experiment-launcher-meco 4.0.1

Launch experiments locally or on SLURM clusters with ease. Fork from 'experiment-launcher'.

Experiment Launcher

Launch experiments locally or on a cluster running SLURM in a single file.
Fork from experiment_launcher

Description

The experiment_launcher package provides a simple way to run multiple experiments using SLURM or Joblib with minimum effort - you just have to set the local parameter to True to run locally, or to False to run with a cluster using SLURM.

It is particularly useful to run multiple seeds and/or test multiple configurations of hyperparameters such as learning rates, batch size.

Installation

You can install the package from pip with:

pip install "experiment-launcher-meco

If you want to install it locally, you can do so with:

pip install -e .

How to Use

Basic Usage

The best way to understand experiment launcher is to look at the basic example in examples/basic.

• The file examples/basic/test.py contains a single experiment
• The file examples/basic/launch_test.py contains the launcher file configurations and call

Single experiment

• examples/basic/test.py consists of:

  • The function experiment is the entry point of your experiment

    • It takes as arguments your experiment settings (e.g., the number of layers in a neural network, the learning rate, ...)
    • The arguments need to be assigned a type and default value in the function definition
      • Current accepted types are int, float, str, bool, list
      • The arguments seed and results_dir must always be included
      • Python kwargs can also be added as **kwargs (accepted types are the same as above)
    • This function must be decorated with a decorator, e.g. @single_experiment. This will take care of creating proper results directories.

  • The if __name__ == '__main__' block

    • This must contain one single line: run_experiment(experiment)
• You can test your code by running

  cd examples/basic
  python test.py
  

Launch file

• examples/basic/launch_test.py consists of:
  • Creating an instance of the LauncherConfig object, that contains the SLURM or Joblib (if run locally) parameters. These are some of the important parameters. For more consult the class definition.
    • exp_name is the experiment name, under which a results directory will be created
    • exp_file is the path to the python file where the experiment is implemented (without the extension .py)
    • n_seeds is the number of random seeds for each single experiment configuration
  • Advanced configs are provided via nested Pydantic objects:
    • ResourceConfig specifies:
      • n_exps_in_parallel is the number of experiments to be run in parallel. This is useful to run multiple jobs in a single GPU in the cluster
      • n_cores is the number of cores for each experiment. Note that if n_exps_in_parallel > 1, then n_exps_in_parallel jobs will share n_cores.
      • memory_per_core is amount of memory in MB requested for each core in SLURM. If you specify this too low, SLURM might crash.
    • SlurmConfig specifies:
      • partition is the SLURM partition, which is cluster dependent
      • gres are special resources asked for a SLURM experiment
      • project_name is the project name in the cluster
    • EnvironmentConfig specifies:
      • conda_env if you are using a conda environment, specify its name here
    • DurationConfig specifies the max runtime of the SLURM job (days, hours, minutes, seconds).
  • Creating an instance of the Launcher object: launcher = Launcher(config)
  • Adding experiments with launcher.add_experiment
    • Use launcher.add_experiment to create an experiment for a particular configuration (e.g., different learning rates)
    • You can use the Sweep class to automatically sweep over a list of parameters.
    • E.g. launcher.add_experiment(learning_rate=Sweep(values=[1e-3, 1e-4]), batch_size=32) creates two experiments: one with 1e-3 and another with 1e-4.
    • Swept parameters automatically are used to organize the results directories. Results directories are going to be created by default as:
      • ./logs/exp_name_DATE/learning_rate_0.001/SEED/ and ./logs/exp_name_DATE/learning_rate_0.0001/SEED/
    • If multiple sweeps are provided, the Cartesian product of all sweep values is calculated to generate the experiments.
  • Running the experiments with launcher.run(LOCAL)
    • This runs your experiment either locally (LOCAL: True) or in the cluster (LOCAL: False)

Running the experiment

• To run the launcher simply call

  cd examples/basic
  python launch_test.py
  

• Log files will be placed in
  • ./logs if running locally or in the IAS cluster
  • /work/scratch/$USERNAME if ran in the Lichtenberg-Hochleistungsrechner of the TU Darmstadt

Integration with Weights and Biases

The experiment launcher provides an easy way to integrate with Weights and Biases.

• In the experiment file add
  • **kwargs in the experiment function definition
• In the launcher file, create wandb options and pass them to the launcher.add_experiment()

  wandb_options = dict(
    wandb_enabled=False,  # If True, runs and logs to wandb.
    wandb_entity='joaocorreiacarvalho',
    wandb_project='experiment_launcher_test',
    wandb_group='group_test'
  )
  

• To use wandb, you need to install it with pip install wandb and log in with wandb login

Running experiments with parameters in configuration files

• If you have many parameters that you need to change, a good idea can be to use configuration files.
• If you want to specify your parameters in a configuration file, look at the example under examples/config_files

Notes

• For reproducibility, the seeds are created sequentially from 0 to n_exps-1.