Using Amazon Sagemaker Jobs

To run a job using the Amazon Sagemaker Operators for Kubernetes, you can either apply a YAML file or use the supplied Helm charts.

All operator sample jobs in the following tutorials use sample data taken from a public MNIST dataset. In order to run these samples, download the dataset into your S3 bucket. You can find the dataset in Download the MNIST Dataset.

Contents

• Using Amazon Sagemaker Jobs
  • TrainingJob operator
    • Create a TrainingJob Using a Simple YAML File
    • Create a TrainingJob Using a Helm Chart
      • Create the Training Job
      • Verify Your Training Helm Chart
    • List Training Jobs
      • Training Job Status Values
      • Secondary Status Values
    • Describe a Training Job
    • View Logs from Training Jobs
    • Delete Training Jobs
  • HyperParameterTuningJobs operator
    • Create a HyperParameterTuningJob Using a Simple YAML File
    • Create a HyperParameterTuningJob using a Helm Chart
      • Create the HPO Job
      • Verify Chart Installation
    • List Hyperparameter Tuning Jobs
      • Hyperparameter Tuning Job Status Values
      • Status Counters
      • Best Training Job
      • Spawned Training Jobs
    • Describe a Hyperparameter Tuning Job
    • View Logs from HyperParameterTuning Jobs
    • Delete HyperParameterTuning jobs
  • BatchTransformJobs operator
    • Create a BatchTransformJob Using a Simple YAML File
    • Create a BatchTransformJob Using a Helm Chart
      • Get the Helm installer directory
      • Configure the Helm Chart
      • Create a Batch Transform Job
    • List Batch Transform Jobs
      • Batch Transform Status Values
    • Describe a Batch Transform Job
    • View Logs from Batch Transform Jobs
    • Delete a Batch Transform Job
  • Real-time inference
    • Configure a HostingDeployment Resource
    • Create a HostingDeployment
    • List HostingDeployments
      • HostingDeployment Status Values
    • Describe a Hostingdeployment
    • Invoking the Endpoint
    • Update HostingDeployment
    • Delete the HostingDeployment

TrainingJob operator

Training job operators reconcile your specified training job spec to Amazon SageMaker by launching it for you in Amazon SageMaker. You can learn more about Amazon SageMaker training jobs in the Amazon SageMaker CreateTrainingJob API documentation.

Create a TrainingJob Using a Simple YAML File

Download the sample YAML file for training using the following command:

wget https://github.com/aws/amazon-sagemaker-operator-for-k8s/blob/master/samples/xgboost-mnist-trainingjob.yaml

Edit the xgboost-mnist-trainingjob.yaml file to replace the roleArn parameter with your <sagemaker-execution-role>, and outputPath with your S3 bucket that the Amazon SageMaker execution role has write access to. The roleArn must have permissions so that Amazon SageMaker can access Amazon S3, Amazon CloudWatch, and other services on your behalf. For more information on creating an Amazon SageMaker ExecutionRole, see Amazon SageMaker Roles. Apply the YAML file using the following command:

kubectl apply -f xgboost-mnist-trainingjob.yaml

Create a TrainingJob Using a Helm Chart

You can use Helm Charts to run TrainingJobs.

Clone the github repo to get the source using the following command:

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

Navigate to the amazon-sagemaker-operator-for-k8s/hack/charts/training-jobs/ folder and edit the values.yaml file to replace values like rolearn and outputpath with values that correspond to your account. The RoleARN must have permissions so that Amazon SageMaker can access Amazon S3, Amazon CloudWatch, and other services on your behalf. For more information on creating an Amazon SageMaker ExecutionRole, see Amazon SageMaker Roles.

Create the Training Job

With the roles and S3 buckets replaced with appropriate values in values.yaml, you can create a training job using the following command:

helm install . --generate-name

Your output should look like the following:

NAME: chart-12345678
LAST DEPLOYED: Wed Nov 20 23:35:49 2019
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thanks for installing the sagemaker-k8s-trainingjob.

Verify Your Training Helm Chart

To verify that the Helm Chart was created successfully, run:

helm ls

Your output should look like the following:

NAME                    NAMESPACE       REVISION        UPDATED                                 STATUS          CHART                           APP VERSION
chart-12345678        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-trainingjob-0.1.0
rolebased-12345678    default         1               2019-11-20 23:14:59.6777082 +0000 UTC   deployed        sagemaker-k8s-operator-0.1.0

helm install creates a TrainingJob k8s resource. The operator launches the actual training job in Amazon SageMaker and updates the TrainingJob k8s resource to reflect the status of the job in Amazon SageMaker. You incur charges for Amazon SageMaker resources used during the duration of your job. You do not incur any charges once your job completes or stops.

