gridsearcher

GridSearcher 𖣯🔍

GridSearcher is a pure Python project designed to simplify the process of running grid searches for Machine Learning projects. It serves as a robust alternative to traditional bash scripts, providing a more flexible and user-friendly way to manage and execute multiple programs in parallel.

⚠️ It is designed for systems where users have direct SSH access to machines and can run their python scripts right away.

Features ✨󠁇󠁇󠁇

• Grid Search Made Easy: Define parameter grids effortlessly and the cartesian product of your hyper-parameters will be computed automatically and an instance of your script will be run for all possible combinations.
• Parallel Execution: Run multiple programs concurrently, maximizing your computational resources.
• GPU Scheduling: Built-in GPU allocation ensures efficient use of available GPUs. Specify the number of GPUs and jobs per GPU, and GridSearcher will handle the rest
• Flexible Configuration: Easily control the number of parallel jobs and GPU assignments through a scheduling dictionary.
• Pure Python: No more dealing with complex bash scripts. GridSearcher is written entirely in Python, making it easy to integrate into your existing Python workflows.

Why GridSearcher? 🤔

• User-Friendly: Simplifies the setup and execution of grid searches, allowing you to focus on your Machine Learning models.
• Efficient Resource Management: Optimize the use of your GPUs and computational resources.
• Pythonic Approach: Seamlessly integrates with your Python projects and leverages Python's rich ecosystem.
• Direct SSH Access: Ideal for systems where users have direct SSH access to machines, providing a straightforward setup and execution process without the need for SLURM or other workload managers, ensuring a smooth and efficient operation.

Installation 🛠️

Install GridSearcher via pip:

pip install gridsearcher

How to use GridSearcher?

We provide a minimal working example in the file example.py. Just set debug=True with debug=False in the run method call to run on GPUs. The output of example.py is the following:

GridSearcher PID: 8940
command 1: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=1_2024-06-19_23-04-23 --seed 1 --lr 1e-2 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=1_2024-06-19_23-04-23
command 2: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=1_2024-06-19_23-04-23 --seed 1 --lr 1e-2 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=1_2024-06-19_23-04-23
command 3: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=1_2024-06-19_23-04-23 --seed 1 --lr 1e-3 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=1_2024-06-19_23-04-23
command 4: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=1_2024-06-19_23-04-23 --seed 1 --lr 1e-3 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=1_2024-06-19_23-04-23
command 5: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=2_2024-06-19_23-04-23 --seed 2 --lr 1e-2 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=2_2024-06-19_23-04-23
command 6: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=2_2024-06-19_23-04-23 --seed 2 --lr 1e-2 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=2_2024-06-19_23-04-23
command 7: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=2_2024-06-19_23-04-23 --seed 2 --lr 1e-3 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=2_2024-06-19_23-04-23
command 8: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=2_2024-06-19_23-04-23 --seed 2 --lr 1e-3 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=2_2024-06-19_23-04-23
command 9: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=3_2024-06-19_23-04-23 --seed 3 --lr 1e-2 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=3_2024-06-19_23-04-23
command 10: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=3_2024-06-19_23-04-23 --seed 3 --lr 1e-2 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-2_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=3_2024-06-19_23-04-23
command 11: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=3_2024-06-19_23-04-23 --seed 3 --lr 1e-3 --wd 1e-2 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-2_beta1=0.9_beta2=0.999_eps=1e-8/seed=3_2024-06-19_23-04-23
command 12: python3 myscript.py --batch_size 128 --epochs 100 --lr_decay_at 82 123 --wandb_project cifar10-training --wandb_group cifar10_rn18_adamw_E=100_bs=128 --wandb_job_type lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8 --wandb_name seed=3_2024-06-19_23-04-23 --seed 3 --lr 1e-3 --wd 1e-3 --beta1 0.9 --beta2 0.999 --eps 1e-8 --root_folder ./results/cifar10-training/cifar10_rn18_adamw_E=100_bs=128/lr=1e-3_wd=1e-3_beta1=0.9_beta2=0.999_eps=1e-8/seed=3_2024-06-19_23-04-23

SBATCH wrapper for SLURM (NEW in version 1.0.4)

We also added a wrapper for SBATCH that allows running SLURM jobs directly from Python!

SBATCH(
    script='script.sh', # the shell script that will be launched using sbatch
    env_vars=dict( # these variables will be set in the --export argument of sbatch and will be available in the h100-eval.sh script
        root=ROOT,
        wandb_project=wandb_project,
        wandb_group=wandb_group,
        wandb_job_type=wandb_job_type,
        wandb_name=wandb_name,
        task='imagenet',
        batch_size=128,
    ),
    sbatch_args={ # the variables below will be added to the sbatch command (e.g. --job_name)
        'exclude': 'machine-1,machine-2',
        'job-name': f'job-name',
        'error': 'slurm_output/%j-%x.err',
        'output': 'slurm_output/%j-%x.out',
        'ntasks': 1,
        'cpus-per-task': 20,
        'time': '1-00:00:00',
        'mem': '200G',
        'partition': 'gpu',
        'gres': 'gpu:H100:1',
    }
).run()

Contribute 🤝

We welcome contributions! If you have suggestions for new features or improvements, feel free to open an issue or submit a pull request.

Versions history:

• 1.1.2 @ 2025-01-23:
  • added create_state_finished parameter to control whether the process writes the file state.finished when returns with error code 0 or not
• 1.1.1: fixed import issues
• 1.1.0: removed specific arguments and replaced them with dictionaries to offer flexibility to use any SBATCH params
• 1.0.4: added SBATCH class, which can be used in a completely separated manner from GridSearcher, allowing running slurm jobs from python
• 1.0.3: do not check whether the script ends with .py extension anymore
• 1.0.2: checking the return code of os.system and create file state.finished only if code == 0
• 1.0.1: added assert statement to make sure that all values in the scheduling["params_values"] are of type list
• 1.0.0: added initial project