Old SageMaker AI Operators for Kubernetes - Amazon SageMaker AI
Install SageMaker AI Operators for Kubernetes

Old SageMaker AI Operators for Kubernetes

This section is based on the original version of SageMaker AI Operators for Kubernetes.

Important

We are stopping the development and technical support of the original version of SageMaker Operators for Kubernetes.

If you are currently using version v1.2.2 or below of SageMaker Operators for Kubernetes, we recommend migrating your resources to the ACK service controller for Amazon SageMaker. The ACK service controller is a new generation of SageMaker Operators for Kubernetes based on AWS Controllers for Kubernetes (ACK).

For information on the migration steps, see Migrate resources to the latest Operators.

For answers to frequently asked questions on the end of support of the original version of SageMaker Operators for Kubernetes, see Announcing the End of Support of the Original Version of SageMaker AI Operators for Kubernetes

Contents

Install SageMaker AI Operators for Kubernetes

Use the following steps to install and use SageMaker AI Operators for Kubernetes to train, tune, and deploy machine learning models with Amazon SageMaker AI.

IAM role-based setup and operator deployment

The following sections describe the steps to set up and deploy the original version of the operator.

Warning

Reminder: The following steps do not install the latest version of SageMaker AI Operators for Kubernetes. To install the new ACK-based SageMaker AI Operators for Kubernetes, see Latest SageMaker AI Operators for Kubernetes.

Prerequisites

This guide assumes that you have completed the following prerequisites:

  • Install the following tools on the client machine used to access your Kubernetes cluster:

    • kubectl Version 1.13 or later. Use a kubectl version that is within one minor version of your Amazon EKS cluster control plane. For example, a 1.13 kubectl client works with Kubernetes 1.13 and 1.14 clusters. OpenID Connect (OIDC) is not supported in versions earlier than 1.13.

    • eksctl Version 0.7.0 or later

    • AWS CLI Version 1.16.232 or later

    • (optional) Helm Version 3.0 or later

    • aws-iam-authenticator

  • Have IAM permissions to create roles and attach policies to roles.

  • Created a Kubernetes cluster on which to run the operators. It should either be Kubernetes version 1.13 or 1.14. For automated cluster creation using eksctl, see Getting Started with eksctl. It takes 20–30 minutes to provision a cluster.

Cluster-scoped deployment

Before you can deploy your operator using an IAM role, associate an OpenID Connect (OIDC) Identity Provider (IdP) with your role to authenticate with the IAM service.

Create an OIDC provider for your cluster

The following instructions show how to create and associate an OIDC provider with your Amazon EKS cluster.

  1. Set the local CLUSTER_NAME and AWS_REGION environment variables as follows:

    # Set the Region and cluster
export CLUSTER_NAME="<your cluster name>"
export AWS_REGION="<your region>"

  2. Use the following command to associate the OIDC provider with your cluster. For more information, see Enabling IAM Roles for Service Accounts on your Cluster. 

    eksctl utils associate-iam-oidc-provider --cluster ${CLUSTER_NAME} \
      --region ${AWS_REGION} --approve

    Your output should look like the following: 

    [_]  eksctl version 0.10.1
  [_]  using region us-east-1
  [_]  IAM OpenID Connect provider is associated with cluster "my-cluster" in "us-east-1"

Now that the cluster has an OIDC identity provider, you can create a role and give a Kubernetes ServiceAccount permission to assume the role.

Get the OIDC ID

To set up the ServiceAccount, obtain the OIDC issuer URL using the following command:

aws eks describe-cluster --name ${CLUSTER_NAME} --region ${AWS_REGION} \
      --query cluster.identity.oidc.issuer --output text

The command returns a URL like the following: 

https://oidc.eks.${AWS_REGION}.amazonaws.com/id/D48675832CA65BD10A532F597OIDCID

In this URL, the value D48675832CA65BD10A532F597OIDCID is the OIDC ID. The OIDC ID for your cluster is different. You need this OIDC ID value to create a role.