Note: Amazon SageMaker does not allow you to update a running training job. You cannot edit any parameter and re-apply the file/config. Either change the metadata name or delete the existing job and create a new one. Similar to existing training job operators like TFJob in Kubeflow, update is not supported.

List Training Jobs

Use the following command to list all jobs created using the k8s operator:

kubectl get TrainingJob

The output listing all jobs should look like the following:

kubectl get trainingjobs
NAME                        STATUS       SECONDARY-STATUS   CREATION-TIME          SAGEMAKER-JOB-NAME
xgboost-mnist-from-for-s3   InProgress   Starting           2019-11-20T23:42:35Z   xgboost-mnist-from-for-s3-examplef11eab94e0ed4671d5a8f

A training job continues to be listed after the job has completed or failed. You can remove a TrainingJob job from the list by following the Delete a Training Job steps. Jobs that have completed or stopped do not incur any charges for Amazon SageMaker resources.

Training Job Status Values

The STATUS field can be one of the following values:

• Completed
• InProgress
• Failed
• Stopped
• Stopping

These statuses come directly from the Amazon SageMaker official API documentation.

In addition to the official Amazon SageMaker status, it is possible for STATUS to be SynchronizingK8sJobWithSageMaker. This means that the operator has not yet processed the job.

Secondary Status Values

The secondary statuses come directly from the Amazon SageMaker official API documentation. They contain more granular information about the status of the job.

Describe a Training Job

You can get more details about the training job by using the describe kubectl verb. This is typically used for debugging a problem or checking the parameters of a training job. To get information about your training job, use the following command:

kubectl describe trainingjob xgboost-mnist-from-for-s3

The output for your training job should look like the following:

Name:         xgboost-mnist-from-for-s3
Namespace:    default
Labels:       <none>
Annotations:  <none>
API Version:  sagemaker.aws.amazon.com/v1
Kind:         TrainingJob
Metadata:
  Creation Timestamp:  2019-11-20T23:42:35Z
  Finalizers:
    sagemaker-operator-finalizer
  Generation:        2
  Resource Version:  23119
  Self Link:         /apis/sagemaker.aws.amazon.com/v1/namespaces/default/trainingjobs/xgboost-mnist-from-for-s3
  UID:               6d7uiui-0bef-11ea-b94e-0ed467example
Spec:
  Algorithm Specification:
    Training Image:       8256416981234.dkr.ecr.us-east-2.amazonaws.com/xgboost:1
    Training Input Mode:  File
  Hyper Parameters:
    Name:   eta
    Value:  0.2
    Name:   gamma
    Value:  4
    Name:   max_depth
    Value:  5
    Name:   min_child_weight
    Value:  6
    Name:   num_class
    Value:  10
    Name:   num_round
    Value:  10
    Name:   objective
    Value:  multi:softmax
    Name:   silent
    Value:  0
  Input Data Config:
    Channel Name:      train
    Compression Type:  None
    Content Type:      text/csv
    Data Source:
      S 3 Data Source:
        S 3 Data Distribution Type:  FullyReplicated
        S 3 Data Type:               S3Prefix
        S 3 Uri:                     https://s3-us-east-2.amazonaws.com/my-bucket/sagemaker/xgboost-mnist/train/
    Channel Name:                    validation
    Compression Type:                None
    Content Type:                    text/csv
    Data Source:
      S 3 Data Source:
        S 3 Data Distribution Type:  FullyReplicated
        S 3 Data Type:               S3Prefix
        S 3 Uri:                     https://s3-us-east-2.amazonaws.com/my-bucket/sagemaker/xgboost-mnist/validation/
  Output Data Config:
    S 3 Output Path:  s3://my-bucket/sagemaker/xgboost-mnist/xgboost/
  Region:             us-east-2
  Resource Config:
    Instance Count:     1
    Instance Type:      ml.m4.xlarge
    Volume Size In GB:  5
  Role Arn:             arn:aws:iam::12345678910:role/service-role/AmazonSageMaker-ExecutionRole
  Stopping Condition:
    Max Runtime In Seconds:  86400
  Training Job Name:         xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0example
Status:
  Cloud Watch Log URL:           https://us-east-2.console.aws.amazon.com/cloudwatch/home?region=us-east-2#logStream:group=/aws/sagemaker/TrainingJobs;prefix=<example>;streamFilter=typeLogStreamPrefix
  Last Check Time:               2019-11-20T23:44:29Z
  Sage Maker Training Job Name:  xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94eexample
  Secondary Status:              Downloading
  Training Job Status:           InProgress
Events:                          <none>

View Logs from Training Jobs

Use the following command to see the logs from the kmeans-mnist training job:

kubectl smlogs trainingjob xgboost-mnist-from-for-s3

Your output will look similar to the following. The logs from instances are ordered chronologically.

