Wallaroo MLOps API Essentials Guide: Pipeline Management

How to use the Wallaroo API for Pipeline Management

Pipeline Naming Requirements

Pipeline names map onto Kubernetes objects, and must be DNS compliant. Pipeline names must be ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.

Pipeline Management

Pipelines are managed through the Wallaroo API or the Wallaroo SDK. Pipelines are the vehicle used for deploying, serving, and monitoring ML models. For more information, see the Wallaroo Glossary.

Create Pipeline in a Workspace

  • Endpoint: /v1/api/pipelines/create

Creates a new pipeline in the specified workspace.

Create Pipeline in a Workspace Parameters

Field TypeDescription
pipeline_id String (Required)Name of the new pipeline.
workspace_id Integer (Required)Numerical id of the workspace for the new pipeline.
definition String (Required)Pipeline definitions, can be {} for none. This is where the pipeline steps are set.
 stepsList[steps]The pipeline steps to add to the pipeline.
Model Inference Pipeline Step

Pipeline steps from models follow the following schema.

{
  "ModelInference": {
    "models": [
      {
        "name": "{name of model: String}",
        "version": {model version: Integer},
        "sha": "{model sha: String}"
      }
    ]
  }
}

Create Pipeline in a Workspace Returns

FieldTypeDescription
pipeline_pk_idIntegerThe pipeline id.
pipeline_variant_pk_idIntegerThe pipeline version id.
pipeline_variant_versionStringThe pipeline version UUID identifier.

Create Pipeline in a Workspace Examples

Two pipelines are created in the workspace id 10. This assumes that the workspace is created and has models uploaded to it.

One pipeline is an empty pipeline without any models.

For the other pipeline, sample models are uploaded then added pipeline.

The pipeline id, variant id, and variant version of each pipeline will be stored for later examples.

Create empty pipeline via Requests.

# Create pipeline in a workspace
# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/pipelines/create"

example_workspace_id = 10

data = {
  "pipeline_id": "api-empty-pipeline",
  "workspace_id": example_workspace_id,
  "definition": {}
}

response = requests.post(endpoint, 
                         json=data, 
                         headers=headers, 
                         verify=True).json()

empty_pipeline_id = response['pipeline_pk_id']
empty_pipeline_variant_id=response['pipeline_variant_pk_id']
example_pipeline_variant_version=['pipeline_variant_version']
display(json.dumps(response))

    '{"pipeline_pk_id": 25, "pipeline_variant_pk_id": 26, "pipeline_variant_version": "c29a277a-10b9-48f9-a738-aafb296df8c2"}'

Create empty pipeline via curl.

curl {wl.api_endpoint}/v1/api/pipelines/create \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{"pipeline_id": "api-empty-pipeline", "workspace_id": {example_workspace_id},"definition": {{}} }}'

{"pipeline_pk_id":25,"pipeline_variant_pk_id":27,"pipeline_variant_version":"f6241f32-85a8-4ad8-9e71-da2763717811"}

Create pipeline with model steps via Requests.

# Create pipeline in a workspace with models

# First upload a model
# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/models/upload_and_convert"

workspace_id = 10

framework='onnx'

example_model_name = f"api-sample-model"

metadata = {
    "name": example_model_name,
    "visibility": "public",
    "workspace_id": workspace_id,
    "conversion": {
        "framework": framework,
        "python_version": "3.8",
        "requirements": []
    }
}

files = {
    "metadata": (None, json.dumps(metadata), "application/json"),
    'file': (example_model_name, open('./models/ccfraud.onnx', 'rb'), "application/octet-stream")
    }

response = requests.post(endpoint, files=files, headers=headers).json()

example_model_id = response['insert_models']['returning'][0]['models'][0]['id']

# Second, get the model version

# Retrieve the token 
headers = wl.auth.auth_header()
endpoint = f"{wl.api_endpoint}/v1/api/models/list_versions"

data = {
  "model_id": example_model_name,
  "models_pk_id": example_model_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
example_model_sha = response[-1]['sha']
example_model_version = response[-1]['model_version']

# Now create the pipeline with the new model
# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/pipelines/create"

data = {
  "pipeline_id": "api-pipeline-with-models",
  "workspace_id": example_workspace_id,
  "definition": {
      'steps': [
          {
            'ModelInference': 
              {
                  'models': 
                    [
                        {
                            'name': example_model_name, 
                            'version': example_model_version, 
                            'sha': example_model_sha
                        }
                    ]
              }
          }
        ]
      }
    }

response = requests.post(endpoint, 
                         json=data, 
                         headers=headers, 
                         verify=True).json()
display(json.dumps(response))

