raise NotImplementedError() + + @abc.abstractmethod + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def stop_container(self, container_id: str, timeout: int = 10): + """Stop a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def remove_container(self, container_id: str, force: bool = True): + """Remove a container.""" + raise NotImplementedError() + + @abc.abstractmethod + def pull_image(self, image: str): + """Pull an image.""" + raise NotImplementedError() + + @abc.abstractmethod + def image_exists(self, image: str) -> bool: + """Check if an image exists locally.""" + raise NotImplementedError() + + @abc.abstractmethod + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """ + Run a short-lived container and return its output. + + Args: + image: Container image + command: Command to run + + Returns: + Container output as string + """ + raise NotImplementedError() + + @abc.abstractmethod + def container_status(self, container_id: str) -> tuple[str, Optional[int]]: + """ + Get container status. + + Returns: + Tuple of (status_string, exit_code) + Status strings: "running", "created", "exited", etc. + Exit code is None if container hasn't exited + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_container_ip(self, container_id: str, network_id: str) -> Optional[str]: + """ + Get container's IP address on a specific network. + + Args: + container_id: Container ID + network_id: Network name or ID + + Returns: + IP address string or None if not found + """ + raise NotImplementedError() + + @abc.abstractmethod + def list_containers(self, filters: Optional[dict[str, list[str]]] = None) -> list[dict]: + """ + List containers, optionally filtered by labels. + + Args: + filters: Dictionary of filters (e.g., {"label": ["key=value"]}) + + Returns: + List of container info dictionaries with keys: + - id: Container ID + - name: Container name + - labels: Dictionary of labels + - status: Container status + - created: Creation timestamp + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_network(self, network_id: str) -> Optional[dict]: + """ + Get network information by ID or name. + + Args: + network_id: Network ID or name + + Returns: + Dictionary with network info including labels, or None if not found + """ + raise NotImplementedError() diff --git a/kubeflow/trainer/backends/container/adapters/docker.py b/kubeflow/trainer/backends/container/adapters/docker.py new file mode 100644 index 000000000..c4c0e1205 --- /dev/null +++ b/kubeflow/trainer/backends/container/adapters/docker.py @@ -0,0 +1,229 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Docker client adapter implementation. + +This module provides the DockerClientAdapter class that implements the +BaseContainerClientAdapter interface for Docker runtime. +""" + +from collections.abc import Iterator +from typing import Optional + +from kubeflow.trainer.backends.container.adapters.base import BaseContainerClientAdapter + + +class DockerClientAdapter(BaseContainerClientAdapter): + """Adapter for Docker client.""" + + def __init__(self, host: Optional[str] = None): + """ + Initialize Docker client. + + Args: + host: Docker host URL, or None to use environment defaults + """ + try: + import docker # type: ignore + except ImportError as e: + raise ImportError( + "The 'docker' Python package is not installed. Install with extras: " + "pip install kubeflow[docker]" + ) from e + + if host: + self.client = docker.DockerClient(base_url=host) + else: + self.client = docker.from_env() + + self._runtime_type = "docker" + + def ping(self): + """Test connection to Docker daemon.""" + self.client.ping() + + def create_network(self, name: str, labels: dict[str, str]) -> str: + """Create a Docker network.""" + try: + self.client.networks.get(name) + return name + except Exception: + pass + + self.client.networks.create( + name=name, + check_duplicate=True, + labels=labels, + ) + return name + + def delete_network(self, network_id: str): + """Delete Docker network.""" + try: + net = self.client.networks.get(network_id) + net.remove() + except Exception: + pass + + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Docker container.""" + container = self.client.containers.run( + image=image, + command=tuple(command), + name=name, + detach=True, + working_dir=working_dir, + network=network_id, + environment=environment, + labels=labels, + volumes=volumes, + auto_remove=False, + ) + return container.id + + def get_container(self, container_id: str): + """Get Docker container by ID.""" + return self.client.containers.get(container_id) + + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from Docker container.""" + container = self.get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def stop_container(self, container_id: str, timeout: int = 10): + """Stop Docker container.""" + container = self.get_container(container_id) + container.stop(timeout=timeout) + + def remove_container(self, container_id: str, force: bool = True): + """Remove Docker container.""" + container = self.get_container(container_id) + container.remove(force=force) + + def pull_image(self, image: str): + """Pull Docker image.""" + self.client.images.pull(image) + + def image_exists(self, image: str) -> bool: + """Check if Docker image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a short-lived Docker container and return output.""" + try: + output = self.client.containers.run( + image=image, + command=tuple(command), + detach=False, + remove=True, + ) + if isinstance(output, (bytes, bytearray)): + return output.decode("utf-8", errors="ignore") + return str(output) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + def container_status(self, container_id: str) -> tuple[str, Optional[int]]: + """Get Docker container status.""" + try: + container = self.get_container(container_id) + status = container.status + # Get exit code if container has exited + exit_code = None + if status == "exited": + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + exit_code = inspect.get("State", {}).get("ExitCode") + return (status, exit_code) + except Exception: + return ("unknown", None) + + def get_container_ip(self, container_id: str, network_id: str) -> Optional[str]: + """Get container's IP address on a specific network.""" + try: + container = self.get_container(container_id) + # Refresh container info + container.reload() + # Get network settings + networks = container.attrs.get("NetworkSettings", {}).get("Networks", {}) + + # Try to find the network by exact name or ID + if network_id in networks: + return networks[network_id].get("IPAddress") + + # Fallback: return first available IP + for _net_name, net_info in networks.items(): + ip = net_info.get("IPAddress") + if ip: + return ip + + return None + except Exception: + return None + + def list_containers(self, filters: Optional[dict[str, str]] = None) -> list[dict]: + """List Docker containers with optional filters.""" + try: + containers = self.client.containers.list(all=True, filters=filters) + result = [] + for c in containers: + result.append( + { + "id": c.id, + "name": c.name, + "labels": c.labels, + "status": c.status, + "created": c.attrs.get("Created", ""), + } + ) + return result + except Exception: + return [] + + def get_network(self, network_id: str) -> Optional[dict]: + """Get Docker network information.""" + try: + network = self.client.networks.get(network_id) + return { + "id": network.id, + "name": network.name, + "labels": network.attrs.get("Labels", {}), + } + except Exception: + return None diff --git a/kubeflow/trainer/backends/container/adapters/podman.py b/kubeflow/trainer/backends/container/adapters/podman.py new file mode 100644 index 000000000..72274e344 --- /dev/null +++ b/kubeflow/trainer/backends/container/adapters/podman.py @@ -0,0 +1,247 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Podman client adapter implementation. + +This module provides the PodmanClientAdapter class that implements the +BaseContainerClientAdapter interface for Podman runtime. + +Key differences from Docker: +- Uses DNS-enabled bridge networks for better container name resolution +- GPU support via CDI (Container Device Interface) instead of NVIDIA Container Toolkit +- Slightly different API for some operations (e.g., container.create + start pattern) +""" + +from collections.abc import Iterator +from typing import Optional + +from kubeflow.trainer.backends.container.adapters.base import BaseContainerClientAdapter + + +class PodmanClientAdapter(BaseContainerClientAdapter): + """Adapter for Podman client.""" + + def __init__(self, host: Optional[str] = None): + """ + Initialize Podman client. + + Args: + host: Podman host URL, or None to use environment defaults + """ + try: + import podman # type: ignore + except ImportError as e: + raise ImportError( + "The 'podman' Python package is not installed. Install with extras: " + "pip install kubeflow[podman]" + ) from e + + if host: + self.client = podman.PodmanClient(base_url=host) + else: + self.client = podman.PodmanClient() + + self._runtime_type = "podman" + + def ping(self): + """Test connection to Podman.""" + self.client.ping() + + def create_network(self, name: str, labels: dict[str, str]) -> str: + """Create a Podman network with DNS enabled.""" + try: + self.client.networks.get(name) + return name + except Exception: + pass + + self.client.networks.create( + name=name, + driver="bridge", + dns_enabled=True, + labels=labels, + ) + return name + + def delete_network(self, network_id: str): + """Delete Podman network.""" + try: + net = self.client.networks.get(network_id) + net.remove() + except Exception: + pass + + def create_and_start_container( + self, + image: str, + command: list[str], + name: str, + network_id: str, + environment: dict[str, str], + labels: dict[str, str], + volumes: dict[str, dict[str, str]], + working_dir: str, + ) -> str: + """Create and start a Podman container.""" + container = self.client.containers.run( + image=image, + command=command, + name=name, + network=network_id, + working_dir=working_dir, + environment=environment, + labels=labels, + volumes=volumes, + detach=True, + remove=False, + ) + return container.id + + def get_container(self, container_id: str): + """Get Podman container by ID.""" + return self.client.containers.get(container_id) + + def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: + """Stream logs from Podman container.""" + container = self.get_container(container_id) + logs = container.logs(stream=bool(follow), follow=bool(follow)) + if follow: + for chunk in logs: + if isinstance(chunk, bytes): + yield chunk.decode("utf-8", errors="ignore") + else: + yield str(chunk) + else: + if isinstance(logs, bytes): + yield logs.decode("utf-8", errors="ignore") + else: + yield str(logs) + + def stop_container(self, container_id: str, timeout: int = 10): + """Stop Podman container.""" + container = self.get_container(container_id) + container.stop(timeout=timeout) + + def remove_container(self, container_id: str, force: bool = True): + """Remove Podman container.""" + container = self.get_container(container_id) + container.remove(force=force) + + def pull_image(self, image: str): + """Pull Podman image.""" + self.client.images.pull(image) + + def image_exists(self, image: str) -> bool: + """Check if Podman image exists locally.""" + try: + self.client.images.get(image) + return True + except Exception: + return False + + def run_oneoff_container(self, image: str, command: list[str]) -> str: + """Run a short-lived Podman container and return output.""" + try: + container = self.client.containers.create( + image=image, + command=command, + detach=False, + remove=True, + ) + container.start() + container.wait() + logs = container.logs() + + if isinstance(logs, (bytes, bytearray)): + return logs.decode("utf-8", errors="ignore") + return str(logs) + except Exception as e: + raise RuntimeError(f"One-off container failed to run: {e}") from e + + def container_status(self, container_id: str) -> tuple[str, Optional[int]]: + """Get Podman container status.""" + try: + container = self.get_container(container_id) + status = container.status + # Get exit code if container has exited + exit_code = None + if status == "exited": + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + exit_code = inspect.get("State", {}).get("ExitCode") + return (status, exit_code) + except Exception: + return ("unknown", None) + + def get_container_ip(self, container_id: str, network_id: str) -> Optional[str]: + """Get container's IP address on a specific network.""" + try: + container = self.get_container(container_id) + # Get container inspect data + inspect = container.attrs if hasattr(container, "attrs") else container.inspect() + + # Get network settings - Podman structure is similar to Docker + networks = inspect.get("NetworkSettings", {}).get("Networks", {}) + + # Try to find the network by exact name or ID + if network_id in networks: + return networks[network_id].get("IPAddress") + + # Fallback: return first available IP + for _net_name, net_info in networks.items(): + ip = net_info.get("IPAddress") + if ip: + return ip + + return None + except Exception: + return None + + def list_containers(self, filters: Optional[dict[str, str]] = None) -> list[dict]: + """List Podman containers with optional filters.""" + try: + containers = self.client.containers.list(all=True, filters=filters) + result = [] + for c in containers: + inspect = c.attrs if hasattr(c, "attrs") else c.inspect() + labels = ( + c.labels + if hasattr(c, "labels") + else inspect.get("Config", {}).get("Labels", {}) + ) + result.append( + { + "id": c.id, + "name": c.name, + "labels": labels, + "status": c.status, + "created": inspect.get("Created", ""), + } + ) + return result + except Exception: + return [] + + def get_network(self, network_id: str) -> Optional[dict]: + """Get Podman network information.""" + try: + network = self.client.networks.get(network_id) + inspect = network.attrs if hasattr(network, "attrs") else network.inspect() + return { + "id": inspect.get("ID", network_id), + "name": inspect.get("Name", network_id), + "labels": inspect.get("Labels", {}), + } + except Exception: + return None diff --git a/kubeflow/trainer/backends/container/backend.py b/kubeflow/trainer/backends/container/backend.py index 2fe96d661..e36e83c9d 100644 --- a/kubeflow/trainer/backends/container/backend.py +++ b/kubeflow/trainer/backends/container/backend.py @@ -24,10 +24,11 @@ Key behaviors: - Auto-detection: Tries Docker first, then Podman. Can be overridden via config. - Multi-node jobs: one container per node connected via a per-job network. -- Entry script generation: we serialize the user's training function to a small - Python file and invoke it inside the container using `torchrun` (preferred) or - `python` as a fallback. -- Runtimes: we use `config/local_runtimes` to define runtime images and +- Entry script generation: we serialize the user's training function and embed it + inline in the container command using a heredoc (no file I/O on the host). The + script is created inside the container at /tmp/train.py and invoked using + `torchrun` (preferred) or `python` as a fallback. +- Runtimes: we use `config/training_runtimes` to define runtime images and characteristics (e.g., torch). Defaults to `torch-distributed` if no runtime is provided. - Image pulling: controlled via `pull_policy` and performed automatically if @@ -42,22 +43,22 @@ from datetime import datetime import logging import os -from pathlib import Path import random import shutil import string +from typing import Optional, Union import uuid -from kubeflow.trainer.backends.base import ExecutionBackend -from kubeflow.trainer.backends.container.client_adapter import ( - ContainerClientAdapter, - DockerClientAdapter, - PodmanClientAdapter, +from kubeflow.trainer.backends.base import RuntimeBackend +from kubeflow.trainer.backends.container import utils as container_utils +from kubeflow.trainer.backends.container.adapters.base import ( + BaseContainerClientAdapter, ) +from kubeflow.trainer.backends.container.adapters.docker import DockerClientAdapter +from kubeflow.trainer.backends.container.adapters.podman import PodmanClientAdapter from kubeflow.trainer.backends.container.runtime_loader import ( - LOCAL_RUNTIMES_DIR, - get_local_runtime, - list_local_runtimes, + get_training_runtime_from_sources, + list_training_runtimes_from_sources, ) from kubeflow.trainer.backends.container.types import ContainerBackendConfig from kubeflow.trainer.constants import constants @@ -66,7 +67,7 @@ logger = logging.getLogger(__name__) -class ContainerBackend(ExecutionBackend): +class ContainerBackend(RuntimeBackend): """ Unified container backend that auto-detects Docker or Podman. @@ -81,7 +82,50 @@ def __init__(self, cfg: ContainerBackendConfig): # Initialize the container client adapter self._adapter = self._create_adapter() - def _create_adapter(self) -> ContainerClientAdapter: + def _get_common_socket_locations(self, runtime_name: str) -> list[Optional[str]]: + """ + Get common socket locations to try for the given runtime. + + Args: + runtime_name: "docker" or "podman" + + Returns: + List of socket URLs to try, including None (for default) + """ + import os + from pathlib import Path + + locations = [self.cfg.container_host] if self.cfg.container_host else [] + + if runtime_name == "docker": + # Common Docker socket locations + colima_sock = Path.home() / ".colima/default/docker.sock" + if colima_sock.exists(): + locations.append(f"unix://{colima_sock}") + # Standard Docker socket + locations.append(None) # Use docker.from_env() default + + elif runtime_name == "podman": + # Common Podman socket locations on macOS + uid = os.getuid() if hasattr(os, "getuid") else None + if uid: + user_sock = f"/run/user/{uid}/podman/podman.sock" + if Path(user_sock).exists(): + locations.append(f"unix://{user_sock}") + # Standard Podman socket + locations.append(None) # Use PodmanClient() default + + # Remove duplicates while preserving order + seen = set() + unique_locations = [] + for loc in locations: + if loc not in seen: + unique_locations.append(loc) + seen.add(loc) + + return unique_locations + + def _create_adapter(self) -> BaseContainerClientAdapter: """ Create the appropriate container client adapter. @@ -90,38 +134,67 @@ def _create_adapter(self) -> ContainerClientAdapter: Raises RuntimeError if neither Docker nor Podman are available. """ - if self.cfg.runtime: - # User specified a runtime explicitly - if self.cfg.runtime == "docker": - adapter = DockerClientAdapter(self.cfg.container_host) - adapter.ping() - logger.info("Using Docker as container runtime") - return adapter - elif self.cfg.runtime == "podman": - adapter = PodmanClientAdapter(self.cfg.container_host) - adapter.ping() - logger.info("Using Podman as container runtime") - return adapter - else: - # Auto-detect: try Docker first, then Podman + runtime_map = { + "docker": DockerClientAdapter, + "podman": PodmanClientAdapter, + } + + # Determine which runtimes to try + runtimes_to_try = ( + [self.cfg.container_runtime] if self.cfg.container_runtime else ["docker", "podman"] + ) + + last_error = None + for runtime_name in runtimes_to_try: + if runtime_name not in runtime_map: + continue + try: - adapter = DockerClientAdapter(self.cfg.container_host) + adapter = runtime_map[runtime_name](self.cfg.container_host) adapter.ping() - logger.info("Using Docker as container runtime") + logger.debug(f"Using {runtime_name} as container runtime") return adapter - except Exception as docker_error: - logger.debug(f"Docker initialization failed: {docker_error}") + except Exception as e: + logger.debug(f"{runtime_name} initialization failed: {e}") + last_error = e + + for host in socket_locations: try: - adapter = PodmanClientAdapter(self.cfg.container_host) + adapter = runtime_map[runtime_name](host) adapter.ping() - logger.info("Using Podman as container runtime") + host_display = host or "default" + logger.debug( + f"Using {runtime_name} as container runtime (host: {host_display})" + ) return adapter - except Exception as podman_error: - logger.debug(f"Podman initialization failed: {podman_error}") - raise RuntimeError( - "Neither Docker nor Podman is available. " - "Please install Docker or Podman, or use LocalProcessBackendConfig instead." - ) from podman_error + except Exception as e: + host_str = host or "default" + logger.debug(f"{runtime_name} initialization failed at {host_str}: {e}") + attempted_connections.append(f"{runtime_name} at {host_str}") + last_error = e + + # Build helpful error message + import platform + + system = platform.system() + + attempted = ", ".join(attempted_connections) + error_msg = f"Could not connect to Docker or Podman (tried: {attempted}).\n" + + if system == "Darwin": # macOS + error_msg += ( + "Ensure Docker/Podman is running " + "(e.g., 'colima start' or 'podman machine start').\n" + ) + else: + error_msg += "Ensure Docker/Podman is installed and running.\n" + + error_msg += ( + "To specify a custom socket: ContainerBackendConfig(container_host='unix:///path/to/socket')\n" + "Or use LocalProcessBackendConfig for non-containerized execution." + ) + + raise RuntimeError(error_msg) from last_error @property def _runtime_type(self) -> str: @@ -130,17 +203,17 @@ def _runtime_type(self) -> str: # ---- Runtime APIs ---- def list_runtimes(self) -> list[types.Runtime]: - return list_local_runtimes() + return list_training_runtimes_from_sources(self.cfg.runtime_source.sources) def get_runtime(self, name: str) -> types.Runtime: - return get_local_runtime(name) + return get_training_runtime_from_sources(name, self.cfg.runtime_source.sources) def get_runtime_packages(self, runtime: types.Runtime): """ Spawn a short-lived container to report Python version, pip list, and nvidia-smi. """ - image = self._resolve_image(runtime) - self._maybe_pull_image(image) + image = container_utils.resolve_image(runtime) + container_utils.maybe_pull_image(self._adapter, image, self.cfg.pull_policy) command = [ "bash", @@ -155,9 +228,9 @@ def get_runtime_packages(self, runtime: types.Runtime): def train( self, - runtime: types.Runtime | None = None, - initializer: types.Initializer | None = None, - trainer: types.CustomTrainer | types.BuiltinTrainer | None = None, + runtime: Optional[types.Runtime] = None, + initializer: Optional[types.Initializer] = None, + trainer: Optional[Union[types.CustomTrainer, types.BuiltinTrainer]] = None, ) -> str: if runtime is None: runtime = self.get_runtime("torch-distributed") @@ -167,43 +240,59 @@ def train( # Generate job name job_name = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11] - logger.info(f"Starting training job: {job_name}") + logger.debug(f"Starting training job: {job_name}") try: - # Create per-job working directory on host - workdir = self._create_workdir(job_name) + # Create per-job working directory on host (for outputs, checkpoints, etc.) + workdir = container_utils.create_workdir(job_name) logger.debug(f"Created working directory: {workdir}") - _ = self._write_training_script(workdir, trainer) - logger.debug(f"Wrote training script to {workdir}/train.py") + # Generate training script code (inline, not written to disk) + training_script_code = container_utils.get_training_script_code(trainer) + logger.debug("Generated training script code") # Resolve image and pull if needed - image = self._resolve_image(runtime) + image = container_utils.resolve_image(runtime) logger.debug(f"Using image: {image}") - self._maybe_pull_image(image) + container_utils.maybe_pull_image(self._adapter, image, self.cfg.pull_policy) logger.debug(f"Image ready: {image}") # Build base environment - env = self._build_environment(trainer) + env = container_utils.build_environment(trainer) # Construct pre-run command to install packages - pre_install_cmd = self._build_pip_install_cmd(trainer) + pre_install_cmd = container_utils.build_pip_install_cmd(trainer) # Create network for multi-node communication num_nodes = trainer.num_nodes or runtime.trainer.num_nodes or 1 logger.debug(f"Creating network for {num_nodes} nodes") + # Determine number of processes per node from GPU count + # For GPU training: spawn one process per GPU for optimal utilization + # For CPU training: use single process (PyTorch parallelizes internally via threads) + nproc_per_node = 1 # Default for CPU training + if trainer.resources_per_node and "gpu" in trainer.resources_per_node: + try: + nproc_per_node = int(trainer.resources_per_node["gpu"]) + logger.debug(f"Using {nproc_per_node} processes per node (1 per GPU)") + except (ValueError, TypeError): + logger.warning( + f"Invalid GPU count in resources_per_node: " + f"{trainer.resources_per_node['gpu']}, defaulting to 1 process per node" + ) + else: + logger.debug("No GPU specified, using 1 process per node") + network_id = self._adapter.create_network( name=f"{job_name}-net", labels={ f"{self.label_prefix}/trainjob-name": job_name, f"{self.label_prefix}/runtime-name": runtime.name, f"{self.label_prefix}/workdir": workdir, - f"{self.label_prefix}/created": datetime.now().isoformat(), }, ) - logger.info(f"Created network: {network_id}") + logger.debug(f"Created network: {network_id}") # Create N containers (one per node) container_ids: list[str] = [] @@ -247,16 +336,20 @@ def train( f"done; " ) + # Embed training script inline using heredoc (no file I/O on host) entry_cmd = ( f"{pre_install_cmd}" f"{wait_for_master}" + f"cat > /tmp/train.py << 'TRAINING_SCRIPT_EOF'\n" + f"{training_script_code}\n" + f"TRAINING_SCRIPT_EOF\n" "if command -v torchrun >/dev/null 2>&1; then " - f" torchrun --nproc_per_node=1 --nnodes={num_nodes} " + f" torchrun --nproc_per_node={nproc_per_node} --nnodes={num_nodes} " f" --node-rank={rank} --rdzv-backend=c10d " f" --rdzv-endpoint={master_addr}:{master_port} " - f" /workspace/train.py; " + f" /tmp/train.py; " "else " - f" python /workspace/train.py; " + f" python /tmp/train.py; " "fi" ) @@ -266,12 +359,11 @@ def train( f"{self.label_prefix}/trainjob-name": job_name, f"{self.label_prefix}/step": f"node-{rank}", f"{self.label_prefix}/network-id": network_id, - f"{self.label_prefix}/num-nodes": str(num_nodes), } volumes = { workdir: { - "bind": "/workspace", + "bind": constants.WORKSPACE_PATH, "mode": "rw", } } @@ -286,10 +378,10 @@ def train( environment=env, labels=labels, volumes=volumes, - working_dir="/workspace", + working_dir=constants.WORKSPACE_PATH, ) - logger.info(f"Started container {container_name} (ID: {container_id[:12]})") + logger.debug(f"Started container {container_name} (ID: {container_id[:12]})") container_ids.append(container_id) # If this is the master node and we're using Podman, get its IP address @@ -299,15 +391,16 @@ def train( # Get master IP for worker nodes to use master_ip = self._adapter.get_container_ip(master_container_id, network_id) if master_ip: - logger.info(f"Master node IP address: {master_ip}") + logger.debug(f"Master node IP address: {master_ip}") else: logger.warning( "Could not retrieve master IP address. " "Worker nodes will fall back to DNS resolution." ) - logger.info( - f"Training job {job_name} created successfully with {len(container_ids)} container(s)" + logger.debug( + f"Training job {job_name} created successfully with " + f"{len(container_ids)} container(s)" ) return job_name @@ -342,104 +435,54 @@ def train( # Re-raise the original exception raise - def list_jobs(self, runtime: types.Runtime | None = None) -> list[types.TrainJob]: - """List all training jobs by querying container runtime.""" - # Get all containers with our label prefix - filters = {"label": [f"{self.label_prefix}/trainjob-name"]} - containers = self._adapter.list_containers(filters=filters) - - # Group containers by job name - jobs_map: dict[str, list[dict]] = {} - for container in containers: - job_name = container["labels"].get(f"{self.label_prefix}/trainjob-name") - if job_name: - if job_name not in jobs_map: - jobs_map[job_name] = [] - jobs_map[job_name].append(container) - - result: list[types.TrainJob] = [] - for job_name, job_containers in jobs_map.items(): - # Get metadata from first container's network - if not job_containers: - continue - - network_id = job_containers[0]["labels"].get(f"{self.label_prefix}/network-id") - if not network_id: - continue + def _get_job_containers(self, name: str) -> list[dict]: + """ + Get containers for a specific training job. - network_info = self._adapter.get_network(network_id) - if not network_info: - continue + Args: + name: Name of the training job - network_labels = network_info.get("labels", {}) - runtime_name = network_labels.get(f"{self.label_prefix}/runtime-name") + Returns: + List of container dictionaries for this job - # Filter by runtime if specified - if runtime and runtime_name != runtime.name: - continue + Raises: + ValueError: If no containers found for the job + """ + filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} + containers = self._adapter.list_containers(filters=filters) - # Get runtime object - try: - job_runtime = self.get_runtime(runtime_name) if runtime_name else None - except Exception: - job_runtime = None + if not containers: + raise ValueError(f"No TrainJob with name {name}") - if not job_runtime: - continue + return containers - # Parse creation timestamp - created_str = network_labels.get(f"{self.label_prefix}/created", "") - try: - from dateutil import parser - creation_timestamp = parser.isoparse(created_str) - except Exception: - creation_timestamp = datetime.now() - - # Build steps from containers - steps = [] - for container in sorted(job_containers, key=lambda c: c["name"]): - step_name = container["labels"].get(f"{self.label_prefix}/step", "") - steps.append( - types.Step( - name=step_name, - pod_name=container["name"], - status=self._container_status(container["id"]), - ) - ) + def __get_trainjob_from_containers( + self, job_name: str, containers: list[dict] + ) -> types.TrainJob: + """ + Build a TrainJob object from a list of containers. - # Get num_nodes from labels - num_nodes = int(job_containers[0]["labels"].get(f"{self.label_prefix}/num-nodes", len(job_containers))) - - result.append( - types.TrainJob( - name=job_name, - creation_timestamp=creation_timestamp, - runtime=job_runtime, - steps=steps, - num_nodes=num_nodes, - status=self._aggregate_status_from_containers(job_containers), - ) - ) + Args: + job_name: Name of the training job + containers: List of container dictionaries for this job - return result - - def get_job(self, name: str) -> types.TrainJob: - """Get a specific training job by querying container runtime.""" - # Find containers for this job - filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} - containers = self._adapter.list_containers(filters=filters) + Returns: + TrainJob object + Raises: + ValueError: If network metadata is missing or runtime not found + """ if not containers: - raise ValueError(f"No TrainJob with name {name}") + raise ValueError(f"No containers found for TrainJob {job_name}") # Get metadata from network network_id = containers[0]["labels"].get(f"{self.label_prefix}/network-id") if not network_id: - raise ValueError(f"TrainJob {name} is missing network metadata") + raise ValueError(f"TrainJob {job_name} is missing network metadata") network_info = self._adapter.get_network(network_id) if not network_info: - raise ValueError(f"TrainJob {name} network not found") + raise ValueError(f"TrainJob {job_name} network not found") network_labels = network_info.get("labels", {}) runtime_name = network_labels.get(f"{self.label_prefix}/runtime-name") @@ -447,16 +490,17 @@ def get_job(self, name: str) -> types.TrainJob: # Get runtime object try: job_runtime = self.get_runtime(runtime_name) if runtime_name else None - except Exception: - raise ValueError(f"Runtime {runtime_name} not found for job {name}") + except Exception as e: + raise ValueError(f"Runtime {runtime_name} not found for job {job_name}") from e if not job_runtime: - raise ValueError(f"Runtime {runtime_name} not found for job {name}") + raise ValueError(f"Runtime {runtime_name} not found for job {job_name}") - # Parse creation timestamp - created_str = network_labels.get(f"{self.label_prefix}/created", "") + # Parse creation timestamp from first container + created_str = containers[0].get("created", "") try: from dateutil import parser + creation_timestamp = parser.isoparse(created_str) except Exception: creation_timestamp = datetime.now() @@ -469,22 +513,68 @@ def get_job(self, name: str) -> types.TrainJob: types.Step( name=step_name, pod_name=container["name"], - status=self._container_status(container["id"]), + status=container_utils.get_container_status(self._adapter, container["id"]), ) ) - # Get num_nodes from labels - num_nodes = int(containers[0]["labels"].get(f"{self.label_prefix}/num-nodes", len(containers))) + # Get num_nodes from container count + num_nodes = len(containers) return types.TrainJob( - name=name, + name=job_name, creation_timestamp=creation_timestamp, runtime=job_runtime, steps=steps, num_nodes=num_nodes, - status=self._aggregate_status_from_containers(containers), + status=container_utils.aggregate_container_statuses(self._adapter, containers), ) + def list_jobs(self, runtime: Optional[types.Runtime] = None) -> list[types.TrainJob]: + """List all training jobs by querying container runtime.""" + # Get all containers with our label prefix + filters = {"label": [f"{self.label_prefix}/trainjob-name"]} + containers = self._adapter.list_containers(filters=filters) + + # Group containers by job name + jobs_map: dict[str, list[dict]] = {} + for container in containers: + job_name = container["labels"].get(f"{self.label_prefix}/trainjob-name") + if job_name: + if job_name not in jobs_map: + jobs_map[job_name] = [] + jobs_map[job_name].append(container) + + result: list[types.TrainJob] = [] + for job_name, job_containers in jobs_map.items(): + # Skip jobs with no containers + if not job_containers: + continue + + # Filter by runtime if specified + if runtime: + network_id = job_containers[0]["labels"].get(f"{self.label_prefix}/network-id") + if network_id: + network_info = self._adapter.get_network(network_id) + if network_info: + network_labels = network_info.get("labels", {}) + runtime_name = network_labels.get(f"{self.label_prefix}/runtime-name") + if runtime_name != runtime.name: + continue + + # Build TrainJob from containers + try: + result.append(self.__get_trainjob_from_containers(job_name, job_containers)) + except Exception as e: + logger.warning(f"Failed to get TrainJob {job_name}: {e}") + continue + + return result + + def get_job(self, name: str) -> types.TrainJob: + """Get a specific training job by querying container runtime.""" + containers = self._get_job_containers(name) + return self.__get_trainjob_from_containers(name, containers) + def get_job_logs( self, name: str, @@ -492,12 +582,7 @@ def get_job_logs( step: str = constants.NODE + "-0", ) -> Iterator[str]: """Get logs for a training job by querying container runtime.""" - # Find containers for this job - filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} - containers = self._adapter.list_containers(filters=filters) - - if not containers: - raise ValueError(f"No TrainJob with name {name}") + containers = self._get_job_containers(name) want_all = step == constants.NODE + "-0" for container in sorted(containers, key=lambda c: c["name"]): @@ -532,12 +617,7 @@ def wait_for_job_status( def delete_job(self, name: str): """Delete a training job by querying container runtime.""" - # Find containers for this job - filters = {"label": [f"{self.label_prefix}/trainjob-name={name}"]} - containers = self._adapter.list_containers(filters=filters) - - if not containers: - raise ValueError(f"No TrainJob with name {name}") + containers = self._get_job_containers(name) # Get network_id and workdir from labels network_id = containers[0]["labels"].get(f"{self.label_prefix}/network-id") @@ -567,150 +647,3 @@ def delete_job(self, name: str): # Remove working directory if configured if self.cfg.auto_remove and workdir_host and os.path.isdir(workdir_host): shutil.rmtree(workdir_host, ignore_errors=True) - - # Helper methods - - def _create_workdir(self, job_name: str) -> str: - """Create per-job working directory on host.""" - workdir_base = self.cfg.workdir_base - if workdir_base: - base = Path(workdir_base) - base.mkdir(parents=True, exist_ok=True) - workdir = str((base / f"{job_name}").resolve()) - os.makedirs(workdir, exist_ok=True) - else: - backend_name = ( - self.__class__.__name__.lower().replace("local", "").replace("backend", "") - ) - home_base = Path.home() / ".kubeflow_trainer" / f"local{backend_name}" - home_base.mkdir(parents=True, exist_ok=True) - workdir = str((home_base / f"{job_name}").resolve()) - os.makedirs(workdir, exist_ok=True) - return workdir - - def _write_training_script(self, workdir: str, trainer: types.CustomTrainer) -> Path: - """Write the training script to the working directory.""" - script_path = Path(workdir) / "train.py" - import inspect - import textwrap - - code = inspect.getsource(trainer.func) - code = textwrap.dedent(code) - if trainer.func_args is None: - code += f"\n{trainer.func.__name__}()\n" - else: - code += f"\n{trainer.func.__name__}({trainer.func_args})\n" - script_path.write_text(code) - return script_path - - def _build_environment(self, trainer: types.CustomTrainer) -> dict[str, str]: - """Build environment variables for containers.""" - env = dict(self.cfg.env or {}) - if trainer.env: - env.update(trainer.env) - return env - - def _build_pip_install_cmd(self, trainer: types.CustomTrainer) -> str: - """Build pip install command for packages.""" - pkgs = trainer.packages_to_install or [] - if not pkgs: - return "" - - index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) - main_idx = index_urls[0] - extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) - quoted = " ".join(f'"{p}"' for p in pkgs) - return ( - "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " - f"--index-url {main_idx} {extras} {quoted} && " - ) - - def _maybe_pull_image(self, image: str): - """Pull image based on pull policy.""" - policy = (self.cfg.pull_policy or "IfNotPresent").lower() - try: - if policy == "never": - if not self._adapter.image_exists(image): - raise RuntimeError( - f"Image '{image}' not found locally and pull policy is Never" - ) - return - if policy == "always": - logger.debug(f"Pulling image (Always): {image}") - self._adapter.pull_image(image) - return - # IfNotPresent - if not self._adapter.image_exists(image): - logger.debug(f"Pulling image (IfNotPresent): {image}") - self._adapter.pull_image(image) - except Exception as e: - raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e - - def _container_status(self, container_id: str) -> str: - """Get the status of a container.""" - try: - status, exit_code = self._adapter.container_status(container_id) - if status == "running": - return constants.TRAINJOB_RUNNING - if status == "created": - return constants.TRAINJOB_CREATED - if status == "exited": - # Exit code 0 -> complete, else failed - return constants.TRAINJOB_COMPLETE if exit_code == 0 else constants.TRAINJOB_FAILED - except Exception: - return constants.UNKNOWN - return constants.UNKNOWN - - def _aggregate_status_from_containers(self, containers: list[dict]) -> str: - """Aggregate status from container info dicts.""" - statuses = [self._container_status(c["id"]) for c in containers] - if constants.TRAINJOB_FAILED in statuses: - return constants.TRAINJOB_FAILED - if constants.TRAINJOB_RUNNING in statuses: - return constants.TRAINJOB_RUNNING - if all(s == constants.TRAINJOB_COMPLETE for s in statuses if s != constants.UNKNOWN): - return constants.TRAINJOB_COMPLETE - if any(s == constants.TRAINJOB_CREATED for s in statuses): - return constants.TRAINJOB_CREATED - return constants.UNKNOWN - - def _resolve_image(self, runtime: types.Runtime) -> str: - """Resolve the container image for a runtime.""" - if self.cfg.image: - return self.cfg.image - - import yaml - - for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): - try: - data = yaml.safe_load(Path(f).read_text()) - if ( - data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} - and data.get("metadata", {}).get("name") == runtime.name - ): - replicated = ( - data.get("spec", {}) - .get("template", {}) - .get("spec", {}) - .get("replicatedJobs", []) - ) - node_jobs = [j for j in replicated if j.get("name") == "node"] - if node_jobs: - node_spec = ( - node_jobs[0] - .get("template", {}) - .get("spec", {}) - .get("template", {}) - .get("spec", {}) - ) - containers = node_spec.get("containers", []) - if containers and containers[0].get("image"): - return str(containers[0]["image"]) - except Exception: - continue - - raise ValueError( - f"No image specified for runtime '{runtime.name}'. " - f"Provide ContainerBackendConfig.image or add an 'image' field " - f"to its YAML in {LOCAL_RUNTIMES_DIR}." - ) diff --git a/kubeflow/trainer/backends/container/backend_test.py b/kubeflow/trainer/backends/container/backend_test.py index dbcce3d76..ac13ca0f5 100644 --- a/kubeflow/trainer/backends/container/backend_test.py +++ b/kubeflow/trainer/backends/container/backend_test.py @@ -19,16 +19,20 @@ """ from collections.abc import Iterator +from contextlib import nullcontext import os from pathlib import Path import shutil import tempfile +from typing import Optional from unittest.mock import Mock, patch import pytest +from kubeflow.trainer.backends.container.adapters.base import ( + BaseContainerClientAdapter, +) from kubeflow.trainer.backends.container.backend import ContainerBackend -from kubeflow.trainer.backends.container.client_adapter import ContainerClientAdapter from kubeflow.trainer.backends.container.types import ContainerBackendConfig from kubeflow.trainer.constants import constants from kubeflow.trainer.test.common import FAILED, SUCCESS, TestCase @@ -36,7 +40,7 @@ # Mock Container Adapter -class MockContainerAdapter(ContainerClientAdapter): +class MockContainerAdapter(BaseContainerClientAdapter): """Mock adapter for testing ContainerBackend without Docker/Podman.""" def __init__(self): @@ -121,27 +125,87 @@ def image_exists(self, image: str) -> bool: def run_oneoff_container(self, image: str, command: list[str]) -> str: return "Python 3.9.0\npip 21.0.1\nnvidia-smi not found\n" - def container_status(self, container_id: str) -> tuple[str, int | None]: + def container_status(self, container_id: str) -> tuple[str, Optional[int]]: for container in self.containers_created: if container["id"] == container_id: return (container["status"], container.get("exit_code")) return ("unknown", None) - def set_container_status(self, container_id: str, status: str, exit_code: int | None = None): + def set_container_status(self, container_id: str, status: str, exit_code: Optional[int] = None): """Helper method to set container status for testing.""" for container in self.containers_created: if container["id"] == container_id: container["status"] = status container["exit_code"] = exit_code + def get_container_ip(self, container_id: str, network_id: str) -> Optional[str]: + """Get container IP address on a specific network.""" + for container in self.containers_created: + if container["id"] == container_id: + return f"192.168.1.