From bd050eebfdb274a48319f701d9d6c9c6f8fdeb9c Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 00:33:20 +0500
Subject: [PATCH 01/30] Create single-node
---
source/cloud/IBM/single-node | 1 +
1 file changed, 1 insertion(+)
create mode 100644 source/cloud/IBM/single-node
diff --git a/source/cloud/IBM/single-node b/source/cloud/IBM/single-node
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/source/cloud/IBM/single-node
@@ -0,0 +1 @@
+
From 2cc09426946595c262217f36b1cdbf9c3cfa3af1 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 00:35:19 +0500
Subject: [PATCH 02/30] Update single-node
---
source/cloud/IBM/single-node | 34 ++++++++++++++++++++++++++++++++++
1 file changed, 34 insertions(+)
diff --git a/source/cloud/IBM/single-node b/source/cloud/IBM/single-node
index 8b137891..1dd9d3ae 100644
--- a/source/cloud/IBM/single-node
+++ b/source/cloud/IBM/single-node
@@ -1 +1,35 @@
+# Single Node
+There are multiple ways you can deploy RAPIDS on a single instance, but the easiest is to use the RAPIDS docker image:
+
+**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
+section for a list of supported instance types. It is recommended to use an AMI
+that already includes the required NVIDIA drivers, such as the **[Amazon Linux 2
+AMI with NVIDIA TESLA GPU
+Driver](https://aws.amazon.com/marketplace/pp/Amazon-Web-Services-Amazon-Linux-2-AMI-with-NVIDIA/B07S5G9S1Z)**
+or the **[AWS Deep Learning
+AMI.](https://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html)**
+
+**2. Credentials.** Using the credentials supplied by AWS, log into the instance
+via SSH. For a short guide on launching your instance and accessing it, read the
+Getting Started with Amazon EC2 documentation.
+
+**3. Install.** Install [Docker and the NVIDIA Docker
+runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+in the AWS instance. This step is not required if you are using AWS Deep
+Learning AMI.
+
+**4. Install.** Install RAPIDS docker image. The docker container can be
+customized by using the options provided in the **[Getting
+Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
+that can be used is provided below:
+
+```shell
+$ docker pull rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04
+$ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
+ rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04-py3.7
+```
+
+**5. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
+notebook instance automatically. You can log into it by going to the IP address
+provided by AWS on port 8888.
From b0e48a70daa664e1058dce66153c65b2f10212e4 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 00:36:39 +0500
Subject: [PATCH 03/30] Rename single-node to single-node.md
---
source/cloud/IBM/{single-node => single-node.md} | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename source/cloud/IBM/{single-node => single-node.md} (100%)
diff --git a/source/cloud/IBM/single-node b/source/cloud/IBM/single-node.md
similarity index 100%
rename from source/cloud/IBM/single-node
rename to source/cloud/IBM/single-node.md
From 91f8358aeb3346b923825fe78bd662a9ddbb6bbc Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:15:58 +0500
Subject: [PATCH 04/30] Update single-node.md
---
source/cloud/IBM/single-node.md | 19 ++++++-------------
1 file changed, 6 insertions(+), 13 deletions(-)
diff --git a/source/cloud/IBM/single-node.md b/source/cloud/IBM/single-node.md
index 1dd9d3ae..4dbabe99 100644
--- a/source/cloud/IBM/single-node.md
+++ b/source/cloud/IBM/single-node.md
@@ -3,21 +3,15 @@
There are multiple ways you can deploy RAPIDS on a single instance, but the easiest is to use the RAPIDS docker image:
**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
-section for a list of supported instance types. It is recommended to use an AMI
-that already includes the required NVIDIA drivers, such as the **[Amazon Linux 2
-AMI with NVIDIA TESLA GPU
-Driver](https://aws.amazon.com/marketplace/pp/Amazon-Web-Services-Amazon-Linux-2-AMI-with-NVIDIA/B07S5G9S1Z)**
-or the **[AWS Deep Learning
-AMI.](https://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html)**
+section for a list of supported instance types.
-**2. Credentials.** Using the credentials supplied by AWS, log into the instance
+**2. Credentials.** Using the credentials supplied by IBM, log into the instance
via SSH. For a short guide on launching your instance and accessing it, read the
-Getting Started with Amazon EC2 documentation.
+[Getting Started with IBM Virtual Server Documentation](https://cloud.ibm.com/docs/virtual-servers?topic=virtual-servers-getting-started-tutorial).
**3. Install.** Install [Docker and the NVIDIA Docker
runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
-in the AWS instance. This step is not required if you are using AWS Deep
-Learning AMI.
+in the IBM virtual server instance.
**4. Install.** Install RAPIDS docker image. The docker container can be
customized by using the options provided in the **[Getting
@@ -25,9 +19,8 @@ Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
that can be used is provided below:
```shell
-$ docker pull rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04
-$ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
- rapidsai/rapidsai:cuda11.2-runtime-ubuntu18.04-py3.7
+$ docker pull rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
+$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
```
**5. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
From 68ece07d518e867f0248fe4062b6c5848943e2dd Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:18:59 +0500
Subject: [PATCH 05/30] Update single-node.md
---
source/cloud/IBM/single-node.md | 7 ++++---
1 file changed, 4 insertions(+), 3 deletions(-)
diff --git a/source/cloud/IBM/single-node.md b/source/cloud/IBM/single-node.md
index 4dbabe99..acf94a63 100644
--- a/source/cloud/IBM/single-node.md
+++ b/source/cloud/IBM/single-node.md
@@ -20,9 +20,10 @@ that can be used is provided below:
```shell
$ docker pull rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
-$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
+$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 \
+ rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
```
**5. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
-notebook instance automatically. You can log into it by going to the IP address
-provided by AWS on port 8888.
+notebook instance automatically. You can log into it by going to the Floating IP address
+associated with the instance on port 8888.
From bb7e96d3726279014535a37710bf8587f3232628 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:21:46 +0500
Subject: [PATCH 06/30] Update single-node.md
---
source/cloud/IBM/single-node.md | 10 ++++++----
1 file changed, 6 insertions(+), 4 deletions(-)
diff --git a/source/cloud/IBM/single-node.md b/source/cloud/IBM/single-node.md
index acf94a63..fb674c70 100644
--- a/source/cloud/IBM/single-node.md
+++ b/source/cloud/IBM/single-node.md
@@ -5,15 +5,17 @@ There are multiple ways you can deploy RAPIDS on a single instance, but the easi
**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
section for a list of supported instance types.
-**2. Credentials.** Using the credentials supplied by IBM, log into the instance
+**2. Create & Associate.** Create a Floating IP and associate that with the created instance to access the instance on the web.
+
+**3. Credentials.** Using the credentials supplied by IBM, log into the instance
via SSH. For a short guide on launching your instance and accessing it, read the
[Getting Started with IBM Virtual Server Documentation](https://cloud.ibm.com/docs/virtual-servers?topic=virtual-servers-getting-started-tutorial).