"xgboost-mnist-from-for-s3" has SageMaker TrainingJobName "xgboost-mnist-from-for-s3-123456789" in region "us-east-2", status "InProgress" and secondary status "Starting"
xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0ed46example/algo-1-1574293123 2019-11-20 23:45:24.7 +0000 UTC Arguments: train
xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0ed46example/algo-1-1574293123 2019-11-20 23:45:24.7 +0000 UTC [2019-11-20:23:45:22:INFO] Running standalone xgboost training.
xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0ed46example/algo-1-1574293123 2019-11-20 23:45:24.7 +0000 UTC [2019-11-20:23:45:22:INFO] File size need to be processed in the node: 1122.95mb. Available memory size in the node: 8586.0mb
xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0ed46example/algo-1-1574293123 2019-11-20 23:45:24.7 +0000 UTC [2019-11-20:23:45:22:INFO] Determined delimiter of CSV input is ','
xgboost-mnist-from-for-s3-6d7fa0af0bef11eab94e0ed46example/algo-1-1574293123 2019-11-20 23:45:24.7 +0000 UTC [23:45:22] S3DistributionType set as FullyReplicated

Delete Training Jobs

Use the following command to stop a training job on Amazon SageMaker:

kubectl delete trainingjob xgboost-mnist-from-for-s3

This command removes the Amazon SageMaker training job from k8s. This command returns the following output:

trainingjob.sagemaker.aws.amazon.com "xgboost-mnist-from-for-s3" deleted

If the job is still in progress on Amazon SageMaker, the job will stop. You do not incur any charges for Amazon SageMaker resources after your job stops or completes.

Note: Amazon SageMaker does not delete training jobs. Stopped jobs continue to show on the Amazon SageMaker console. The delete command takes about 2 minutes to clean up the resources from Amazon SageMaker.

HyperParameterTuningJobs operator

Hyperparameter tuning job operators reconcile your specified hyperparameter tuning job spec to Amazon SageMaker by launching it in Amazon SageMaker. You can learn more about Amazon SageMaker hyperparameter tuning jobs in the Amazon SageMaker CreateHyperParameterTuningJob API documentation.

Create a HyperParameterTuningJob Using a Simple YAML File

Download the sample YAML file for the hyperparameter tuning job using the following command:

wget https://github.com/aws/amazon-sagemaker-operator-for-k8s/blob/master/samples/xgboost-mnist-hpo.yaml

Edit the xgboost-mnist-hpo.yaml file to replace the roleArn parameter with your <sagemaker-execution-role>. For HyperparameterTuningJob to succeed, you must also change the s3InputPath and s3OutputPath to values that correspond to your account. Apply the updates YAML file using the following command:

kubectl apply -f xgboost-mnist-hpo.yaml

Create a HyperParameterTuningJob using a Helm Chart

You can use Helm Charts to run HyperParameterTuningJobs.

Clone the github repo to get the source using the following command:

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

Navigate to the amazon-sagemaker-operator-for-k8s/hack/charts/hyperparameter-tuning-jobs/ folder.

Edit the values.yaml file to replace the roleArn parameter with your <sagemaker-execution-role>. For HyperparameterTuningJob to succeed, you must also change the s3InputPath and s3OutputPath to values that correspond to your account.

Create the HPO Job

With the roles and Amazon S3 paths replaced with appropriate values in values.yaml, you can create a hyperparameter tuning job using the following command:

helm install . --generate-name

Your output will look similar to the following:

NAME: chart-1574292948
LAST DEPLOYED: Wed Nov 20 23:35:49 2019
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thanks for installing the sagemaker-k8s-hyperparametertuningjob.

Verify Chart Installation

To verify that the Helm Chart was created successfully, run the following command:

helm ls

Your output should look like the following:

NAME                    NAMESPACE       REVISION        UPDATED
chart-1474292948        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-hyperparametertuningjob-0.1.0                               STATUS          CHART                           APP VERSION
chart-1574292948        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-trainingjob-0.1.0
rolebased-1574291698    default         1               2019-11-20 23:14:59.6777082 +0000 UTC   deployed        sagemaker-k8s-operator-0.1.0

helm install creates a HyperParameterTuningJob k8s resource. The operator launches the actual hyperparameter optimization job in Amazon SageMaker and updates the HyperParameterTuningJob k8s resource to reflect the status of the job in Amazon SageMaker. You incur charges for Amazon SageMaker resources used during the duration of your job. You do not incur any charges once your job completes or stops.

Note: Amazon SageMaker does not allow you to update a running hyperparameter tuning job. You cannot edit any parameter and re-apply the file/config. You must either change the metadata name or delete the existing job and create a new one. Similar to existing training job operators like TFJob in Kubeflow, update is not supported.