{len(self.containers_created)}" + return None + + def list_containers(self, filters: Optional[dict[str, list[str]]] = None) -> list[dict]: + """List containers with optional filters.""" + if not filters: + return [ + { + "id": c["id"], + "name": c["name"], + "labels": c["labels"], + "status": c["status"], + "created": "2025-01-01T00:00:00Z", + } + for c in self.containers_created + ] + + # Simple label filtering + result = [] + for container in self.containers_created: + if "label" in filters: + match = True + for label_filter in filters["label"]: + if "=" in label_filter: + key, value = label_filter.split("=", 1) + if container["labels"].get(key) != value: + match = False + break + else: + if label_filter not in container["labels"]: + match = False + break + if match: + result.append( + { + "id": container["id"], + "name": container["name"], + "labels": container["labels"], + "status": container["status"], + "created": "2025-01-01T00:00:00Z", + } + ) + return result + + def get_network(self, network_id: str) -> Optional[dict]: + """Get network information.""" + for network in self.networks_created: + if network["id"] == network_id or network["name"] == network_id: + return { + "id": network["id"], + "name": network["name"], + "labels": network["labels"], + } + return None + # Fixtures @pytest.fixture def container_backend(): """Provide ContainerBackend with mocked adapter.""" - backend = ContainerBackend(ContainerBackendConfig()) - backend._adapter = MockContainerAdapter() - return backend + with patch("kubeflow.trainer.backends.container.backend.DockerClientAdapter") as mock_docker: + mock_docker.return_value = MockContainerAdapter() + backend = ContainerBackend(ContainerBackendConfig()) + return backend @pytest.fixture @@ -196,7 +260,8 @@ def test_backend_initialization(test_case): _ = ContainerBackend(ContainerBackendConfig()) - mock_docker.assert_called_once_with(None) + # Docker should be called (could be with Colima socket or None) + assert mock_docker.call_count == 1 mock_docker_instance.ping.assert_called_once() mock_podman.assert_not_called() assert test_case.expected_status == SUCCESS @@ -219,7 +284,8 @@ def test_backend_initialization(test_case): _ = ContainerBackend(ContainerBackendConfig()) - mock_docker.assert_called_once() + # Docker may be tried multiple times (different socket locations) + assert mock_docker.call_count >= 1 mock_podman.assert_called_once_with(None) mock_podman_instance.ping.assert_called_once() assert test_case.expected_status == SUCCESS @@ -334,6 +400,46 @@ def test_get_runtime_packages(container_backend): "expected_containers": 1, }, ), + TestCase( + name="train with single GPU", + expected_status=SUCCESS, + config={ + "num_nodes": 1, + "resources_per_node": {"gpu": "1"}, + "expected_containers": 1, + "expected_nproc_per_node": 1, + }, + ), + TestCase( + name="train with multiple GPUs", + expected_status=SUCCESS, + config={ + "num_nodes": 1, + "resources_per_node": {"gpu": "4"}, + "expected_containers": 1, + "expected_nproc_per_node": 4, + }, + ), + TestCase( + name="train multi-node with GPUs", + expected_status=SUCCESS, + config={ + "num_nodes": 2, + "resources_per_node": {"gpu": "2"}, + "expected_containers": 2, + "expected_nproc_per_node": 2, + }, + ), + TestCase( + name="train with CPU resources (nproc=1)", + expected_status=SUCCESS, + config={ + "num_nodes": 1, + "resources_per_node": {"cpu": "16"}, + "expected_containers": 1, + "expected_nproc_per_node": 1, + }, + ), ], ) def test_train(container_backend, test_case): @@ -345,6 +451,7 @@ def test_train(container_backend, test_case): num_nodes=test_case.config.get("num_nodes", 1), env=test_case.config.get("env"), packages_to_install=test_case.config.get("packages"), + resources_per_node=test_case.config.get("resources_per_node"), ) runtime = container_backend.get_runtime("torch-distributed") @@ -373,6 +480,15 @@ def test_train(container_backend, test_case): for package in test_case.config["packages"]: assert package in command_str + # Check nproc_per_node if specified + if "expected_nproc_per_node" in test_case.config: + container = container_backend._adapter.containers_created[0] + command_str = container["command"][2] # Get bash script content + expected_nproc = test_case.config["expected_nproc_per_node"] + assert f"--nproc_per_node={expected_nproc}" in command_str, ( + f"Expected --nproc_per_node={expected_nproc} in command, but got: {command_str}" + ) + except Exception as e: assert type(e) is test_case.expected_error print("test execution complete") @@ -583,7 +699,6 @@ def test_delete_job(container_backend, temp_workdir, test_case): """Test deleting a job.""" print("Executing test:", test_case.name) try: - container_backend.cfg.workdir_base = temp_workdir container_backend.cfg.auto_remove = test_case.config["auto_remove"] trainer = types.CustomTrainer( @@ -592,7 +707,7 @@ def test_delete_job(container_backend, temp_workdir, test_case): runtime = container_backend.get_runtime("torch-distributed") job_name = container_backend.train(runtime=runtime, trainer=trainer) - job_workdir = Path(temp_workdir) / job_name + job_workdir = Path.home() / ".kubeflow" / "trainer" / "containers" / job_name assert job_workdir.exists() container_backend.delete_job(job_name) @@ -665,3 +780,84 @@ def test_container_status_mapping(container_backend, test_case): except Exception as e: assert type(e) is test_case.expected_error print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="docker socket locations with colima", + expected_status=SUCCESS, + config={ + "runtime_name": "docker", + "container_host": None, + "create_colima_socket": True, + "expected_contains_none": True, + "expected_has_colima": True, + }, + ), + TestCase( + name="custom host has priority", + expected_status=SUCCESS, + config={ + "runtime_name": "docker", + "container_host": "unix:///custom/path/docker.sock", + "create_colima_socket": False, + "expected_first": "unix:///custom/path/docker.sock", + }, + ), + ], +) +def test_get_common_socket_locations(test_case, tmp_path): + """Test common socket location detection.""" + print("Executing test:", test_case.name) + + # Setup + if test_case.config.get("create_colima_socket"): + colima_dir = tmp_path / ".colima" / "default" + colima_dir.mkdir(parents=True) + colima_sock = colima_dir / "docker.sock" + colima_sock.touch() + + cfg = ContainerBackendConfig(container_host=test_case.config["container_host"]) + + # Test the method directly without creating the backend + context_manager = ( + patch("pathlib.Path.home", return_value=tmp_path) + if test_case.config.get("create_colima_socket") + else nullcontext() + ) + + with context_manager: + backend = ContainerBackend.__new__(ContainerBackend) + backend.cfg = cfg + locations = backend._get_common_socket_locations(test_case.config["runtime_name"]) + + # Assertions + if "expected_contains_none" in test_case.config: + assert None in locations + + if "expected_has_colima" in test_case.config: + assert f"unix://{colima_sock}" in locations + + if "expected_first" in test_case.config: + assert locations[0] == test_case.config["expected_first"] + + print("test execution complete") + + +def test_create_adapter_error_message_format(): + """Test that error message includes attempted connections.""" + cfg = ContainerBackendConfig(container_runtime="docker") + + docker_adapter = "kubeflow.trainer.backends.container.adapters.docker.DockerClientAdapter" + with patch(docker_adapter) as mock_docker: + mock_docker.side_effect = Exception("Connection failed") + + with pytest.raises(RuntimeError) as exc_info: + ContainerBackend(cfg) + + # Error message should be helpful + error_msg = str(exc_info.value) + assert "Could not connect" in error_msg + assert "tried:" in error_msg diff --git a/kubeflow/trainer/backends/container/client_adapter.py b/kubeflow/trainer/backends/container/client_adapter.py deleted file mode 100644 index c742f203d..000000000 --- a/kubeflow/trainer/backends/container/client_adapter.py +++ /dev/null @@ -1,606 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Container client adapters for Docker and Podman. - -This module implements the adapter pattern to abstract away differences between -Docker and Podman APIs, allowing the backend to work with either runtime through -a common interface. -""" - -from __future__ import annotations - -import abc -from collections.abc import Iterator - - -class ContainerClientAdapter(abc.ABC): - """ - Abstract adapter interface for container clients. - - This adapter abstracts the container runtime API, allowing the backend - to work with Docker and Podman through a unified interface. - """ - - @abc.abstractmethod - def ping(self): - """Test the connection to the container runtime.""" - raise NotImplementedError() - - @abc.abstractmethod - def create_network( - self, - name: str, - labels: dict[str, str], - ) -> str: - """ - Create a container network. - - Args: - name: Network name - labels: Labels to attach to the network - - Returns: - Network ID or name - """ - raise NotImplementedError() - - @abc.abstractmethod - def delete_network(self, network_id: str): - """Delete a network.""" - raise NotImplementedError() - - @abc.abstractmethod - def create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """ - Create and start a container. - - Args: - image: Container image - command: Command to run - name: Container name - network_id: Network to attach to - environment: Environment variables - labels: Container labels - volumes: Volume mounts - working_dir: Working directory - - Returns: - Container ID - """ - raise NotImplementedError() - - @abc.abstractmethod - def get_container(self, container_id: str): - """Get container object by ID.""" - raise NotImplementedError() - - @abc.abstractmethod - def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def stop_container(self, container_id: str, timeout: int = 10): - """Stop a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def remove_container(self, container_id: str, force: bool = True): - """Remove a container.""" - raise NotImplementedError() - - @abc.abstractmethod - def pull_image(self, image: str): - """Pull an image.""" - raise NotImplementedError() - - @abc.abstractmethod - def image_exists(self, image: str) -> bool: - """Check if an image exists locally.""" - raise NotImplementedError() - - @abc.abstractmethod - def run_oneoff_container(self, image: str, command: list[str]) -> str: - """ - Run a short-lived container and return its output. - - Args: - image: Container image - command: Command to run - - Returns: - Container output as string - """ - raise NotImplementedError() - - @abc.abstractmethod - def container_status(self, container_id: str) -> tuple[str, int | None]: - """ - Get container status. - - Returns: - Tuple of (status_string, exit_code) - Status strings: "running", "created", "exited", etc. - Exit code is None if container hasn't exited - """ - raise NotImplementedError() - - @abc.abstractmethod - def get_container_ip(self, container_id: str, network_id: str) -> str | None: - """ - Get container's IP address on a specific network. - - Args: - container_id: Container ID - network_id: Network name or ID - - Returns: - IP address string or None if not found - """ - raise NotImplementedError() - - @abc.abstractmethod - def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: - """ - List containers, optionally filtered by labels. - - Args: - filters: Dictionary of filters (e.g., {"label": ["key=value"]}) - - Returns: - List of container info dictionaries with keys: - - id: Container ID - - name: Container name - - labels: Dictionary of labels - - status: Container status - - created: Creation timestamp - """ - raise NotImplementedError() - - @abc.abstractmethod - def get_network(self, network_id: str) -> dict | None: - """ - Get network information by ID or name. - - Args: - network_id: Network ID or name - - Returns: - Dictionary with network info including labels, or None if not found - """ - raise NotImplementedError() - - -class DockerClientAdapter(ContainerClientAdapter): - """Adapter for Docker client.""" - - def __init__(self, host: str | None = None): - """ - Initialize Docker client. - - Args: - host: Docker host URL, or None to use environment defaults - """ - try: - import docker # type: ignore - except ImportError as e: - raise ImportError( - "The 'docker' Python package is not installed. Install with extras: " - "pip install kubeflow[docker]" - ) from e - - if host: - self.client = docker.DockerClient(base_url=host) - else: - self.client = docker.from_env() - - self._runtime_type = "docker" - - def ping(self): - """Test connection to Docker daemon.""" - self.client.ping() - - def create_network(self, name: str, labels: dict[str, str]) -> str: - """Create a Docker network.""" - try: - self.client.networks.get(name) - return name - except Exception: - pass - - self.client.networks.create( - name=name, - check_duplicate=True, - labels=labels, - ) - return name - - def delete_network(self, network_id: str): - """Delete Docker network.""" - try: - net = self.client.networks.get(network_id) - net.remove() - except Exception: - pass - - def create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """Create and start a Docker container.""" - container = self.client.containers.run( - image=image, - command=tuple(command), - name=name, - detach=True, - working_dir=working_dir, - network=network_id, - environment=environment, - labels=labels, - volumes=volumes, - auto_remove=False, - ) - return container.id - - def get_container(self, container_id: str): - """Get Docker container by ID.""" - return self.client.containers.get(container_id) - - def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from Docker container.""" - container = self.get_container(container_id) - logs = container.logs(stream=bool(follow), follow=bool(follow)) - if follow: - for chunk in logs: - if isinstance(chunk, bytes): - yield chunk.decode("utf-8", errors="ignore") - else: - yield str(chunk) - else: - if isinstance(logs, bytes): - yield