ai-factory-sdk 0.2.0.dev4

Python SDK for the AI Factory Compute API

AI Factory SDK

Python SDK for the AI Factory Compute API — submit and manage HPC jobs from Python.

Features

• Synchronous and asynchronous clients (AIFactoryClient, AsyncAIFactoryClient)
• Typed request/response models with Pydantic validation
• Job polling with configurable timeout and retry (client.wait())
• Automatic retry on transient errors (429, 5xx)
• PEP 561 compatible — full type annotation coverage
• ai-factory CLI for shell workflows (ai-factory jobs list/get/submit-container/cancel)

Installation

pip install ai-factory-sdk

Or with uv:

uv add ai-factory-sdk

Pre-release versions

Development builds published from the dev branch use PEP 440 pre-release suffixes (e.g., 0.2.0.dev1). Install them with:

pip install ai-factory-sdk --pre

Quick Start

from ai_factory.sdk import AIFactoryClient, JobRequest

# Credentials resolve from ~/.ai-factory/config.yaml, env vars, or constructor
# args (see "Configuration" below). Passed explicitly here for clarity:
with AIFactoryClient(
    api_key="dev-portal-api-key",
    slurm_token="slurm-jwt",
    slurm_user="jane",
) as client:
    # Submit a job
    resp = client.submit_job(
        JobRequest(name="hello", script="#!/bin/bash\necho Hello from SLURM")
    )
    print(f"Submitted job {resp.job_id}")

    # Wait for completion
    if resp.job_id is not None:
        detail = client.wait(str(resp.job_id), timeout=3600)
        print(f"Job finished with status: {detail.status}")

Async Usage

import asyncio
from ai_factory.sdk import AsyncAIFactoryClient, JobRequest

async def main():
    async with AsyncAIFactoryClient(
        api_key="dev-portal-api-key",
        slurm_token="slurm-jwt",
        slurm_user="jane",
    ) as client:
        resp = await client.submit_job(
            JobRequest(name="async-job", script="#!/bin/bash\nsleep 10 && echo done")
        )
        if resp.job_id is not None:
            detail = await client.wait(str(resp.job_id))
            print(detail.status)

asyncio.run(main())

Container Jobs

from ai_factory.sdk import AIFactoryClient, ContainerJobRequest

with AIFactoryClient(
    api_key="dev-portal-api-key",
    slurm_token="slurm-jwt",
    slurm_user="jane",
) as client:
    resp = client.submit_container(
        ContainerJobRequest(
            name="gpu-training",
            image="docker://nvcr.io/nvidia/pytorch:24.01-py3",
            container_command="python train.py",
            gres="gpu:a40:1",
            time_limit=120,
        )
    )

Configuration

The Compute API sits behind an APISIX gateway, so two distinct credentials are required (see onboarding & auth flow):

• api_key — the Developer Portal API key. The SDK sends it as the apikey header so APISIX's key-auth plugin lets the request through.
• slurm_token — the Slurm JWT (scontrol token). The Compute API forwards it to the upstream Slurm REST endpoints.

Credentials resolve from three sources, in priority order:

1. Explicit constructor arguments — Client(api_key=..., slurm_token=..., slurm_user=...).
2. Environment variables.
3. YAML config file at ~/.ai-factory/config.yaml.

If a required value is missing from all three sources, Client() raises ValueError with a message listing all three options.

Parameter	Environment Variable	Config File Key	Default
base_url	AI_FACTORY_API_URL	api_url	https://aifactory.ai-factory.datalab.tuwien.ac.at/compute-api/v1
api_key	AI_FACTORY_API_KEY	api_key	(required — Developer Portal key, sent as apikey)
slurm_token	AI_FACTORY_SLURM_TOKEN	slurm_token	(required — Slurm JWT, sent as X-SLURM-USER-TOKEN)
slurm_user	AI_FACTORY_SLURM_USER	slurm_user	(required)
timeout	—	—	30.0 (HTTP timeout in seconds)

