aihpi 0.1.2

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.

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
• Flexible Configuration: Dataclass-based configuration system

Setup and Installation

Prerequisites

• Python ≥ 3.8
• submitit ≥ 1.4.0
• Access to SLURM cluster with Pyxis/Enroot (for container jobs)

Installation with pip

1. Clone the repository:

   git clone https://github.com/aihpi/aihpi-cluster.git
   cd aihpi-cluster
   

2. Install the package:

   # Basic installation
   pip install -e .
   
   # With experiment tracking support
   pip install -e ".[tracking]"
   
   # With all optional dependencies
   pip install -e ".[all]"
   

Installation with UV (Recommended)

UV is a fast Python package manager that provides better dependency resolution and faster installs:

1. Install UV (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh
   # or
   pip install uv
   

2. Clone and setup:

   git clone https://github.com/aihpi/aihpi-cluster.git
   cd aihpi-cluster
   

3. Install with UV:

   # Basic installation
   uv pip install -e .
   
   # With experiment tracking support
   uv pip install -e ".[tracking]"
   
   # With all optional dependencies (recommended)
   uv pip install -e ".[all]"
   

Quick Start

After installation, start using aihpi:

from aihpi import SlurmJobExecutor, JobConfig

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

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

User Guide

Using the Tool

1. Configure your job using JobConfig with resource requirements and SLURM parameters
   • Important: Set login_node to your SLURM login node IP for remote job submission
2. Create an executor with SlurmJobExecutor(config)
3. Submit your function with executor.submit_function(func) or executor.submit_distributed_training(func)
4. Monitor progress using JobMonitor for real-time status updates
5. Track experiments with Weights & Biases, MLflow, or local tracking

Basic Example

from aihpi import SlurmJobExecutor, JobConfig, ContainerConfig

# Configure multi-node distributed training
config = JobConfig(
    job_name="distributed-training",
    num_nodes=4,
    gpus_per_node=2,
    walltime="04:00:00",
    partition="aisc",
    login_node="10.130.0.6",  # Your SLURM login node IP
)

# Configure container
config.container = ContainerConfig(
    name="torch2412",
    mounts=["/data:/workspace/data"]
)

executor = SlurmJobExecutor(config)

def distributed_training():
    import os
    print(f"Node rank: {os.getenv('NODE_RANK')}")
    print(f"World size: {os.getenv('WORLD_SIZE')}")
    # Your distributed training code here

job = executor.submit_distributed_training(distributed_training)

Command Line Interface

The aihpi CLI provides a convenient command-line interface for job submission and management:

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

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

# Submit distributed job (automatically detected from config)
aihpi run train.py --config distributed_config.py

# Submit LlamaFactory job with app config
aihpi run llamafactory-cli train --config job_config.py --app-config train.yaml

# Monitor a running job
aihpi monitor 12345 --follow

# Check job status
aihpi status

# Cancel a job
aihpi cancel 12345

CLI Configuration Files

The CLI uses Python configuration files containing a JobConfig object:

# config.py
from aihpi import JobConfig
from pathlib import Path

config = JobConfig(
    job_name="my_job",
    num_nodes=1,
    gpus_per_node=2,
    walltime="02:00:00",
    partition="gpu",
    log_dir=Path("./logs"),
    login_node="10.130.0.6"
)

The CLI automatically determines the submission mode:

• Function mode: Single-node Python scripts
• Distributed mode: Multi-node Python scripts (when num_nodes > 1)
• CLI mode: Non-Python executables

Advanced Features

• Job Monitoring: Real-time status tracking and log streaming
• Experiment Tracking: Automatic logging of metrics, parameters, and artifacts
• Remote Submission: Submit jobs via SSH from any machine
• LlamaFactory Integration: Built-in support for LLM fine-tuning

See aihpi/examples/ for comprehensive usage examples.

Recommendations

• Use containerized jobs for reproducible environments
• Enable experiment tracking for better ML workflow management
• Monitor long-running jobs with the built-in monitoring utilities
• Configure SSH keys for seamless remote job submission

Package Structure

aihpi/
├── cli.py              # Command-line interface
├── core/               # Core job submission functionality
│   ├── config.py      # Configuration classes
│   └── executor.py    # Job executors
├── monitoring/        # Job monitoring utilities
│   └── monitoring.py  # Real-time job status and log streaming
├── tracking/          # Experiment tracking integrations
│   └── tracking.py    # W&B, MLflow, and local tracking
└── examples/          # Usage examples
    ├── basic.py       # Basic job submission examples
    └── monitoring.py  # Monitoring and tracking examples

Limitations

• SLURM Dependency: Requires access to a SLURM cluster environment
• Container Runtime: Container features require Pyxis/Enroot setup
• Network Access: Remote submission requires SSH connectivity to login nodes

References

• submitit Documentation
• SLURM Workload Manager
• Weights & Biases
• MLflow

Author

• Felix Boelter

License

MIT License - see LICENSE file for details.

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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.