Wallaroo MLOps API Essentials Guide: Model Management

How to use the Wallaroo API for Model Management

Model Naming Requirements

Model names map onto Kubernetes objects, and must be DNS compliant. The strings for model names must be lower case ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.

This tutorial and the assets are available as part of the Wallaroo Tutorials repository.

Wallaroo MLOps API User Management Tutorial

This tutorial focuses on using the Wallaroo MLOps API for model management. For this tutorial, we will be using the Wallaroo SDK to provide authentication credentials for ease of use examples. See the Wallaroo API Guide for full details on using the Wallaroo MLOps API.

References

The following references are available for more information about Wallaroo and the Wallaroo MLOps API:

  • Wallaroo Documentation Site: The Wallaroo Documentation Site.
  • Wallaroo MLOps API Documentation from a Wallaroo instance: A Swagger UI based documentation is available from your Wallaroo instance at https://{Wallaroo Domain}/v1/api/docs. For example, if the Wallaroo Domain is example.wallaroo.ai, the Wallaroo MLOps API Documentation is at https://example.wallaroo.ai/v1/api/docs. Note the . is part of the prefix.

IMPORTANT NOTE: The Wallaroo MLOps API is provided as an early access features. Future iterations may adjust the methods and returns to provide a better user experience. Please refer to this guide for updates.

Prerequisites

  • An installed Wallaroo instance.
  • The following Python libraries installed:
    • requests
    • json
    • wallaroo: The Wallaroo SDK. Included with the Wallaroo JupyterHub service by default.

Connection Steps

Import Libraries

For these examples, we will rely on the wallaroo SDK and requests library for making connections to our sample Wallaroo Ops instance.

pyarrow is the Apache Arrow library used for data schemas in Wallaroo, while base64 is used to convert data schemas to base64 format for model uploads.

import wallaroo

import requests

import json

import pyarrow as pa

import base64

Connect to the Wallaroo Instance

The next step is to connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.

This is accomplished using the wallaroo.Client() command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.

If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client(). For more information on Wallaroo Client settings, see the Client Connection guide.

# Login through local Wallaroo instance

wl = wallaroo.Client()

Retrieve API Service URL

The Wallaroo SDK provides the API endpoint through the wallaroo.client.api_endpoint variable. This is derived from the Wallaroo OPs DNS settings.

The method wallaroo.client.auth.auth_header() retrieves the HTTP authorization headers for the API connection.

Both of these are used to authenticate for the Wallaroo MLOps API calls used in the future examples.

display(wl.api_endpoint)
display(wl.auth.auth_header())

'https://autoscale-uat-gcp.wallaroo.dev'

{'Authorization': 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCIgOiAiSldUIiwia2lkIiA6ICJoVUcyQ1puTTZpa0EtQlNRVFNsVkJnaEd0dk45QXItN0g2R3NLcHlrY0ZjIn0.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.ix3iHG7s8VJVxhU6EnSjh3Kmmx2AX5yiaDHSt4Cvnz5renlbDyA6iIITg1wd1I7Ub2xzMQe03eVjc3erFob_Xg_12dhL4JLrkXJGwQJgfzN-UqA4Zr1b0YgiiAaIxFKWc75_AtHmCiK9R8mVSqjrCQzjyfuQgtQypcRUfkHvS26k0Ps3Ov1J9V1qlvh9Sdhvc84vjLBxiNtvBXQInjIj-PgjMyVQ0HR4cyMgiSY5u4Hr9wMXN2gMvGQHlZBMER8VoOwuV5Z-EyhjIfYbkc1f9bfzdySJ9zoPh5e2xD0ZkxBc8zyFLbdor8f8HQ0JMyADh-6ydTW48b28gEcW3RzsUQ'}

Models

The Wallaroo MLOps API allows users to upload different types of ML models and frameworks into Wallaroo.