If your output is None, it means that your client version is old. To work around this, run the following command: 

aws eks describe-cluster --region ${AWS_REGION} --query cluster --name ${CLUSTER_NAME} --output text | grep OIDC

The OIDC URL is returned as follows: 

OIDC https://oidc.eks.us-east-1.amazonaws.com/id/D48675832CA65BD10A532F597OIDCID
Create an IAM role

  1. Create a file named trust.json and insert the following trust relationship code block into it. Be sure to replace all <OIDC ID>, <AWS account number>, and <EKS Cluster region> placeholders with values corresponding to your cluster. 

    {
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Principal": {
          "Federated": "arn:aws:iam::<AWS account number>:oidc-provider/oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>"
        },
        "Action": "sts:AssumeRoleWithWebIdentity",
        "Condition": {
          "StringEquals": {
            "oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>:aud": "sts.amazonaws.com",
            "oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>:sub": "system:serviceaccount:sagemaker-k8s-operator-system:sagemaker-k8s-operator-default"
          }
        }
      }
    ]
  }

  2. Run the following command to create a role with the trust relationship defined in trust.json. This role allows the Amazon EKS cluster to get and refresh credentials from IAM. 

    aws iam create-role --region ${AWS_REGION} --role-name <role name> --assume-role-policy-document file://trust.json --output=text

    Your output should look like the following: 

    ROLE    arn:aws:iam::123456789012:role/my-role 2019-11-22T21:46:10Z    /       ABCDEFSFODNN7EXAMPLE   my-role
ASSUMEROLEPOLICYDOCUMENT        2012-10-17
STATEMENT       sts:AssumeRoleWithWebIdentity   Allow
STRINGEQUALS    sts.amazonaws.com       system:serviceaccount:sagemaker-k8s-operator-system:sagemaker-k8s-operator-default
PRINCIPAL       arn:aws:iam::123456789012:oidc-provider/oidc.eks.us-east-1.amazonaws.com/id/

    Take note of ROLE ARN; you pass this value to your operator.

Attach the AmazonSageMakerFullAccess policy to the role

To give the role access to SageMaker AI, attach the AmazonSageMakerFullAccess policy. If you want to limit permissions to the operator, you can create your own custom policy and attach it.

To attach AmazonSageMakerFullAccess, run the following command: 

aws iam attach-role-policy --role-name <role name>  --policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess

The Kubernetes ServiceAccount sagemaker-k8s-operator-default should have AmazonSageMakerFullAccess permissions. Confirm this when you install the operator.

Deploy the operator

When deploying your operator, you can use either a YAML file or Helm charts.

Deploy the operator using YAML

This is the simplest way to deploy your operators. The process is as follows:

  1. Download the installer script using the following command: 

    wget https://raw.githubusercontent.com/aws/amazon-sagemaker-operator-for-k8s/master/release/rolebased/installer.yaml

  2. Edit the installer.yaml file to replace eks.amazonaws.com/role-arn. Replace the ARN here with the Amazon Resource Name (ARN) for the OIDC-based role you’ve created.

  3. Use the following command to deploy the cluster: 

    kubectl apply -f installer.yaml
Deploy the operator using Helm Charts

Use the provided Helm Chart to install the operator.

  1. Clone the Helm installer directory using the following command: 

    git clone https://github.com/aws/amazon-sagemaker-operator-for-k8s.git

  2. Navigate to the amazon-sagemaker-operator-for-k8s/hack/charts/installer folder. Edit the rolebased/values.yaml file, which includes high-level parameters for the chart. Replace the role ARN here with the Amazon Resource Name (ARN) for the OIDC-based role you've created.

  3. Install the Helm Chart using the following command: 

    kubectl create namespace sagemaker-k8s-operator-system
  helm install --namespace sagemaker-k8s-operator-system sagemaker-operator rolebased/

    If you decide to install the operator into a namespace other than the one specified, you need to adjust the namespace defined in the IAM role trust.json file to match.

