# ML Project Manager
The intent of this project is to provide a quick and easy to use framework to run machine learning experiments in a systematic way, while keeping track of all the important details that are necessary for reproducibility.
## Installation This project is uploaded to the Python Package Index, so you can simply run the following command: python3 -m pip install mlproj_manager
## Usage Here is a quick list of steps to create and run a new experiment:
1. Write a python script with a class that is a child of the Experiment abstract class in ./mlproj_manager/experiments/abstract_experiment.py. See ./examples/non_stationary_cifar_example for an example. 2. Register the experiment using the command python -m mlproj_manager.experiments.register_experiment with the arguments –experiment-name followed by a named of your choosing, –experiment-path followed by the path to the script created in step 1, and –experiment-class-name followed by the name of the class defined in the script created in step 1. 3. Create a config.json file for your experiment that contains all the relevant details for running the experiment. See ./examples/non_stationary_cifar_example/config_files/backprop.json for an example. 4. Finally, run the experiment using the command python -m mlproj_manager.main with the arguments –experiment-name followed by the experiment name used in step 2, –experiment-config-path followed by the path to the config file created in step 3, –use-slurm (optional) to indicate whether to schedule the experiment using slurm, and –slurm-config-path (required only if using slurm) followed by the path to a similar file as the one created for step 3 but with parameters relevant to the slurm scheduler.
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Release files for mlproj-manager 0.0.21
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