logs.decode("utf-8", errors="ignore") - else: - yield str(logs) - - def stop_container(self, container_id: str, timeout: int = 10): - """Stop Docker container.""" - container = self.get_container(container_id) - container.stop(timeout=timeout) - - def remove_container(self, container_id: str, force: bool = True): - """Remove Docker container.""" - container = self.get_container(container_id) - container.remove(force=force) - - def pull_image(self, image: str): - """Pull Docker image.""" - self.client.images.pull(image) - - def image_exists(self, image: str) -> bool: - """Check if Docker image exists locally.""" - try: - self.client.images.get(image) - return True - except Exception: - return False - - def run_oneoff_container(self, image: str, command: list[str]) -> str: - """Run a short-lived Docker container and return output.""" - try: - output = self.client.containers.run( - image=image, - command=tuple(command), - detach=False, - remove=True, - ) - if isinstance(output, (bytes, bytearray)): - return output.decode("utf-8", errors="ignore") - return str(output) - except Exception as e: - raise RuntimeError(f"One-off container failed to run: {e}") from e - - def container_status(self, container_id: str) -> tuple[str, int | None]: - """Get Docker container status.""" - try: - container = self.get_container(container_id) - status = container.status - # Get exit code if container has exited - exit_code = None - if status == "exited": - inspect = container.attrs if hasattr(container, "attrs") else container.inspect() - exit_code = inspect.get("State", {}).get("ExitCode") - return (status, exit_code) - except Exception: - return ("unknown", None) - - def get_container_ip(self, container_id: str, network_id: str) -> str | None: - """Get container's IP address on a specific network.""" - try: - container = self.get_container(container_id) - # Refresh container info - container.reload() - # Get network settings - networks = container.attrs.get("NetworkSettings", {}).get("Networks", {}) - - # Try to find the network by exact name or ID - if network_id in networks: - return networks[network_id].get("IPAddress") - - # Fallback: return first available IP - for net_name, net_info in networks.items(): - ip = net_info.get("IPAddress") - if ip: - return ip - - return None - except Exception: - return None - - def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: - """List Docker containers with optional filters.""" - try: - containers = self.client.containers.list(all=True, filters=filters) - result = [] - for c in containers: - result.append({ - "id": c.id, - "name": c.name, - "labels": c.labels, - "status": c.status, - "created": c.attrs.get("Created", ""), - }) - return result - except Exception: - return [] - - def get_network(self, network_id: str) -> dict | None: - """Get Docker network information.""" - try: - network = self.client.networks.get(network_id) - return { - "id": network.id, - "name": network.name, - "labels": network.attrs.get("Labels", {}), - } - except Exception: - return None - - -class PodmanClientAdapter(ContainerClientAdapter): - """Adapter for Podman client.""" - - def __init__(self, host: str | None = None): - """ - Initialize Podman client. - - Args: - host: Podman host URL, or None to use environment defaults - """ - try: - import podman # type: ignore - except ImportError as e: - raise ImportError( - "The 'podman' Python package is not installed. Install with extras: " - "pip install kubeflow[podman]" - ) from e - - if host: - self.client = podman.PodmanClient(base_url=host) - else: - self.client = podman.PodmanClient() - - self._runtime_type = "podman" - - def ping(self): - """Test connection to Podman.""" - self.client.ping() - - def create_network(self, name: str, labels: dict[str, str]) -> str: - """Create a Podman network with DNS enabled.""" - try: - self.client.networks.get(name) - return name - except Exception: - pass - - self.client.networks.create( - name=name, - driver="bridge", - dns_enabled=True, - labels=labels, - ) - return name - - def delete_network(self, network_id: str): - """Delete Podman network.""" - try: - net = self.client.networks.get(network_id) - net.remove() - except Exception: - pass - - def create_and_start_container( - self, - image: str, - command: list[str], - name: str, - network_id: str, - environment: dict[str, str], - labels: dict[str, str], - volumes: dict[str, dict[str, str]], - working_dir: str, - ) -> str: - """Create and start a Podman container.""" - container = self.client.containers.run( - image=image, - command=command, - name=name, - network=network_id, - working_dir=working_dir, - environment=environment, - labels=labels, - volumes=volumes, - detach=True, - remove=False, - ) - return container.id - - def get_container(self, container_id: str): - """Get Podman container by ID.""" - return self.client.containers.get(container_id) - - def container_logs(self, container_id: str, follow: bool) -> Iterator[str]: - """Stream logs from Podman container.""" - container = self.get_container(container_id) - logs = container.logs(stream=bool(follow), follow=bool(follow)) - if follow: - for chunk in logs: - if isinstance(chunk, bytes): - yield chunk.decode("utf-8", errors="ignore") - else: - yield str(chunk) - else: - if isinstance(logs, bytes): - yield logs.decode("utf-8", errors="ignore") - else: - yield str(logs) - - def stop_container(self, container_id: str, timeout: int = 10): - """Stop Podman container.""" - container = self.get_container(container_id) - container.stop(timeout=timeout) - - def remove_container(self, container_id: str, force: bool = True): - """Remove Podman container.""" - container = self.get_container(container_id) - container.remove(force=force) - - def pull_image(self, image: str): - """Pull Podman image.""" - self.client.images.pull(image) - - def image_exists(self, image: str) -> bool: - """Check if Podman image exists locally.""" - try: - self.client.images.get(image) - return True - except Exception: - return False - - def run_oneoff_container(self, image: str, command: list[str]) -> str: - """Run a short-lived Podman container and return output.""" - try: - container = self.client.containers.create( - image=image, - command=command, - detach=False, - remove=True, - ) - container.start() - container.wait() - logs = container.logs() - - if isinstance(logs, (bytes, bytearray)): - return logs.decode("utf-8", errors="ignore") - return str(logs) - except Exception as e: - raise RuntimeError(f"One-off container failed to run: {e}") from e - - def container_status(self, container_id: str) -> tuple[str, int | None]: - """Get Podman container status.""" - try: - container = self.get_container(container_id) - status = container.status - # Get exit code if container has exited - exit_code = None - if status == "exited": - inspect = container.attrs if hasattr(container, "attrs") else container.inspect() - exit_code = inspect.get("State", {}).get("ExitCode") - return (status, exit_code) - except Exception: - return ("unknown", None) - - def get_container_ip(self, container_id: str, network_id: str) -> str | None: - """Get container's IP address on a specific network.""" - try: - container = self.get_container(container_id) - # Get container inspect data - inspect = container.attrs if hasattr(container, "attrs") else container.inspect() - - # Get network settings - Podman structure is similar to Docker - networks = inspect.get("NetworkSettings", {}).get("Networks", {}) - - # Try to find the network by exact name or ID - if network_id in networks: - return networks[network_id].get("IPAddress") - - # Fallback: return first available IP - for net_name, net_info in networks.items(): - ip = net_info.get("IPAddress") - if ip: - return ip - - return None - except Exception: - return None - - def list_containers(self, filters: dict[str, str] | None = None) -> list[dict]: - """List Podman containers with optional filters.""" - try: - containers = self.client.containers.list(all=True, filters=filters) - result = [] - for c in containers: - inspect = c.attrs if hasattr(c, "attrs") else c.inspect() - result.append({ - "id": c.id, - "name": c.name, - "labels": c.labels if hasattr(c, "labels") else inspect.get("Config", {}).get("Labels", {}), - "status": c.status, - "created": inspect.get("Created", ""), - }) - return result - except Exception: - return [] - - def get_network(self, network_id: str) -> dict | None: - """Get Podman network information.""" - try: - network = self.client.networks.get(network_id) - inspect = network.attrs if hasattr(network, "attrs") else network.inspect() - return { - "id": inspect.get("ID", network_id), - "name": inspect.get("Name", network_id), - "labels": inspect.get("Labels", {}), - } - except Exception: - return None diff --git a/kubeflow/trainer/backends/container/runtime_loader.py b/kubeflow/trainer/backends/container/runtime_loader.py index 6b9bdf06c..3e614400e 100644 --- a/kubeflow/trainer/backends/container/runtime_loader.py +++ b/kubeflow/trainer/backends/container/runtime_loader.py @@ -13,15 +13,600 @@ # limitations under the License. """ -Runtime loader for the Container backend. +Runtime loader for container backends (Docker, Podman). -This module provides container-agnostic imports for the shared local runtime loader. +We support loading training runtime definitions from multiple sources: +1. GitHub: Fetches latest runtimes from kubeflow/trainer repository (with caching) +2. Local bundled: Falls back to `kubeflow/trainer/config/training_runtimes/` YAML files +3. User custom: Additional YAML files in the local directory + +The loader tries GitHub first (with 24-hour cache), then falls back to bundled files +if the network is unavailable or GitHub fetch fails. """ -from kubeflow.trainer.backends.local_runtime_loader import ( - LOCAL_RUNTIMES_DIR, - get_local_runtime, - list_local_runtimes, +from __future__ import annotations + +from datetime import datetime, timedelta +import json +import logging +from pathlib import Path +from typing import Any, Optional +import urllib.error +import urllib.request + +import yaml + +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types as base_types + +logger = logging.getLogger(__name__) + +TRAINING_RUNTIMES_DIR = Path(__file__).parents[2] / "config" / "training_runtimes" +CACHE_DIR = Path.home() / ".kubeflow" / "trainer" / "cache" +CACHE_DURATION = timedelta(hours=24) + +# GitHub runtimes configuration +GITHUB_RUNTIMES_BASE_URL = ( + "https://raw.githubusercontent.com/kubeflow/trainer/master/manifests/base/runtimes" ) +GITHUB_RUNTIMES_TREE_URL = "https://github.com/kubeflow/trainer/tree/master/manifests/base/runtimes" + +__all__ = [ + "TRAINING_RUNTIMES_DIR", + "get_training_runtime_from_sources", + "list_training_runtimes_from_sources", +] + + +def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: + with open(path) as f: + data: dict[str, Any] = yaml.safe_load(f) + return data + + +def _discover_github_runtime_files( + owner: str = "kubeflow", + repo: str = "trainer", + branch: str = "master", + path: str = "manifests/base/runtimes", +) -> list[str]: + """ + Discover available runtime YAML files from GitHub repository. + + Fetches the directory listing from GitHub and extracts .yaml filenames, + excluding kustomization.yaml and other non-runtime files. + + Args: + owner: GitHub repository owner (default: "kubeflow") + repo: GitHub repository name (default: "trainer") + branch: Git branch name (default: "master") + path: Path to runtimes directory (default: "manifests/base/runtimes") + + Returns: + List of YAML filenames (e.g., ['torch_distributed.yaml', ...]) + Returns empty list if discovery fails. + """ + tree_url = f"https://github.com/{owner}/{repo}/tree/{branch}/{path}" + try: + logger.debug(f"Discovering runtimes from GitHub: {tree_url}") + with urllib.request.urlopen(tree_url, timeout=5) as response: + html_content = response.read().decode("utf-8") + + # Parse HTML to find .yaml files + # Look for .yaml filenames in the HTML content + import re + + # Pattern to match .yaml files in the HTML + # Matches word characters, hyphens, underscores followed by .yaml + pattern = r"([\w-]+\.yaml)" + matches = re.findall(pattern, html_content) + + # Filter out kustomization.yaml, config files, and duplicates + # Keep only runtime files (typically named *_distributed.yaml or similar) + runtime_files = [] + seen = set() + exclude_files = {"kustomization.yaml", "golangci.yaml", "pre-commit-config.yaml"} + + for match in matches: + filename = match + if filename not in seen and filename not in exclude_files: + runtime_files.append(filename) + seen.add(filename) + + logger.debug(f"Discovered {len(runtime_files)} runtime files: {runtime_files}") + return runtime_files + + except Exception as e: + logger.debug(f"Failed to discover GitHub runtime files from {tree_url}: {e}") + return [] + + +def _fetch_runtime_from_github( + runtime_file: str, + owner: str = "kubeflow", + repo: str = "trainer", + branch: str = "master", + path: str = "manifests/base/runtimes", +) -> Optional[dict[str, Any]]: + """ + Fetch a runtime YAML from GitHub. + + Args: + runtime_file: YAML filename to fetch + owner: GitHub repository owner (default: "kubeflow") + repo: GitHub repository name (default: "trainer") + branch: Git branch name (default: "master") + path: Path to runtimes directory (default: "manifests/base/runtimes") + + Returns None if fetch fails (network error, timeout, etc.) + """ + url = f"https://raw.githubusercontent.com/{owner}/{repo}/{branch}/{path}/{runtime_file}" + try: + logger.debug(f"Fetching runtime from GitHub: {url}") + with urllib.request.urlopen(url, timeout=5) as response: + content = response.read().decode("utf-8") + data = yaml.safe_load(content) + logger.debug(f"Successfully fetched {runtime_file} from GitHub") + return data + except (urllib.error.URLError, TimeoutError, Exception) as e: + logger.debug(f"Failed to fetch {runtime_file} from GitHub: {e}") + return None + + +def _get_cached_runtime_list() -> Optional[list[str]]: + """ + Get cached runtime file list if it exists and is not expired. + + Returns None if cache doesn't exist or is expired. + """ + if not CACHE_DIR.exists(): + return None + + cache_file = CACHE_DIR / "runtime_list.json" + + if not cache_file.exists(): + return None + + try: + with