Upload Model to Workspace

  • Endpoint: /v1/api/models/upload_and_convert
  • Content-Type: multipart/form-data

Models uploaded through this method that are not Wallaroo Native Runtimes (ONNX, Tensorflow, and Python script) are containerized within the Wallaroo instance then run by the Wallaroo engine. See Wallaroo MLOps API Essentials Guide: Pipeline Management for details on pipeline configurations and deployments.

Upload Model to Workspace Parameters

Field TypeDescription
name String (Required)The model name.
visibility String (Required)Either public or private.
workspace_id String (Required)The numerical ID of the workspace to upload the model to.
conversion String (Required)The conversion parameters that include the following:
 frameworkString (Required)The framework of the model being uploaded. See the list of supported models for more details.
 python_versionString (Required)The version of Python required for model.
 requirementsString (Required)Required libraries. Can be [] if the requirements are default Wallaroo JupyterHub libraries.
 input_schemaString (Optional)The input schema from the Apache Arrow pyarrow.lib.Schema format, encoded with base64.b64encode. Only required for Containerized Wallaroo Runtime models.
 output_schemaString (Optional)The output schema from the Apache Arrow pyarrow.lib.Schema format, encoded with base64.b64encode. Only required for non-native runtime models.

Files are uploaded in the multipart/form-data format with two parts:

  • metadata: Contains the parameters listed above as application/json.
  • file: The binary file (ONNX, .zip, etc) as Content-Type application/octet-stream.

Upload Model to Workspace Returns

Field TypeDescription
insert_models{‘returning’: [models]} List[models]The uploaded models details.
 idIntegerThe model’s numerical id.

Upload Model to Workspace Examples

The following example shows uploading an ONNX model to a Wallaroo instance. Note that the input_schema and output_schema encoded details are not required.

This example assumes the workspace id of 18. Modify this code block based on your Wallaroo Ops instance.

Upload model via Requests library.

workspace = wl.get_current_workspace()
workspace.id()

108

When using the requests library, the files data must be in the following format:

'file': (model_file_name, open(model_file_path, 'rb'), "application/octet-stream")

For example, if the file name is test.onnx, and the path is models/test.onnx, the command would be:

'file': ('test.onnx', open(`models/test.onnx`, 'rb'), "application/octet-stream")

Three model aspects to track:

  • The model name: The model name unique to the workspace; models uploaded to the same workspace with the same model name are saved as new model versions.
  • The model file name: The file name for the specific model (onnx, zip, etc).
  • The model file path: The path to the model file location.
# Retrieve the token 
headers = wl.auth.auth_header()

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

display(endpoint)

workspace_id = workspace.id()

framework='onnx'

model_name = f"api-sample-model"
model_file_name = 'ccfraud.onnx'
model_file_path = './models/ccfraud.onnx'

metadata = {
    "name": 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': (model_file_name, open(model_file_path, 'rb'), "application/octet-stream")
    }

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

display(f"Uploaded Model Name: {model_name}.")
display(f"Sample model file: ./models/ccfraud.onnx")
display(response)

'https://autoscale-uat-gcp.wallaroo.dev/v1/api/models/upload_and_convert'

'Uploaded Model Name: api-sample-model.'

'Sample model file: ./models/ccfraud.onnx'

{'insert_models': {'returning': [{'models': [{'id': 1215}]}]}}

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

1215

Upload ONNX model via curl.

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

# save metadata to a file
with open("data/onnx_file_upload.json", "w") as outfile:
    json.dump(metadata, outfile)

!curl -H 'Authorization: {wl.auth.auth_header()["Authorization"]}' \
    -F 'metadata={json.dumps(metadata)};type=application/json' \
    -F 'file=@{model_file_path};type=application/octet-stream' \
    {wl.api_endpoint}/v1/api/models/upload_and_convert

HTTP/1.1 202 Accepted
content-type: application/json
content-length: 58
date: Tue, 06 Jan 2026 16:48:12 GMT
x-envoy-upstream-service-time: 315
server: opscenter-https

{"insert_models":{"returning":[{"models":[{"id":1216}]}]}}

The following example shows uploading a Pytorch model to a Wallaroo instance. Note that the input_schema and output_schema encoded details are required.