-**3. Install.** Install [Docker and the NVIDIA Docker
+**4. Install.** Install [Docker and the NVIDIA Docker
runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
in the IBM virtual server instance.
-**4. Install.** Install RAPIDS docker image. The docker container can be
+**5. Install.** Install RAPIDS docker image. The docker container can be
customized by using the options provided in the **[Getting
Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
that can be used is provided below:
@@ -24,6 +26,6 @@ $ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 88
rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
```
-**5. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
+**6. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
notebook instance automatically. You can log into it by going to the Floating IP address
associated with the instance on port 8888.
From a263762677ad1e66feb6645b5b2c0e4c59fc86a7 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:24:16 +0500
Subject: [PATCH 07/30] Create index.md
---
source/cloud/IBM/index.md | 32 ++++++++++++++++++++++++++++++++
1 file changed, 32 insertions(+)
create mode 100644 source/cloud/IBM/index.md
diff --git a/source/cloud/IBM/index.md b/source/cloud/IBM/index.md
new file mode 100644
index 00000000..be09a7eb
--- /dev/null
+++ b/source/cloud/IBM/index.md
@@ -0,0 +1,32 @@
+# IBM Cloud
+
+```{toctree}
+---
+maxdepth: 2
+caption: IBM Cloud
+---
+single-node
+iks
+```
+
+RAPIDS can be deployed on IBM Cloud in several ways. See the
+list of accelerated instance types below:
+
+| Cloud
Provider | Inst.
Type | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
+| :------------------ | --------------- | --------------- | -------------- | ------------- | ------------- | ------------------: |
+| AWS | G4dn | g4dn\.xlarge | 1 | T4 | 16 (GB) | 16 (GB) |
+| AWS | G4dn | g4dn\.12xlarge | 4 | T4 | 16 (GB) | 64 (GB) |
+| AWS | G4dn | g4dn\.metal | 8 | T4 | 16 (GB) | 128 (GB) |
+| AWS | P3 | p3\.2xlarge | 1 | V100 | 16 (GB) | 16 (GB) |
+| AWS | P3 | p3\.8xlarge | 4 | V100 | 16 (GB) | 64 (GB) |
+| AWS | P3 | p3\.16xlarge | 8 | V100 | 16 (GB) | 128 (GB) |
+| AWS | P3 | p3dn\.24xlarge | 8 | V100 | 32 (GB) | 256 (GB) |
+| AWS | P4 | p4d\.24xlarge | 8 | A100 | 40 (GB) | 320 (GB) |
+| AWS | G5 | g5\.xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
+| AWS | G5 | g5\.2xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
+| AWS | G5 | g5\.4xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
+| AWS | G5 | g5\.8xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
+| AWS | G5 | g5\.16xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
+| AWS | G5 | g5\.12xlarge | 4 | A10G | 24 (GB) | 96 (GB) |
+| AWS | G5 | g5\.24xlarge | 4 | A10G | 24 (GB) | 96 (GB) |
+| AWS | G5 | g5\.48xlarge | 8 | A10G | 24 (GB) | 192 (GB) |
From f4f36d4840daeddf27b5178e2741374e023fe1be Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:41:03 +0500
Subject: [PATCH 08/30] Update index.md
---
source/cloud/IBM/index.md | 25 +++++++------------------
1 file changed, 7 insertions(+), 18 deletions(-)
diff --git a/source/cloud/IBM/index.md b/source/cloud/IBM/index.md
index be09a7eb..ccd1330a 100644
--- a/source/cloud/IBM/index.md
+++ b/source/cloud/IBM/index.md
@@ -12,21 +12,10 @@ iks
RAPIDS can be deployed on IBM Cloud in several ways. See the
list of accelerated instance types below:
-| Cloud
Provider | Inst.
Type | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
-| :------------------ | --------------- | --------------- | -------------- | ------------- | ------------- | ------------------: |
-| AWS | G4dn | g4dn\.xlarge | 1 | T4 | 16 (GB) | 16 (GB) |
-| AWS | G4dn | g4dn\.12xlarge | 4 | T4 | 16 (GB) | 64 (GB) |
-| AWS | G4dn | g4dn\.metal | 8 | T4 | 16 (GB) | 128 (GB) |
-| AWS | P3 | p3\.2xlarge | 1 | V100 | 16 (GB) | 16 (GB) |
-| AWS | P3 | p3\.8xlarge | 4 | V100 | 16 (GB) | 64 (GB) |
-| AWS | P3 | p3\.16xlarge | 8 | V100 | 16 (GB) | 128 (GB) |
-| AWS | P3 | p3dn\.24xlarge | 8 | V100 | 32 (GB) | 256 (GB) |
-| AWS | P4 | p4d\.24xlarge | 8 | A100 | 40 (GB) | 320 (GB) |
-| AWS | G5 | g5\.xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
-| AWS | G5 | g5\.2xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
-| AWS | G5 | g5\.4xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
-| AWS | G5 | g5\.8xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
-| AWS | G5 | g5\.16xlarge | 1 | A10G | 24 (GB) | 24 (GB) |
-| AWS | G5 | g5\.12xlarge | 4 | A10G | 24 (GB) | 96 (GB) |
-| AWS | G5 | g5\.24xlarge | 4 | A10G | 24 (GB) | 96 (GB) |
-| AWS | G5 | g5\.48xlarge | 8 | A10G | 24 (GB) | 192 (GB) |
+| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
+| :------------------ | --------------- | ---- |----------------- | -------------- | ------------- | ------------- | ------------------: |
+| IBM | V100 GPU | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
+| IBM | V100 GPU | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
+
From e209dbe6705e6e537d2eb26cda844a6bab3046f8 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:53:11 +0500
Subject: [PATCH 09/30] Update index.md
---
source/cloud/IBM/index.md | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
diff --git a/source/cloud/IBM/index.md b/source/cloud/IBM/index.md
index ccd1330a..c84bc3a4 100644
--- a/source/cloud/IBM/index.md
+++ b/source/cloud/IBM/index.md
@@ -12,10 +12,12 @@ iks
RAPIDS can be deployed on IBM Cloud in several ways. See the
list of accelerated instance types below:
-| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
-| :------------------ | --------------- | ---- |----------------- | -------------- | ------------- | ------------- | ------------------: |
-| IBM | V100 GPU | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
-| IBM | V100 GPU | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
+| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
+| :------------------ | ------------------- | ---- |------------------- | -------------- | ------------- | ------------- | ------------------: |
+| IBM | V100 GPU Virtual | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
+| IBM | P100 GPU Bare Metal | 32 | mg4c.32x384.2xp100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
+| IBM | V100 GPU Bare Metal | 48 | mg4c.48x384.2xv100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
From ac4358ec53e879d317133050efbeddeb9e3801ca Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 01:55:54 +0500
Subject: [PATCH 10/30] Create iks.md
---
source/cloud/IBM/iks.md | 89 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 89 insertions(+)
create mode 100644 source/cloud/IBM/iks.md
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
new file mode 100644
index 00000000..c2255280
--- /dev/null
+++ b/source/cloud/IBM/iks.md
@@ -0,0 +1,89 @@
+# IBM Kubernetes Service (IKS)
+
+RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using Helm. More details can be found at our **[helm docs.](https://helm.rapids.ai/docs/csp.html)**
+
+**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, and IBM cloud cli.