  4. After a moment, the chart is installed with a randomly generated name. Verify that the installation succeeded by running the following command: 

    helm ls

    Your output should look like the following: 

    NAME                    NAMESPACE                       REVISION        UPDATED                                 STATUS          CHART                           APP VERSION
  sagemaker-operator      sagemaker-k8s-operator-system   1               2019-11-20 23:14:59.6777082 +0000 UTC   deployed        sagemaker-k8s-operator-0.1.0
Verify the operator deployment

  1. You should be able to see the SageMaker AI Custom Resource Definitions (CRDs) for each operator deployed to your cluster by running the following command: 

    kubectl get crd | grep sagemaker

    Your output should look like the following: 

    batchtransformjobs.sagemaker.aws.amazon.com         2019-11-20T17:12:34Z
endpointconfigs.sagemaker.aws.amazon.com            2019-11-20T17:12:34Z
hostingdeployments.sagemaker.aws.amazon.com         2019-11-20T17:12:34Z
hyperparametertuningjobs.sagemaker.aws.amazon.com   2019-11-20T17:12:34Z
models.sagemaker.aws.amazon.com                     2019-11-20T17:12:34Z
trainingjobs.sagemaker.aws.amazon.com               2019-11-20T17:12:34Z

  2. Ensure that the operator pod is running successfully. Use the following command to list all pods: 

    kubectl -n sagemaker-k8s-operator-system get pods

    You should see a pod named sagemaker-k8s-operator-controller-manager-***** in the namespace sagemaker-k8s-operator-system as follows: 

    NAME                                                         READY   STATUS    RESTARTS   AGE
sagemaker-k8s-operator-controller-manager-12345678-r8abc     2/2     Running   0          23s

Namespace-scoped deployment

You have the option to install your operator within the scope of an individual Kubernetes namespace. In this mode, the controller only monitors and reconciles resources with SageMaker AI if the resources are created within that namespace. This allows for finer-grained control over which controller is managing which resources. This is useful for deploying to multiple AWS accounts or controlling which users have access to particular jobs.

This guide outlines how to install an operator into a particular, predefined namespace. To deploy a controller into a second namespace, follow the guide from beginning to end and change out the namespace in each step.

Create an OIDC provider for your Amazon EKS cluster

The following instructions show how to create and associate an OIDC provider with your Amazon EKS cluster.

  1. Set the local CLUSTER_NAME and AWS_REGION environment variables as follows: 

    # Set the Region and cluster
export CLUSTER_NAME="<your cluster name>"
export AWS_REGION="<your region>"

  2. Use the following command to associate the OIDC provider with your cluster. For more information, see Enabling IAM Roles for Service Accounts on your Cluster. 

    eksctl utils associate-iam-oidc-provider --cluster ${CLUSTER_NAME} \
      --region ${AWS_REGION} --approve

    Your output should look like the following: 

    [_]  eksctl version 0.10.1
  [_]  using region us-east-1
  [_]  IAM OpenID Connect provider is associated with cluster "my-cluster" in "us-east-1"

Now that the cluster has an OIDC identity provider, create a role and give a Kubernetes ServiceAccount permission to assume the role.

Get your OIDC ID

To set up the ServiceAccount, first obtain the OpenID Connect issuer URL using the following command: 

aws eks describe-cluster --name ${CLUSTER_NAME} --region ${AWS_REGION} \
      --query cluster.identity.oidc.issuer --output text

The command returns a URL like the following: 

https://oidc.eks.${AWS_REGION}.amazonaws.com/id/D48675832CA65BD10A532F597OIDCID

In this URL, the value D48675832CA65BD10A532F597OIDCID is the OIDC ID. The OIDC ID for your cluster is different. You need this OIDC ID value to create a role.

