Skip to main content

A package with utilities for managing and running machine learning projects

Project description

# 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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mlproj_manager-0.0.29.tar.gz (40.0 kB view details)

Uploaded Source

Built Distribution

mlproj_manager-0.0.29-py3-none-any.whl (55.2 kB view details)

Uploaded Python 3

File details

Details for the file mlproj_manager-0.0.29.tar.gz.

File metadata

  • Download URL: mlproj_manager-0.0.29.tar.gz
  • Upload date:
  • Size: 40.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.6

File hashes

Hashes for mlproj_manager-0.0.29.tar.gz
Algorithm Hash digest
SHA256 0913946049fc808233e04bb1c65e2f11eaf9c0de0d288a30ed6da7be2bf8fdcc
MD5 45da5cf603ce2902094d5974ca16a0a4
BLAKE2b-256 2b26104c6d15a7adbfc1e1d744078b3a12fe29cb3072c526ace1f4ac1f5fdd10

See more details on using hashes here.

File details

Details for the file mlproj_manager-0.0.29-py3-none-any.whl.

File metadata

File hashes

Hashes for mlproj_manager-0.0.29-py3-none-any.whl
Algorithm Hash digest
SHA256 ee197f393af0c2c1594e3262b006485061d91741e466cade845637250c826c6e
MD5 112130658869957baf1b72c86ac03a70
BLAKE2b-256 c48db6632c10c50a238b68c9834c3e83666b5c25b22e02cb9009c54543437199

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page