+
+**2. Public Key.** Create a public key if you don't have one.
+
+**3. Create your cluster:**
+
+```shell
+$ eksctl create cluster \
+ --name [CLUSTER_NAME] \
+ --version 1.14 \
+ --region [REGION] \
+ --nodegroup-name gpu-workers \
+ --node-type [NODE_INSTANCE] \
+ --nodes [NUM_NODES] \
+ --nodes-min 1 \
+ --nodes-max [MAX_NODES] \
+ --node-volume-size [NODE_SIZE] \
+ --ssh-access \
+ --ssh-public-key ~/path/to/id_rsa.pub \
+ --managed
+```
+
+[CLUSTER_NAME] = Name of the EKS cluster. This will be auto generated if not specified.
+[NODE_INSTANCE] = Node instance type to be used. Select one of the instance types supported by RAPIDS Refer to the introduction section for a list of supported instance types.
+[NUM_NODES] = Number of nodes to be used.
+[MAX_NODES] = Maximum size of the nodes.
+[NODE_SIZE] = Size of the nodes.
+
+Update the path to the ssh-public-key to point to the folder and file where your public key is saved.
+
+**4. Install GPU addon:**
+
+```shell
+$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
+```
+
+**5. Install RAPIDS helm repo:**
+
+```shell
+$ helm repo add rapidsai https://helm.rapids.ai
+$ helm repo update
+```
+
+**6. Install helm chart:**
+
+```shell
+$ helm install --set dask.scheduler.serviceType="LoadBalancer" --set dask.jupyter.serviceType="LoadBalancer" rapidstest rapidsai/rapidsai
+```
+
+**7. Accessing your cluster:**
+
+```shell
+$ kubectl get svc
+NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+kubernetes ClusterIP 10.100.0.1 443/TCP 12m
+rapidsai-jupyter LoadBalancer 10.100.251.155 a454a9741455544cfa37fc4ac71caa53-868718558.us-east-1.elb.amazonaws.com 80:30633/TCP 85s
+rapidsai-scheduler LoadBalancer 10.100.11.182 a9c703f1c002f478ea60d9acaf165bab-1146605388.us-east-1.elb.amazonaws.com 8786:30346/TCP,8787:32444/TCP 85s
+```
+
+**7. ELB IP address:** **[Convert the DNS address provided above as the
+EXTERNAL-IP address to an IPV4
+address](https://aws.amazon.com/premiumsupport/knowledge-center/elb-find-load-balancer-IP/)**.
+Then use the obtained IPV4 address to visit the rapidsai-jupyter service in your
+browser!
+
+**8. Delete the cluster:** List and delete services running in the cluster to release resources
+
+```shell
+$ kubectl get svc --all-namespaces
+$ kubectl delete svc [SERVICE_NAME]
+```
+
+[SERVICE_NAME] = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
+
+Delete the cluster and its associated nodes
+
+```shell
+$ eksctl delete cluster --region=[REGION] --name=[CLUSTER_NAME]
+```
+
+**9. Uninstall the helm chart:**
+
+```shell
+$ helm uninstall rapidstest
+```
From ca89675dffe25223b024754483c6b99328807532 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 02:16:36 +0500
Subject: [PATCH 11/30] Update iks.md
---
source/cloud/IBM/iks.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index c2255280..e67f17ea 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -2,7 +2,7 @@
RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using Helm. More details can be found at our **[helm docs.](https://helm.rapids.ai/docs/csp.html)**
-**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, and IBM cloud cli.
+**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
**2. Public Key.** Create a public key if you don't have one.
From ea4537aa83ce62ceeaa5bf05a5acd13593e35b6d Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 02:20:15 +0500
Subject: [PATCH 12/30] Update iks.md
---
source/cloud/IBM/iks.md | 8 ++++++++
1 file changed, 8 insertions(+)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index e67f17ea..d20a5994 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -9,6 +9,14 @@ RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IK
**3. Create your cluster:**
```shell
+$ ibmcloud ks cluster create classic
+ --name my_cluster
+ --zone dal10
+ --flavor mb2c.4x32
+ --hardware dedicated
+ --workers 3
+ --public-vlan
+ --private-vlan
$ eksctl create cluster \
--name [CLUSTER_NAME] \
--version 1.14 \
From 387530ddd15d62fb1cbbac70d2c9b9f33160453e Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 02:23:03 +0500
Subject: [PATCH 13/30] Update iks.md
---
source/cloud/IBM/iks.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index d20a5994..d5c06097 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -10,7 +10,7 @@ RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IK
```shell
$ ibmcloud ks cluster create classic
- --name my_cluster
+ --name [CLUSTER_NAME] \
--zone dal10
--flavor mb2c.4x32
--hardware dedicated
@@ -32,7 +32,7 @@ $ eksctl create cluster \
--managed
```
-[CLUSTER_NAME] = Name of the EKS cluster. This will be auto generated if not specified.
+[CLUSTER_NAME] = Name of the IKS cluster. This will be auto generated if not specified.
[NODE_INSTANCE] = Node instance type to be used. Select one of the instance types supported by RAPIDS Refer to the introduction section for a list of supported instance types.
[NUM_NODES] = Number of nodes to be used.
[MAX_NODES] = Maximum size of the nodes.
From 89b342e5f34022ab9d76d24e6ebc021a19aa2c2b Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 18:38:19 +0500
Subject: [PATCH 14/30] Update iks.md
---
source/cloud/IBM/iks.md | 4 +++-
1 file changed, 3 insertions(+), 1 deletion(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index d5c06097..247f9137 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -4,7 +4,9 @@ RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IK
**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
-**2. Public Key.** Create a public key if you don't have one.
+**2. Login.** Login to IBM cloud on CLI using below command.
+
+$ ibmcloud login
**3. Create your cluster:**
From 22275234e74a34b9a6cd1d0b0c43dda5240860ad Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 18:46:37 +0500
Subject: [PATCH 15/30] Update iks.md
---
source/cloud/IBM/iks.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 247f9137..540ab935 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -4,9 +4,9 @@ RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IK
**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
-**2. Login.** Login to IBM cloud on CLI using below command.
+**2. Login to IBM CLI.** Login to IBM cloud on CLI using below command.
