azure-jobs 0.1.18

A fast CLI for submitting and managing Azure ML jobs via pure REST APIs

Azure Jobs

A fast, lightweight CLI for submitting and managing Azure ML jobs via pure REST APIs. Uses YAML template inheritance, sensible defaults, and rich terminal output. Supports both Azure ML compute clusters and Singularity virtual clusters.

Install

pipx install azure_jobs

For development:

uv pip install -e .

Quick Start

aj ws set                             # configure workspace (interactive)
aj template pull user/templates       # clone shared templates
aj template list                      # show available templates
aj run -t gpu python train.py         # submit a job
aj job list                           # monitor jobs
aj job logs <id>                      # view logs

Commands

Job Submission

aj run -t gpu python train.py       # submit using "gpu" template
aj run python train.py              # re-use last template
aj run -d python train.py           # dry run — inspect config without submitting
aj run -L python train.py           # run locally for testing

Flag	Purpose
-t	Template name
-n	Number of nodes
-p	Processes per node
-d	Dry run
-y	Skip confirmation
-L	Run locally

Job Management

aj job list                         # list recent cloud jobs
aj job list -s Running              # filter by status
aj job show <id>                    # show job details
aj job cancel <id>                  # cancel a running job
aj job logs <id>                    # download and display logs
aj list                             # show local submission history

Templates

aj template list                    # list available templates
aj template show <name>             # display resolved config after inheritance
aj template validate                # check all templates for errors
aj template pull <repo>             # clone template repository
aj template push -m "msg"           # commit and push local changes

Workspace & Auth

aj ws list                          # list workspaces in subscription
aj ws set                           # set active workspace (interactive picker)
aj auth status                      # show login and credential status
aj auth login                       # delegate to az login

Resources

aj env list                         # list registered environments
aj env show <name>                  # show environment versions and images
aj ds list                          # list datastores
aj ds show <name>                   # show datastore details
aj quota list                       # show Singularity VC quota and availability
aj quota list --aml                 # show AML compute cluster availability
aj exp list                         # list experiments (aggregated from jobs)
aj image list                       # list Singularity curated base images

Other

aj config show                      # print all configuration
aj config timezone Asia/Shanghai    # set display timezone
aj dash                             # open interactive TUI dashboard

How It Works

1. Templates define reusable job configs as YAML with optional base inheritance
2. aj run loads a template, resolves the inheritance chain, merges configs, and applies CLI overrides
3. Code upload — zips your code, uploads to workspace blob storage, and registers a code asset
4. Environment — registers Docker images as Azure ML environment versions (with SHA-based dedup)
5. Submission — builds a REST job body and submits via PUT /jobs/{name} to Azure Resource Manager
6. Tracking — records each submission locally to record.jsonl and prints the portal URL

No dependency on amlt or the azure-ai-ml SDK. All Azure interactions use lightweight REST APIs with AzureCliCredential.

Documentation

Document	Contents
Architecture	Module layout, data flow, design decisions
Configuration	Templates, inheritance, merge rules, environment variables
REST API	REST client design, endpoints, authentication
Roadmap	Planned features and milestones

Testing

uv run pytest