If your output is None, it means that your client version is old. To work around this, run the following command: 

aws eks describe-cluster --region ${AWS_REGION} --query cluster --name ${CLUSTER_NAME} --output text | grep OIDC

The OIDC URL is returned as follows: 

OIDC https://oidc.eks.us-east-1.amazonaws.com/id/D48675832CA65BD10A532F597OIDCID
Create your IAM role

  1. Create a file named trust.json and insert the following trust relationship code block into it. Be sure to replace all <OIDC ID>, <AWS account number>, <EKS Cluster region>, and <Namespace> placeholders with values corresponding to your cluster. For the purposes of this guide, my-namespace is used for the <Namespace> value. 

    {
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Principal": {
          "Federated": "arn:aws:iam::<AWS account number>:oidc-provider/oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>"
        },
        "Action": "sts:AssumeRoleWithWebIdentity",
        "Condition": {
          "StringEquals": {
            "oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>:aud": "sts.amazonaws.com",
            "oidc.eks.<EKS Cluster region>.amazonaws.com/id/<OIDC ID>:sub": "system:serviceaccount:<Namespace>:sagemaker-k8s-operator-default"
          }
        }
      }
    ]
  }

  2. Run the following command to create a role with the trust relationship defined in trust.json. This role allows the Amazon EKS cluster to get and refresh credentials from IAM. 

    aws iam create-role --region ${AWS_REGION} --role-name <role name> --assume-role-policy-document file://trust.json --output=text

    Your output should look like the following: 

    ROLE    arn:aws:iam::123456789012:role/my-role 2019-11-22T21:46:10Z    /       ABCDEFSFODNN7EXAMPLE   my-role
  ASSUMEROLEPOLICYDOCUMENT        2012-10-17
  STATEMENT       sts:AssumeRoleWithWebIdentity   Allow
  STRINGEQUALS    sts.amazonaws.com       system:serviceaccount:my-namespace:sagemaker-k8s-operator-default
  PRINCIPAL       arn:aws:iam::123456789012:oidc-provider/oidc.eks.us-east-1.amazonaws.com/id/

Take note of ROLE ARN. You pass this value to your operator.

Attach the AmazonSageMakerFullAccess policy to your role

To give the role access to SageMaker AI, attach the AmazonSageMakerFullAccess policy. If you want to limit permissions to the operator, you can create your own custom policy and attach it.

To attach AmazonSageMakerFullAccess, run the following command: 

aws iam attach-role-policy --role-name <role name>  --policy-arn arn:aws:iam::aws:policy/AmazonSageMakerFullAccess

The Kubernetes ServiceAccount sagemaker-k8s-operator-default should have AmazonSageMakerFullAccess permissions. Confirm this when you install the operator.

Deploy the operator to your namespace

When deploying your operator, you can use either a YAML file or Helm charts.

Deploy the operator to your namespace using YAML

There are two parts to deploying an operator within the scope of a namespace. The first is the set of CRDs that are installed at a cluster level. These resource definitions only need to be installed once per Kubernetes cluster. The second part is the operator permissions and deployment itself.

If you have not already installed the CRDs into the cluster, apply the CRD installer YAML using the following command: 

kubectl apply -f https://raw.githubusercontent.com/aws/amazon-sagemaker-operator-for-k8s/master/release/rolebased/namespaced/crd.yaml

To install the operator onto the cluster:

  1. Download the operator installer YAML using the following command: 

    wget https://raw.githubusercontent.com/aws/amazon-sagemaker-operator-for-k8s/master/release/rolebased/namespaced/operator.yaml

  2. Update the installer YAML to place the resources into your specified namespace using the following command: 

    sed -i -e 's/PLACEHOLDER-NAMESPACE/<YOUR NAMESPACE>/g' operator.yaml

  3. Edit the operator.yaml file to place resources into your eks.amazonaws.com/role-arn. Replace the ARN here with the Amazon Resource Name (ARN) for the OIDC-based role you've created.