Config file

Example ~/.ai-factory/config.yaml:

api_url: "https://aifactory.ai-factory.datalab.tuwien.ac.at/compute-api/v1"
api_key: "your-developer-portal-api-key"
slurm_token: "eyJhbGciOiJSUzI1NiIs..."   # scontrol token output
slurm_user: "jane.doe"

Secure the file so only your user can read it:

chmod 600 ~/.ai-factory/config.yaml

The SDK emits a UserWarning when a Client() is constructed if the file is group- or world-accessible. A malformed or unreadable file raises ConfigFileError (a subclass of SDKError).

Command-Line Interface

Installing the SDK also registers the ai-factory console script for users who prefer shell workflows or want to drive the platform from bash:

# Set credentials once (or use ~/.ai-factory/config.yaml — same resolution chain as Client())
export AI_FACTORY_API_KEY="your-developer-portal-api-key"     # -> apikey header
export AI_FACTORY_SLURM_TOKEN="eyJhbGciOi..."                 # -> X-SLURM-USER-TOKEN
export AI_FACTORY_SLURM_USER="jane.doe"

ai-factory --version                    # print SDK version and exit
ai-factory jobs list                    # table output
ai-factory jobs list --json             # machine-readable
ai-factory jobs get 459381              # single job detail
ai-factory jobs submit-container \
    --name training-run \
    --image docker://nvcr.io/nvidia/pytorch:24.01-py3 \
    --command "python train.py" \
    --partition GPU-a100 \
    --gres gpu:a40:1 \
    --time-limit 120
ai-factory jobs cancel 459381

Every subcommand supports --help and --json. Errors map to distinct exit codes so shell scripts can branch on the failure mode (codes start at 10 so they do not collide with Click/Typer's argument-parse exit 2):

Exit code	Meaning
0	success
2	usage error (raised by Typer for unknown options/missing arguments)
10	configuration error (missing credentials, bad config file)
11	authentication failed (expired/invalid token)
12	resource not found (e.g. unknown job ID)
13	API error (server returned non-success status)
14	other SDK error

The CLI shares the credential resolution chain with AIFactoryClient: explicit env vars take precedence over ~/.ai-factory/config.yaml.

There is no ai-factory jobs wait subcommand yet. Poll with jobs get --json in a script until .status is one of completed / errored / cancelled, or use the Python client.wait() method directly.

API Reference

Clients

Class	Description
AIFactoryClient	Synchronous client (context manager)
AsyncAIFactoryClient	Asynchronous client (async context manager)

Methods

Method	Description
submit_job(request)	Submit a Slurm job script
submit_container(request)	Submit a containerised job
get_job(job_id)	Get job details by ID
list_jobs(...)	List jobs with optional filters and pagination
cancel_job(job_id)	Cancel a running or pending job
wait(job_id, ...)	Poll until the job reaches a terminal state

Request Models

Model	Fields
JobRequest	name, script, partition, tasks, cpus_per_task, time_limit, gres, standard_output, standard_error
ContainerJobRequest	name, image, container_command, partition, tasks, cpus_per_task, time_limit, gres, standard_output, standard_error

Response Models

Model	Fields
SubmitJobResponse	job_id, output_dir, logs_url
JobDetail	job_id, name, status, partition, nodes, exit_code, duration, start_time, end_time, submit_time, working_directory, standard_output, standard_error, gres, output_dir, logs_url
JobListItem	job_id, name, status, duration, start_time, end_time
JobList	jobs, total, limit, offset
CancelJobResponse	message

Exceptions

Exception	When
SDKError	Base for all SDK errors
APIError	Non-2xx HTTP response
AuthError	401 or 403 response
NotFoundError	404 response
WaitTimeoutError	wait() exceeded its deadline
ConfigFileError	~/.ai-factory/config.yaml unreadable or malformed

Requirements

• Python >= 3.11
• httpx >= 0.28
• pydantic >= 2.0

License

MIT