-$ ibmcloud login
+$ ibmcloud login -a cloud.ibm.com -r -g
**3. Create your cluster:**
From 99582b10cb3bac3af41f6e108c73148bd1302d44 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 18:48:59 +0500
Subject: [PATCH 16/30] Update iks.md
---
source/cloud/IBM/iks.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 540ab935..10aed0d3 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -6,7 +6,9 @@ RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IK
**2. Login to IBM CLI.** Login to IBM cloud on CLI using below command.
+```shell
$ ibmcloud login -a cloud.ibm.com -r -g
+```
**3. Create your cluster:**
From 041333f6b0ec24c196257e6ef819d2a078e40be2 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 18:59:28 +0500
Subject: [PATCH 17/30] Update iks.md
---
source/cloud/IBM/iks.md | 28 +++++++++-------------------
1 file changed, 9 insertions(+), 19 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 10aed0d3..d1074a6b 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -16,24 +16,12 @@ $ ibmcloud login -a cloud.ibm.com -r -g
$ ibmcloud ks cluster create classic
--name [CLUSTER_NAME] \
--zone dal10
- --flavor mb2c.4x32
+ --flavor gx2-8x64x1v100
--hardware dedicated
- --workers 3
+ --workers 1
+ --version \
--public-vlan
--private-vlan
-$ eksctl create cluster \
- --name [CLUSTER_NAME] \
- --version 1.14 \
- --region [REGION] \
- --nodegroup-name gpu-workers \
- --node-type [NODE_INSTANCE] \
- --nodes [NUM_NODES] \
- --nodes-min 1 \
- --nodes-max [MAX_NODES] \
- --node-volume-size [NODE_SIZE] \
- --ssh-access \
- --ssh-public-key ~/path/to/id_rsa.pub \
- --managed
```
[CLUSTER_NAME] = Name of the IKS cluster. This will be auto generated if not specified.
@@ -44,26 +32,28 @@ $ eksctl create cluster \
Update the path to the ssh-public-key to point to the folder and file where your public key is saved.
-**4. Install GPU addon:**
+**4. Connect your cluster:**
+
+**5. Install GPU addon:**
```shell
$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
```
-**5. Install RAPIDS helm repo:**
+**6. Install RAPIDS helm repo:**
```shell
$ helm repo add rapidsai https://helm.rapids.ai
$ helm repo update
```
-**6. Install helm chart:**
+**7. Install helm chart:**
```shell
$ helm install --set dask.scheduler.serviceType="LoadBalancer" --set dask.jupyter.serviceType="LoadBalancer" rapidstest rapidsai/rapidsai
```
-**7. Accessing your cluster:**
+**8. Accessing your cluster:**
```shell
$ kubectl get svc
From d19704df53a01d906ca5b652cd60c88f81d29ce8 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 20:47:57 +0500
Subject: [PATCH 18/30] Update iks.md
---
source/cloud/IBM/iks.md | 34 ++++++++++++++--------------------
1 file changed, 14 insertions(+), 20 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index d1074a6b..55b77ce7 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -15,25 +15,25 @@ $ ibmcloud login -a cloud.ibm.com -r -g
```shell
$ ibmcloud ks cluster create classic
--name [CLUSTER_NAME] \
- --zone dal10
- --flavor gx2-8x64x1v100
- --hardware dedicated
- --workers 1
- --version \
- --public-vlan
- --private-vlan
+ --zone dal10 \
+ --flavor gx2-8x64x1v100 \
+ --hardware dedicated \
+ --workers 1 \
+ --version [kubernetes-version] \
```
[CLUSTER_NAME] = Name of the IKS cluster. This will be auto generated if not specified.
-[NODE_INSTANCE] = Node instance type to be used. Select one of the instance types supported by RAPIDS Refer to the introduction section for a list of supported instance types.
-[NUM_NODES] = Number of nodes to be used.
-[MAX_NODES] = Maximum size of the nodes.
-[NODE_SIZE] = Size of the nodes.
+[kubernetes-version] = Kubernetes version, the tested version for this deployment is 1.21.14.
-Update the path to the ssh-public-key to point to the folder and file where your public key is saved.
+Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
**4. Connect your cluster:**
+```shell
+$ ibmcloud ks cluster config --cluster [clusterId]
+```
+[clusterid] = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+
**5. Install GPU addon:**
```shell
@@ -63,13 +63,7 @@ rapidsai-jupyter LoadBalancer 10.100.251.155 a454a9741455544cfa37fc4ac71caa
rapidsai-scheduler LoadBalancer 10.100.11.182 a9c703f1c002f478ea60d9acaf165bab-1146605388.us-east-1.elb.amazonaws.com 8786:30346/TCP,8787:32444/TCP 85s
```
-**7. ELB IP address:** **[Convert the DNS address provided above as the
-EXTERNAL-IP address to an IPV4
-address](https://aws.amazon.com/premiumsupport/knowledge-center/elb-find-load-balancer-IP/)**.
-Then use the obtained IPV4 address to visit the rapidsai-jupyter service in your
-browser!
-
-**8. Delete the cluster:** List and delete services running in the cluster to release resources
+**9. Delete the cluster:** List and delete services running in the cluster to release resources
```shell
$ kubectl get svc --all-namespaces
@@ -81,7 +75,7 @@ $ kubectl delete svc [SERVICE_NAME]
Delete the cluster and its associated nodes
```shell
-$ eksctl delete cluster --region=[REGION] --name=[CLUSTER_NAME]
+$ ibmlclud ks delete cluster --region=[REGION] --name=[CLUSTER_NAME]
```
**9. Uninstall the helm chart:**
From 225d05f63ed8f3b7630e3f98e6a9e0ae607adae8 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 20:51:17 +0500
Subject: [PATCH 19/30] Update iks.md
---
source/cloud/IBM/iks.md | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 55b77ce7..574eb7ca 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -14,25 +14,25 @@ $ ibmcloud login -a cloud.ibm.com -r -g
```shell
$ ibmcloud ks cluster create classic
- --name [CLUSTER_NAME] \
+ --name \
--zone dal10 \
--flavor gx2-8x64x1v100 \
--hardware dedicated \
--workers 1 \
- --version [kubernetes-version] \
+ --version \
```
-[CLUSTER_NAME] = Name of the IKS cluster. This will be auto generated if not specified.
-[kubernetes-version] = Kubernetes version, the tested version for this deployment is 1.21.14.
+ = Name of the IKS cluster. This will be auto generated if not specified.
+ = Kubernetes version, the tested version for this deployment is 1.21.14.
Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
**4. Connect your cluster:**
```shell
-$ ibmcloud ks cluster config --cluster [clusterId]
+$ ibmcloud ks cluster config --cluster
```
-[clusterid] = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+ = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
**5. Install GPU addon:**
@@ -67,15 +67,15 @@ rapidsai-scheduler LoadBalancer 10.100.11.182 a9c703f1c002f478ea60d9acaf165b
```shell
$ kubectl get svc --all-namespaces
-$ kubectl delete svc [SERVICE_NAME]
+$ kubectl delete svc
```
-[SERVICE_NAME] = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
+ = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
Delete the cluster and its associated nodes
```shell
-$ ibmlclud ks delete cluster --region=[REGION] --name=[CLUSTER_NAME]
+$ ibmcloud ks cluster rm --cluster
```
**9. Uninstall the helm chart:**
From fb4a85b4d441335101d14f6d4ecdc19f19cfb503 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 20:52:12 +0500
Subject: [PATCH 20/30] Update iks.md
---
source/cloud/IBM/iks.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 574eb7ca..ecf59fb5 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -19,11 +19,11 @@ $ ibmcloud ks cluster create classic
--flavor gx2-8x64x1v100 \
--hardware dedicated \
--workers 1 \
- --version \
+ --version \
```
= Name of the IKS cluster. This will be auto generated if not specified.