Upload Pytorch via Requests.

input_schema = pa.schema([
    pa.field('input_1', pa.list_(pa.float32(), list_size=10)),
    pa.field('input_2', pa.list_(pa.float32(), list_size=5))
])
output_schema = pa.schema([
    pa.field('output_1', pa.list_(pa.float32(), list_size=3)),
    pa.field('output_2', pa.list_(pa.float32(), list_size=2))
])

encoded_input_schema = base64.b64encode(
                bytes(input_schema.serialize())
            ).decode("utf8")

encoded_output_schema = base64.b64encode(
                bytes(output_schema.serialize())
            ).decode("utf8")

framework = 'pytorch'

model_name = 'api-upload-pytorch-multi-io'
model_file_name = 'model-auto-conversion_pytorch_multi_io_model.pt'
model_file_path = './models/model-auto-conversion_pytorch_multi_io_model.pt'

metadata = {
    "name": model_name,
    "visibility": "private",
    "workspace_id": workspace_id,
    "conversion": {
        "framework": framework,
        "python_version": "3.8",
        "requirements": []
    },
    "input_schema": encoded_input_schema,
    "output_schema": encoded_output_schema,
}

headers = wl.auth.auth_header()

files = {
    'metadata': (None, json.dumps(metadata), "application/json"),
    'file': (model_file_name, open(model_file_path,'rb'),'application/octet-stream')
}

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

display(f"Uploaded Model Name: {model_name}.")
display(f"Sample model file: ./models/model-auto-conversion_pytorch_multi_io_model.pt")
display(response)

'Uploaded Model Name: api-upload-pytorch-multi-io.'

'Sample model file: ./models/model-auto-conversion_pytorch_multi_io_model.pt'

{'insert_models': {'returning': [{'models': [{'id': 1217}]}]}}

Upload Pytorch via curl.

!curl -H 'Authorization: {wl.auth.auth_header()["Authorization"]}' \
    -F 'metadata={json.dumps(metadata)};type=application/json' \
    -F 'file=@{model_file_path};type=application/octet-stream' \
    {wl.api_endpoint}/v1/api/models/upload_and_convert

{"insert_models":{"returning":[{"models":[{"id":1218}]}]}}

List Models in Workspace

  • Endpoint: /v1/api/models/list

Returns a list of models added to a specific workspace.

List Models in Workspace Parameters

FieldTypeDescription
workspace_idInteger (REQUIRED)The workspace id to list.

List Models in Workspace Returns

Field TypeDescription
models List[models]List of models in the workspace.
 idIntegerThe numerical id of the model.
 **owner_idStringIdentifer of the model owner.
 created_atStringDateTime of the model’s creation.
 updated_atStringDateTime of the model’s last update.

List Models in Workspace Examples

Display the models for the workspace. This is assumed to be workspace_id of 10. Adjust the script for your own use.

List models in workspace via Requests.