List Hyperparameter Tuning Jobs

Use the following command to list all jobs created using the k8s operator:

kubectl get hyperparametertuningjob

Your output will look like the following:

NAME         STATUS      CREATION-TIME          COMPLETED   INPROGRESS   ERRORS   STOPPED   BEST-TRAINING-JOB                               SAGEMAKER-JOB-NAME
xgboost-mnist-hpo   Completed   2019-10-17T01:15:52Z   10          0            0        0         xgboostha92f5e3cf07b11e9bf6c06d6-009-4c7a123   xgboostha92f5e3cf07b11e9bf6c123

A hyper parameter tuning job will continue to be listed after the job has completed or failed. You can remove a hyperparametertuningjob from the list by following the steps in Delete a Hyper Parameter Tuning Job. Jobs that have completed or stopped do not incur any charges for Amazon SageMaker resources.

Hyperparameter Tuning Job Status Values

The STATUS field can be one of the following values:

• Completed
• InProgress
• Failed
• Stopped
• Stopping

These statuses come directly from the Amazon SageMaker official API documentation.

In addition to the official Amazon SageMaker status, it is possible for STATUS to be SynchronizingK8sJobWithSageMaker. This means that the operator has not yet processed the job.

Status Counters

The output has several counters, like COMPLETED and INPROGRESS. These represent how many training jobs have completed and are in progress, respectively. For more information about how these are determined, see TrainingJobStatusCounters in the Amazon SageMaker API documentation.

Best Training Job

This column contains the name of the TrainingJob that best optimized the selected metric.

To see a summary of the tuned hyperparameters, run:

kubectl describe hyperparametertuningjob xgboost-mnist-hpo

To see detailed information about the TrainingJob, run:

kubectl describe trainingjobs <job name>

Spawned Training Jobs

You can also track all 10 training jobs in k8s launched by HyperparameterTuningJob by running the following command:

kubectl get trainingjobs

Describe a Hyperparameter Tuning Job

You can obtain debugging details using the describe kubectl verb by running the following command.

kubectl describe hyperparametertuningjob xgboost-mnist-hpo

In addition to information about the tuning job, the Amazon SageMaker Operator for Kubernetes also exposes the best training job found by the hyperparameter tuning job in the describe output as follows:

Name:         xgboost-mnist-hpo
Namespace:    default
Labels:       <none>
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"sagemaker.aws.amazon.com/v1","kind":"HyperparameterTuningJob","metadata":{"annotations":{},"name":"xgboost-mnist-hpo","namespace":...
API Version:  sagemaker.aws.amazon.com/v1
Kind:         HyperparameterTuningJob
Metadata:
  Creation Timestamp:  2019-10-17T01:15:52Z
  Finalizers:
    sagemaker-operator-finalizer
  Generation:        2
  Resource Version:  8167
  Self Link:         /apis/sagemaker.aws.amazon.com/v1/namespaces/default/hyperparametertuningjobs/xgboost-mnist-hpo
  UID:               a92f5e3c-f07b-11e9-bf6c-06d6f303uidu
Spec:
  Hyper Parameter Tuning Job Config:
    Hyper Parameter Tuning Job Objective:
      Metric Name:  validation:error
      Type:         Minimize
    Parameter Ranges:
      Integer Parameter Ranges:
        Max Value:     20
        Min Value:     10
        Name:          num_round
        Scaling Type:  Linear
    Resource Limits:
      Max Number Of Training Jobs:     10
      Max Parallel Training Jobs:      10
    Strategy:                          Bayesian
    Training Job Early Stopping Type:  Off
  Hyper Parameter Tuning Job Name:     xgboostha92f5e3cf07b11e9bf6c06d6
  Region:                              us-east-2
  Training Job Definition:
    Algorithm Specification:
      Training Image:       12345678910.dkr.ecr.us-east-2.amazonaws.com/xgboost:1
      Training Input Mode:  File
    Input Data Config:
      Channel Name:  train
      Content Type:  text/csv
      Data Source:
        s3DataSource:
          s3DataDistributionType:  FullyReplicated
          s3DataType:              S3Prefix
          s3Uri:                   https://s3-us-east-2.amazonaws.com/my-bucket/sagemaker/xgboost-mnist/train/
      Channel Name:                validation
      Content Type:                text/csv
      Data Source:
        s3DataSource:
          s3DataDistributionType:  FullyReplicated
          s3DataType:              S3Prefix
          s3Uri:                   https://s3-us-east-2.amazonaws.com/my-bucket/sagemaker/xgboost-mnist/validation/
    Output Data Config:
      s3OutputPath:  https://s3-us-east-2.amazonaws.com/my-bucket/sagemaker/xgboost-mnist/xgboost
    Resource Config:
      Instance Count:     1
      Instance Type:      ml.m4.xlarge
      Volume Size In GB:  5
    Role Arn:             arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole
    Static Hyper Parameters:
      Name:   base_score
      Value:  0.5
      Name:   booster
      Value:  gbtree
      Name:   csv_weights
      Value:  0
      Name:   dsplit
      Value:  row
      Name:   grow_policy
      Value:  depthwise
      Name:   lambda_bias
      Value:  0.0
      Name:   max_bin
      Value:  256
      Name:   max_leaves
      Value:  0
      Name:   normalize_type
      Value:  tree
      Name:   objective
      Value:  reg:linear
      Name:   one_drop
      Value:  0
      Name:   prob_buffer_row
      Value:  1.0
      Name:   process_type
      Value:  default
      Name:   rate_drop
      Value:  0.0
      Name:   refresh_leaf
      Value:  1
      Name:   sample_type
      Value:  uniform
      Name:   scale_pos_weight
      Value:  1.0
      Name:   silent
      Value:  0
      Name:   sketch_eps
      Value:  0.03
      Name:   skip_drop
      Value:  0.0
      Name:   tree_method
      Value:  auto
      Name:   tweedie_variance_power
      Value:  1.5
    Stopping Condition:
      Max Runtime In Seconds:  86400
Status:
  Best Training Job:
    Creation Time:  2019-10-17T01:16:14Z
    Final Hyper Parameter Tuning Job Objective Metric:
      Metric Name:        validation:error
      Value:
    Objective Status:     Succeeded
    Training End Time:    2019-10-17T01:20:24Z
    Training Job Arn:     arn:aws:sagemaker:us-east-2:123456789012:training-job/xgboostha92f5e3cf07b11e9bf6c06d6-009-4sample
    Training Job Name:    xgboostha92f5e3cf07b11e9bf6c06d6-009-4c7a3059
    Training Job Status:  Completed
    Training Start Time:  2019-10-17T01:18:35Z
    Tuned Hyper Parameters:
      Name:                                    num_round
      Value:                                   18
  Hyper Parameter Tuning Job Status:           Completed
  Last Check Time:                             2019-10-17T01:21:01Z
  Sage Maker Hyper Parameter Tuning Job Name:  xgboostha92f5e3cf07b11e9bf6c06d6
  Training Job Status Counters:
    Completed:            10
    In Progress:          0
    Non Retryable Error:  0
    Retryable Error:      0
    Stopped:              0
    Total Error:          0
Events:                   <none>