# saved for later steps

model_pipeline_id = response['pipeline_pk_id']
model_pipeline_variant_id=response['pipeline_variant_pk_id']
model_pipeline_variant_version=['pipeline_variant_version']

'{"pipeline_pk_id": 28, "pipeline_variant_pk_id": 29, "pipeline_variant_version": "5d0326fa-6753-4252-bb56-3b2106a8c671"}'

Create pipeline with model steps via curl.

curl {wl.api_endpoint}/v1/api/pipelines/create \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
    "pipeline_id": "api-pipeline-with-models", \
    "workspace_id": {example_workspace_id}, \
    "definition": {{ \
        "steps": [ \
            {{ \
              "ModelInference": \
                {{ \
                    "models": \
                      [ \
                          {{ \
                              "name": "{example_model_name}", \
                              "version": "{example_model_version}", \
                              "sha": "{example_model_sha}" \
                          }} \
                      ] \
                }} \
            }} \
          ] \
        }} \
      }}'

{
  "pipeline_pk_id": 28,
  "pipeline_variant_pk_id": 30,
  "pipeline_variant_version": "82148e63-3950-4ec4-99f7-b9a22212bfdf"
}

GPU Support

Wallaroo 2023.2.1 and above supports Kubernetes nodepools with Nvidia Cuda GPUs.

See the Create GPU Nodepools for Kubernetes Clusters guide for instructions on adding GPU enabled nodepools to a Kubernetes cluster.

Deploy a Pipeline

  • Endpoint: /v1/api/pipelines/deploy

Deploy a an existing pipeline. Note that for any pipeline that has model steps, they must be included either in model_configs, model_ids or models.

Deploy a Pipeline Parameters

Field TypeDescription
deploy_id String (REQUIRED)The name for the pipeline deployment. This must match the name of the pipeline being deployed.
engine_config String (OPTIONAL)Additional configuration options for the pipeline.
pipeline_version_pk_id Integer REQUIRED)Pipeline version id.
model_configs List[Integer] (OPTIONAL)Ids of model configs to apply.
model_ids List[Integer] (OPTIONAL)Ids of models to apply to the pipeline. If passed in, model_configs will be created automatically.
models List[models] (OPTIONAL)If the model ids are not available as a pipeline step, the models’ data can be passed to it through this method. The options below are only required if models are provided as a parameter.
 nameString (REQUIRED)Name of the uploaded model that is in the same workspace as the pipeline.
 versionString (REQUIRED)Version of the model to use.
 shaString (REQUIRED)SHA value of the model.
pipeline_id *Integer (REQUIRED)Numerical value of the pipeline to deploy.

Deploy a Pipeline Returns

FieldTypeDescription
idIntegerThe deployment id.

Deploy a Pipeline Returns

The pipeline with models created in the step Create Pipeline in a Workspace will be deployed and their deployment information saved for later examples.

Deploy pipeline via Requests.

# Deploy a pipeline with models

# Retrieve the token 
headers = wl.auth.auth_header()
endpoint = f"{wl.api_endpoint}/v1/api/pipelines/deploy"

# verify this matches the pipeline with model created earlier
pipeline_with_models_id = "api-pipeline-with-models"

data = {
    "deploy_id": pipeline_with_models_id,
    "pipeline_version_pk_id": model_pipeline_variant_id,
    "models": [
        {
            "name": example_model_name,
            "version":example_model_version,
            "sha":example_model_sha
        }
    ],
    "pipeline_id": model_pipeline_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
display(response)
model_deployment_id=response['id']

{'id': 14}

Deploy pipeline via curl.

curl {wl.api_endpoint}/v1/api/pipelines/deploy \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
        "deploy_id": "{pipeline_with_models_id}", \
        "pipeline_version_pk_id": {model_pipeline_variant_id}, \
        "models": [ \
            {{ \
                "name": "{example_model_name}", \
                "version": "{example_model_version}", \
                "sha": "{example_model_sha}" \
            }} \
        ], \
        "pipeline_id": {model_pipeline_id} \
    }}'

{"id":14}

Get Deployment Status

  • Endpoint: /v1/api/status/get_deployment

Get Deployment Status Parameters

FieldTypeDescription
idString (Required)The deployment in the format {deployment_name}-{deployment-id}.