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

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

data = {
  "workspace_id": workspace_id
}

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

{'models': [{'model': {'id': 839,
    'name': 'yolov8n-openvino',
    'owner_id': '""',
    'created_at': '2025-07-14T20:43:07.104895+00:00',
    'updated_at': '2025-07-14T20:48:48.255132+00:00',
    'workspace_id': 108},
   'model_versions': [{'model_version': {'name': 'yolov8n-openvino',
      'visibility': 'private',
      'workspace_id': 108,
      'conversion': {'arch': 'x86',
       'accel': 'openvino',
       'python_version': '3.8',
       'requirements': [],
       'framework': 'onnx',
       'framework_config': None},
      'id': 838,
      'image_path': None,
      'status': 'ready',
      'task_id': None,
      'file_info': {'version': '87ca2f41-5645-4040-82bc-6fc431f85ece',
       'sha': '3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08',
       'file_name': 'yolov8n.onnx',
       'size': 12823491},
      'created_on_version': '2025.1.2',
      'created_by': 'john.hummel@wallaroo.ai',
      'created_at': '2025-07-14T20:48:48.255132+00:00',
      'deployed': False,
      'error_summary': None},
     'config': {'id': 1259,
      'model_version_id': 838,
      'runtime': 'onnx',
      'filter_threshold': None,
      'tensor_fields': None,
      'input_schema': None,
      'output_schema': None,
      'batch_config': None,
      'dynamic_batching_config': None,
      'continuous_batching_config': None,
      'queue_depth': None,
      'sidekick_uri': None,
      'openai': None}},
    {'model_version': {'name': 'yolov8n-openvino',
      'visibility': 'private',
      'workspace_id': 108,
      'conversion': {'arch': 'x86',
       'accel': 'none',
       'python_version': '3.8',
       'requirements': [],
       'framework': 'onnx',
       'framework_config': None},
      'id': 837,
      'image_path': None,
      'status': 'ready',
      'task_id': None,
      'file_info': {'version': '36a26352-e497-46ed-a693-b01a0324da55',
       'sha': '3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08',
       'file_name': 'yolov8n.onnx',
       'size': 12823491},
      'created_on_version': '2025.1.2',
      'created_by': 'john.hummel@wallaroo.ai',
      'created_at': '2025-07-14T20:43:07.104895+00:00',
      'deployed': False,
      'error_summary': None},
     'config': {'id': 1257,
      'model_version_id': 837,
      'runtime': 'onnx',
      'filter_threshold': None,
      'tensor_fields': None,
      'input_schema': None,
      'output_schema': None,
      'batch_config': None,
      'dynamic_batching_config': None,
      'continuous_batching_config': None,
      'queue_depth': None,
      'sidekick_uri': None,
      'openai': None}}],
   'workspace': {'id': 108,
    'name': 'john.hummel@wallaroo.ai - Default Workspace',
    'created_by': 'john.hummel@wallaroo.ai',
    'archived': False,
    'created_at': '2024-10-25T21:54:29.172083+00:00',
    'group_id': None}},
  ...]}

List models in workspace via curl.

!curl {wl.api_endpoint}/v1/api/models/list \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    --data '{{"workspace_id": {workspace_id}}}'

{"models":[{"model":{"id":839,"name":"yolov8n-openvino","owner_id":"\"\"","created_at":"2025-07-14T20:43:07.104895+00:00","updated_at":"2025-07-14T20:48:48.255132+00:00","workspace_id":108},"model_versions":[{"model_version":{"name":"yolov8n-openvino","visibility":"private","workspace_id":108,"conversion":{"arch":"x86","accel":"openvino","python_version":"3.8","requirements":[],"framework":"onnx","framework_config":null},"id":838,"image_path":null,"status":"ready","task_id":null,"file_info":{"version":"87ca2f41-5645-4040-82bc-6fc431f85ece","sha":"3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08","file_name":"yolov8n.onnx","size":12823491},"created_on_version":"2025.1.2","created_by":"john.hummel@wallaroo.ai","created_at":"2025-07-14T20:48:48.255132+00:00","deployed":false,"error_summary":null},"config":{"id":1259,"model_version_id":838,"runtime":"onnx","filter_threshold":null,"tensor_fields":null,"input_schema":null,"output_schema":null,"batch_config":null,"dynamic_batching_config":null,"continuous_batching_config":null,"queue_depth":null,"sidekick_uri":null,"openai":null}},{"model_version":{"name":"yolov8n-openvino","visibility":"private","workspace_id":108,"conversion":{"arch":"x86","accel":"none","python_version":"3.8","requirements":[],"framework":"onnx","framework_config":null},"id":837,"image_path":null,"status":"ready","task_id":null,"file_info":{"version":"36a26352-e497-46ed-a693-b01a0324da55","sha":"3ed5cd199e0e6e419bd3d474cf74f2e378aacbf586e40f24d1f8c89c2c476a08","file_name":"yolov8n.onnx","size":12823491},"created_on_version":"2025.1.2","created_by":"john.hummel@wallaroo.ai","created_at":"2025-07-14T20:43:07.104895+00:00","deployed":false,"error_summary":null},"config":{"id":1257,"model_version_id":837,"runtime":"onnx","filter_threshold":null,"tensor_fields":null,"input_schema":null,"output_schema":null,"batch_config":null,"dynamic_batching_config":null,"continuous_batching_config":null,"queue_depth":null,"sidekick_uri":null,"openai":null}}],"workspace":{"id":108,"name":"john.hummel@wallaroo.ai - Default Workspace","created_by":"john.hummel@wallaroo.ai","archived":false,"created_at":"2024-10-25T21:54:29.172083+00:00","group_id":null}},...]}