open(cache_file) as f: + data = json.load(f) + + cached_time = datetime.fromisoformat(data["cached_at"]) + if datetime.now() - cached_time > CACHE_DURATION: + logger.debug("Runtime list cache expired") + return None + + logger.debug(f"Using cached runtime list: {data['files']}") + return data["files"] + except (json.JSONDecodeError, KeyError, ValueError, Exception) as e: + logger.debug(f"Failed to read runtime list cache: {e}") + return None + + +def _cache_runtime_list(files: list[str]) -> None: + """Cache the discovered runtime file list.""" + try: + CACHE_DIR.mkdir(parents=True, exist_ok=True) + cache_file = CACHE_DIR / "runtime_list.json" + + data = { + "cached_at": datetime.now().isoformat(), + "files": files, + } + with open(cache_file, "w") as f: + json.dump(data, f) + + logger.debug(f"Cached runtime list: {files}") + except Exception as e: + logger.debug(f"Failed to cache runtime list: {e}") + + +def _get_github_runtime_files() -> list[str]: + """ + Get list of runtime files from GitHub with caching. + + Priority: + 1. Check cache (if not expired) + 2. Discover from GitHub (and cache if successful) + 3. Return empty list if both fail + """ + # Try cache first + cached = _get_cached_runtime_list() + if cached is not None: + return cached + + # Try GitHub discovery + files = _discover_github_runtime_files() + if files: + _cache_runtime_list(files) + return files + + return [] + + +def _get_cached_runtime(runtime_file: str) -> Optional[dict[str, Any]]: + """ + Get cached runtime if it exists and is not expired. + + Returns None if cache doesn't exist or is expired. + """ + if not CACHE_DIR.exists(): + return None + + cache_file = CACHE_DIR / runtime_file + metadata_file = CACHE_DIR / f"{runtime_file}.metadata" + + if not cache_file.exists() or not metadata_file.exists(): + return None + + try: + # Check if cache is expired + with open(metadata_file) as f: + metadata = json.load(f) + + cached_time = datetime.fromisoformat(metadata["cached_at"]) + if datetime.now() - cached_time > CACHE_DURATION: + logger.debug(f"Cache expired for {runtime_file}") + return None + + # Load cached runtime + with open(cache_file) as f: + data = yaml.safe_load(f) + + logger.debug(f"Using cached runtime: {runtime_file}") + return data + except (json.JSONDecodeError, KeyError, ValueError, Exception) as e: + logger.debug(f"Failed to read cache for {runtime_file}: {e}") + return None + + +def _cache_runtime(runtime_file: str, data: dict[str, Any]) -> None: + """Cache a runtime YAML with metadata.""" + try: + CACHE_DIR.mkdir(parents=True, exist_ok=True) + + cache_file = CACHE_DIR / runtime_file + metadata_file = CACHE_DIR / f"{runtime_file}.metadata" + + # Write runtime data + with open(cache_file, "w") as f: + yaml.safe_dump(data, f) + + # Write metadata + metadata = { + "cached_at": datetime.now().isoformat(), + "source": "github", + } + with open(metadata_file, "w") as f: + json.dump(metadata, f) + + logger.debug(f"Cached runtime: {runtime_file}") + except Exception as e: + logger.debug(f"Failed to cache {runtime_file}: {e}") + + +def _load_runtime_from_github_with_cache(runtime_file: str) -> Optional[dict[str, Any]]: + """ + Load runtime from GitHub with caching. + + Priority: + 1. Check cache (if not expired) + 2. Fetch from GitHub (and cache if successful) + 3. Return None if both fail + + Args: + runtime_file: YAML filename to load + """ + # Try cache first + cached = _get_cached_runtime(runtime_file) + if cached is not None: + return cached + + # Try GitHub + data = _fetch_runtime_from_github(runtime_file) + if data is not None: + _cache_runtime(runtime_file, data) + return data + + return None + + +def _create_default_runtimes() -> list[base_types.Runtime]: + """ + Create default Runtime objects from DEFAULT_FRAMEWORK_IMAGES constant. + + Returns: + List of default Runtime objects for each framework. + """ + default_runtimes = [] + + for framework, image in constants.DEFAULT_FRAMEWORK_IMAGES.items(): + runtime = base_types.Runtime( + name=f"{framework}-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework=framework, + num_nodes=1, + ), + pretrained_model=None, + ) + default_runtimes.append(runtime) + logger.debug(f"Created default runtime: {runtime.name} with image {image}") + + return default_runtimes + + +def _parse_runtime_yaml(data: dict[str, Any], source: str = "unknown") -> base_types.Runtime: + """ + Parse a runtime YAML dict into a Runtime object. + + Args: + data: The YAML data as a dictionary + source: Source of the YAML (for error messages) + + Returns: + Runtime object + + Raises: + ValueError: If the YAML is malformed or missing required fields + """ + # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime + # and TrainingRuntime kinds. + if not ( + data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} and data.get("metadata") + ): + raise ValueError( + f"Runtime YAML from {source} must be a ClusterTrainingRuntime CRD-shaped document" + ) + + name = data["metadata"].get("name") + if not name: + raise ValueError(f"Runtime YAML from {source} missing metadata.name") + + labels = data["metadata"].get("labels", {}) + framework = labels.get("trainer.kubeflow.org/framework") + if not framework: + raise ValueError( + f"Runtime {name} from {source} must set " + f"metadata.labels['trainer.kubeflow.org/framework']" + ) + + spec = data.get("spec", {}) + ml_policy = spec.get("mlPolicy", {}) + num_nodes = int(ml_policy.get("numNodes", 1)) + + # Validate presence of a 'node' replicated job with a container image + templ = spec.get("template", {}).get("spec", {}) + replicated = templ.get("replicatedJobs", []) + node_jobs = [j for j in replicated if j.get("name") == "node"] + if not node_jobs: + raise ValueError( + f"Runtime {name} from {source} must define replicatedJobs with a 'node' entry" + ) + node_spec = node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) + containers = node_spec.get("containers", []) + if not containers or not containers[0].get("image"): + raise ValueError(f"Runtime {name} from {source} 'node' must specify containers[0].image") + + return base_types.Runtime( + name=name, + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework=framework, + num_nodes=num_nodes, + ), + pretrained_model=None, + ) + + +def _parse_source_url(source: str) -> tuple[str, str]: + """ + Parse a source URL to determine its type and path. + + Args: + source: Source URL with scheme (github://, https://, file://, or absolute path) + + Returns: + Tuple of (source_type, path) where source_type is one of: + 'github', 'http', 'https', 'file' + """ + if source.startswith("github://"): + return ("github", source[9:]) # Remove 'github://' + elif source.startswith("https://"): + return ("https", source) + elif source.startswith("http://"): + return ("http", source) + elif source.startswith("file://"): + return ("file", source[7:]) # Remove 'file://' + elif source.startswith("/"): + # Absolute path without file:// prefix + return ("file", source) + else: + raise ValueError( + f"Unsupported source URL scheme: {source}. " + f"Supported: github://, https://, http://, file://, or absolute paths" + ) + + +def _load_from_github_url(github_path: str) -> list[base_types.Runtime]: + """ + Load runtimes from a GitHub URL (github://owner/repo[/path]). + + Args: + github_path: Path after github:// (e.g., "kubeflow/trainer" or "myorg/myrepo") + + Returns: + List of Runtime objects loaded from GitHub + """ + runtimes = [] + runtime_names_seen = set() + + # Parse the GitHub path + # Format: owner/repo[/path/to/runtimes] + parts = github_path.split("/") + if len(parts) < 2: + logger.warning(f"Invalid GitHub path format: {github_path}. Expected owner/repo[/path]") + return runtimes + + owner = parts[0] + repo = parts[1] + # Custom path if provided (default to manifests/base/runtimes) + custom_path = "/".join(parts[2:]) if len(parts) > 2 else "manifests/base/runtimes" + + # Discover runtime files from the specified GitHub repo + logger.debug(f"Loading runtimes from GitHub: {owner}/{repo}/{custom_path}") + github_runtime_files = _discover_github_runtime_files(owner=owner, repo=repo, path=custom_path) + + for runtime_file in github_runtime_files: + try: + data = _fetch_runtime_from_github( + runtime_file, owner=owner, repo=repo, path=custom_path + ) + if data is not None: + runtime = _parse_runtime_yaml(data, source=f"github://{github_path}/{runtime_file}") + if runtime.name not in runtime_names_seen: + runtimes.append(runtime) + runtime_names_seen.add(runtime.name) + logger.debug(f"Loaded runtime from GitHub: {runtime.name}") + except Exception as e: + logger.debug(f"Failed to parse GitHub runtime {runtime_file}: {e}") + + return runtimes + + +def _load_from_http_url(url: str) -> list[base_types.Runtime]: + """ + Load runtimes from an HTTP(S) URL. + + Args: + url: HTTP(S) URL to a runtime YAML file or directory listing + + Returns: + List of Runtime objects loaded from HTTP(S) + """ + runtimes = [] + + try: + import urllib.request + + logger.debug(f"Fetching runtime from HTTP: {url}") + with urllib.request.urlopen(url, timeout=5) as response: + content = response.read().decode("utf-8") + import yaml + + data = yaml.safe_load(content) + runtime = _parse_runtime_yaml(data, source=url) + runtimes.append(runtime) + logger.debug(f"Loaded runtime from HTTP: {runtime.name}") + except Exception as e: + logger.debug(f"Failed to load runtime from HTTP {url}: {e}") + + return runtimes + + +def _load_from_filesystem(path: str) -> list[base_types.Runtime]: + """ + Load runtimes from local filesystem path. + + Args: + path: Local filesystem path to a directory or YAML file + + Returns: + List of Runtime objects loaded from filesystem + """ + from pathlib import Path + + runtimes = [] + runtime_path = Path(path).expanduser() + + try: + if runtime_path.is_dir(): + # Load all YAML files from directory + for yaml_file in sorted(runtime_path.glob("*.yaml")): + try: + data = _load_runtime_from_yaml(yaml_file) + runtime = _parse_runtime_yaml(data, source=str(yaml_file)) + runtimes.append(runtime) + logger.debug(f"Loaded runtime from file: {runtime.name}") + except Exception as e: + logger.warning(f"Failed to load runtime from {yaml_file}: {e}") + elif runtime_path.is_file(): + # Load single YAML file + data = _load_runtime_from_yaml(runtime_path) + runtime = _parse_runtime_yaml(data, source=str(runtime_path)) + runtimes.append(runtime) + logger.debug(f"Loaded runtime from file: {runtime.name}") + else: + logger.warning(f"Path does not exist: {runtime_path}") + except Exception as e: + logger.warning(f"Failed to load runtimes from {path}: {e}") + + return runtimes + + +def list_training_runtimes_from_sources(sources: list[str]) -> list[base_types.Runtime]: + """ + List all available training runtimes from configured sources. + + Args: + sources: List of source URLs with schemes (github://, https://, http://, file://, or paths) + + Returns: + List of Runtime objects (built-in runtimes used as default if not found in sources) + """ + runtimes: list[base_types.Runtime] = [] + runtime_names_seen = set() + + # Load from each configured source in priority order + for source in sources: + try: + source_type, source_path = _parse_source_url(source) + + if source_type == "github": + source_runtimes = _load_from_github_url(source_path) + elif source_type in ("http", "https"): + source_runtimes = _load_from_http_url(source) + elif source_type == "file": + source_runtimes = _load_from_filesystem(source_path) + else: + logger.warning(f"Unsupported source type: {source_type}") + continue + + # Add runtimes, skipping duplicates + for runtime in source_runtimes: + if runtime.name not in runtime_names_seen: + runtimes.append(runtime) + runtime_names_seen.add(runtime.name) + except Exception as e: + logger.debug(f"Failed to load from source {source}: {e}") + + # Fallback to default runtimes from constants if not found in sources + for default_runtime in _create_default_runtimes(): + if default_runtime.name not in runtime_names_seen: + runtimes.append(default_runtime) + runtime_names_seen.add(default_runtime.name) + + return runtimes + + +def get_training_runtime_from_sources(name: str, sources: list[str]) -> base_types.Runtime: + """ + Get a specific training runtime by name from configured sources. + + Args: + name: The name of the runtime to get + sources: List of source URLs with schemes + + Returns: + Runtime object -__all__ = ["LOCAL_RUNTIMES_DIR", "get_local_runtime", "list_local_runtimes"] + Raises: + ValueError: If the runtime is not found + """ + for rt in list_training_runtimes_from_sources(sources): + if rt.name == name: + return rt + raise ValueError( + f"Runtime '{name}' not found. Available runtimes: " + f"{[rt.name for rt in list_training_runtimes_from_sources(sources)]}" + ) diff --git a/kubeflow/trainer/backends/container/runtime_loader_test.py b/kubeflow/trainer/backends/container/runtime_loader_test.py new file mode 100644 index 000000000..ea0ec2cff --- /dev/null +++ b/kubeflow/trainer/backends/container/runtime_loader_test.py @@ -0,0 +1,526 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Unit tests for runtime_loader module. + +Tests runtime loading from various sources including GitHub, HTTP, and filesystem. +""" + +from unittest.mock import MagicMock, patch + +import pytest + +from kubeflow.trainer.backends.container import runtime_loader +from kubeflow.trainer.constants import constants +from kubeflow.trainer.test.common import FAILED, SUCCESS, TestCase +from kubeflow.trainer.types import types as base_types + +# Sample runtime YAML data for testing +SAMPLE_RUNTIME_YAML = { + "apiVersion": "trainer.kubeflow.org/v1alpha1", + "kind": "ClusterTrainingRuntime", + "metadata": { + "name": "torch-distributed", + "labels": {"trainer.kubeflow.org/framework": "torch"}, + }, + "spec": { + "mlPolicy": {"numNodes": 1}, + "template": { + "spec": { + "replicatedJobs": [ + { + "name": "node", + "template": { + "spec": { + "template": { + "spec": { + "containers": [ + { + "name": "trainer", + "image": "pytorch/pytorch:2.0.0", + } + ] + } + } + } + }, + } + ] + } + }, + }, +} + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="parse github url", + expected_status=SUCCESS, + config={ + "url": "github://kubeflow/trainer", + "expected_type": "github", + "expected_path": "kubeflow/trainer", + }, + ), + TestCase( + name="parse