Get Deployment Status Returns

FieldTypeDescription
statusStringStatus of the pipeline deployment. Values are: Running: the Deployment successfully started. Starting: The Deployment is still loading. Error: There is an error with the deployment.
detailsList[details]The list of deployment details.
enginesList[engines]A list of engines deployed in the pipeline.
engine_lbsList[engine_lbs]A list of engine load balancers.
sidekicksList[sidekicks]A list of deployment sidekicks. These are used for Containerized Deployment Runtimes.

Get Deployment Status Examples

The deployed pipeline with model details from the previous step is displayed.

Get deployment status via Requests.

# Retrieve the token 
headers = wl.auth.auth_header()

# Get model pipeline deployment

endpoint = f"{wl.api_endpoint}/v1/api/status/get_deployment"

data = {
  "name": f"{pipeline_with_models_id}-{model_deployment_id}"
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

    {'status': 'Running',
     'details': [],
     'engines': [{'ip': '10.244.3.145',
       'name': 'engine-797d8958d9-fsszh',
       'status': 'Running',
       'reason': None,
       'details': [],
       'pipeline_statuses': {'pipelines': [{'id': 'api-pipeline-with-models',
          'status': 'Running'}]},
       'model_statuses': {'models': [{'name': 'api-sample-model',
          'version': 'bdfc8c60-b5bc-4c0e-aa87-157cd52895b6',
          'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
          'status': 'Running'}]}}],
     'engine_lbs': [{'ip': '10.244.4.157',
       'name': 'engine-lb-584f54c899-qfjgp',
       'status': 'Running',
       'reason': None,
       'details': []}],
     'sidekicks': []}

Get deployment status via curl.

curl {wl.api_endpoint}/v1/api/status/get_deployment \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
        "name": "{pipeline_with_models_id}-{model_deployment_id}" \
}}'

{
  "status": "Running",
  "details": [],
  "engines": [
    {
      "ip": "10.244.3.145",
      "name": "engine-797d8958d9-fsszh",
      "status": "Running",
      "reason": null,
      "details": [],
      "pipeline_statuses": {
        "pipelines": [{ "id": "api-pipeline-with-models", "status": "Running" }]
      },
      "model_statuses": {
        "models": [
          {
            "name": "api-sample-model",
            "version": "bdfc8c60-b5bc-4c0e-aa87-157cd52895b6",
            "sha": "bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507",
            "status": "Running"
          }
        ]
      }
    }
  ],
  "engine_lbs": [
    {
      "ip": "10.244.4.157",
      "name": "engine-lb-584f54c899-qfjgp",
      "status": "Running",
      "reason": null,
      "details": []
    }
  ],
  "sidekicks": []
}

Target Architecture for ARM Deployment

Model deployments inherit the architecture of the model. No additional deployment configurations are required to set the architecture.

Note that that architecture must be available in the cluster Wallaroo is deployed on. For instructions on adding ARM nodepools to a cluster, see Create ARM Nodepools for Kubernetes Clusters.

The following example shows a model uploaded with the architecture set to arm, and the deployment configuration inheriting that setting.

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/models/upload_and_convert"

workspace_id = 10

framework='onnx'

model_name = f"api-sample-model"

metadata = {
    "name": model_name,
    "visibility": "public",
    "workspace_id": workspace_id,
    "conversion": {
        "framework": framework,
        "python_version": "3.8",
        "requirements": [],
        "arch": "arm"
    }
}

files = {
    "metadata": (None, json.dumps(metadata), "application/json"),
    'file': (model_name, open('./models/ccfraud.onnx', 'rb'), "application/octet-stream")
    }

response = requests.post(endpoint, files=files, headers=headers).json()

# Create the pipeline and set the model. Note that the architecture setting is not required to deploy on ARM nodepools

# Retrieve the token 
headers = wl.auth.auth_header()
endpoint = f"{wl.api_endpoint}/v1/api/pipelines/deploy"

# verify this matches the pipeline with model created earlier
pipeline_with_models_id = "api-pipeline-with-models"

data = {
    "deploy_id": pipeline_with_models_id,
    "pipeline_version_pk_id": model_pipeline_variant_id,
    "models": [
        {
            "name": model_name,
            "version":example_model_version,
            "sha":example_model_sha
        }
    ],
    "pipeline_id": model_pipeline_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
display(response)
model_deployment_id=response['id']

## deploy the pipeline.  No additional deployment configurations are required to support the architecture

# Retrieve the token 
headers = wl.auth.auth_header()
endpoint = f"{wl.api_endpoint}/v1/api/pipelines/deploy"

# verify this matches the pipeline with model created earlier
pipeline_with_models_id = "api-pipeline-with-models"

data = {
    "deploy_id": pipeline_with_models_id,
    "pipeline_version_pk_id": model_pipeline_variant_id,
    "models": [
        {
            "name": model_name,
            "version":example_model_version,
            "sha":example_model_sha
        }
    ],
    "pipeline_id": model_pipeline_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()

Get External Inference URL

  • Endpoint: /v1/api/admin/get_pipeline_external_url

Retrieves the external inference URL for a specific pipeline in a workspace.

