GPU Task Scheduler

A simple Python lib to schedule and execute deep learning tasks on available GPUs. It supports:

• Custom selection of GPUs.
• Maximum concurrency per GPU.
• Dynamic task dispatch using threading.

📦 Installation

pip install gpu_task_scheduler

🚀 Usage Example

from gpu_task_scheduler import GPUTaskScheduler

tasks = [
    "python train_model.py --epochs 10",
    "bash run_simulation.sh",
    "python evaluate.py --model checkpoint.pth"
]

scheduler = GPUTaskScheduler(
    wait_interval=10,
    allowed_gpu_ids=[0, 1],
    max_tasks_per_gpu=2,
    min_memory=0.8
)
scheduler.run_tasks(tasks)

⚙️ Parameters

GPUTaskScheduler(wait_interval=30, allowed_gpu_ids=None, max_tasks_per_gpu=1, min_memory=0.8)

Parameter	Type	Default	Description
wait_interval	int	30	Number of seconds to wait before checking again if no GPU is available.
allowed_gpu_ids	list[int]	None	List of GPU indices (e.g., [0, 1]) that the scheduler is allowed to use. If None, all GPUs are considered.
max_tasks_per_gpu	int	1	Maximum number of concurrent tasks allowed on a single GPU. Helps share GPU among multiple tasks.
min_memory	int or float	0.8	Minimum free memory (in MiB or as a ratio) required on a GPU to be considered available.

🛠️ Requirements

• Python 3.6+
• nvidia-smi available in PATH (NVIDIA drivers installed)