HyperDrive

Name: HyperDrive API
Brand: HyperDrive
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth22 EndpointsREST

For Agents

Submit and cancel Azure Machine Learning HyperDrive hyperparameter tuning runs against an ARM-scoped AML workspace.

Connect the HyperDrive to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the HyperDrive, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with HyperDrive API.

Submit a new HyperDrive hyperparameter tuning run scoped to an Azure Machine Learning workspace

Cancel an active HyperDrive run by its runId before it completes

Pass workspace and resource group context through the {armScope} path parameter

Authenticate calls with the Azure AD user_impersonation scope used across AML

GET STARTED

Start building with HyperDrive API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for HyperDrive, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for HyperDrive, keeping it validated and agent-ready. HyperDrive is the Azure Machine Learning hyperparameter tuning service that submits and tracks parallel training runs against an ARM-scoped workspace. The API exposes two operations under /hyperdrive/v1.0/{armScope}/runs to launch a new tuning experiment and to cancel an in-flight run by runId.

Use Cases

Patterns agents use HyperDrive API for, with concrete tasks.

★ Hyperparameter Sweep from CI

Kick off Azure Machine Learning HyperDrive runs from a CI/CD pipeline by POSTing the run definition to /hyperdrive/v1.0/{armScope}/runs with the workspace ARM scope. This lets ML teams launch tuning experiments on every model change without depending on the AML Python SDK in the build agent. Sweeps then run on the workspace's compute target and report results back into AML.

POST a HyperDrive run definition with a random sampling space over learning_rate and batch_size to /hyperdrive/v1.0/{armScope}/runs and return the runId.

Cancel Stuck Tuning Runs

Free expensive GPU compute by cancelling HyperDrive runs that exceed their time budget. The API accepts a POST to /hyperdrive/v1.0/{armScope}/runs/{runId}/cancel and stops the parent run plus its child trials. This is the operational lever for ML platform teams whose users frequently mis-configure search spaces.

Call cancel on runId 'HD_abc123' under the workspace armScope and confirm a 200 response.

Cost-Aware Tuning Orchestration

Build a thin orchestrator that submits a HyperDrive sweep, monitors total elapsed time outside of HyperDrive, and cancels the run once a cost ceiling is reached. Because the API exposes only submit and cancel, an external scheduler enforces the budget and the workspace handles compute. Setup is roughly half a day per orchestrator.

Submit a HyperDrive run, wait 60 minutes, then cancel the runId if it is still active.

Agent-Triggered Model Tuning via Jentic

An AI agent can launch and stop HyperDrive runs through Jentic without touching Azure AD tokens directly. The agent searches Jentic for 'submit a hyperparameter tuning run', loads the submit-run schema, and executes with the workspace ARM scope and run definition. Jentic's MAXsystem holds the OAuth client secret and supplies a scoped bearer token per call.

Use the Jentic MCP tool azure_hyperdrive_submit_run to start a tuning experiment in workspace 'aml-prod' and return the runId.

Key Endpoints

2 endpoints — jentic publishes the only available openapi specification for hyperdrive, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/hyperdrive/v1.0/{armScope}/runs

Submit a new HyperDrive hyperparameter tuning run

POST

/hyperdrive/v1.0/{armScope}/runs/{runId}/cancel

Cancel an active HyperDrive run by runId

POST

/hyperdrive/v1.0/{armScope}/runs

Submit a new HyperDrive hyperparameter tuning run

POST

/hyperdrive/v1.0/{armScope}/runs/{runId}/cancel

Cancel an active HyperDrive run by runId

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Azure AD OAuth 2.0 client secrets and refresh tokens for HyperDrive are stored encrypted in the Jentic vault (MAXsystem). Agents receive scoped bearer tokens — raw client secrets never enter the agent's context.

Intent-based discovery

Agents search Jentic by intent (e.g. 'submit a hyperparameter tuning run') and Jentic returns the matching HyperDrive operation with its parameter schema, so the agent can call submit or cancel without parsing AML docs.

Time to first call

Direct HyperDrive integration: 1-2 days to wire AAD token acquisition and run definition payloads. Through Jentic: under 30 minutes — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure IoT Hub

IoT Hub feeds device telemetry that AML and HyperDrive can train and tune models against.

Use IoT Hub when the training data originates from connected devices that need ingest before tuning.

Complementary

Azure Key Vault

Key Vault stores datastore secrets that HyperDrive runs need to access training data.

Use Key Vault to manage data access secrets referenced by AML datastores instead of inlining them.

Alternative

Azure HybridData Management

HybridData moves on-premises data into Azure storage that an AML workspace then trains on.

Choose HybridData when the bottleneck is getting training data into Azure rather than tuning the model.

FAQs

Specific to using HyperDrive API through Jentic.

Why is there no official OpenAPI spec for HyperDrive?

Microsoft Azure does not publish an OpenAPI specification for the HyperDrive REST surface. Jentic generates and maintains this spec so that AI agents and developers can call HyperDrive via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the HyperDrive API use?

It uses Azure Active Directory OAuth 2.0 with the user_impersonation scope, the same auth used across Azure Machine Learning. Through Jentic the AAD client secret is held in the Jentic vault (MAXsystem) and a scoped bearer token is injected into each call so the agent never sees the raw secret.

Can I cancel a HyperDrive run mid-flight?

Yes. POST to /hyperdrive/v1.0/{armScope}/runs/{runId}/cancel with the workspace ARM scope and the runId returned from the submit call. The cancel terminates the parent run and all of its child trials.

What are the rate limits for HyperDrive?

HyperDrive does not publish dedicated limits — it inherits the throughput of the Azure Machine Learning workspace and the underlying compute target. Practical concurrency is capped by the max_concurrent_runs you set in the run definition and by the workspace's compute quota.

How do I submit a tuning run through Jentic?

Run pip install jentic, then search Jentic with 'submit a hyperparameter tuning run', load the submit-run operation, and execute with the workspace armScope and a run definition that includes the sampling method and parameter space. Jentic returns the runId for tracking.

Does HyperDrive replace the Azure ML Python SDK?

No — it complements it. The REST API is useful when you cannot run the AML SDK in your environment (for example, lightweight CI jobs or agents) and need only to submit or cancel runs. For richer experiment tracking, metrics, and model registry, continue using the AML SDK or the AML control-plane APIs.