The HPE Machine Learning Development Environment REST API provides a way to interact with an HPE Machine Learning Development Environment cluster programmatically. The API reference documentation includes detailed information about the REST API endpoints and a playground for interacting with the API.

The protobuf mechanism is used to define language-agnostic message structures. These type definitions are used with gRPC-gateway to provide consistent REST endpoints that serve various needs.

These tools are used to autogenerate an OpenAPI v2 specification, which inlines documentation for each endpoint and response message. The specification can be served to different tools to generate code for different languages and to provide web-based explorers, such as the Swagger UI, for the API.


REST API Reference

HPE Machine Learning Development Environment REST API

The REST API reference documentation lists available endpoints grouped by workflow. Click an endpoint method to see the expected input parameters and response. You can also use Try it out button to make an HTTP request against the endpoint. You need to have the appropriate cookie set and a running cluster for an interactive request.

If you have access to a running HPE Machine Learning Development Environment cluster you can try the live-interact version by clicking the API icon from the HPE Machine Learning Development Environment WebUI or by navigating to /docs/rest-api/ on your HPE Machine Learning Development Environment cluster.


Most of the API calls to an HPE Machine Learning Development Environment cluster require authentication. On each API call, the server expects a Bearer token.

To receive a token, POST a valid username and password combination to the login endpoint, /api/v1/auth/login using the following format:

  "username": "string",
  "password": "string"

Example request:

curl -s "${DET_MASTER}/api/v1/auth/login" \
  -H 'Content-Type: application/json' \
  --data-binary '{"username":"determined","password":""}'

Example response:

  "token": "string",
  "user": {
    "username": "string",
    "admin": true,
    "active": true,
    "agent_user_group": {
      "agent_uid": 0,
      "agent_gid": 0

When you receive the token, store it and attach it to future API calls under the Authorization header in the Bearer $TOKEN format.


This example shows how to use the REST API to unarchive a previously archived experiment.

To find an archived experiment, look up the experiment endpoint to find which filtering options are provided. They are archived and limit. Including a bearer token to authenticate the request, use the archived and limit query parameters to limit the result set to only show a single archived experiment:

curl -H "Authorization: Bearer ${token}" "${DET_MASTER}/api/v1/experiments?archived=true&limit=1"

JSON response:

  "experiments": [
      "id": 16,
      "description": "mnist_pytorch_const",
      "labels": [],
      "startTime": "2020-08-26T20:12:35.337160Z",
      "endTime": "2020-08-26T20:12:51.951720Z",
      "state": "STATE_COMPLETED",
      "archived": true,
      "numTrials": 1,
      "progress": 0,
      "username": "determined"
  "pagination": {
    "offset": 0,
    "limit": 1,
    "startIndex": 0,
    "endIndex": 1,
    "total": 1

In the archive endpoint entry, you can see that all that you need is an experiment ID.

With the experiment ID you want, you can now unarchive the experiment using the unarchive endpoint in a POST request:

curl -H "Authorization: Bearer ${token}" -X POST "${DET_MASTER}/api/v1/experiments/16/unarchive"