# 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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"description": "# ML Project Manager\n\nThe intent of this project is to provide a quick and easy to use framework to run machine learning experiments in a \nsystematic way, while keeping track of all the important details that are necessary for reproducibility.\n\n## Installation\nThis project is uploaded to the Python Package Index, so you can simply run the following command:\n`python3 -m pip install mlproj_manager`\n\n## Usage \nHere is a quick list of steps to create and run a new experiment:\n\n1. Write a python script with a class that is a child of the `Experiment` abstract class in \n`./mlproj_manager/experiments/abstract_experiment.py`. See `./examples/non_stationary_cifar_example` for an example. \n2. Register the experiment using the command `python -m mlproj_manager.experiments.register_experiment` with the arguments\n`--experiment-name` followed by a named of your choosing, `--experiment-path` followed by the path to the script\ncreated in step 1, and `--experiment-class-name` followed by the name of the class defined in the script created in \nstep 1.\n3. Create a `config.json` file for your experiment that contains all the relevant details for running the experiment.\nSee `./examples/non_stationary_cifar_example/config_files/backprop.json` for an example.\n4. Finally, run the experiment using the command `python -m mlproj_manager.main` with the arguments `--experiment-name` \nfollowed by the experiment name used in step 2, `--experiment-config-path` followed by the path to the config file\ncreated in step 3, `--use-slurm` (optional) to indicate whether to schedule the experiment using slurm, and\n`--slurm-config-path` (required only if using slurm) followed by the path to a similar file as the one created for step\n3 but with parameters relevant to the slurm scheduler. \n",
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