Microsoft Azure BatchAI
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AI ReadinessFoundational (D)44/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferenceoauth229 EndpointsREST
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For Agents
Provision GPU clusters, file servers, experiments, and training jobs for distributed deep learning on Azure BatchAI workspaces.
Use for: I need to provision a GPU cluster for distributed training, Submit a training job to an existing BatchAI experiment, List the output files from a completed BatchAI job, Terminate a runaway training job before it consumes more credits
Not supported: Does not handle model registry, hyperparameter tuning, or inference deployment - use for BatchAI workspace, cluster, and job lifecycle only.
Jentic publishes the only available OpenAPI specification for BatchAI, keeping it validated and agent-ready. The Azure BatchAI Management API provisions GPU-enabled clusters, file servers, experiments, and jobs for distributed deep-learning training on Azure. Operators model their training environment as workspaces containing clusters and file servers, then submit experiments and jobs that run on the cluster, with operations to terminate jobs and list output files for inspection.
Install Jentic One Beta
Connect the BatchAI to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the BatchAI, 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%2Fbatchai" | 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%2Fbatchai" | 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 BatchAI API.
Provision and resize GPU-backed clusters scoped to a BatchAI workspace
Create file servers that mount as shared storage for training jobs
Submit, terminate, and inspect training jobs grouped under experiments
List remote login credentials for cluster nodes during debugging
List output files for a completed job to retrieve checkpoints and logs
Manage workspace lifecycle including create, update, and delete with full ARM tagging
Use Cases
Patterns agents use BatchAI API for, with concrete tasks.
★ Run a distributed deep-learning experiment
ML platform teams use BatchAI to spin up a GPU cluster, attach a shared file server with training data, and submit jobs grouped under an experiment. The API exposes Workspaces_Create, Clusters_Create, FileServers_Create, and Jobs_Create as ARM operations, so the entire training environment can be provisioned reproducibly without portal clicks.
Create a workspace, provision a 4-node GPU cluster, create a file server, then submit a Jobs_Create call for a PyTorch training script
Debug a stuck job
When a training job is not progressing, on-call engineers list its remote login credentials via Jobs_ListRemoteLoginInformation, SSH into the node, and inspect logs. If the job needs to be killed, Jobs_Terminate stops execution while preserving any partial output files for later inspection through Jobs_ListOutputFiles.
Call Jobs_ListRemoteLoginInformation for the job, attempt SSH, and if the job is hung call Jobs_Terminate followed by Jobs_ListOutputFiles to capture partial outputs
Cluster cost control
Finance-aware ML teams resize BatchAI clusters down to zero between training runs to avoid GPU charges. They list workspaces with Workspaces_List, then call Clusters_Update on each cluster with a new scaleSettings target. Combined with experiment cleanup, this keeps idle BatchAI spend close to zero.
Walk every cluster under a workspace and call Clusters_Update with target node count 0 if the cluster has no active jobs
AI agent integration via Jentic
An ML ops agent uses Jentic to run training jobs on demand without learning ARM. The agent searches Jentic for 'submit a BatchAI training job', loads the Jobs_Create schema, and executes with structured input. Jentic mints AAD tokens for management.azure.com so the agent never holds the underlying client secret.
Use Jentic search 'submit a BatchAI training job', load Jobs_Create, and execute with cluster ID, job script, and input directories
Key Endpoints
29 endpoints — jentic publishes the only available openapi specification for batchai, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/clusters/{clusterName}
Create a GPU cluster
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}
Create a training job under an experiment
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}/terminate
Terminate a running job
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}/listOutputFiles
List the output files for a completed job
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/fileServers/{fileServerName}
Create a file server in a workspace
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/clusters/{clusterName}
Update a cluster, including resizing nodes
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/clusters/{clusterName}
Create a GPU cluster
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}
Create a training job under an experiment
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}/terminate
Terminate a running job
POST
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}/listOutputFiles
List the output files for a completed job
PUT
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/fileServers/{fileServerName}
Create a file server in a workspace
PATCH
/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/clusters/{clusterName}
Update a cluster, including resizing nodes
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.
B
BatchAI
- Foundational (D)44/100
59
Foundational Compliance62
Developer Experience & Jentic Compatibility23
AI-Readiness & Agent Experience94
Agent Usability35
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
59
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: C+Signals: 4
62%
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.
62
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
48%
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.
23
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
91%
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.
0%
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 the BatchAI API by hand means setting up Azure AD OAuth2 against management.azure.com, handling long-running-operation polling for cluster and job provisioning, and threading workspace, experiment, and job ids through every Microsoft.BatchAI path yourself. Through Jentic you install once, import BatchAI from the API Directory, store the Azure AD credential once, and your agent calls it.
Permission scoping
This API puts the workspace, cluster, experiment, and job ids in the URL path (/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.BatchAI/workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}), so a rule can pin your agent to one workspace and its jobs. You choose the operations it may call, so destructive ones like terminating a job or deleting a cluster are not included unless you add them.
Credential isolation
Your Azure AD credential 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 BatchAI training job' or 'create a training cluster', and Jentic returns the matching Jobs or Clusters operation with its input schema so the agent calls the right ARM path without browsing the Microsoft.BatchAI reference.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using BatchAI API through Jentic.
Why is there no official OpenAPI spec for BatchAI?
Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call BatchAI 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 BatchAI API use?
The API uses Azure Active Directory OAuth 2.0 declared as azure_auth with the implicit flow. Tokens are presented as Bearer headers against management.azure.com. Through Jentic, the AAD client secret stays in the vault and only scoped, short-lived tokens are used by the agent.
Can I terminate a running training job with this API?
Yes. Call POST on /workspaces/{workspaceName}/experiments/{experimentName}/jobs/{jobName}/terminate (operationId Jobs_Terminate). The job stops and any output files written so far remain available via Jobs_ListOutputFiles.
What are the rate limits for the BatchAI API?
Azure Resource Manager applies subscription-level throttling, typically 12,000 read and 1,200 write requests per hour, surfaced via x-ms-ratelimit-remaining headers. BatchAI also has GPU vCPU quotas per region returned via Usages_List.
How do I submit a training job through Jentic?
Run pip install jentic, then use the async client to search 'submit a BatchAI training job', load Jobs_Create, and execute with the workspace name, experiment name, job name, cluster ID, container settings, and input directories. Jentic returns the job resource so you can poll its state.
Is BatchAI still the recommended Azure ML training service?
BatchAI is the legacy GPU training service for Azure; Microsoft has migrated most users to Azure Machine Learning compute. The API remains usable for existing workspaces, but new projects should evaluate Azure Machine Learning instead.
Can I limit what my agent is allowed to do with the Microsoft Azure BatchAI API?
Yes. Because you run Jentic One yourself, your own rules decide which BatchAI operations and credentials the agent may use. Since this API carries the workspace, cluster, experiment, and job ids in the URL path, you can pin the agent to a single workspace and its jobs. You also pick exactly which operations it can call, so destructive ones such as Jobs_Terminate or deleting a cluster stay out of reach unless you explicitly add them.
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