Microsoft Azure HyperDrive
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AI ReadinessFoundational (C-)52/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferenceoauth22 EndpointsREST
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For Agents
Submit and cancel Azure Machine Learning HyperDrive hyperparameter tuning runs against an ARM-scoped AML workspace.
Use for: I need to submit a new hyperparameter tuning run for my AML model, Cancel the HyperDrive run that has been running too long, Start a HyperDrive sweep against my Azure ML workspace, Stop an in-flight hyperparameter search and free its compute
Not supported: Does not handle workspace creation, compute target provisioning, or model deployment - use for HyperDrive run submission and cancellation only.
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.
Install Jentic One Beta
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.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fhyperdrive" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fhyperdrive" | sh
jentic register # connects your agent to your Jentic One instanceJentic 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
Drive parallel training sweeps from agents or pipelines without the AML Python SDK
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 your Jentic One instance 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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
H
HyperDrive
- Foundational (C-)52/100
68
Foundational Compliance63
Developer Experience & Jentic Compatibility33
AI-Readiness & Agent Experience94
Agent Usability35
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
68
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: B+Signals: 4
95%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
75%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
1%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
33
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
81%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
50%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
35
Security
Trust, risk posture, and security compliance.
Grade: FSignals: 1
35%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring HyperDrive by hand means registering an Azure AD app, obtaining OAuth 2.0 bearer tokens, and constructing the correct ARM scope for every run you submit. Through Jentic you install once, import HyperDrive from the API Directory, store the Azure AD credential once, and your agent calls it.
Permission scoping
HyperDrive puts the run id in the URL path (/hyperdrive/v1.0/{armScope}/runs/{runId}/...), so a rule can pin your agent to run submission for a given scope: it can submit tuning runs and read them. You choose the operations it may call, so cancelling a run is not included unless you add it.
Credential isolation
Your Azure AD credential for HyperDrive is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'submit a hyperparameter tuning run' or 'cancel a HyperDrive run', and Jentic returns the matching HyperDrive operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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 with Jentic One, the self-hosted execution layer.
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 your Jentic One instance 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.
Can I limit what my agent is allowed to do with the HyperDrive API?
Yes. Because you run Jentic One yourself, you decide which HyperDrive operations your agent may call, and your own rules govern which credentials it uses. Since HyperDrive puts the run id in the URL path, you can pin an agent to submitting and reading tuning runs for a given ARM scope while withholding the cancel operation on /hyperdrive/v1.0/{armScope}/runs/{runId}/cancel until you explicitly add it. The stored Azure AD credential is injected at execution time and never reaches the agent's prompt or logs.
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