  4. Use the following command to deploy the cluster: 

    kubectl apply -f operator.yaml
Deploy the operator to your namespace using Helm Charts

There are two parts needed to deploy an operator within the scope of a namespace. The first is the set of CRDs that are installed at a cluster level. These resource definitions only need to be installed once per Kubernetes cluster. The second part is the operator permissions and deployment itself. When using Helm Charts you have to first create the namespace using kubectl.

  1. Clone the Helm installer directory using the following command: 

    git clone https://github.com/aws/amazon-sagemaker-operator-for-k8s.git

  2. Navigate to the amazon-sagemaker-operator-for-k8s/hack/charts/installer/namespaced folder. Edit the rolebased/values.yaml file, which includes high-level parameters for the chart. Replace the role ARN here with the Amazon Resource Name (ARN) for the OIDC-based role you've created.

  3. Install the Helm Chart using the following command: 

    helm install crds crd_chart/

  4. Create the required namespace and install the operator using the following command: 

    kubectl create namespace <namespace>
helm install --n <namespace> op operator_chart/

  5. After a moment, the chart is installed with the name sagemaker-operator. Verify that the installation succeeded by running the following command: 

    helm ls

    Your output should look like the following: 

    NAME                    NAMESPACE                       REVISION        UPDATED                                 STATUS          CHART                           APP VERSION
sagemaker-operator      my-namespace                    1               2019-11-20 23:14:59.6777082 +0000 UTC   deployed        sagemaker-k8s-operator-0.1.0
Verify the operator deployment to your namespace

  1. You should be able to see the SageMaker AI Custom Resource Definitions (CRDs) for each operator deployed to your cluster by running the following command: 

    kubectl get crd | grep sagemaker

    Your output should look like the following: 

    batchtransformjobs.sagemaker.aws.amazon.com         2019-11-20T17:12:34Z
endpointconfigs.sagemaker.aws.amazon.com            2019-11-20T17:12:34Z
hostingdeployments.sagemaker.aws.amazon.com         2019-11-20T17:12:34Z
hyperparametertuningjobs.sagemaker.aws.amazon.com   2019-11-20T17:12:34Z
models.sagemaker.aws.amazon.com                     2019-11-20T17:12:34Z
trainingjobs.sagemaker.aws.amazon.com               2019-11-20T17:12:34Z

  2. Ensure that the operator pod is running successfully. Use the following command to list all pods: 

    kubectl -n my-namespace get pods

    You should see a pod named sagemaker-k8s-operator-controller-manager-***** in the namespace my-namespace as follows: 

    NAME                                                         READY   STATUS    RESTARTS   AGE
sagemaker-k8s-operator-controller-manager-12345678-r8abc     2/2     Running   0          23s

Install the SageMaker AI logs kubectl plugin

As part of the SageMaker AI Operators for Kubernetes, you can use the smlogs plugin for kubectl. This allows SageMaker AI CloudWatch logs to be streamed with kubectl. kubectl must be installed onto your PATH. The following commands place the binary in the sagemaker-k8s-bin directory in your home directory, and add that directory to your PATH. 

export os="linux"
  
wget https://amazon-sagemaker-operator-for-k8s-us-east-1.s3.amazonaws.com/kubectl-smlogs-plugin/v1/${os}.amd64.tar.gz
tar xvzf ${os}.amd64.tar.gz
  
# Move binaries to a directory in your homedir.
mkdir ~/sagemaker-k8s-bin
cp ./kubectl-smlogs.${os}.amd64/kubectl-smlogs ~/sagemaker-k8s-bin/.
  
# This line adds the binaries to your PATH in your .bashrc.
  
echo 'export PATH=$PATH:~/sagemaker-k8s-bin' >> ~/.bashrc
  
# Source your .bashrc to update environment variables:
source ~/.bashrc

Use the following command to verify that the kubectl plugin is installed correctly: 

kubectl smlogs

If the kubectl plugin is installed correctly, your output should look like the following: 

View SageMaker AI logs via Kubernetes
  
Usage:
  smlogs [command]
  
Aliases:
  smlogs, SMLogs, Smlogs
  
Available Commands:
  BatchTransformJob       View BatchTransformJob logs via Kubernetes
  TrainingJob             View TrainingJob logs via Kubernetes
  help                    Help about any command
  
Flags:
   -h, --help   help for smlogs
  
Use "smlogs [command] --help" for more information about a command.