github url with path", + expected_status=SUCCESS, + config={ + "url": "github://myorg/myrepo/custom/path", + "expected_type": "github", + "expected_path": "myorg/myrepo/custom/path", + }, + ), + TestCase( + name="parse https url", + expected_status=SUCCESS, + config={ + "url": "https://example.com/runtime.yaml", + "expected_type": "https", + "expected_path": "https://example.com/runtime.yaml", + }, + ), + TestCase( + name="parse http url", + expected_status=SUCCESS, + config={ + "url": "http://example.com/runtime.yaml", + "expected_type": "http", + "expected_path": "http://example.com/runtime.yaml", + }, + ), + TestCase( + name="parse file url", + expected_status=SUCCESS, + config={ + "url": "file:///path/to/runtime.yaml", + "expected_type": "file", + "expected_path": "/path/to/runtime.yaml", + }, + ), + TestCase( + name="parse absolute path", + expected_status=SUCCESS, + config={ + "url": "/absolute/path/to/runtime.yaml", + "expected_type": "file", + "expected_path": "/absolute/path/to/runtime.yaml", + }, + ), + TestCase( + name="parse unsupported scheme", + expected_status=FAILED, + config={"url": "ftp://example.com/runtime.yaml"}, + expected_error=ValueError, + ), + ], +) +def test_parse_source_url(test_case): + """Test parsing various source URL formats.""" + print("Executing test:", test_case.name) + try: + source_type, path = runtime_loader._parse_source_url(test_case.config["url"]) + + assert test_case.expected_status == SUCCESS + assert source_type == test_case.config["expected_type"] + assert path == test_case.config["expected_path"] + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="load from default github", + expected_status=SUCCESS, + config={ + "github_path": "kubeflow/trainer", + "discovered_files": ["torch_distributed.yaml"], + "expected_runtime_name": "torch-distributed", + "expected_framework": "torch", + }, + ), + TestCase( + name="load from custom github", + expected_status=SUCCESS, + config={ + "github_path": "myorg/myrepo", + "discovered_files": ["custom_runtime.yaml"], + "expected_runtime_name": "custom-runtime", + "expected_framework": "custom", + }, + ), + TestCase( + name="load from github no files", + expected_status=SUCCESS, + config={ + "github_path": "kubeflow/trainer", + "discovered_files": [], + "expected_count": 0, + }, + ), + TestCase( + name="load from github invalid path", + expected_status=SUCCESS, + config={ + "github_path": "invalid", + "expected_count": 0, + }, + ), + ], +) +def test_load_from_github_url(test_case): + """Test loading runtimes from GitHub URLs.""" + print("Executing test:", test_case.name) + try: + with ( + patch( + "kubeflow.trainer.backends.container.runtime_loader._discover_github_runtime_files" + ) as mock_discover, + patch( + "kubeflow.trainer.backends.container.runtime_loader._fetch_runtime_from_github" + ) as mock_fetch, + ): + if test_case.name == "load from github invalid path": + # Don't set up mocks for invalid path test + runtimes = runtime_loader._load_from_github_url(test_case.config["github_path"]) + assert len(runtimes) == test_case.config["expected_count"] + else: + mock_discover.return_value = test_case.config.get("discovered_files", []) + + # Create runtime YAML with custom name/framework if specified + runtime_yaml = SAMPLE_RUNTIME_YAML.copy() + if "expected_runtime_name" in test_case.config: + runtime_yaml["metadata"]["name"] = test_case.config["expected_runtime_name"] + runtime_yaml["metadata"]["labels"]["trainer.kubeflow.org/framework"] = ( + test_case.config["expected_framework"] + ) + mock_fetch.return_value = runtime_yaml + + runtimes = runtime_loader._load_from_github_url(test_case.config["github_path"]) + + if "expected_count" in test_case.config: + assert len(runtimes) == test_case.config["expected_count"] + else: + assert len(runtimes) == 1 + assert runtimes[0].name == test_case.config["expected_runtime_name"] + assert runtimes[0].trainer.framework == test_case.config["expected_framework"] + + assert test_case.expected_status == SUCCESS + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="priority order github sources", + expected_status=SUCCESS, + config={ + "sources": ["github://myorg/myrepo", "github://kubeflow/trainer"], + "expected_count": 2, + "expected_names": ["torch-distributed", "deepspeed-distributed"], + }, + ), + TestCase( + name="duplicate runtime names skipped", + expected_status=SUCCESS, + config={ + "sources": ["github://myorg/myrepo", "github://kubeflow/trainer"], + "duplicate_names": True, + "expected_count": 1, + "expected_names": ["torch-distributed"], + }, + ), + TestCase( + name="fallback to defaults", + expected_status=SUCCESS, + config={ + "sources": ["github://myorg/myrepo"], + "no_github_runtimes": True, + "expected_count": 1, + "expected_names": ["torch-distributed"], + }, + ), + ], +) +def test_list_training_runtimes_from_sources(test_case): + """Test listing runtimes from multiple sources.""" + print("Executing test:", test_case.name) + try: + with ( + patch( + "kubeflow.trainer.backends.container.runtime_loader._load_from_github_url" + ) as mock_github, + patch( + "kubeflow.trainer.backends.container.runtime_loader._create_default_runtimes" + ) as mock_defaults, + ): + if test_case.name == "priority order github sources": + torch_runtime = base_types.Runtime( + name="torch-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework="torch", + num_nodes=1, + ), + ) + deepspeed_runtime = base_types.Runtime( + name="deepspeed-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework="deepspeed", + num_nodes=1, + ), + ) + mock_github.side_effect = [[torch_runtime], [deepspeed_runtime]] + mock_defaults.return_value = [] + + elif test_case.name == "duplicate runtime names skipped": + torch_runtime_1 = base_types.Runtime( + name="torch-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework="torch", + num_nodes=1, + ), + ) + torch_runtime_2 = base_types.Runtime( + name="torch-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework="torch", + num_nodes=2, + ), + ) + mock_github.side_effect = [[torch_runtime_1], [torch_runtime_2]] + mock_defaults.return_value = [] + + elif test_case.name == "fallback to defaults": + mock_github.return_value = [] + default_runtime = base_types.Runtime( + name="torch-distributed", + trainer=base_types.RuntimeTrainer( + trainer_type=base_types.TrainerType.CUSTOM_TRAINER, + framework="torch", + num_nodes=1, + ), + ) + mock_defaults.return_value = [default_runtime] + + runtimes = runtime_loader.list_training_runtimes_from_sources( + test_case.config["sources"] + ) + + assert len(runtimes) == test_case.config["expected_count"] + runtime_names = [r.name for r in runtimes] + for expected_name in test_case.config["expected_names"]: + assert expected_name in runtime_names + + assert test_case.expected_status == SUCCESS + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +def test_create_default_runtimes(): + """Test creating default runtimes from constants.""" + print("Executing test: create default runtimes") + runtimes = runtime_loader._create_default_runtimes() + + assert len(runtimes) == len(constants.DEFAULT_FRAMEWORK_IMAGES) + + # Check torch runtime + torch_runtimes = [r for r in runtimes if r.trainer.framework == "torch"] + assert len(torch_runtimes) == 1 + assert torch_runtimes[0].name == "torch-distributed" + assert torch_runtimes[0].trainer.trainer_type == base_types.TrainerType.CUSTOM_TRAINER + assert torch_runtimes[0].trainer.num_nodes == 1 + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="discover runtime files", + expected_status=SUCCESS, + config={ + "html_content": """ + + torch_distributed.yaml + deepspeed_distributed.yaml + kustomization.yaml + + """, + "expected_files": ["torch_distributed.yaml", "deepspeed_distributed.yaml"], + "excluded_files": ["kustomization.yaml"], + }, + ), + TestCase( + name="discover runtime files custom repo", + expected_status=SUCCESS, + config={ + "html_content": """ + + custom_runtime.yaml + + """, + "expected_files": ["custom_runtime.yaml"], + "owner": "myorg", + "repo": "myrepo", + "path": "custom/path", + }, + ), + TestCase( + name="discover runtime files network error", + expected_status=SUCCESS, + config={ + "network_error": True, + "expected_files": [], + }, + ), + ], +) +def test_discover_github_runtime_files(test_case): + """Test discovering runtime files from GitHub.""" + print("Executing test:", test_case.name) + try: + with patch("urllib.request.urlopen") as mock_urlopen: + if test_case.config.get("network_error"): + mock_urlopen.side_effect = Exception("Network error") + else: + mock_response = MagicMock() + mock_response.read.return_value = test_case.config["html_content"].encode("utf-8") + mock_response.__enter__.return_value = mock_response + mock_urlopen.return_value = mock_response + + kwargs = {} + if "owner" in test_case.config: + kwargs["owner"] = test_case.config["owner"] + kwargs["repo"] = test_case.config["repo"] + kwargs["path"] = test_case.config["path"] + + files = runtime_loader._discover_github_runtime_files(**kwargs) + + for expected_file in test_case.config["expected_files"]: + assert expected_file in files + + for excluded_file in test_case.config.get("excluded_files", []): + assert excluded_file not in files + + if "owner" in test_case.config and not test_case.config.get("network_error"): + called_url = mock_urlopen.call_args[0][0] + assert f"{kwargs['owner']}/{kwargs['repo']}" in called_url + assert kwargs["path"] in called_url + + assert test_case.expected_status == SUCCESS + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="fetch runtime success", + expected_status=SUCCESS, + config={ + "yaml_content": """ +apiVersion: trainer.kubeflow.org/v1alpha1 +kind: ClusterTrainingRuntime +metadata: + name: torch-distributed +""", + "expected_name": "torch-distributed", + }, + ), + TestCase( + name="fetch runtime custom repo", + expected_status=SUCCESS, + config={ + "yaml_content": """ +apiVersion: trainer.kubeflow.org/v1alpha1 +kind: ClusterTrainingRuntime +metadata: + name: custom-runtime +""", + "expected_name": "custom-runtime", + "runtime_file": "custom.yaml", + "owner": "myorg", + "repo": "myrepo", + "path": "custom/path", + }, + ), + TestCase( + name="fetch runtime network error", + expected_status=SUCCESS, + config={ + "network_error": True, + "expected_none": True, + }, + ), + ], +) +def test_fetch_runtime_from_github(test_case): + """Test fetching runtime YAML from GitHub.""" + print("Executing test:", test_case.name) + try: + with patch("urllib.request.urlopen") as mock_urlopen: + if test_case.config.get("network_error"): + mock_urlopen.side_effect = Exception("Network error") + else: + mock_response = MagicMock() + mock_response.read.return_value = test_case.config["yaml_content"].encode("utf-8") + mock_response.__enter__.return_value = mock_response + mock_urlopen.return_value = mock_response + + default_runtime_file = "torch_distributed.yaml" + kwargs = {"runtime_file": test_case.config.get("runtime_file", default_runtime_file)} + if "owner" in test_case.config: + kwargs["owner"] = test_case.config["owner"] + kwargs["repo"] = test_case.config["repo"] + kwargs["path"] = test_case.config["path"] + + data = runtime_loader._fetch_runtime_from_github(**kwargs) + + if test_case.config.get("expected_none"): + assert data is None + else: + assert data is not None + assert data["metadata"]["name"] == test_case.config["expected_name"] + + if "owner" in test_case.config: + called_url = mock_urlopen.call_args[0][0] + assert "raw.githubusercontent.com" in called_url + assert f"{kwargs['owner']}/{kwargs['repo']}" in called_url + assert f"{kwargs['path']}/{kwargs['runtime_file']}" in called_url + + assert test_case.expected_status == SUCCESS + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") diff --git a/kubeflow/trainer/backends/container/types.py b/kubeflow/trainer/backends/container/types.py index ae1ce2075..f30025cb9 100644 --- a/kubeflow/trainer/backends/container/types.py +++ b/kubeflow/trainer/backends/container/types.py @@ -20,37 +20,48 @@ of the underlying runtime. Configuration options: - - image: Optional explicit image. If omitted, use the image referenced by the - selected runtime (e.g., torch_distributed) from `config/local_runtimes`. - pull_policy: Controls image pulling. Supported values: "IfNotPresent", "Always", "Never". The default is "IfNotPresent". - auto_remove: Whether to remove containers and networks when jobs are deleted. Defaults to True. - - gpus: GPU support (implementation varies between Docker and Podman). - Defaults to None. - - env: Optional global environment variables applied to all containers. - container_host: Optional override for connecting to a remote/local container daemon. By default, auto-detects from environment or uses system defaults. For Docker: uses DOCKER_HOST or default socket. For Podman: uses CONTAINER_HOST or default socket. - - workdir_base: Base directory on the host to place per-job working dirs that - are bind-mounted into containers as /workspace. Defaults to a path under the - user's home directory for compatibility. - - runtime: Force use of a specific container runtime ("docker" or "podman"). + - container_runtime: Force use of a specific container runtime ("docker" or "podman"). If not set, auto-detects based on availability (tries Docker first, then Podman). + - runtime_source: Configuration for training runtime sources using URL schemes. + Supports github://, https://, http://, file://, and absolute paths. + Built-in runtimes packaged with kubeflow-trainer are used as default fallback. """ -from typing import Literal, Optional, Union +from typing import Literal, Optional from pydantic import BaseModel, Field +class TrainingRuntimeSource(BaseModel): + """Configuration for training runtime sources using URL schemes.""" + + sources: list[str] = Field( + default_factory=lambda: ["github://kubeflow/trainer"], + description=( + "Runtime sources with URL schemes (checked in priority order):\n" + " - github://owner/repo[/path] - GitHub repository\n" + " - https://url or http://url - HTTP(S) endpoint\n" + " - file:///path or /absolute/path - Local filesystem\n" + "If a runtime is not found in configured sources, built-in runtimes " + "packaged with kubeflow-trainer are used as default." + ), + ) + + class ContainerBackendConfig(BaseModel): - image: Optional[str] = Field(default=None) pull_policy: str = Field(default="IfNotPresent") auto_remove: bool = Field(default=True) - gpus: Optional[Union[int, bool]] = Field(default=None) - env: Optional[dict[str, str]] = Field(default=None) container_host: Optional[str] = Field(default=None) - workdir_base: Optional[str] = Field(default=None) - runtime: Optional[Literal["docker", "podman"]] = Field(default=None) + container_runtime: Optional[Literal["docker", "podman"]] = Field(default=None) + runtime_source: TrainingRuntimeSource = Field( + default_factory=TrainingRuntimeSource, + description="Configuration for training runtime sources", + ) diff --git a/kubeflow/trainer/backends/container/utils.py b/kubeflow/trainer/backends/container/utils.py new file mode 100644 index 000000000..8642f8651 --- /dev/null +++ b/kubeflow/trainer/backends/container/utils.py @@ -0,0 +1,236 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Utility functions for the Container backend. +""" + +import logging +import os +from pathlib import Path + +from kubeflow.common.constants import UNKNOWN +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types + +logger = logging.getLogger(__name__) + + +def create_workdir(job_name: str) -> str: + """ + Create per-job working directory on host. + + Working directories are created under ~/.kubeflow/trainer/containers/ + + Args: + job_name: Name of the training job. + + Returns: + Absolute path to the working directory. + """ + home_base = Path.home() / ".kubeflow" / "trainer" / "containers" + home_base.mkdir(parents=True, exist_ok=True) + workdir = str((home_base / f"{job_name}").resolve()) + os.makedirs(workdir, exist_ok=True) + return workdir + + +def get_training_script_code(trainer: types.CustomTrainer) -> str: + """ + Generate the training script code from the trainer function. + + This extracts the function source and appends a function call, + similar to how the Kubernetes backend handles training scripts. + + Args: + trainer: CustomTrainer configuration. + + Returns: + Complete Python code as a string to execute. + """ + import inspect + import textwrap + + code = inspect.getsource(trainer.func) + code = textwrap.dedent(code) + if trainer.func_args is None: + code += f"\n{trainer.func.__name__}()\n" + else: + code += f"\n{trainer.func.__name__}(**{trainer.func_args})\n" + return code + + +def build_environment(trainer: types.CustomTrainer) -> dict[str, str]: + """ + Build environment variables for containers. + + Args: + trainer: CustomTrainer configuration. + + Returns: + Dictionary of environment variables. + """ + return dict(trainer.env or {}) + + +def build_pip_install_cmd(trainer: types.CustomTrainer) -> str: + """ + Build pip install command for packages. + + Args: + trainer: CustomTrainer configuration. + + Returns: + Pip install command string (empty if no packages to install). + """ + pkgs = trainer.packages_to_install or [] + if not pkgs: + return "" + + index_urls = trainer.pip_index_urls or list(constants.DEFAULT_PIP_INDEX_URLS) + main_idx = index_urls[0] + extras = " ".join(f"--extra-index-url {u}" for u in index_urls[1:]) + quoted = " ".join(f'"{p}"' for p in pkgs) + return ( + "PIP_DISABLE_PIP_VERSION_CHECK=1 pip install --no-warn-script-location " + f"--index-url {main_idx} {extras} {quoted} && " + ) + + +def container_status_to_trainjob_status(status: str, exit_code: int) -> str: + """ + Convert container status to TrainJob status. + + Args: + status: Container status (e.g., "running", "exited", "created"). + exit_code: Container exit code. + + Returns: + TrainJob status constant. + """ + if status == "running": + return constants.TRAINJOB_RUNNING + if status == "created": + return constants.TRAINJOB_CREATED + if status == "exited": + # Exit code 0 -> complete, else failed + return constants.TRAINJOB_COMPLETE if exit_code == 0 else constants.TRAINJOB_FAILED + return constants.UNKNOWN + + +def aggregate_status_from_containers(container_statuses: list[str]) -> str: + """ + Aggregate status from multiple container statuses. + + Args: + container_statuses: List of container status strings. + + Returns: + Aggregated TrainJob status. + """ + if constants.TRAINJOB_FAILED in container_statuses: + return constants.TRAINJOB_FAILED + if constants.TRAINJOB_RUNNING in container_statuses: + return constants.TRAINJOB_RUNNING + if all(s == constants.TRAINJOB_COMPLETE for s in container_statuses if s != UNKNOWN): + return constants.TRAINJOB_COMPLETE + if any(s == constants.TRAINJOB_CREATED for s in container_statuses): + return constants.TRAINJOB_CREATED + return UNKNOWN + + +def resolve_image(runtime: types.Runtime) -> str: + """ + Resolve the container image for a runtime from DEFAULT_FRAMEWORK_IMAGES. + + Args: + runtime: Runtime object. + + Returns: + Container image name. + + Raises: + ValueError: If no image is found for the runtime's framework. + """ + framework = runtime.trainer.framework + if framework in constants.DEFAULT_FRAMEWORK_IMAGES: + return constants.DEFAULT_FRAMEWORK_IMAGES[framework] + + raise ValueError( + f"No default image found for framework '{framework}'. " + f"Supported frameworks: {list(constants.DEFAULT_FRAMEWORK_IMAGES.keys())}" + ) + + +def maybe_pull_image(adapter, image: str, pull_policy: str): + """ + Pull image based on pull policy. + + Args: + adapter: Container client adapter (DockerClientAdapter or PodmanClientAdapter). + image: Container image name. + pull_policy: Pull policy ("IfNotPresent", "Always", or "Never"). + + Raises: + RuntimeError: If image is not found or pull fails. + """ + policy = pull_policy.lower() + try: + if policy == "never": + if not adapter.image_exists(image): + raise RuntimeError(f"Image '{image}' not found locally and pull policy is Never") + return + if policy == "always": + logger.debug(f"Pulling image (Always): {image}") + adapter.pull_image(image) + return + # IfNotPresent + if not adapter.image_exists(image): + logger.debug(f"Pulling image (IfNotPresent): {image}") + adapter.pull_image(image) + except Exception as e: + raise RuntimeError(f"Failed to ensure image '{image}': {e}") from e + + +def get_container_status(adapter, container_id: str) -> str: + """ + Get the TrainJob status of a container. + + Args: + adapter: Container client adapter (DockerClientAdapter or PodmanClientAdapter). + container_id: Container ID. + + Returns: + TrainJob status constant. + """ + try: + status, exit_code = adapter.container_status(container_id) + return container_status_to_trainjob_status(status, exit_code) + except Exception: + return constants.UNKNOWN + + +def aggregate_container_statuses(adapter, containers: list[dict]) -> str: + """ + Aggregate TrainJob status from container info dicts. + + Args: + adapter: Container client adapter (DockerClientAdapter or PodmanClientAdapter). + containers: List of container info dicts with 'id' key. + + Returns: + Aggregated TrainJob status. + """ + statuses = [get_container_status(adapter, c["id"]) for c in containers] + return aggregate_status_from_containers(statuses) diff --git a/kubeflow/trainer/backends/local_runtime_loader.py b/kubeflow/trainer/backends/local_runtime_loader.py deleted file mode 100644 index 24f34c113..000000000 --- a/kubeflow/trainer/backends/local_runtime_loader.py +++ /dev/null @@ -1,110 +0,0 @@ -# Copyright 2025 The Kubeflow Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Shared runtime loader for local container backends (Docker, Podman). - -We support loading local runtime definitions from -`kubeflow/trainer/config/local_runtimes/` (YAML files). The schema mirrors the -essential fields from the upstream CRD manifest but is tailored for local -container backends to capture the container image and trainer characteristics -needed to construct a `types.Runtime` object. - -We ship a built-in `torch_distributed.yaml` as the default runtime. Users can -add additional YAML files to the same directory to define custom local runtimes. -""" - -from __future__ import annotations - -from pathlib import Path -from typing import Any - -import yaml - -from kubeflow.trainer.types import types as base_types - -LOCAL_RUNTIMES_DIR = Path(__file__).parents[1] / "config" / "local_runtimes" - - -def _load_runtime_from_yaml(path: Path) -> dict[str, Any]: - with open(path) as f: - data: dict[str, Any] = yaml.safe_load(f) - return data - - -def list_local_runtimes() -> list[base_types.Runtime]: - runtimes: list[base_types.Runtime] = [] - if not LOCAL_RUNTIMES_DIR.exists(): - return runtimes - - for f in sorted(LOCAL_RUNTIMES_DIR.glob("*.yaml")): - data = _load_runtime_from_yaml(f) - - # Require CRD-like schema strictly. Accept both ClusterTrainingRuntime - # and TrainingRuntime kinds. - if not ( - data.get("kind") in {"ClusterTrainingRuntime", "TrainingRuntime"} - and data.get("metadata") - ): - raise ValueError( - f"Runtime YAML {f} must be a ClusterTrainingRuntime CRD-shaped document" - ) - - name = data["metadata"].get("name") - if not name: - raise ValueError(f"Runtime YAML {f} missing metadata.name") - - labels = data["metadata"].get("labels", {}) - framework = labels.get("trainer.kubeflow.org/framework") - if not framework: - raise ValueError( - f"Runtime {name} must set metadata.labels['trainer.kubeflow.org/framework']" - ) - - spec = data.get("spec", {}) - ml_policy = spec.get("mlPolicy", {}) - num_nodes = int(ml_policy.get("numNodes", 1)) - - # Validate presence of a 'node' replicated job with a container image - templ = spec.get("template", {}).get("spec", {}) - replicated = templ.get("replicatedJobs", []) - node_jobs = [j for j in replicated if j.get("name") == "node"] - if not node_jobs: - raise ValueError(f"Runtime {name} must define replicatedJobs with a 'node' entry") - node_spec = ( - node_jobs[0].get("template", {}).get("spec", {}).get("template", {}).get("spec", {}) - ) - containers = node_spec.get("containers", []) - if not containers or not containers[0].get("image"): - raise ValueError(f"Runtime {name} 'node' must specify containers[0].image") - - runtimes.append( - base_types.Runtime( - name=name, - trainer=base_types.RuntimeTrainer( - trainer_type=base_types.TrainerType.CUSTOM_TRAINER, - framework=framework, - num_nodes=num_nodes, - ), - pretrained_model=None, - ) - ) - return runtimes - - -def get_local_runtime(name: str) -> base_types.Runtime: - for rt in list_local_runtimes(): - if rt.name == name: - return rt - raise ValueError(f"Runtime '{name}' not found in {LOCAL_RUNTIMES_DIR}") diff --git a/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml b/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml deleted file mode 100644 index 60d36cfbf..000000000 --- a/kubeflow/trainer/config/local_runtimes/torch_distributed.yaml +++ /dev/null @@ -1,25 +0,0 @@ -apiVersion: trainer.kubeflow.org/v1alpha1 -kind: ClusterTrainingRuntime -metadata: - name: torch-distributed - labels: - trainer.kubeflow.org/framework: torch -spec: - mlPolicy: - numNodes: 1 - torch: - numProcPerNode: auto - template: - spec: - replicatedJobs: - - name: node - template: - metadata: - labels: - trainer.kubeflow.org/trainjob-ancestor-step: trainer - spec: - template: - spec: - containers: - - name: node - image: pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime diff --git a/kubeflow/trainer/constants/constants.py b/kubeflow/trainer/constants/constants.py index 5356bc5a2..a15d402ae 100644 --- a/kubeflow/trainer/constants/constants.py +++ b/kubeflow/trainer/constants/constants.py @@ -162,3 +162,8 @@ # The Instruct Datasets class in torchtune TORCH_TUNE_INSTRUCT_DATASET = "torchtune.datasets.instruct_dataset" + +# Default container images for each framework (used as fallback when runtime not provided) +DEFAULT_FRAMEWORK_IMAGES = { + "torch": "pytorch/pytorch:2.7.1-cuda12.8-cudnn9-runtime", +} diff --git a/pyproject.toml b/pyproject.toml index ee68d0b54..570d16f90 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -108,7 +108,9 @@ select = [ ] ignore = [ - "B006", # mutable-argument-default + "B006", # mutable-argument-default + "UP007", # Use X | Y instead of Union[X, Y] (requires Python 3.10+) + "UP045", # Use X | None instead of Optional[X] (requires Python 3.10+) ] From 525d4c9a2784eead9d4b366d86f9e34f5f1370fa Mon Sep 17 00:00:00 2001 From: Fiona Waters Date: Thu, 30 Oct 2025 13:16:48 +0000 Subject: [PATCH 7/7] Checking common sockets before failing Signed-off-by: Fiona Waters --- README.md | 2 -- kubeflow/trainer/backends/container/backend.py | 15 ++++++--------- 2 files changed, 6 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 9f59f6e50..7e94d6535 100644 --- a/README.md +++ b/README.md @@ -94,8 +94,6 @@ client = TrainerClient(backend_config=ContainerBackendConfig()) job_id = client.train(trainer=CustomTrainer(func=train_fn)) ``` -For detailed configuration options and platform-specific setup (macOS, Linux), see the [ContainerBackend documentation](kubeflow/trainer/backends/container/README.md). - ## Supported Kubeflow Projects | Project | Status | Version Support | Description | diff --git a/kubeflow/trainer/backends/container/backend.py b/kubeflow/trainer/backends/container/backend.py index e36e83c9d..7408d83aa 100644 --- a/kubeflow/trainer/backends/container/backend.py +++ b/kubeflow/trainer/backends/container/backend.py @@ -130,7 +130,8 @@ def _create_adapter(self) -> BaseContainerClientAdapter: Create the appropriate container client adapter. Tries Docker first, then Podman if Docker fails, unless a specific - runtime is requested in the config. + runtime is requested in the config. Automatically tries common socket + locations (e.g., Colima for Docker on macOS, user socket for Podman). Raises RuntimeError if neither Docker nor Podman are available. """ @@ -144,19 +145,15 @@ def _create_adapter(self) -> BaseContainerClientAdapter: [self.cfg.container_runtime] if self.cfg.container_runtime else ["docker", "podman"] ) + attempted_connections = [] last_error = None + for runtime_name in runtimes_to_try: if runtime_name not in runtime_map: continue - try: - adapter = runtime_map[runtime_name](self.cfg.container_host) - adapter.ping() - logger.debug(f"Using {runtime_name} as container runtime") - return adapter - except Exception as e: - logger.debug(f"{runtime_name} initialization failed: {e}") - last_error = e + # Try common socket locations for this runtime + socket_locations = self._get_common_socket_locations(runtime_name) for host in socket_locations: try: