From a1cd349873fcdac06a402c7faf4a15bcdce221d7 Mon Sep 17 00:00:00 2001 From: wylerz Date: Fri, 18 Oct 2024 11:49:33 -0600 Subject: [PATCH 1/3] Add system metrics examples colab dir; add TPU colab; ruff format and remove global wandb use; --- .../WandB_Llama3_1_Training_Colab_TPU.ipynb | 6930 +++++++++++++++++ 1 file changed, 6930 insertions(+) create mode 100644 colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb diff --git a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb new file mode 100644 index 00000000..69ba34b4 --- /dev/null +++ b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb @@ -0,0 +1,6930 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "TYiKDjBFjaX-" + }, + "source": [ + "This is a demo of Weights and Biases' support for automatic loggign of system metrics for TPUs.\n", + "\n", + "This combines the work of Felafax.ai with Weights and Biases instrumentation for logging.\n", + "\n", + "Please see the original colab and backend from Felfax [here](https://github.com/felafax/felfax), and checkout their website at [Felefax.ai](felefax.ai)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wk3kTwlqjVG5" + }, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Cb4zyReNm7LS", + "outputId": "02d1f8f6-3225-4066-8609-8ed0433c27df" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + 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modules.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "import sys\n", + "from typing import Any, Dict, List\n", + "\n", + "# Add the parent directory of the current working directory to the Python path\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \".\")))\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"..\")))\n", + "\n", + "from llama3_jax.trainer_engine import setup\n", + "\n", + "setup.setup_environment(base_dir=\"/\")\n", + "\n", + "from llama3_jax.trainer_engine import automodel_lib, jax_utils, trainer_lib\n", + "\n", + "setup.reload_modules(\"llama3_jax\")\n", + "\n", + "import jax\n", + "import jax.numpy as jnp\n", + "import chex\n", + "import optax\n", + "\n", + "import torch\n", + "from datasets import load_dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dYv1s-cRk7_m" + }, + "source": [ + "# Step 0: Configure LoRA params and precision for training (jnp.bfloat16 or jnp.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "D0AaGj9Y6uPQ" + }, + "outputs": [], + "source": [ + "MODEL_NAME = \"colab-llama-3.1-8B-Instruct-JAX\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 340, + "referenced_widgets": [ + "cc85bb83384e49d089e30c34418e1647", + "ed0d812297be44689aff539e24614884", + "431b1f509bed4bb3b6ac462888edab51", + "d01e433cda254779be719352e464210d", + "96a708992bb44c248d9dd973821ff572", + "4542d8c0a51e4c39a7adb5fc023d6ff8", + "0788f9522fb3449b999eb6576b8e4af3", + "d8ff35d2a09b473ca03add878f61ee9d", + "1757167fb4bf44dba5cc4862dacc340e", + "0c062cafbd1444888a2c516ee085a82b", + "6cb955aa798140169cea13791a95328d", + "9bb3bc92c8774f6aa59b001cfc855162", + "421e82a7deb1471daa4970bd08d28e8d", + "ade6ca70bbd64e76838487ca4ab78b42", + "83c2ac3d75e843efac02867311d34974", + 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"7c1d18ad4c3243e18a9ca94414fdf217", + "e37601fe85494040b5e9ef2a9756e0cf", + "3842719d55564345bb337d53569316f2", + "e9b1100c3efc41ad84898442dc91e40b", + "015d4bfc6fd94885bc6db2ab440ddf3a", + "c9f19b2a746b4efb97b7882f6de95a21" + ] + }, + "id": "IDbUl_n86uPQ", + "outputId": "7f4725e2-898e-4b35-bf0a-442801a7dd11" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading model colab-llama-3.1-8B-Instruct-JAX...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cc85bb83384e49d089e30c34418e1647", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 8 files: 0%| | 0/8 [00:00 Dict[str, jnp.ndarray]:\n", + " \"\"\"\n", + " Collates batch items and converts to JAX arrays.\n", + " \"\"\"\n", + " collated = {\"input_tokens\": [], \"target_tokens\": [], \"loss_masks\": []}\n", + " for item in batch:\n", + " for key in collated:\n", + " collated[key].append(item[key])\n", + " jax_batch = {}\n", + " for key, value in collated.items():\n", + " jax_batch[key] = jnp.array(value)\n", + " return jax_batch\n", + "\n", + " # Load and preprocess the dataset\n", + " dataset = load_dataset(\"yahma/alpaca-cleaned\", split=\"train\")\n", + " if max_examples:\n", + " dataset = dataset.select(range(max_examples))\n", + " dataset = dataset.map(_format_prompts, batched=True)\n", + "\n", + " # Create train and test dataset.\n", + " ds = dataset.train_test_split(test_size=0.15)\n", + " for split in [\"train\", \"test\"]:\n", + " ds[split] = ds[split].map(\n", + " _tokenize, batched=True, remove_columns=dataset.column_names\n", + " )\n", + "\n", + " # Create DataLoaders\n", + " dataloader_args = dict(\n", + " shuffle=True, batch_size=batch_size, collate_fn=_custom_collate_fn\n", + " )\n", + " train_dataloader = torch.utils.data.DataLoader(ds[\"train\"], **dataloader_args)\n", + " test_dataloader = torch.utils.data.DataLoader(ds[\"test\"], **dataloader_args)\n", + "\n", + " return train_dataloader, test_dataloader" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MaxHZh7zlhRH" + }, + "source": [ + "### Uncomment below code ⬇️ if you'd like to run and test your dataset pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3nd25pbO6uPQ" + }, + "outputs": [], + "source": [ + "# def test_dataset_pipeline(tokenizer):\n", + "# \"\"\"Print shapes of first batch to verify dataset pipeline.\"\"\"\n", + "# train_loader, _ = get_dataset(tokenizer=tokenizer, batch_size=1, seq_length=32, max_examples=32)\n", + "# batch = next(iter(train_loader))\n", + "\n", + "# print(\"Input tokens shape:\", batch['input_tokens'].shape)\n", + "# print(\"Target mask shape:\", batch['target_tokens'].shape)\n", + "# test_dataset_pipeline(tokenizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vtyzzGEh6uPQ" + }, + "source": [ + "# Step 2: Configure hyperparameters below and train!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lX0kBNsLa7d1" + }, + "outputs": [], + "source": [ + "@chex.dataclass(frozen=True)\n", + "class TrainerConfig:\n", + " # dataset pipeline knobs\n", + " batch_size: int = 64 # 8 Is the minimum\n", + " seq_length: int = 32\n", + " dataset_size_limit: int | None = None\n", + "\n", + " # training pipeline knobs\n", + " learning_rate: float = 1e-3\n", + " num_epochs: int = 2\n", + " max_steps: int | None = 20\n", + "\n", + " print_every_n_steps: int = 5\n", + "\n", + " # eval\n", + " eval_every_n_steps: int = 10\n", + " max_eval_steps: int | None = 1\n", + "\n", + "\n", + "trainer_config = TrainerConfig()\n", + "optimizer = optax.sgd(trainer_config.learning_rate)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "456b506a50514880815540addf58cc8c", + "f29147f862734766a3d93b20c7de3d64", + 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backend. Please refer to https://wandb.me/wandb-core for more information.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "!pip install wandb\n", + "import wandb\n", + "from google.colab import userdata\n", + "\n", + "wandb.login(key=userdata.get(\"WANDB_API_KEY\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 185, + "referenced_widgets": [ + "54cd43fde898469d9a1cfd30fbe5e61d", + "49385e54e7b34584a511439e17fa14d5", + "0ea08db1792a4f2492ca5c8d4ad90b5a", + "c2f30a80a0844be6be72a99dffe49371", + "dcc750a4d208454d8e8663d2b9348770", + "94ba578ae08a45e79cb3d22fe41a5df3", + "7621a661bf6544d2a12fa992737fd48e", + "17b3fe6155c444aba37edd08b4f878e8" + ] + }, + "id": "CjGjKgpq77UL", + "outputId": "b621d2ff-6dcb-4847-e6ea-bda01856db54" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mwyler-zahm\u001b[0m (\u001b[33mwandb\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "54cd43fde898469d9a1cfd30fbe5e61d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.01111395401111142, max=1.0)…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.18.3" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /content/wandb/run-20241011_222640-eigmtbl5" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run smart-cosmos-12 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU/runs/eigmtbl5" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PROJECT_NAME = \"llama-3.1-8b-fine-tune-TPU\"\n", + "ENTITY = \"wandb\"\n", + "run = wandb.init(project=PROJECT_NAME, entity=ENTITY, tags=[\"Colab\"], save_code=False)\n", + "\n", + "# Organize Metrics\n", + "run.define_metric(\"train_step\")\n", + "run.define_metric(\"epoch\")\n", + "\n", + "run.define_metric(\"train/*\", step_metric=\"train_step\")\n", + "run.define_metric(\"val/*\", step_metric=\"train_step\")\n", + "\n", + "# Log config\n", + "from dataclasses import asdict\n", + "\n", + "config_dict = asdict(trainer_config)\n", + "config_dict.update({\"model_name\": MODEL_NAME})\n", + "run.config.update(config_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "zyMTNYKJ6uPR" + }, + "outputs": [], + "source": [ + "trainer = trainer_lib.CausalLMTrainer(\n", + " model_name=MODEL_NAME,\n", + " model=model,\n", + " model_ckpt_path=model_path,\n", + " model_configurator=model_configurator,\n", + " optimizer=optimizer,\n", + " training_config=trainer_config,\n", + " mesh=jax_utils.MESH,\n", + " dtype=jnp.bfloat16, # precision to use for training\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DATT8rQj4WE8" + }, + "outputs": [], + "source": [ + "# Redefine the CausalLMTrainer.train function to instrument with W&B and add eval\n", + "\n", + "import time\n", + "from jax.sharding import NamedSharding\n", + "from jax.sharding import PartitionSpec as PS\n", + "\n", + "\n", + "def train(trainer, train_dataloader, eval_dataloader):\n", + " total_training_time = 0\n", + " total_steps = 0\n", + "\n", + " for epoch in range(trainer.training_config.num_epochs):\n", + " print(f\"Starting epoch {epoch} of training...\")\n", + "\n", + " for step, train_batch in enumerate(train_dataloader):\n", + " trainer.current_step = epoch * len(train_dataloader) + step\n", + "\n", + " train_batch = jax.device_put(\n", + " train_batch, NamedSharding(trainer.mesh, PS(\"dp\", \"fsdp\"))\n", + " )\n", + "\n", + " sharded_rng = jax_utils.next_rng()\n", + "\n", + " # Start timing\n", + " step_start_time = time.time()\n", + "\n", + " trainer.train_state, sharded_rng, metrics = trainer.train_step(\n", + " trainer.train_state, train_batch, sharded_rng, run_jitted=False\n", + " )\n", + "\n", + " # End timing\n", + " step_end_time = time.time()\n", + "\n", + " # Calculate step duration\n", + " step_duration = step_end_time - step_start_time\n", + " total_training_time += step_duration\n", + " total_steps += 1\n", + "\n", + " # Calculate steps per second\n", + " steps_per_sec = 1 / step_duration\n", + "\n", + " to_log = {}\n", + " to_log.update(\n", + " {\n", + " \"train_step\": step,\n", + " \"train/loss\": metrics[\"loss\"],\n", + " \"train/accuracy\": metrics[\"accuracy\"],\n", + " \"train/step_time\": step_duration,\n", + " \"train/step_hz\": steps_per_sec,\n", + " \"epoch\": epoch,\n", + " }\n", + " )\n", + "\n", + " if step % trainer.training_config.print_every_n_steps == 0:\n", + " print(\n", + " f\"Epoch {epoch}, Step {step}, \"\n", + " f\"Train Loss: {metrics['loss']:.4f}, \"\n", + " f\"Accuracy: {metrics['accuracy']:.4f}, \"\n", + " f\"Step Time: {step_duration:.4f}s, \"\n", + " f\"Steps/sec: {steps_per_sec:.2f}\"\n", + " )\n", + "\n", + " # Evaluate if applicable (ADDED)\n", + " # if step % trainer.training_config.eval_every_n_steps == 0:\n", + " # eval_step_start_time = time.time()\n", + " # #{'loss': avg_loss, 'accuracy': avg_accuracy}\n", + " # eval_metrics = trainer.evaluate(trainer.train_state, eval_dataloader, run_jitted=False)\n", + " # eval_time = time.time()-eval_step_start_time\n", + "\n", + " # print(f\"Epoch {epoch}, Step {step}, \"\n", + " # f\"Val Loss: {eval_metrics['avg_loss']:.4f}, \"\n", + " # f\"Val Accuracy: {eval_metrics['avg_accuracy']:.4f}, \"\n", + " # f\"Step Time: {eval_time:.4f}s\")\n", + "\n", + " # to_log.update({\n", + " # \"val/loss\": eval_metrics['avg_loss'],\n", + " # \"val/accuracy\": eval_metrics['avg_accuracy'],\n", + " # \"val/eval_time\": eval_time})\n", + "\n", + " # Log metrics to W&B\n", + " print(\"Logging:\", to_log)\n", + " run.log(to_log)\n", + "\n", + " if (\n", + " trainer.training_config.max_steps\n", + " and step >= trainer.training_config.max_steps\n", + " ):\n", + " break\n", + "\n", + " avg_steps_per_sec = total_steps / total_training_time\n", + " print(f\"Average Steps per Second: {avg_steps_per_sec:.2f}\")\n", + "\n", + " return trainer.train_state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "7cdd15cd6e3a47bdaaef269652e9c1ea", + "81d25136db79469ca924369f2b7e2edd", + "8a7f0f0043364a2fa259f2d263400ea9", + "e8e4d266d6c04af887e4351e5a3b3d0d", + "9ded4c9d058a4a65a9cedea7ac52a2bc", + "15acb3fbfb1b47c697c855ce5f43e7c0", + "cd74bda12b88498291e204895ff86ecd", + "cce2164fd4fe45ec9892dea732e2e227" + ] + }, + "id": "PtZxDbB1vl6O", + "outputId": "e92d065e-1928-4ba8-ed93-63af42c6f648" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting epoch 0 of training...