View Logs from HyperParameterTuning Jobs

Hyperparameter tuning jobs do not have logs, but all training jobs launched by them do have logs. These logs can be accessed as if they were a normal training job. For more information, see View Logs from Training Jobs.

Delete HyperParameterTuning jobs

Use the following command to stop a hyperparameter job in Amazon SageMaker.

kubectl delete hyperparametertuningjob xgboost-mnist-hpo

This command removes the hyperparameter tuning job and associated training jobs from your Kubernetes cluster, as well as stops them in Amazon SageMaker. Jobs that have stopped or completed do not incur any charges for Amazon SageMaker resources. Amazon SageMaker does not delete hyperparameter tuning jobs. Stopped jobs continue to show on the Amazon SageMaker Console.

Your output should look like the following:

hyperparametertuningjob.sagemaker.aws.amazon.com "xgboost-mnist-hpo" deleted

Note: The delete command takes about 2 minutes to clean up the resources from Amazon SageMaker.

BatchTransformJobs operator

Batch transform job operators reconcile your specified batch transform job spec to Amazon SageMaker by launching it in Amazon SageMaker. You can learn more about Amazon SageMaker batch transform job in the Amazon SageMaker CreateTransformJob API documentation.

Create a BatchTransformJob Using a Simple YAML File

Download the sample YAML file for the batch transform job using the following command:

wget https://github.com/aws/amazon-sagemaker-operator-for-k8s/blob/master/samples/xgboost-mnist-batchtransform.yaml

Edit the file xgboost-mnist-batchtransform.yaml to change necessary parameters to replace the inputdataconfig with your input data and s3OutputPath with your S3 buckets that the Amazon SageMaker execution role has write access to.

Apply the YAML file using the following command:

kubectl apply -f xgboost-mnist-batchtransform.yaml

Create a BatchTransformJob Using a Helm Chart

You can use Helm Charts to run batch transform jobs.

Get the Helm installer directory

Clone the github repo to get the source using the following command:

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

Configure the Helm Chart

Navigate to the amazon-sagemaker-operator-for-k8s/hack/charts/batch-transform-jobs/ folder.

Edit the values.yaml file to replace the inputdataconfig with your input data and outputPath with your S3 buckets that the Amazon SageMaker execution role has write access to.

Create a Batch Transform Job

Use the following command to create a batch transform job:

helm install . --generate-name

Your output should look like the following:

NAME: chart-1574292948
LAST DEPLOYED: Wed Nov 20 23:35:49 2019
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
Thanks for installing the sagemaker-k8s-batch-transform-job.