- = Kubernetes version, the tested version for this deployment is 1.21.14.
+ = Kubernetes version, the tested version for this deployment is 1.21.14.
Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
From aea3f04c1713842f461ba162b667bc82f3ce8d7f Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 20:53:17 +0500
Subject: [PATCH 21/30] Update iks.md
---
source/cloud/IBM/iks.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index ecf59fb5..27bdef5c 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -30,9 +30,9 @@ Upon successful creation, you would get the cluster id, note that down, it would
**4. Connect your cluster:**
```shell
-$ ibmcloud ks cluster config --cluster
+$ ibmcloud ks cluster config --cluster
```
- = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+ = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
**5. Install GPU addon:**
From 066048983a0be01857b41ebdb26dffaae8a05ee5 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:03:11 +0500
Subject: [PATCH 22/30] Update iks.md
---
source/cloud/IBM/iks.md | 22 +++++++++++++---------
1 file changed, 13 insertions(+), 9 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 27bdef5c..1276d07f 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -30,14 +30,16 @@ Upon successful creation, you would get the cluster id, note that down, it would
**4. Connect your cluster:**
```shell
-$ ibmcloud ks cluster config --cluster
+$ ibmcloud ks cluster config --cluster
```
- = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+ = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
**5. Install GPU addon:**
```shell
-$ kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/1.0.0-beta4/nvidia-device-plugin.yml
+$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
+$ helm repo update
+$ helm install — version=0.6.0 — generate-name nvdp/nvidia-device-plugin
```
**6. Install RAPIDS helm repo:**
@@ -47,22 +49,24 @@ $ helm repo add rapidsai https://helm.rapids.ai
$ helm repo update
```
-**7. Install helm chart:**
+**7. Install helm RAPIDS chart:**
```shell
-$ helm install --set dask.scheduler.serviceType="LoadBalancer" --set dask.jupyter.serviceType="LoadBalancer" rapidstest rapidsai/rapidsai
+$ helm install — set dask.scheduler.serviceType=”LoadBalancer” — set \ dask.jupyter.serviceType=”LoadBalancer” rapidstest rapidsai/rapidsai
```
**8. Accessing your cluster:**
```shell
$ kubectl get svc
-NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
-kubernetes ClusterIP 10.100.0.1 443/TCP 12m
-rapidsai-jupyter LoadBalancer 10.100.251.155 a454a9741455544cfa37fc4ac71caa53-868718558.us-east-1.elb.amazonaws.com 80:30633/TCP 85s
-rapidsai-scheduler LoadBalancer 10.100.11.182 a9c703f1c002f478ea60d9acaf165bab-1146605388.us-east-1.elb.amazonaws.com 8786:30346/TCP,8787:32444/TCP 85s
+NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+kubernetes ClusterIP 172.21.0.1 443/TCP 12m
+rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.286.45 80:31118/TCP 85s
+rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.286.43 8786:30346/TCP,8787:32444/TCP 85s
```
+You can now visit the external IP of the rapidsai-jupyter service in your browser!
+
**9. Delete the cluster:** List and delete services running in the cluster to release resources
```shell
From 26066a11dd51b3db00a7b1bc74f9734cee24f651 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:06:30 +0500
Subject: [PATCH 23/30] Update iks.md
---
source/cloud/IBM/iks.md | 14 ++++++++------
1 file changed, 8 insertions(+), 6 deletions(-)
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
index 1276d07f..090983d6 100644
--- a/source/cloud/IBM/iks.md
+++ b/source/cloud/IBM/iks.md
@@ -61,12 +61,18 @@ $ helm install — set dask.scheduler.serviceType=”LoadBalancer” — set \ d
$ kubectl get svc
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kubernetes ClusterIP 172.21.0.1 443/TCP 12m
-rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.286.45 80:31118/TCP 85s
-rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.286.43 8786:30346/TCP,8787:32444/TCP 85s
+rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.206.45 80:31118/TCP 85s
+rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.206.43 8786:30962/TCP,8787:30136/TCP 85s
```
You can now visit the external IP of the rapidsai-jupyter service in your browser!
+**8. Uninstall the helm chart:**
+
+```shell
+$ helm uninstall rapidstest
+```
+
**9. Delete the cluster:** List and delete services running in the cluster to release resources
```shell
@@ -82,8 +88,4 @@ Delete the cluster and its associated nodes
$ ibmcloud ks cluster rm --cluster
```
-**9. Uninstall the helm chart:**
-```shell
-$ helm uninstall rapidstest
-```
From 03837169d88759ae7d44ce06f279c8759026387d Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:10:16 +0500
Subject: [PATCH 24/30] Delete source/cloud/IBM directory
---
source/cloud/IBM/iks.md | 91 ---------------------------------
source/cloud/IBM/index.md | 23 ---------
source/cloud/IBM/single-node.md | 31 -----------
3 files changed, 145 deletions(-)
delete mode 100644 source/cloud/IBM/iks.md
delete mode 100644 source/cloud/IBM/index.md
delete mode 100644 source/cloud/IBM/single-node.md
diff --git a/source/cloud/IBM/iks.md b/source/cloud/IBM/iks.md
deleted file mode 100644
index 090983d6..00000000
--- a/source/cloud/IBM/iks.md
+++ /dev/null
@@ -1,91 +0,0 @@
-# IBM Kubernetes Service (IKS)
-
-RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using Helm. More details can be found at our **[helm docs.](https://helm.rapids.ai/docs/csp.html)**
-
-**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
-
-**2. Login to IBM CLI.** Login to IBM cloud on CLI using below command.
-
-```shell
-$ ibmcloud login -a cloud.ibm.com -r -g
-```
-
-**3. Create your cluster:**
-
-```shell
-$ ibmcloud ks cluster create classic
- --name \
- --zone dal10 \
- --flavor gx2-8x64x1v100 \
- --hardware dedicated \
- --workers 1 \
- --version \
-```
-
- = Name of the IKS cluster. This will be auto generated if not specified.
- = Kubernetes version, the tested version for this deployment is 1.21.14.
-
-Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
-
-**4. Connect your cluster:**
-
-```shell
-$ ibmcloud ks cluster config --cluster
-```
- = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
-
-**5. Install GPU addon:**
-
-```shell
-$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
-$ helm repo update
-$ helm install — version=0.6.0 — generate-name nvdp/nvidia-device-plugin
-```
-
-**6. Install RAPIDS helm repo:**
-
-```shell
-$ helm repo add rapidsai https://helm.rapids.ai
-$ helm repo update
-```
-
-**7. Install helm RAPIDS chart:**
-
-```shell
-$ helm install — set dask.scheduler.serviceType=”LoadBalancer” — set \ dask.jupyter.serviceType=”LoadBalancer” rapidstest rapidsai/rapidsai
-```
-
-**8. Accessing your cluster:**
-
-```shell
-$ kubectl get svc
-NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
-kubernetes ClusterIP 172.21.0.1 443/TCP 12m
-rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.206.45 80:31118/TCP 85s
-rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.206.43 8786:30962/TCP,8787:30136/TCP 85s
-```
-
-You can now visit the external IP of the rapidsai-jupyter service in your browser!
