gpurun 0.1.1

GPU job scheduler using filelock

🚀 GPU Runner (gpurun)

Overview

gpurun is a sophisticated command-line tool designed to simplify scheduling GPU jobs on a single machine for machine learning, data science, and computational tasks. It provides intelligent GPU allocation, concurrency control, and seamless integration with CUDA applications using CUDA_VISIBLE_DEVICES. It is inspired by the sem tool from GNU parallel.

Why Use GPU Runner?

Imagine you are on an 8-GPU machine, and you want to run 100 inference jobs. You want to run them in parallel. Each job needs 2 GPUs and each GPU can only run three jobs at a time to avoid GPU memory overflow. You think you need a cluster scheduler like sbatch but for a single machine on GPUs. If this is your problem, GPU Runner is the perfect solution for you!

Think of GPU Runner as a local GPU scheduler that intelligently coordinates job execution, preventing memory overflow and maximizing computational efficiency.

🌟 Key Features

• Flexible GPU Allocation: Automatically assign jobs to GPUs with CUDA_VISIBLE_DEVICES
• Simple Interface: Minimalistic command-line interface
• Concurrent Job Management: Limit and control GPU usage across multiple jobs
• Automatic GPU Detection: Automatically identifies available GPUs
• GNU Parallel Compatibility: Easy to be used with GNU parallel
• Session-based Locking: Prevent GPU contention between different computational tasks

Installation

pip install git+https://github.com/zhou13/gpurun

Usage Examples

Basic Usage

# 1. Run infer.py (needs 1 GPU) on 100 images with all the GPUs in parallel, 1 job per GPU.
for i in $(seq 1 100); do gpurun python infer.py $i.jpg & done

# 2. Same as 1, but put 2 jobs per GPU at the same time.
for i in $(seq 1 100); do gpurun -j2 python infer.py $i.jpg & done

# 3. Same as 2, but use gnu-parallel to simplify the command.
parallel -j0 gpurun -j2 python infer.py {} ::: $(seq 1 100)

# 4. Same as 1, but infer.py now will see 2 GPUs.
parallel -j0 gpurun -g2 python infer.py {} ::: $(seq 1 100)

# 5. You can customize the GPUs to be used with --gpus.
parallel -j0 gpurun --gpus 0,1 python infer.py {} ::: $(seq 1 100)

# 6. You can customize the name of lockfile with --session.
parallel -j0 gpurun --session ml-seesion python infer.py {} ::: $(seq 1 100)

Configuration Options

$ gpurun --help
usage: gpurun [-h] [--session SESSION] [--gpus GPUS] [-j MAX_JOBS_PER_GPU] [-g GPUS_PER_JOB] ...

Run a command with GPU management and scheduling

positional arguments:
  command               Command to run (e.g., "python run.py")

options:
  -h, --help            show this help message and exit
  --session SESSION     String representing the session name used in locks (default: "default-session")
  --gpus GPUS           Comma-separated list of GPU IDs to use (e.g., "0,1,2") or "auto" for autodetection
  -j MAX_JOBS_PER_GPU, --max-jobs-per-gpu MAX_JOBS_PER_GPU
                        Maximum number of concurrent jobs allowed per GPU group (default: 1)
  -g GPUS_PER_JOB, --gpus-per-job GPUS_PER_JOB
                        Number of GPUs needed for each job (default: 1)

Contributing

Contributions welcome! Feel free to open issues and submit pull requests.

License

This project is licensed under the MIT License.