To verify that the Helm Chart was created successfully, run the following command:

helm ls
NAME                    NAMESPACE       REVISION        UPDATED                                 STATUS          CHART                           APP VERSION
chart-1474292948        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-batchtransformjob-0.1.0
chart-1474292948        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-hyperparametertuningjob-0.1.0
chart-1574292948        default         1               2019-11-20 23:35:49.9136092 +0000 UTC   deployed        sagemaker-k8s-trainingjob-0.1.0
rolebased-1574291698    default         1               2019-11-20 23:14:59.6777082 +0000 UTC   deployed        sagemaker-k8s-operator-0.1.0

The previous command creates a BatchTransformJob k8s resource. The operator launches the actual transform job in Amazon SageMaker and updates the BatchTransformJob k8s resource to reflect the status of the job in Amazon SageMaker. You incur charges for Amazon SageMaker resources used during the duration of your job. You do not incur any charges once your job completes or stops.

Note: Amazon SageMaker does not allow you to update a running batch transform job. You cannot edit any parameter and re-apply the file/config. You must either change the metadata name or delete the existing job and create a new one. Similar to existing training job operators like TFJob in Kubeflow, update is not supported.

List Batch Transform Jobs

Use the following command to list all jobs created using the k8s operator:

kubectl get batchtransformjob

Your output should look like the following:

NAME                                STATUS      CREATION-TIME          SAGEMAKER-JOB-NAME
xgboost-mnist-batch-transform       Completed   2019-11-18T03:44:00Z   xgboost-mnist-a88fb19809b511eaac440aa8axgboost

A batch transform job will continue to be listed after the job has completed or failed. You can remove a hyperparametertuningjob from the list by following the Delete a Batch Transform Job steps. Jobs that have completed or stopped do not incur any charges for Amazon SageMaker resources.

Batch Transform Status Values

The STATUS field can be one of the following values:

• Completed
• InProgress
• Failed
• Stopped
• Stopping

These statuses come directly from the Amazon SageMaker official API documentation.

In addition to the official Amazon SageMaker status, it is possible for STATUS to be SynchronizingK8sJobWithSageMaker. This means that the operator has not yet processed the job and will get to it soon.

Describe a Batch Transform Job

You can obtain debugging details using the describe kubectl verb by running the following command.

kubectl describe batchtransformjob xgboost-mnist-batch-transform

Your output should look like the following:

Name:         xgboost-mnist-batch-transform
Namespace:    default
Labels:       <none>
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"sagemaker.aws.amazon.com/v1","kind":"BatchTransformJob","metadata":{"annotations":{},"name":"xgboost-mnist","namespace"...
API Version:  sagemaker.aws.amazon.com/v1
Kind:         BatchTransformJob
Metadata:
  Creation Timestamp:  2019-11-18T03:44:00Z
  Finalizers:
    sagemaker-operator-finalizer
  Generation:        2
  Resource Version:  21990924
  Self Link:         /apis/sagemaker.aws.amazon.com/v1/namespaces/default/batchtransformjobs/xgboost-mnist
  UID:               a88fb198-09b5-11ea-ac44-0aa8a9UIDNUM
Spec:
  Model Name:  TrainingJob-20190814SMJOb-IKEB
  Region:      us-east-1
  Transform Input:
    Content Type:  text/csv
    Data Source:
      S 3 Data Source:
        S 3 Data Type:  S3Prefix
        S 3 Uri:        s3://my-bucket/mnist_kmeans_example/input
  Transform Job Name:   xgboost-mnist-a88fb19809b511eaac440aa8a9SMJOB
  Transform Output:
    S 3 Output Path:  s3://my-bucket/mnist_kmeans_example/output
  Transform Resources:
    Instance Count:  1
    Instance Type:   ml.m4.xlarge
Status:
  Last Check Time:                2019-11-19T22:50:40Z
  Sage Maker Transform Job Name:  xgboost-mnist-a88fb19809b511eaac440aaSMJOB
  Transform Job Status:           Completed
Events:                           <none>

View Logs from Batch Transform Jobs

Use the following command to see the logs from the xgboost-mnist batch transform job:

kubectl smlogs batchtransformjob xgboost-mnist-batch-transform

Delete a Batch Transform Job

Use the following command to stop a batch transform job in Amazon SageMaker.

kubectl delete batchTransformJob xgboost-mnist-batch-transform

Your output will look like the following:

batchtransformjob.sagemaker.aws.amazon.com "xgboost-mnist" deleted

This command removes the batch transform job from your Kubernetes cluster, as well as stops them in Amazon SageMaker. Jobs that have stopped or completed do not incur any charges for Amazon SageMaker resources. Delete takes about 2 minutes to clean up the resources from Amazon SageMaker.

Note: Amazon SageMaker does not delete batch transform jobs. Stopped jobs continue to show on the Amazon SageMaker console.

Real-time inference

HostingDeployments support creating and deleting an endpoint, as well as updating an existing endpoint. The hosting deployment operator reconciles your specified hosting deployment job spec to Amazon SageMaker by creating models, endpoint-configs and endpoints in Amazon SageMaker. You can learn more about Amazon SageMaker inference in the Amazon SageMaker CreateEndpoint API documentaiton.

Configure a HostingDeployment Resource

Download the sample YAML file for the hosting deployment job using the following command:

wget https://github.com/aws/amazon-sagemaker-operator-for-k8s/blob/master/samples/xgboost-mnist-hostingdeployment.yaml

The xgboost-mnist-hostingdeployment.yaml file has the following components that can be edited as required:

• ProductionVariants. A production variant is a set of instances serving a single model. Amazon SageMaker will load-balance between all production variants according to set weights.
• Models. A model is the containers and execution role ARN necessary to serve a model. It requires at least a single container.
• Containers. A container specifies the dataset and serving image. If you are using your own custom algorithm instead of an algorithm provided by Amazon SageMaker, the inference code must meet Amazon SageMaker requirements. For more information, see Using Your Own Algorithms with Amazon SageMaker.

Create a HostingDeployment

To create a HostingDeployment, use kubectl to apply the file hosting.yaml with the following command:

kubectl apply -f hosting.yaml

Amazon SageMaker create an endpoint with the specified configuration. You incur charges for Amazon SageMaker resources used during the lifetime of your endpoint. You do not incur any charges once your endpoint is deleted.

The creation process will take approximately 10 minutes.

List HostingDeployments

To verify that the HostingDeployment was created, use the following command:

kubectl get hostingdeployments

Your output should look like the following:

NAME           STATUS     SAGEMAKER-ENDPOINT-NAME
host-xgboost   Creating   host-xgboost-def0e83e0d5f11eaaa450aSMLOGS

HostingDeployment Status Values

The status field can be one of several values:

• SynchronizingK8sJobWithSageMaker: The operator is preparing to create the endpoint.
• ReconcilingEndpoint: The operator is creating, updating, or deleting endpoint resources. If the HostingDeployment remains in this state, use kubectl describe to see the reason in the Additional field.
• OutOfService: Endpoint is not available to take incoming requests.
• Creating: CreateEndpoint is executing.
• Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.
• SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.
• RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.
• InService: Endpoint is available to process incoming requests.
• Deleting: DeleteEndpoint is executing.
• Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpoint:FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.

Describe a Hostingdeployment

You can obtain debugging details using the describe kubectl verb by running the following command.

kubectl describe hostingdeployment

Your output should look like the following:

Name:         host-xgboost
Namespace:    default
Labels:       <none>
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"sagemaker.aws.amazon.com/v1","kind":"HostingDeployment","metadata":{"annotations":{},"name":"host-xgboost","namespace":"def..."
API Version:  sagemaker.aws.amazon.com/v1
Kind:         HostingDeployment
Metadata:
  Creation Timestamp:  2019-11-22T19:40:00Z
  Finalizers:
    sagemaker-operator-finalizer
  Generation:        1
  Resource Version:  4258134
  Self Link:         /apis/sagemaker.aws.amazon.com/v1/namespaces/default/hostingdeployments/host-xgboost
  UID:               def0e83e-0d5f-11ea-aa45-0a3507uiduid
Spec:
  Containers:
    Container Hostname:  xgboost
    Image:               123456789012.dkr.ecr.us-east-2.amazonaws.com/xgboost:latest
    Model Data URL:      s3://my-bucket/inference/xgboost-mnist/model.tar.gz
  Models:
    Containers:
      xgboost
    Execution Role Arn:  arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole
    Name:                xgboost-model
    Primary Container:   xgboost
  Production Variants:
    Initial Instance Count:  1
    Instance Type:           ml.c5.large
    Model Name:              xgboost-model
    Variant Name:            all-traffic
  Region:                    us-east-2
Status:
  Creation Time:         2019-11-22T19:40:04Z
  Endpoint Arn:          arn:aws:sagemaker:us-east-2:123456789012:endpoint/host-xgboost-def0e83e0d5f11eaaaexample
  Endpoint Config Name:  host-xgboost-1-def0e83e0d5f11e-e08f6c510d5f11eaaa450aexample
  Endpoint Name:         host-xgboost-def0e83e0d5f11eaaa450a350733ba06
  Endpoint Status:       Creating
  Endpoint URL:          https://runtime.sagemaker.us-east-2.amazonaws.com/endpoints/host-xgboost-def0e83e0d5f11eaaaexample/invocations
  Last Check Time:       2019-11-22T19:43:57Z
  Last Modified Time:    2019-11-22T19:40:04Z
  Model Names:
    Name:   xgboost-model
    Value:  xgboost-model-1-def0e83e0d5f11-df5cc9fd0d5f11eaaa450aexample
Events:     <none>