Get Model Version Details

  • Endpoint: /v1/api/models/get_version_by_id

Get Model Details Parameters

Returns details regarding a single model, including versions.

FieldTypeDescription
model_version_idInteger (REQUIRED)The numerical value of the model’s id.

Get Model Details Examples

Submit the model id for the model uploaded in the Upload Model to Workspace step to retrieve configuration details.

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

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

data = {
  "model_version_id": model_version_id
}

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

{'model_version': {'model_version': {'name': 'api-sample-model',
   'visibility': 'public',
   'workspace_id': 108,
   'conversion': {'python_version': '3.8',
    'requirements': [],
    'framework': 'onnx',
    'framework_config': None},
   'id': 1215,
   'image_path': None,
   'status': 'attempting_load_container',
   'task_id': '3f04e6b6-b0f2-41c9-8acf-6509e194ae80',
   'file_info': {'version': 'b935b572-04d5-4071-a174-985be94c724d',
    'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
    'file_name': 'ccfraud.onnx',
    'size': 1928},
   'created_on_version': '2025.2.1',
   'created_by': 'john.hummel@wallaroo.ai',
   'created_at': '2026-01-06T16:34:30.953078+00:00',
   'deployed': False,
   'error_summary': None},
  'config': {'id': 1788,
   'model_version_id': 1215,
   'runtime': 'onnx',
   'filter_threshold': None,
   'tensor_fields': None,
   'input_schema': None,
   'output_schema': None,
   'batch_config': None,
   'dynamic_batching_config': None,
   'continuous_batching_config': None,
   'queue_depth': None,
   'sidekick_uri': None,
   'openai': None}}}

Get Model Versions

  • Endpoint: /v1/api/models/list_versions

Retrieves all versions of a model based on either the name of the model or the model_pk_id.

Get Model Versions Parameters

FieldTypeDescription
model_idString (REQUIRED)The model name.
models_pk_idInteger (REQUIRED)The model’s numerical id.

Get Model Versions Returns

Field TypeDescription
Unnamed List[models]A list of model versions for the requested model.
 shaStringThe sha hash of the model version.
 models_pk_idIntegerThe pk id of the model.
 model_versionStringThe UUID identifier of the model version.
 owner_idStringThe Keycloak user id of the model’s owner.
 model_idStringThe name of the model.
 idIntegerThe integer id of the model.
 file_nameStringThe filename used when uploading the model.
 image_pathStringThe image path of the model.

Retrieve the versions for a previously uploaded model. This assumes a workspace with id 10 has models already loaded into it.

Retrieve model versions via Requests.