\n", + "Epoch 0, Step 0, Train Loss: 3.7673, Accuracy: 0.3438, Step Time: 70.6932s, Steps/sec: 0.01\n", + "Logging: {'train_step': 0, 'train/loss': Array(3.7673268, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 70.69318437576294, 'train/step_hz': 0.014145635238110008, 'epoch': 0}\n", + "Logging: {'train_step': 1, 'train/loss': Array(3.6964705, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.022202968597412, 'train/step_hz': 0.05874680274020946, 'epoch': 0}\n", + "Logging: {'train_step': 2, 'train/loss': Array(3.609604, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.354307889938354, 'train/step_hz': 0.057622580303520944, 'epoch': 0}\n", + "Logging: {'train_step': 3, 'train/loss': Array(3.5263896, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.398077964782715, 'train/step_hz': 0.05747761344811798, 'epoch': 0}\n", + "Logging: {'train_step': 4, 'train/loss': Array(3.4427636, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.52925419807434, 'train/step_hz': 0.06049879734540595, 'epoch': 0}\n", + "Epoch 0, Step 5, Train Loss: 3.3614, Accuracy: 0.3750, Step Time: 17.2696s, Steps/sec: 0.06\n", + "Logging: {'train_step': 5, 'train/loss': Array(3.3614292, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.269606590270996, 'train/step_hz': 0.05790519863743508, 'epoch': 0}\n", + "Logging: {'train_step': 6, 'train/loss': Array(3.256286, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.259114027023315, 'train/step_hz': 0.057940401716696364, 'epoch': 0}\n", + "Logging: {'train_step': 7, 'train/loss': Array(3.161405, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 15.967827320098877, 'train/step_hz': 0.06262592774542904, 'epoch': 0}\n", + "Logging: {'train_step': 8, 'train/loss': Array(3.0304565, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 16.90745973587036, 'train/step_hz': 0.05914549054808215, 'epoch': 0}\n", + "Logging: {'train_step': 9, 'train/loss': Array(2.8449862, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 16.333918571472168, 'train/step_hz': 0.061222296145551955, 'epoch': 0}\n", + "Epoch 0, Step 10, Train Loss: 2.7451, Accuracy: 0.4375, Step Time: 16.1461s, Steps/sec: 0.06\n", + "Logging: {'train_step': 10, 'train/loss': Array(2.7451093, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 16.146111726760864, 'train/step_hz': 0.06193441597103416, 'epoch': 0}\n", + "Logging: {'train_step': 11, 'train/loss': Array(2.776972, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 17.18953561782837, 'train/step_hz': 0.05817492817914382, 'epoch': 0}\n", + "Logging: {'train_step': 12, 'train/loss': Array(2.7720864, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 17.1419460773468, 'train/step_hz': 0.05833643365157395, 'epoch': 0}\n", + "Logging: {'train_step': 13, 'train/loss': Array(2.438384, dtype=float32), 'train/accuracy': Array(0.5, dtype=float32), 'train/step_time': 16.319708108901978, 'train/step_hz': 0.06127560574778454, 'epoch': 0}\n", + "Logging: {'train_step': 14, 'train/loss': Array(2.3391292, dtype=float32), 'train/accuracy': Array(0.50146484, dtype=float32), 'train/step_time': 17.214398622512817, 'train/step_hz': 0.05809090528972706, 'epoch': 0}\n", + "Epoch 0, Step 15, Train Loss: 2.2142, Accuracy: 0.5332, Step Time: 17.2439s, Steps/sec: 0.06\n", + "Logging: {'train_step': 15, 'train/loss': Array(2.2141843, dtype=float32), 'train/accuracy': Array(0.5332031, dtype=float32), 'train/step_time': 17.24386715888977, 'train/step_hz': 0.05799163208494492, 'epoch': 0}\n", + "Logging: {'train_step': 16, 'train/loss': Array(2.0796099, dtype=float32), 'train/accuracy': Array(0.5317383, dtype=float32), 'train/step_time': 16.338812112808228, 'train/step_hz': 0.061203959816398514, 'epoch': 0}\n", + "Logging: {'train_step': 17, 'train/loss': Array(1.943904, dtype=float32), 'train/accuracy': Array(0.5625, dtype=float32), 'train/step_time': 17.83607769012451, 'train/step_hz': 0.056066138383871277, 'epoch': 0}\n", + "Logging: {'train_step': 18, 'train/loss': Array(1.8160772, dtype=float32), 'train/accuracy': Array(0.5957031, dtype=float32), 'train/step_time': 16.623939275741577, 'train/step_hz': 0.06015421395693176, 'epoch': 0}\n", + "Logging: {'train_step': 19, 'train/loss': Array(1.5971501, dtype=float32), 'train/accuracy': Array(0.62597656, dtype=float32), 'train/step_time': 17.373450994491577, 'train/step_hz': 0.05755908830761717, 'epoch': 0}\n", + "Epoch 0, Step 20, Train Loss: 1.4374, Accuracy: 0.6577, Step Time: 17.2673s, Steps/sec: 0.06\n", + "Logging: {'train_step': 20, 'train/loss': Array(1.4374148, dtype=float32), 'train/accuracy': Array(0.65771484, dtype=float32), 'train/step_time': 17.267250537872314, 'train/step_hz': 0.05791309958737767, 'epoch': 0}\n", + "Starting epoch 1 of training...