The status field provides more information using the following fields:

• Additional: Additional information about the status of the hosting deployment. This field is optional and only gets populated in case of error.
• Creation Time: When the endpoint was created in Amazon SageMaker.
• Endpoint ARN: The Amazon SageMaker endpoint ARN.
• Endpoint Config Name: The Amazon SageMaker name of the endpoint configuration.
• Endpoint Name: The Amazon SageMaker name of the endpoint.
• Endpoint Status: The Status of the endpoint.
• Endpoint URL: The HTTPS URL that can be used to access the endpoint. For more information, see Deploy a Model on Amazon SageMaker Hosting Services.
• FailureReason: If a create, update, or delete command fails, the cause will be shown here.
• Last Check Time: The last time the operator checked the status of the endpoint.
• Last Modified Time: The last time the endpoint was modified.
• Model Names: A key-value pair of HostingDeployment model names to Amazon SageMaker model names.

Invoking the Endpoint

Once the endpoint status is InService, you can invoke the endpoint in two ways: using the AWS CLI, which does authentication and URL request signing, or using an HTTP client like curl. If you use your own client, you will need to do AWSv4 URL signing and authentication on your own.

To invoke the endpoint using the AWS CLI, run the following command. Make sure to replace the Region and endpoint-name with your endpoint’s Region and Amazon SageMaker endpoint name. This information can be obtained from the output of kubectl describe.

# Invoke the endpoint with mock input data.
aws sagemaker-runtime invoke-endpoint \
  --region us-east-2 \
  --endpoint-name <endpoint name> \
  --body $(seq 784 | xargs echo | sed 's/ /,/g') \
  >(cat) \
  --content-type text/csv > /dev/null

For example, if your Region were us-east-2 and your endpoint config name were host-xgboost-f56b6b280d7511ea824b129926example, then the following command would invoke the endpoint:

aws sagemaker-runtime invoke-endpoint \
  --region us-east-2 \
  --endpoint-name host-xgboost-f56b6b280d7511ea824b1299example \
  --body $(seq 784 | xargs echo | sed 's/ /,/g') \
  >(cat) \
  --content-type text/csv > /dev/null
4.95847082138

Here, 4.95847082138 is the prediction from the model for the mock data.

Update HostingDeployment

Once a HostingDeployment has a status of InService, it can be updated. It might take about 10 minutes for HostingDeployment to be in service. To verify that the status is InService, use the following command:

kubectl get hostingdeployments

The HostingDeployment can be updated before the status is InService. The operator will wait until the Amazon SageMaker endpoint is InService before applying the update.

To apply an update, modify the hosting.yaml file. For example, change the initialInstanceCount field from 1 to 2 as follows:

apiVersion: sagemaker.aws.amazon.com/v1
kind: HostingDeployment
metadata:
  name: host-xgboost
spec:
    region: us-east-2
    productionVariants:
        - variantName: all-traffic
          modelName: xgboost-model
          initialInstanceCount: 2
          instanceType: ml.c5.large
    models:
        - name: xgboost-model
          executionRoleArn: arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole
          primaryContainer: xgboost
          containers:
            - xgboost
    containers:
        - containerHostname: xgboost
          modelDataUrl: s3://my-bucket/inference/xgboost-mnist/model.tar.gz
          image: 123456789012.dkr.ecr.us-east-2.amazonaws.com/xgboost:latest

Save the file, then use kubectl to apply your update as follows. You should see the status change from InService to ReconcilingEndpoint, then Updating.

$ kubectl apply -f hosting.yaml
hostingdeployment.sagemaker.aws.amazon.com/host-xgboost configured

$ kubectl get hostingdeployments
NAME           STATUS                SAGEMAKER-ENDPOINT-NAME
host-xgboost   ReconcilingEndpoint   host-xgboost-def0e83e0d5f11eaaa450a350abcdef

$ kubectl get hostingdeployments
NAME           STATUS     SAGEMAKER-ENDPOINT-NAME
host-xgboost   Updating   host-xgboost-def0e83e0d5f11eaaa450a3507abcdef

Amazon SageMaker deploys a new set of instances with your models, switches traffic to use the new instances, and drains the old instances. As soon as this process begins, the status becomes Updating. After the update is complete, your endpoint becomes InService. This process takes approximately 10 minutes.

Delete the HostingDeployment

Use kubectl to delete a HostingDeployment with the following command:

kubectl delete hostingdeployments host-xgboost

Your output should look like the following:

hostingdeployment.sagemaker.aws.amazon.com "host-xgboost" deleted

To verify that the hosting deployment has been deleted, use the following command:

kubectl get hostingdeployments
No resources found.

Endpoints that have been deleted do not incur any charges for Amazon SageMaker resources.