## List model versions

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

data = {
  "model_id": "api-sample-model",
  "models_pk_id": model_version_id
}

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

[{'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 1140,
  'model_version': 'c1074d11-510c-4f72-8e39-43cd437cf827',
  'owner_id': '13443b0a-de12-406b-a718-10bd26decce6',
  'model_id': 'api-sample-model',
  'id': 1137,
  'file_name': 'keras_ccfraud.onnx',
  'image_path': None,
  'status': 'ready'},
 {'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 1218,
  'model_version': '1d7191d4-5996-4750-b209-3a3a757bd4dd',
  'owner_id': '7d603858-88e0-472e-8f71-e41094afd7ec',
  'model_id': 'api-sample-model',
  'id': 1216,
  'file_name': 'ccfraud.onnx',
  'image_path': None,
  'status': 'attempting_load_container'},
 {'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 1218,
  'model_version': 'b935b572-04d5-4071-a174-985be94c724d',
  'owner_id': '7d603858-88e0-472e-8f71-e41094afd7ec',
  'model_id': 'api-sample-model',
  'id': 1215,
  'file_name': 'ccfraud.onnx',
  'image_path': None,
  'status': 'attempting_load_container'},
 {'sha': 'bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507',
  'models_pk_id': 1215,
  'model_version': 'fa08ab20-2dbf-431e-8ad0-90630ff21a5c',
  'owner_id': '7d603858-88e0-472e-8f71-e41094afd7ec',
  'model_id': 'ccfraud',
  'id': 1212,
  'file_name': 'ccfraud.onnx',
  'image_path': None,
  'status': 'attempting_load_container'}]

Retrieve model versions via curl.

!curl {wl.api_endpoint}/v1/api/models/list_versions \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    -d '{json.dumps(data)}'

[{"sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507","models_pk_id":1140,"model_version":"c1074d11-510c-4f72-8e39-43cd437cf827","owner_id":"13443b0a-de12-406b-a718-10bd26decce6","model_id":"api-sample-model","id":1137,"file_name":"keras_ccfraud.onnx","image_path":null,"status":"ready"},{"sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507","models_pk_id":1218,"model_version":"1d7191d4-5996-4750-b209-3a3a757bd4dd","owner_id":"7d603858-88e0-472e-8f71-e41094afd7ec","model_id":"api-sample-model","id":1216,"file_name":"ccfraud.onnx","image_path":null,"status":"attempting_load_container"},{"sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507","models_pk_id":1218,"model_version":"b935b572-04d5-4071-a174-985be94c724d","owner_id":"7d603858-88e0-472e-8f71-e41094afd7ec","model_id":"api-sample-model","id":1215,"file_name":"ccfraud.onnx","image_path":null,"status":"attempting_load_container"},{"sha":"bc85ce596945f876256f41515c7501c399fd97ebcb9ab3dd41bf03f8937b4507","models_pk_id":1215,"model_version":"fa08ab20-2dbf-431e-8ad0-90630ff21a5c","owner_id":"7d603858-88e0-472e-8f71-e41094afd7ec","model_id":"ccfraud","id":1212,"file_name":"ccfraud.onnx","image_path":null,"status":"attempting_load_container"}]

Get Model Configuration by Id

  • Endpoints: /v1/api/models/get_config_by_id

Returns the model’s configuration details.

Get Model Configuration by Id Parameters

FieldTypeDescription
model_idInteger (Required)The numerical value of the model’s id.

Get Model Configuration by Id Returns

FieldTypeDescription

Get Model Configuration by Id Examples

Submit the model id for the model uploaded in the Upload Model to Workspace step to retrieve configuration details.

Retrieve model configuration via Requests.

## Get model config by id

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

data = {
  "model_id": model_version_id
}

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

{'model_config': {'id': 1788,
  'runtime': 'onnx',
  'tensor_fields': None,
  'filter_threshold': None}}

Retrieve model configuration via curl.

!curl {wl.api_endpoint}/v1/api/models/get_config_by_id \
    -H "Authorization: {wl.auth.auth_header()['Authorization']}" \
    -H "Content-Type: application/json" \
    -d '{json.dumps(data)}'

{"model_config":{"id":1788,"runtime":"onnx","tensor_fields":null,"filter_threshold":null}}