\n", + "Epoch 1, Step 0, Train Loss: 1.2817, Accuracy: 0.6592, Step Time: 17.0802s, Steps/sec: 0.06\n", + "Logging: {'train_step': 0, 'train/loss': Array(1.2817158, dtype=float32), 'train/accuracy': Array(0.6591797, dtype=float32), 'train/step_time': 17.080212831497192, 'train/step_hz': 0.05854727981819553, 'epoch': 1}\n", + "Logging: {'train_step': 1, 'train/loss': Array(1.1303419, dtype=float32), 'train/accuracy': Array(0.75146484, dtype=float32), 'train/step_time': 17.35130786895752, 'train/step_hz': 0.05763254318073954, 'epoch': 1}\n", + "Logging: {'train_step': 2, 'train/loss': Array(1.0906806, dtype=float32), 'train/accuracy': Array(0.7207031, dtype=float32), 'train/step_time': 18.079795837402344, 'train/step_hz': 0.0553103590877538, 'epoch': 1}\n", + "Logging: {'train_step': 3, 'train/loss': Array(0.81101084, dtype=float32), 'train/accuracy': Array(0.7817383, dtype=float32), 'train/step_time': 16.434743642807007, 'train/step_hz': 0.06084670511046699, 'epoch': 1}\n", + "Logging: {'train_step': 4, 'train/loss': Array(1.1070464, dtype=float32), 'train/accuracy': Array(0.81347656, dtype=float32), 'train/step_time': 17.03373122215271, 'train/step_hz': 0.05870704351020168, 'epoch': 1}\n", + "Epoch 1, Step 5, Train Loss: 1.4578, Accuracy: 0.5938, Step Time: 17.0086s, Steps/sec: 0.06\n", + "Logging: {'train_step': 5, 'train/loss': Array(1.4577665, dtype=float32), 'train/accuracy': Array(0.59375, dtype=float32), 'train/step_time': 17.008564949035645, 'train/step_hz': 0.05879390783387038, 'epoch': 1}\n", + "Logging: {'train_step': 6, 'train/loss': Array(1.0049247, dtype=float32), 'train/accuracy': Array(0.75, dtype=float32), 'train/step_time': 16.599169731140137, 'train/step_hz': 0.06024397703000737, 'epoch': 1}\n", + "Logging: {'train_step': 7, 'train/loss': Array(0.7702064, dtype=float32), 'train/accuracy': Array(0.8754883, dtype=float32), 'train/step_time': 16.905812978744507, 'train/step_hz': 0.059151251776965064, 'epoch': 1}\n", + "Logging: {'train_step': 8, 'train/loss': Array(0.5257302, dtype=float32), 'train/accuracy': Array(0.9086914, dtype=float32), 'train/step_time': 16.937358140945435, 'train/step_hz': 0.0590410849011061, 'epoch': 1}\n", + "Logging: {'train_step': 9, 'train/loss': Array(0.34922788, dtype=float32), 'train/accuracy': Array(0.93896484, dtype=float32), 'train/step_time': 16.276695489883423, 'train/step_hz': 0.061437531999141815, 'epoch': 1}\n", + "Epoch 1, Step 10, Train Loss: 0.2343, Accuracy: 0.9707, Step Time: 17.0346s, Steps/sec: 0.06\n", + "Logging: {'train_step': 10, 'train/loss': Array(0.23427255, dtype=float32), 'train/accuracy': Array(0.9707031, dtype=float32), 'train/step_time': 17.034571886062622, 'train/step_hz': 0.0587041462907666, 'epoch': 1}\n", + "Logging: {'train_step': 11, 'train/loss': Array(0.21187244, dtype=float32), 'train/accuracy': Array(0.9707031, dtype=float32), 'train/step_time': 17.143501043319702, 'train/step_hz': 0.05833114236544287, 'epoch': 1}\n", + "Logging: {'train_step': 12, 'train/loss': Array(0.18872786, dtype=float32), 'train/accuracy': Array(0.97265625, dtype=float32), 'train/step_time': 16.369540452957153, 'train/step_hz': 0.06108906984125814, 'epoch': 1}\n", + "Logging: {'train_step': 13, 'train/loss': Array(0.18209873, dtype=float32), 'train/accuracy': Array(0.96972656, dtype=float32), 'train/step_time': 16.66157364845276, 'train/step_hz': 0.06001834047007096, 'epoch': 1}\n", + "Logging: {'train_step': 14, 'train/loss': Array(0.20132065, dtype=float32), 'train/accuracy': Array(0.96972656, dtype=float32), 'train/step_time': 17.326436042785645, 'train/step_hz': 0.057715273789174806, 'epoch': 1}\n", + "Epoch 1, Step 15, Train Loss: 0.1833, Accuracy: 0.9692, Step Time: 17.2097s, Steps/sec: 0.06\n", + "Logging: {'train_step': 15, 'train/loss': Array(0.18326175, dtype=float32), 'train/accuracy': Array(0.9692383, dtype=float32), 'train/step_time': 17.209666967391968, 'train/step_hz': 0.058106876902077824, 'epoch': 1}\n", + "Logging: {'train_step': 16, 'train/loss': Array(0.18982112, dtype=float32), 'train/accuracy': Array(0.97021484, dtype=float32), 'train/step_time': 16.359159469604492, 'train/step_hz': 0.06112783495130123, 'epoch': 1}\n", + "Logging: {'train_step': 17, 'train/loss': Array(0.18973069, dtype=float32), 'train/accuracy': Array(0.96875, dtype=float32), 'train/step_time': 17.213257789611816, 'train/step_hz': 0.05809475534628308, 'epoch': 1}\n", + "Logging: {'train_step': 18, 'train/loss': Array(0.18263203, dtype=float32), 'train/accuracy': Array(0.97021484, dtype=float32), 'train/step_time': 16.991802215576172, 'train/step_hz': 0.058851909133176734, 'epoch': 1}\n", + "Logging: {'train_step': 19, 'train/loss': Array(0.16862138, dtype=float32), 'train/accuracy': Array(0.9707031, dtype=float32), 'train/step_time': 17.140758991241455, 'train/step_hz': 0.05834047375095686, 'epoch': 1}\n", + "Epoch 1, Step 20, Train Loss: 0.1783, Accuracy: 0.9712, Step Time: 17.1517s, Steps/sec: 0.06\n", + "Logging: {'train_step': 20, 'train/loss': Array(0.17833142, dtype=float32), 'train/accuracy': Array(0.9711914, dtype=float32), 'train/step_time': 17.151716947555542, 'train/step_hz': 0.058303200959861905, 'epoch': 1}\n", + "Average Steps per Second: 0.05\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cdd15cd6e3a47bdaaef269652e9c1ea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='0.019 MB of 0.019 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "

Run history:


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Run summary:


epoch1
train/accuracy0.97119
train/loss0.17833
train/step_hz0.0583
train/step_time17.15172
train_step20

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run smart-cosmos-12 at: https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU/runs/eigmtbl5
View project at: https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20241011_222640-eigmtbl5/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "state = train(trainer, train_dataloader, val_dataloader)\n", + "run.finish()" + ] + } + ], + "metadata": { + "accelerator": "TPU", + "colab": { + "gpuType": "V28", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + 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00:00:00 2001 From: wylerz Date: Fri, 18 Oct 2024 11:58:34 -0600 Subject: [PATCH 2/3] minor fix to train step logic --- colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb index 69ba34b4..3d447ae8 100644 --- a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb +++ b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb @@ -1027,7 +1027,6 @@ "from jax.sharding import NamedSharding\n", "from jax.sharding import PartitionSpec as PS\n", "\n", - "\n", "def train(trainer, train_dataloader, eval_dataloader):\n", " total_training_time = 0\n", " total_steps = 0\n", @@ -1065,7 +1064,7 @@ " to_log = {}\n", " to_log.update(\n", " {\n", - " \"train_step\": step,\n", + " \"train_step\": total_steps,\n", " \"train/loss\": metrics[\"loss\"],\n", " \"train/accuracy\": metrics[\"accuracy\"],\n", " \"train/step_time\": step_duration,\n", From b5e8d9ee58f99bd556476776d4622c500d47e41b Mon Sep 17 00:00:00 2001 From: wylerz Date: Fri, 18 Oct 2024 13:43:27 -0600 Subject: [PATCH 3/3] minor fixes to conform to wandb standards --- .../WandB_Llama3_1_Training_Colab_TPU.ipynb | 3862 +++++++---------- 1 file changed, 1650 insertions(+), 2212 deletions(-) diff --git a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb index 3d447ae8..be541fff 100644 --- a/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb +++ b/colabs/system_metrics/WandB_Llama3_1_Training_Colab_TPU.ipynb @@ -24,13 +24,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Cb4zyReNm7LS", - "outputId": "02d1f8f6-3225-4066-8609-8ed0433c27df" + "outputId": "596dca3f-c2a2-47e2-8630-2798adb99309" }, "outputs": [ { @@ 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"\u001b[?25hDownloading smmap-5.0.1-py3-none-any.whl (24 kB)\n", - "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, gitpython, wandb\n", - "Successfully installed docker-pycreds-0.4.0 gitdb-4.0.11 gitpython-3.1.43 sentry-sdk-2.16.0 setproctitle-1.3.3 smmap-5.0.1 wandb-0.18.3\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -862,7 +857,6 @@ } ], "source": [ - "!pip install wandb\n", "import wandb\n", "from google.colab import userdata\n", "\n", @@ -871,24 +865,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 185, - "referenced_widgets": [ - "54cd43fde898469d9a1cfd30fbe5e61d", - "49385e54e7b34584a511439e17fa14d5", - "0ea08db1792a4f2492ca5c8d4ad90b5a", - "c2f30a80a0844be6be72a99dffe49371", - "dcc750a4d208454d8e8663d2b9348770", - "94ba578ae08a45e79cb3d22fe41a5df3", - "7621a661bf6544d2a12fa992737fd48e", - "17b3fe6155c444aba37edd08b4f878e8" - ] + "height": 121 }, "id": "CjGjKgpq77UL", - "outputId": "b621d2ff-6dcb-4847-e6ea-bda01856db54" + "outputId": "717aa44f-c6f4-4ca7-c404-f98f8cc2658a" }, "outputs": [ { @@ -898,24 +882,10 @@ "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mwyler-zahm\u001b[0m (\u001b[33mwandb\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" ] }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "54cd43fde898469d9a1cfd30fbe5e61d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.01111395401111142, max=1.0)…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "text/html": [ - "Tracking run with wandb version 0.18.3" + "Tracking run with wandb version 0.18.5" ], "text/plain": [ "" @@ -927,7 +897,7 @@ { "data": { "text/html": [ - "Run data is saved locally in /content/wandb/run-20241011_222640-eigmtbl5" + "Run data is saved locally in /content/wandb/run-20241018_190904-kv0rlmxz" ], "text/plain": [ "" @@ -939,7 +909,7 @@ { "data": { "text/html": [ - "Syncing run smart-cosmos-12 to Weights & Biases (docs)