Get External Inference URL Parameters

FieldTypeDescription
workspace_idInteger (*REQUIRED)The workspace integer id.
pipeline_nameString (REQUIRED)The name of the deployment.

Get External Inference URL Returns

FieldTypeDescription
urlStringThe pipeline’s external inference URL.

Get External Inference URL Examples

In this example, the pipeline’s external inference URL from the previous example is retrieved.

Get external inference url via Requests.

## Retrieve the pipeline's External Inference URL

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/admin/get_pipeline_external_url"

data = {
    "workspace_id": example_workspace_id,
    "pipeline_name": pipeline_with_models_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
print(response)
deployurl = response['url']

{'url': 'https://doc-test.wallarooexample.ai/v1/api/pipelines/infer/api-pipeline-with-models-14/api-pipeline-with-models'}

Get external inference url via Requests.

curl {wl.api_endpoint}/v1/api/admin/get_pipeline_external_url \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
        "workspace_id": {example_workspace_id}, \
        "pipeline_name": "{pipeline_with_models_id}" \
}}'

{"url":"https://doc-test.wallarooexample.ai/v1/api/pipelines/infer/api-pipeline-with-models-14/api-pipeline-with-models"}

Perform Inference Through External URL

The inference can now be performed through the External Inference URL. This URL will accept the same inference data file that is used with the Wallaroo SDK, or with an Internal Inference URL as used in the Internal Pipeline Inference URL Tutorial.

Deployed pipelines have their own Inference URL that accepts HTTP POST submissions.

For connections that are external to the Kubernetes cluster hosting the Wallaroo instance, model endpoints must be enabled.

HTTP Headers

The following headers are required for connecting the the Pipeline Deployment URL:

  • Authorization: This requires the JWT token in the format 'Bearer ' + token. For example:

    Authorization: Bearer abcdefg==

  • Content-Type:

  • For DataFrame formatted JSON:

    Content-Type:application/json; format=pandas-records

  • For Arrow binary files, the Content-Type is application/vnd.apache.arrow.file.

    Content-Type:application/vnd.apache.arrow.file

  • IMPORTANT NOTE: Verify that the pipeline deployed has status Running before attempting an inference.

Perform inference via external url via Requests.

# Retrieve the token
headers = wl.auth.auth_header()

# set Content-Type type
headers['Content-Type']='application/json; format=pandas-records'

## Inference through external URL using dataframe

# retrieve the json data to submit
data = [
    {
        "dense_input":[
            1.0678324729,
            0.2177810266,
            -1.7115145262,
            0.682285721,
            1.0138553067,
            -0.4335000013,
            0.7395859437,
            -0.2882839595,
            -0.447262688,
            0.5146124988,
            0.3791316964,
            0.5190619748,
            -0.4904593222,
            1.1656456469,
            -0.9776307444,
            -0.6322198963,
            -0.6891477694,
            0.1783317857,
            0.1397992467,
            -0.3554220649,
            0.4394217877,
            1.4588397512,
            -0.3886829615,
            0.4353492889,
            1.7420053483,
            -0.4434654615,
            -0.1515747891,
            -0.2668451725,
            -1.4549617756
        ]
    }
]

# submit the request via POST, import as pandas DataFrame
response = pd.DataFrame.from_records(
    requests.post(
        deployurl, 
        json=data, 
        headers=headers)
        .json()
    )

display(response)
timeinoutcheck_failuresmetadata
01701376879347{'dense_input': [1.0678324729, 0.2177810266, -...{'dense_1': [0.0014974177]}[]{'last_model': '{"model_name":"api-sample-mode...