-
-**8. Uninstall the helm chart:**
-
-```shell
-$ helm uninstall rapidstest
-```
-
-**9. Delete the cluster:** List and delete services running in the cluster to release resources
-
-```shell
-$ kubectl get svc --all-namespaces
-$ kubectl delete svc
-```
-
- = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
-
-Delete the cluster and its associated nodes
-
-```shell
-$ ibmcloud ks cluster rm --cluster
-```
-
-
diff --git a/source/cloud/IBM/index.md b/source/cloud/IBM/index.md
deleted file mode 100644
index c84bc3a4..00000000
--- a/source/cloud/IBM/index.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# IBM Cloud
-
-```{toctree}
----
-maxdepth: 2
-caption: IBM Cloud
----
-single-node
-iks
-```
-
-RAPIDS can be deployed on IBM Cloud in several ways. See the
-list of accelerated instance types below:
-
-| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
-| :------------------ | ------------------- | ---- |------------------- | -------------- | ------------- | ------------- | ------------------: |
-| IBM | V100 GPU Virtual | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
-| IBM | V100 GPU Virtual | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU Virtual | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU Virtual | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
-| IBM | P100 GPU Bare Metal | 32 | mg4c.32x384.2xp100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
-| IBM | V100 GPU Bare Metal | 48 | mg4c.48x384.2xv100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
-
diff --git a/source/cloud/IBM/single-node.md b/source/cloud/IBM/single-node.md
deleted file mode 100644
index fb674c70..00000000
--- a/source/cloud/IBM/single-node.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Single Node
-
-There are multiple ways you can deploy RAPIDS on a single instance, but the easiest is to use the RAPIDS docker image:
-
-**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
-section for a list of supported instance types.
-
-**2. Create & Associate.** Create a Floating IP and associate that with the created instance to access the instance on the web.
-
-**3. Credentials.** Using the credentials supplied by IBM, log into the instance
-via SSH. For a short guide on launching your instance and accessing it, read the
-[Getting Started with IBM Virtual Server Documentation](https://cloud.ibm.com/docs/virtual-servers?topic=virtual-servers-getting-started-tutorial).
-
-**4. Install.** Install [Docker and the NVIDIA Docker
-runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
-in the IBM virtual server instance.
-
-**5. Install.** Install RAPIDS docker image. The docker container can be
-customized by using the options provided in the **[Getting
-Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
-that can be used is provided below:
-
-```shell
-$ docker pull rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
-$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 \
- rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
-```
-
-**6. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
-notebook instance automatically. You can log into it by going to the Floating IP address
-associated with the instance on port 8888.
From c21aede34501e3a372c7acea95425b94efa5edc1 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:11:48 +0500
Subject: [PATCH 25/30] Create index.md
---
source/cloud/ibm/index.md | 23 +++++++++++++++++++++++
1 file changed, 23 insertions(+)
create mode 100644 source/cloud/ibm/index.md
diff --git a/source/cloud/ibm/index.md b/source/cloud/ibm/index.md
new file mode 100644
index 00000000..c84bc3a4
--- /dev/null
+++ b/source/cloud/ibm/index.md
@@ -0,0 +1,23 @@
+# IBM Cloud
+
+```{toctree}
+---
+maxdepth: 2
+caption: IBM Cloud
+---
+single-node
+iks
+```
+
+RAPIDS can be deployed on IBM Cloud in several ways. See the
+list of accelerated instance types below:
+
+| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
+| :------------------ | ------------------- | ---- |------------------- | -------------- | ------------- | ------------- | ------------------: |
+| IBM | V100 GPU Virtual | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
+| IBM | P100 GPU Bare Metal | 32 | mg4c.32x384.2xp100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
+| IBM | V100 GPU Bare Metal | 48 | mg4c.48x384.2xv100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
+
From e3dc7b647a2e7e5976d700f986212e18ffbc919a Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:12:23 +0500
Subject: [PATCH 26/30] Create single-node.md
---
source/cloud/ibm/single-node.md | 31 +++++++++++++++++++++++++++++++
1 file changed, 31 insertions(+)
create mode 100644 source/cloud/ibm/single-node.md
diff --git a/source/cloud/ibm/single-node.md b/source/cloud/ibm/single-node.md
new file mode 100644
index 00000000..fb674c70
--- /dev/null
+++ b/source/cloud/ibm/single-node.md
@@ -0,0 +1,31 @@
+# Single Node
+
+There are multiple ways you can deploy RAPIDS on a single instance, but the easiest is to use the RAPIDS docker image:
+
+**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
+section for a list of supported instance types.
+
+**2. Create & Associate.** Create a Floating IP and associate that with the created instance to access the instance on the web.
+
+**3. Credentials.** Using the credentials supplied by IBM, log into the instance
+via SSH. For a short guide on launching your instance and accessing it, read the
+[Getting Started with IBM Virtual Server Documentation](https://cloud.ibm.com/docs/virtual-servers?topic=virtual-servers-getting-started-tutorial).
+
+**4. Install.** Install [Docker and the NVIDIA Docker
+runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+in the IBM virtual server instance.
+
+**5. Install.** Install RAPIDS docker image. The docker container can be
+customized by using the options provided in the **[Getting
+Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
+that can be used is provided below:
+
+```shell
+$ docker pull rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
+$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 \
+ rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
+```
+
+**6. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
+notebook instance automatically. You can log into it by going to the Floating IP address
+associated with the instance on port 8888.
From 656cd0a52385eebbfaae218ebea94b0225f205d5 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Wed, 7 Sep 2022 21:12:54 +0500
Subject: [PATCH 27/30] Create iks.md
---
source/cloud/ibm/iks.md | 91 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 91 insertions(+)
create mode 100644 source/cloud/ibm/iks.md
diff --git a/source/cloud/ibm/iks.md b/source/cloud/ibm/iks.md
new file mode 100644
index 00000000..090983d6
--- /dev/null
+++ b/source/cloud/ibm/iks.md
@@ -0,0 +1,91 @@
+# IBM Kubernetes Service (IKS)
+
+RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using Helm. More details can be found at our **[helm docs.](https://helm.rapids.ai/docs/csp.html)**
+
+**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
+
+**2. Login to IBM CLI.** Login to IBM cloud on CLI using below command.
+
+```shell
+$ ibmcloud login -a cloud.ibm.com -r -g
+```
+
+**3. Create your cluster:**
+
+```shell
+$ ibmcloud ks cluster create classic
+ --name \
+ --zone dal10 \
+ --flavor gx2-8x64x1v100 \
+ --hardware dedicated \
+ --workers 1 \
+ --version \
+```
+
+ = Name of the IKS cluster. This will be auto generated if not specified.