" + "Syncing run northern-wave-1 to Weights & Biases (docs)
" ], "text/plain": [ "" @@ -951,7 +921,7 @@ { "data": { "text/html": [ - " View project at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU" + " View project at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing" ], "text/plain": [ "" @@ -963,7 +933,7 @@ { "data": { "text/html": [ - " View run at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU/runs/eigmtbl5" + " View run at https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing/runs/kv0rlmxz" ], "text/plain": [ "" @@ -979,9 +949,6 @@ "run = wandb.init(project=PROJECT_NAME, entity=ENTITY, tags=[\"Colab\"], save_code=False)\n", "\n", "# Organize Metrics\n", - "run.define_metric(\"train_step\")\n", - "run.define_metric(\"epoch\")\n", - "\n", "run.define_metric(\"train/*\", step_metric=\"train_step\")\n", "run.define_metric(\"val/*\", step_metric=\"train_step\")\n", "\n", @@ -995,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "id": "zyMTNYKJ6uPR" }, @@ -1015,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "DATT8rQj4WE8" }, @@ -1027,6 +994,7 @@ "from jax.sharding import NamedSharding\n", "from jax.sharding import PartitionSpec as PS\n", "\n", + "\n", "def train(trainer, train_dataloader, eval_dataloader):\n", " total_training_time = 0\n", " total_steps = 0\n", @@ -1117,24 +1085,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000, - "referenced_widgets": [ - "7cdd15cd6e3a47bdaaef269652e9c1ea", - "81d25136db79469ca924369f2b7e2edd", - "8a7f0f0043364a2fa259f2d263400ea9", - "e8e4d266d6c04af887e4351e5a3b3d0d", - "9ded4c9d058a4a65a9cedea7ac52a2bc", - "15acb3fbfb1b47c697c855ce5f43e7c0", - "cd74bda12b88498291e204895ff86ecd", - "cce2164fd4fe45ec9892dea732e2e227" - ] + "height": 1000 }, "id": "PtZxDbB1vl6O", - "outputId": "e92d065e-1928-4ba8-ed93-63af42c6f648" + "outputId": "60a10327-55fa-4152-cb88-706ada41110f" }, "outputs": [ { @@ -1142,76 +1100,62 @@ "output_type": "stream", "text": [ "Starting epoch 0 of training...\n", - "Epoch 0, Step 0, Train Loss: 3.7673, Accuracy: 0.3438, Step Time: 70.6932s, Steps/sec: 0.01\n", - "Logging: {'train_step': 0, 'train/loss': Array(3.7673268, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 70.69318437576294, 'train/step_hz': 0.014145635238110008, 'epoch': 0}\n", - "Logging: {'train_step': 1, 'train/loss': Array(3.6964705, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.022202968597412, 'train/step_hz': 0.05874680274020946, 'epoch': 0}\n", - "Logging: {'train_step': 2, 'train/loss': Array(3.609604, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.354307889938354, 'train/step_hz': 0.057622580303520944, 'epoch': 0}\n", - "Logging: {'train_step': 3, 'train/loss': Array(3.5263896, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.398077964782715, 'train/step_hz': 0.05747761344811798, 'epoch': 0}\n", - "Logging: {'train_step': 4, 'train/loss': Array(3.4427636, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.52925419807434, 'train/step_hz': 0.06049879734540595, 'epoch': 0}\n", - "Epoch 0, Step 5, Train Loss: 3.3614, Accuracy: 0.3750, Step Time: 17.2696s, Steps/sec: 0.06\n", - "Logging: {'train_step': 5, 'train/loss': Array(3.3614292, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.269606590270996, 'train/step_hz': 0.05790519863743508, 'epoch': 0}\n", - "Logging: {'train_step': 6, 'train/loss': Array(3.256286, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.259114027023315, 'train/step_hz': 0.057940401716696364, 'epoch': 0}\n", - "Logging: {'train_step': 7, 'train/loss': Array(3.161405, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 15.967827320098877, 'train/step_hz': 0.06262592774542904, 'epoch': 0}\n", - "Logging: {'train_step': 8, 'train/loss': Array(3.0304565, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 16.90745973587036, 'train/step_hz': 0.05914549054808215, 'epoch': 0}\n", - "Logging: {'train_step': 9, 'train/loss': Array(2.8449862, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 16.333918571472168, 'train/step_hz': 0.061222296145551955, 'epoch': 0}\n", - "Epoch 0, Step 10, Train Loss: 2.7451, Accuracy: 0.4375, Step Time: 16.1461s, Steps/sec: 0.06\n", - "Logging: {'train_step': 10, 'train/loss': Array(2.7451093, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 16.146111726760864, 'train/step_hz': 0.06193441597103416, 'epoch': 0}\n", - "Logging: {'train_step': 11, 'train/loss': Array(2.776972, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 17.18953561782837, 'train/step_hz': 0.05817492817914382, 'epoch': 0}\n", - "Logging: {'train_step': 12, 'train/loss': Array(2.7720864, dtype=float32), 'train/accuracy': Array(0.4375, dtype=float32), 'train/step_time': 17.1419460773468, 'train/step_hz': 0.05833643365157395, 'epoch': 0}\n", - "Logging: {'train_step': 13, 'train/loss': Array(2.438384, dtype=float32), 'train/accuracy': Array(0.5, dtype=float32), 'train/step_time': 16.319708108901978, 'train/step_hz': 0.06127560574778454, 'epoch': 0}\n", - "Logging: {'train_step': 14, 'train/loss': Array(2.3391292, dtype=float32), 'train/accuracy': Array(0.50146484, dtype=float32), 'train/step_time': 17.214398622512817, 'train/step_hz': 0.05809090528972706, 'epoch': 0}\n", - "Epoch 0, Step 15, Train Loss: 2.2142, Accuracy: 0.5332, Step Time: 17.2439s, Steps/sec: 0.06\n", - "Logging: {'train_step': 15, 'train/loss': Array(2.2141843, dtype=float32), 'train/accuracy': Array(0.5332031, dtype=float32), 'train/step_time': 17.24386715888977, 'train/step_hz': 0.05799163208494492, 'epoch': 0}\n", - "Logging: {'train_step': 16, 'train/loss': Array(2.0796099, dtype=float32), 'train/accuracy': Array(0.5317383, dtype=float32), 'train/step_time': 16.338812112808228, 'train/step_hz': 0.061203959816398514, 'epoch': 0}\n", - "Logging: {'train_step': 17, 'train/loss': Array(1.943904, dtype=float32), 'train/accuracy': Array(0.5625, dtype=float32), 'train/step_time': 17.83607769012451, 'train/step_hz': 0.056066138383871277, 'epoch': 0}\n", - "Logging: {'train_step': 18, 'train/loss': Array(1.8160772, dtype=float32), 'train/accuracy': Array(0.5957031, dtype=float32), 'train/step_time': 16.623939275741577, 'train/step_hz': 0.06015421395693176, 'epoch': 0}\n", - "Logging: {'train_step': 19, 'train/loss': Array(1.5971501, dtype=float32), 'train/accuracy': Array(0.62597656, dtype=float32), 'train/step_time': 17.373450994491577, 'train/step_hz': 0.05755908830761717, 'epoch': 0}\n", - "Epoch 0, Step 20, Train Loss: 1.4374, Accuracy: 0.6577, Step Time: 17.2673s, Steps/sec: 0.06\n", - "Logging: {'train_step': 20, 'train/loss': Array(1.4374148, dtype=float32), 'train/accuracy': Array(0.65771484, dtype=float32), 'train/step_time': 17.267250537872314, 'train/step_hz': 0.05791309958737767, 'epoch': 0}\n", + "Epoch 0, Step 0, Train Loss: 3.7602, Accuracy: 0.3438, Step Time: 71.0879s, Steps/sec: 0.01\n", + "Logging: {'train_step': 1, 'train/loss': Array(3.7602315, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 71.08793210983276, 'train/step_hz': 0.014067085232623917, 'epoch': 0}\n", + "Logging: {'train_step': 2, 'train/loss': Array(3.697931, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.17443823814392, 'train/step_hz': 0.061825949394750286, 'epoch': 0}\n", + "Logging: {'train_step': 3, 'train/loss': Array(3.6042004, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.501534700393677, 'train/step_hz': 0.060600424030629284, 'epoch': 0}\n", + "Logging: {'train_step': 4, 'train/loss': Array(3.5397573, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.845587253570557, 'train/step_hz': 0.05936272716096864, 'epoch': 0}\n", + "Logging: {'train_step': 5, 'train/loss': Array(3.4478605, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 17.577601194381714, 'train/step_hz': 0.056890584155455046, 'epoch': 0}\n", + "Epoch 0, Step 5, Train Loss: 3.3534, Accuracy: 0.3438, Step Time: 16.8048s, Steps/sec: 0.06\n", + "Logging: {'train_step': 6, 'train/loss': Array(3.353401, dtype=float32), 'train/accuracy': Array(0.34375, dtype=float32), 'train/step_time': 16.804821491241455, 'train/step_hz': 0.05950673147710569, 'epoch': 0}\n", + "Logging: {'train_step': 7, 'train/loss': Array(3.2514594, dtype=float32), 'train/accuracy': Array(0.375, dtype=float32), 'train/step_time': 17.086888551712036, 'train/step_hz': 0.05852440583161667, 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Run history:


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Run summary:


epoch1
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Run history:


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Run summary:


epoch1
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train/loss0.185
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train/step_time17.42172
train_step42

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View project at: https://wandb.ai/wandb/llama-3.1-8b-fine-tune-TPU-testing
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