Clean up resources

To uninstall the operator from your cluster, you must first make sure to delete all SageMaker AI resources from the cluster. Failure to do so causes the operator delete operation to hang. Run the following commands to stop all jobs: 

# Delete all SageMaker AI jobs from Kubernetes
kubectl delete --all --all-namespaces hyperparametertuningjob.sagemaker.aws.amazon.com
kubectl delete --all --all-namespaces trainingjobs.sagemaker.aws.amazon.com
kubectl delete --all --all-namespaces batchtransformjob.sagemaker.aws.amazon.com
kubectl delete --all --all-namespaces hostingdeployment.sagemaker.aws.amazon.com

You should see output similar to the following: 

$ kubectl delete --all --all-namespaces trainingjobs.sagemaker.aws.amazon.com
trainingjobs.sagemaker.aws.amazon.com "xgboost-mnist-from-for-s3" deleted
  
$ kubectl delete --all --all-namespaces hyperparametertuningjob.sagemaker.aws.amazon.com
hyperparametertuningjob.sagemaker.aws.amazon.com "xgboost-mnist-hpo" deleted
  
$ kubectl delete --all --all-namespaces batchtransformjob.sagemaker.aws.amazon.com
batchtransformjob.sagemaker.aws.amazon.com "xgboost-mnist" deleted
  
$ kubectl delete --all --all-namespaces hostingdeployment.sagemaker.aws.amazon.com
hostingdeployment.sagemaker.aws.amazon.com "host-xgboost" deleted

After you delete all SageMaker AI jobs, see Delete operators to delete the operator from your cluster.

Delete operators

Delete cluster-based operators

Operators installed using YAML

To uninstall the operator from your cluster, make sure that all SageMaker AI resources have been deleted from the cluster. Failure to do so causes the operator delete operation to hang.

Note

Before deleting your cluster, be sure to delete all SageMaker AI resources from the cluster. See Clean up resources for more information.

After you delete all SageMaker AI jobs, use kubectl to delete the operator from the cluster:

# Delete the operator and its resources
kubectl delete -f /installer.yaml

You should see output similar to the following: 

$ kubectl delete -f raw-yaml/installer.yaml
namespace "sagemaker-k8s-operator-system" deleted
customresourcedefinition.apiextensions.k8s.io "batchtransformjobs.sagemaker.aws.amazon.com" deleted
customresourcedefinition.apiextensions.k8s.io "endpointconfigs.sagemaker.aws.amazon.com" deleted
customresourcedefinition.apiextensions.k8s.io "hostingdeployments.sagemaker.aws.amazon.com" deleted
customresourcedefinition.apiextensions.k8s.io "hyperparametertuningjobs.sagemaker.aws.amazon.com" deleted
customresourcedefinition.apiextensions.k8s.io "models.sagemaker.aws.amazon.com" deleted
customresourcedefinition.apiextensions.k8s.io "trainingjobs.sagemaker.aws.amazon.com" deleted
role.rbac.authorization.k8s.io "sagemaker-k8s-operator-leader-election-role" deleted
clusterrole.rbac.authorization.k8s.io "sagemaker-k8s-operator-manager-role" deleted
clusterrole.rbac.authorization.k8s.io "sagemaker-k8s-operator-proxy-role" deleted
rolebinding.rbac.authorization.k8s.io "sagemaker-k8s-operator-leader-election-rolebinding" deleted
clusterrolebinding.rbac.authorization.k8s.io "sagemaker-k8s-operator-manager-rolebinding" deleted
clusterrolebinding.rbac.authorization.k8s.io "sagemaker-k8s-operator-proxy-rolebinding" deleted
service "sagemaker-k8s-operator-controller-manager-metrics-service" deleted
deployment.apps "sagemaker-k8s-operator-controller-manager" deleted
secrets "sagemaker-k8s-operator-abcde" deleted
Operators installed using Helm Charts