+ = Kubernetes version, the tested version for this deployment is 1.21.14.
+
+Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
+
+**4. Connect your cluster:**
+
+```shell
+$ ibmcloud ks cluster config --cluster
+```
+ = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+
+**5. Install GPU addon:**
+
+```shell
+$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
+$ helm repo update
+$ helm install — version=0.6.0 — generate-name nvdp/nvidia-device-plugin
+```
+
+**6. Install RAPIDS helm repo:**
+
+```shell
+$ helm repo add rapidsai https://helm.rapids.ai
+$ helm repo update
+```
+
+**7. Install helm RAPIDS chart:**
+
+```shell
+$ helm install — set dask.scheduler.serviceType=”LoadBalancer” — set \ dask.jupyter.serviceType=”LoadBalancer” rapidstest rapidsai/rapidsai
+```
+
+**8. Accessing your cluster:**
+
+```shell
+$ kubectl get svc
+NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
+kubernetes ClusterIP 172.21.0.1 443/TCP 12m
+rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.206.45 80:31118/TCP 85s
+rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.206.43 8786:30962/TCP,8787:30136/TCP 85s
+```
+
+You can now visit the external IP of the rapidsai-jupyter service in your browser!
+
+**8. Uninstall the helm chart:**
+
+```shell
+$ helm uninstall rapidstest
+```
+
+**9. Delete the cluster:** List and delete services running in the cluster to release resources
+
+```shell
+$ kubectl get svc --all-namespaces
+$ kubectl delete svc
+```
+
+ = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
+
+Delete the cluster and its associated nodes
+
+```shell
+$ ibmcloud ks cluster rm --cluster
+```
+
+
From fdda35dd06a5f420244a69bd34b592125e9c0583 Mon Sep 17 00:00:00 2001
From: marifse <50728232+marifse@users.noreply.github.com>
Date: Thu, 8 Sep 2022 00:16:05 +0500
Subject: [PATCH 28/30] Update iks.md
---
source/cloud/ibm/iks.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/source/cloud/ibm/iks.md b/source/cloud/ibm/iks.md
index 090983d6..098a428e 100644
--- a/source/cloud/ibm/iks.md
+++ b/source/cloud/ibm/iks.md
@@ -67,13 +67,13 @@ rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.206.43 8786:
You can now visit the external IP of the rapidsai-jupyter service in your browser!
-**8. Uninstall the helm chart:**
+**9. Uninstall the helm chart:**
```shell
$ helm uninstall rapidstest
```
-**9. Delete the cluster:** List and delete services running in the cluster to release resources
+**10. Delete the cluster:** List and delete services running in the cluster to release resources
```shell
$ kubectl get svc --all-namespaces
From 4848cd7ca728d639bfaaf8b55fceea74bd063907 Mon Sep 17 00:00:00 2001
From: Jacob Tomlinson
Date: Wed, 19 Oct 2022 13:46:07 +0100
Subject: [PATCH 29/30] Fix linting
---
source/cloud/ibm/iks.md | 7 +++----
source/cloud/ibm/index.md | 17 ++++++++---------
2 files changed, 11 insertions(+), 13 deletions(-)
diff --git a/source/cloud/ibm/iks.md b/source/cloud/ibm/iks.md
index 098a428e..f270e62c 100644
--- a/source/cloud/ibm/iks.md
+++ b/source/cloud/ibm/iks.md
@@ -13,11 +13,11 @@ $ ibmcloud login -a cloud.ibm.com -r -g
**3. Create your cluster:**
```shell
-$ ibmcloud ks cluster create classic
+$ ibmcloud ks cluster create classic
--name \
--zone dal10 \
--flavor gx2-8x64x1v100 \
- --hardware dedicated \
+ --hardware dedicated \
--workers 1 \
--version \
```
@@ -32,6 +32,7 @@ Upon successful creation, you would get the cluster id, note that down, it would
```shell
$ ibmcloud ks cluster config --cluster
```
+
= When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
**5. Install GPU addon:**
@@ -87,5 +88,3 @@ Delete the cluster and its associated nodes
```shell
$ ibmcloud ks cluster rm --cluster
```
-
-
diff --git a/source/cloud/ibm/index.md b/source/cloud/ibm/index.md
index c84bc3a4..e8546793 100644
--- a/source/cloud/ibm/index.md
+++ b/source/cloud/ibm/index.md
@@ -12,12 +12,11 @@ iks
RAPIDS can be deployed on IBM Cloud in several ways. See the
list of accelerated instance types below:
-| Cloud
Provider | Inst.
Type |vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
-| :------------------ | ------------------- | ---- |------------------- | -------------- | ------------- | ------------- | ------------------: |
-| IBM | V100 GPU Virtual | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
-| IBM | V100 GPU Virtual | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU Virtual | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
-| IBM | V100 GPU Virtual | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
-| IBM | P100 GPU Bare Metal | 32 | mg4c.32x384.2xp100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
-| IBM | V100 GPU Bare Metal | 48 | mg4c.48x384.2xv100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
-
+| Cloud
Provider | Inst.
Type | vCPUs | Inst.
Name | GPU
Count | GPU
Type | xGPU
RAM | xGPU
RAM Total |
+| :------------------ | ------------------- | ----- | ------------------ | -------------- | ------------- | ------------- | ------------------: |
+| IBM | V100 GPU Virtual | 8 | gx2-8x64x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 64 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x1v100 | 1 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 16 | gx2-16x128x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 128 (GB) |
+| IBM | V100 GPU Virtual | 32 | gx2-32x256x2v100 | 2 | NVIDIA Tesla | 16 (GB) | 256 (GB) |
+| IBM | P100 GPU Bare Metal | 32 | mg4c.32x384.2xp100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
+| IBM | V100 GPU Bare Metal | 48 | mg4c.48x384.2xv100 | 2 | NVIDIA Tesla | 16 (GB) | 384 (GB) |
From 0a39e349540285c371195704b2094477a6e67e79 Mon Sep 17 00:00:00 2001
From: Jacob Tomlinson
Date: Wed, 19 Oct 2022 14:36:11 +0100
Subject: [PATCH 30/30] Formatting, layout and reuse of existing documentation
---
source/cloud/ibm/iks.md | 76 ++++++++++++++-------------------
source/cloud/ibm/single-node.md | 32 +++++++++-----
source/cloud/index.rst | 1 +
3 files changed, 54 insertions(+), 55 deletions(-)
diff --git a/source/cloud/ibm/iks.md b/source/cloud/ibm/iks.md
index f270e62c..5a7771e8 100644
--- a/source/cloud/ibm/iks.md
+++ b/source/cloud/ibm/iks.md
@@ -1,89 +1,75 @@
# IBM Kubernetes Service (IKS)
-RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using Helm. More details can be found at our **[helm docs.](https://helm.rapids.ai/docs/csp.html)**
+RAPIDS can be deployed on IBM Cloud via IBM Cloud managed Kubernetes service (IKS) using any of the [supported Kubernetes installation methods](../../platforms/kubernetes).
