aihpi 0.1.4

AI High Performance Infrastructure - Distributed job submission for SLURM clusters

aihpi - AI High Performance Infrastructure

A Python package for simplified distributed job submission on SLURM clusters with container support. Built on top of submitit with additional features specifically designed for AI/ML workloads.

Installation

# Basic installation
pip install aihpi

# With experiment tracking support
pip install aihpi[tracking]

# With all optional dependencies
pip install aihpi[all]

Quick Start

from aihpi import SlurmJobExecutor, JobConfig

config = JobConfig(
    job_name="my-training",
    num_nodes=1,
    gpus_per_node=2,
    walltime="01:00:00",
    partition="gpu",
    login_node="10.130.0.6"  # Your SLURM login node IP
)

executor = SlurmJobExecutor(config)
job = executor.submit_function(my_training_function)

Features

• Simple API: Configure and submit jobs with minimal code
• Command Line Interface: aihpi CLI for easy job submission and management
• Distributed Training: Automatic setup for multi-node distributed training
• Container Support: First-class support for Pyxis/Enroot containers
• Remote Submission: Submit jobs via SSH from remote machines
• LlamaFactory Integration: Built-in support for LlamaFactory training
• Job Monitoring: Real-time job status tracking and log streaming
• Experiment Tracking: Integration with Weights & Biases, MLflow, and local tracking

Command Line Usage

# Submit a Python job
aihpi run train.py --config config.py

# Submit with monitoring
aihpi run train.py --config config.py --monitor

# Submit distributed job
aihpi run train.py --config distributed_config.py

# Monitor a running job
aihpi monitor 12345 --follow

Documentation & Examples

For detailed documentation, examples, and setup instructions, visit:

• GitHub Repository: aihpi/aihpi-cluster
• Full Documentation: README.md

Requirements

• Python ≥ 3.8
• Access to SLURM cluster
• submitit ≥ 1.4.0

License

MIT License

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Acknowledgements

The AI Service Centre Berlin Brandenburg is funded by the Federal Ministry of Research, Technology and Space under the funding code 01IS22092.