To delete the operator CRDs, first delete all the running jobs. Then delete the Helm Chart that was used to deploy the operators using the following commands: 

# get the helm charts
helm ls
  
# delete the charts
helm delete <chart_name>

Delete namespace-based operators

Operators installed with YAML

To uninstall the operator from your cluster, first make sure that all SageMaker AI resources have been deleted from the cluster. Failure to do so causes the operator delete operation to hang.

Note

Before deleting your cluster, be sure to delete all SageMaker AI resources from the cluster. See Clean up resources for more information.

After you delete all SageMaker AI jobs, use kubectl to first delete the operator from the namespace and then the CRDs from the cluster. Run the following commands to delete the operator from the cluster: 

# Delete the operator using the same yaml file that was used to install the operator
kubectl delete -f operator.yaml
  
# Now delete the CRDs using the CRD installer yaml
kubectl delete -f https://raw.githubusercontent.com/aws/amazon-sagemaker-operator-for-k8s/master/release/rolebased/namespaced/crd.yaml
  
# Now you can delete the namespace if you want
kubectl delete namespace <namespace>
Operators installed with Helm Charts

To delete the operator CRDs, first delete all the running jobs. Then delete the Helm Chart that was used to deploy the operators using the following commands: 

# Delete the operator
helm delete <chart_name>
  
# delete the crds
helm delete crds
  
# optionally delete the namespace
kubectl delete namespace <namespace>

Troubleshooting

Debugging a failed job

Use these steps to debug a failed job.

  • Check the job status by running the following: 

    kubectl get <CRD Type> <job name>

  • If the job was created in SageMaker AI, you can use the following command to see the STATUS and the SageMaker Job Name: 

    kubectl get <crd type> <job name>

  • You can use smlogs to find the cause of the issue using the following command: 

    kubectl smlogs <crd type> <job name>

  • You can also use the describe command to get more details about the job using the following command. The output has an additional field that has more information about the status of the job. 

    kubectl describe <crd type> <job name>

  • If the job was not created in SageMaker AI, then use the logs of the operator's pod to find the cause of the issue as follows: 

    $ kubectl get pods -A | grep sagemaker
# Output:
sagemaker-k8s-operator-system   sagemaker-k8s-operator-controller-manager-5cd7df4d74-wh22z   2/2     Running   0          3h33m
  
$ kubectl logs -p <pod name> -c manager -n sagemaker-k8s-operator-system

Deleting an operator CRD

If deleting a job is not working, check if the operator is running. If the operator is not running, then you have to delete the finalizer using the following steps:

  1. In a new terminal, open the job in an editor using kubectl edit as follows: 

    kubectl edit <crd type> <job name>

  2. Edit the job to delete the finalizer by removing the following two lines from the file. Save the file and the job is be deleted. 

    finalizers:
  - sagemaker-operator-finalizer

Images and SMlogs in each Region

The following table lists the available operator images and SMLogs in each Region.

Region Controller Image Linux SMLogs
us-east-1 957583890962.dkr.ecr.us-east-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1 https://s3.us-east-1.amazonaws.com/amazon-sagemaker-operator-for-k8s-us-east-1/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz
us-east-2 922499468684.dkr.ecr.us-east-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1 https://s3.us-east-2.amazonaws.com/amazon-sagemaker-operator-for-k8s-us-east-2/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz
us-west-2 640106867763.dkr.ecr.us-west-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1 https://s3.us-west-2.amazonaws.com/amazon-sagemaker-operator-for-k8s-us-west-2/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz
eu-west-1 613661167059.dkr.ecr.eu-west-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1 https://s3.eu-west-1.amazonaws.com/amazon-sagemaker-operator-for-k8s-eu-west-1/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz