# 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.
Raw data
{
"_id": null,
"home_page": "https://github.com/JFernando4/Research_Project_Manager",
"name": "mlproj-manager",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "ml, rl",
"author": "J. Fernando Hernandez-Garcia",
"author_email": "jfhernan@ualberta.ca",
"download_url": "https://files.pythonhosted.org/packages/ae/17/956852354643fa07b724037e1dcb65d47eefe92f3077e0fe0f66980fe636/mlproj_manager-0.0.28.tar.gz",
"platform": null,
"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",
"bugtrack_url": null,
"license": null,
"summary": "A package with utilities for managing and running machine learning projects",
"version": "0.0.28",
"project_urls": {
"Homepage": "https://github.com/JFernando4/Research_Project_Manager"
},
"split_keywords": [
"ml",
" rl"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "d0580df174772c880b547d2128ee774e8448bec3fac104f5f016b3c2a1c7329e",
"md5": "a058f31c3dcff25dd329ad61db2daa98",
"sha256": "68d2554039636110e166c14e3f7e2b5821986aab1d3e1a244e576a05025144f7"
},
"downloads": -1,
"filename": "mlproj_manager-0.0.28-py3-none-any.whl",
"has_sig": false,
"md5_digest": "a058f31c3dcff25dd329ad61db2daa98",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 55184,
"upload_time": "2024-04-12T14:34:41",
"upload_time_iso_8601": "2024-04-12T14:34:41.645698Z",
"url": "https://files.pythonhosted.org/packages/d0/58/0df174772c880b547d2128ee774e8448bec3fac104f5f016b3c2a1c7329e/mlproj_manager-0.0.28-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ae17956852354643fa07b724037e1dcb65d47eefe92f3077e0fe0f66980fe636",
"md5": "91ea6cc7a38edfa633d4a5cfb0b44d9d",
"sha256": "90586fe8ea5cec543f636fa041d74203bbafc5659c618c5d3062209db0595c80"
},
"downloads": -1,
"filename": "mlproj_manager-0.0.28.tar.gz",
"has_sig": false,
"md5_digest": "91ea6cc7a38edfa633d4a5cfb0b44d9d",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 39952,
"upload_time": "2024-04-12T14:34:43",
"upload_time_iso_8601": "2024-04-12T14:34:43.480337Z",
"url": "https://files.pythonhosted.org/packages/ae/17/956852354643fa07b724037e1dcb65d47eefe92f3077e0fe0f66980fe636/mlproj_manager-0.0.28.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-12 14:34:43",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "JFernando4",
"github_project": "Research_Project_Manager",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "matplotlib",
"specs": [
[
"==",
"3.6.3"
]
]
},
{
"name": "numpy",
"specs": [
[
"==",
"1.24.2"
]
]
},
{
"name": "torch",
"specs": [
[
">=",
"1.10.0"
]
]
},
{
"name": "torchvision",
"specs": [
[
">=",
"0.11.1"
]
]
}
],
"lcname": "mlproj-manager"
}
JSON