Perform inference via external url via curl.

curl {deployurl} \
    -H "Content-Type: application/json; format=pandas-records" \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    --data '[ \
    {{ \
        "dense_input":[ \
            1.0678324729, \
            0.2177810266, \
            -1.7115145262, \
            0.682285721, \
            1.0138553067, \
            -0.4335000013, \
            0.7395859437, \
            -0.2882839595, \
            -0.447262688, \
            0.5146124988, \
            0.3791316964, \
            0.5190619748, \
            -0.4904593222, \
            1.1656456469, \
            -0.9776307444, \
            -0.6322198963, \
            -0.6891477694, \
            0.1783317857, \
            0.1397992467, \
            -0.3554220649, \
            0.4394217877, \
            1.4588397512, \
            -0.3886829615, \
            0.4353492889, \
            1.7420053483, \
            -0.4434654615, \
            -0.1515747891, \
            -0.2668451725, \
            -1.4549617756 \
        ] \
    }} \
]'

[
  {
    "time": 1701377058894,
    "in": {
      "dense_input": [
        1.0678324729, 0.2177810266, -1.7115145262, 0.682285721, 1.0138553067,
        -0.4335000013, 0.7395859437, -0.2882839595, -0.447262688, 0.5146124988,
        0.3791316964, 0.5190619748, -0.4904593222, 1.1656456469, -0.9776307444,
        -0.6322198963, -0.6891477694, 0.1783317857, 0.1397992467, -0.3554220649,
        0.4394217877, 1.4588397512, -0.3886829615, 0.4353492889, 1.7420053483,
        -0.4434654615, -0.1515747891, -0.2668451725, -1.4549617756
      ]
    },
    "out": { "dense_1": [0.0014974177] },
    "check_failures": [],
    "metadata": {
      "last_model": "{\"model_name\":\"api-sample-model\",\"model_sha\":\"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507\"}",
      "pipeline_version": "",
      "elapsed": [53101, 318104],
      "dropped": [],
      "partition": "engine-797d8958d9-fsszh"
    }
  }
]

Undeploy a Pipeline

  • Endpoint: /v1/api/pipelines/undeploy

Undeploys a deployed pipeline.

Undeploy a Pipeline Parameters

FieldTypeDescription
pipeline_idInteger (*REQUIRED)The numerical id of the pipeline.
deployment_idInteger (*REQUIRED)The numerical id of the deployment.

Undeploy a Pipeline Returns

Nothing if the call is successful.

Undeploy a Pipeline Examples

The pipeline with models deployed is undeployed.

Undeploy the pipeline via Requests.

# Undeploy pipeline with models
# Retrieve the token 
headers = wl.auth.auth_header()
endpoint = f"{wl.api_endpoint}/v1/api/pipelines/undeploy"

data = {
    "pipeline_id": model_pipeline_id,
    "deployment_id":model_deployment_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
display(response)

Undeploy the pipeline via curl.

curl {wl.api_endpoint}/v1/api/pipelines/undeploy \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
        "pipeline_id": {model_pipeline_id}, \
        "deployment_id": {model_deployment_id} \
}}'

null

Copy a Pipeline

Copies an existing pipeline into a new one in the same workspace. A new engine configuration can be set for the copied pipeline.

Copy a Pipeline Parameters

Copy a Pipeline Returns

  • Parameters
    • name - (REQUIRED string): The name of the new pipeline.
    • workspace_id - (REQUIRED int): The numerical id of the workspace to copy the source pipeline from.
    • source_pipeline - (REQUIRED int): The numerical id of the pipeline to copy from.
    • deploy - (OPTIONAL string): Name of the deployment.
    • engine_config - (OPTIONAL string): Engine configuration options.
    • pipeline_version - (OPTIONAL string): Optional version of the copied pipeline to create.

Copy a Pipeline Examples

The pipeline with models created in the step Create Pipeline in a Workspace will be copied into a new one.

Copy a pipeline via Requests.

## Copy a pipeline

# Retrieve the token 
headers = wl.auth.auth_header()

endpoint = f"{wl.api_endpoint}/v1/api/pipelines/copy"

data = {
  "name": "api-copied-pipeline-requests",
  "workspace_id": example_workspace_id,
  "source_pipeline": model_pipeline_id
}

response = requests.post(endpoint, json=data, headers=headers, verify=True).json()
response

    {'pipeline_pk_id': 9,
     'pipeline_variant_pk_id': 9,
     'pipeline_version': None,
     'deployment': None}

Copy a pipeline via curl.

curl {wl.api_endpoint}/v1/api/pipelines/copy \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{ \
        "name": "api-copied-pipeline-curl", \
        "workspace_id": {example_workspace_id}, \
        "source_pipeline": {model_pipeline_id} \
}}'

{
  "pipeline_pk_id": 32,
  "pipeline_variant_pk_id": 32,
  "pipeline_version": null,
  "deployment": null
}