-**1. Install.** Install and configure dependencies in your local environment: kubectl, helm, IBM cloud cli and IBM Kubernetes Service (KS) plugin.
+## Install pre-requisites
-**2. Login to IBM CLI.** Login to IBM cloud on CLI using below command.
+Install and configure dependencies in your local environment: [kubectl](https://kubernetes.io/docs/tasks/tools/), [helm](https://helm.sh/), [IBM cloud cli](https://cloud.ibm.com/docs/cli?topic=cli-getting-started) and [IBM Kubernetes Service (KS) plugin](https://cloud.ibm.com/docs/containers?topic=containers-cs_cli_install).
+
+## Login to IBM CLI
```shell
-$ ibmcloud login -a cloud.ibm.com -r -g
+$ ibmcloud login -a cloud.ibm.com -r
+$ ibmcloud target -g
+```
+
+```{note}
+You can list regions with `$ ibmcloud regions` and resource groups with `$ ibmcloud resource groups`.
```
-**3. Create your cluster:**
+## Create a Kubernetes cluster
```shell
-$ ibmcloud ks cluster create classic
+$ ibmcloud ks cluster create classic \
--name \
--zone dal10 \
--flavor gx2-8x64x1v100 \
--hardware dedicated \
--workers 1 \
- --version \
+ --version
```
- = Name of the IKS cluster. This will be auto generated if not specified.
- = Kubernetes version, the tested version for this deployment is 1.21.14.
+`` = Name of the IKS cluster. This will be auto generated if not specified.
+`` = Kubernetes version, the tested version for this deployment is 1.21.14.
-Upon successful creation, you would get the cluster id, note that down, it would be required in next step to connect to the cluster.
+Upon successful creation, you would get the cluster id, note that down, it will be required in the next step to connect to the cluster.
-**4. Connect your cluster:**
+## Connect to the cluster
```shell
$ ibmcloud ks cluster config --cluster
```
- = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
+`` = When creating the cluster using IBM KS CLI, use that cluster id to connect to the cluster.
-**5. Install GPU addon:**
+## Install GPU drivers
```shell
-$ helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
+$ helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
$ helm repo update
-$ helm install — version=0.6.0 — generate-name nvdp/nvidia-device-plugin
+$ helm install --wait --generate-name \
+ -n gpu-operator --create-namespace \
+ nvidia/gpu-operator
```
-**6. Install RAPIDS helm repo:**
+## Install RAPIDS
-```shell
-$ helm repo add rapidsai https://helm.rapids.ai
-$ helm repo update
-```
-
-**7. Install helm RAPIDS chart:**
+Follow any of the [Kubernetes installation methods to install and use RAPIDS](../../platforms/kubernetes).
-```shell
-$ helm install — set dask.scheduler.serviceType=”LoadBalancer” — set \ dask.jupyter.serviceType=”LoadBalancer” rapidstest rapidsai/rapidsai
-```
+## Delete the cluster
-**8. Accessing your cluster:**
-
-```shell
-$ kubectl get svc
-NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
-kubernetes ClusterIP 172.21.0.1 443/TCP 12m
-rapidsai-jupyter LoadBalancer 172.21.164.106 169.59.206.45 80:31118/TCP 85s
-rapidsai-scheduler LoadBalancer 172.21.111.67 169.59.206.43 8786:30962/TCP,8787:30136/TCP 85s
-```
-
-You can now visit the external IP of the rapidsai-jupyter service in your browser!
-
-**9. Uninstall the helm chart:**
-
-```shell
-$ helm uninstall rapidstest
-```
+When you are finished delete the Kubernetes cluster.
-**10. Delete the cluster:** List and delete services running in the cluster to release resources
+Before you delete the cluster you need to manually delete services running in the cluster with external IPs to release network resources.
```shell
$ kubectl get svc --all-namespaces
$ kubectl delete svc
```
- = Name of the services which have an EXTERNAL-IP value and are required to be removed to release resources.
+`` = Name of the services which have an `EXTERNAL-IP` value.
-Delete the cluster and its associated nodes
+Delete the cluster and its associated nodes.
```shell
$ ibmcloud ks cluster rm --cluster
diff --git a/source/cloud/ibm/single-node.md b/source/cloud/ibm/single-node.md
index fb674c70..1dfde88b 100644
--- a/source/cloud/ibm/single-node.md
+++ b/source/cloud/ibm/single-node.md
@@ -2,30 +2,42 @@
There are multiple ways you can deploy RAPIDS on a single instance, but the easiest is to use the RAPIDS docker image:
-**1. Initiate.** Initiate an instance supported by RAPIDS. See the introduction
-section for a list of supported instance types.
+## Launch a VM instance
-**2. Create & Associate.** Create a Floating IP and associate that with the created instance to access the instance on the web.
+Launch an instance supported by RAPIDS. See the [introduction
+section for a list of supported instance types](index).
-**3. Credentials.** Using the credentials supplied by IBM, log into the instance
+## Configure networking
+
+Create a Floating IP and associate that with the created instance to access the instance on the web.
+
+## Login
+
+Using the credentials supplied by IBM, log into the instance
via SSH. For a short guide on launching your instance and accessing it, read the
[Getting Started with IBM Virtual Server Documentation](https://cloud.ibm.com/docs/virtual-servers?topic=virtual-servers-getting-started-tutorial).
-**4. Install.** Install [Docker and the NVIDIA Docker
+## Install pre-requisites
+
+Install the [NVIDIA drivers](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#nvidia-drivers) and [Docker and the NVIDIA Docker
runtime](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
in the IBM virtual server instance.
-**5. Install.** Install RAPIDS docker image. The docker container can be
+## Install RAPIDS
+
+Install RAPIDS docker image. The docker container can be
customized by using the options provided in the **[Getting
Started](https://rapids.ai/start.html)** page of RAPIDS. Example of an image
that can be used is provided below:
```shell
-$ docker pull rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
-$ docker run — gpus all — rm -it — shm-size=1g — ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 \
- rapidsai/rapidsai:22.06-cuda11.5-runtime-ubuntu20.04-py3.9
+$ docker pull rapidsai/rapidsai:22.10-cuda11.5-runtime-ubuntu20.04-py3.9
+$ docker run --gpus all --rm -it --shm-size=1g --ulimit memlock=-1 -p 8888:8888 -p 8787:8787 -p 8786:8786 \
+ rapidsai/rapidsai:22.10-cuda11.5-runtime-ubuntu20.04-py3.9
```
-**6. Test RAPIDS.** Test it! The RAPIDS docker image will start a Jupyter
+## Test RAPIDS
+
+Test it! The RAPIDS docker image will start a Jupyter
notebook instance automatically. You can log into it by going to the Floating IP address
associated with the instance on port 8888.
diff --git a/source/cloud/index.rst b/source/cloud/index.rst
index 7ed786d9..0692be24 100644
--- a/source/cloud/index.rst
+++ b/source/cloud/index.rst
@@ -8,3 +8,4 @@ Cloud
:caption: Cloud
aws/index
+ ibm/index