/ Machine Learning

Experiment tracking for machine and deep learning projects

Experiment tracking for machine and deep learning projects

modelchimp

ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

ModelChimp

ModelChimp provides the following features:

  • Real-time tracking of parameters and metrics
  • Realtime charts for experiment metrics at epoch level
  • Code used for the experiment
  • Experiment comparison
  • Collaborate and share experiments with team members
  • Python objects storage such as data objects and model objects which can be used pulled for other experiments
  • Storage of test and validation images for computer vision use cases. Useful for post experiment forensics of deep learning models
  • Server based solution with user registration and authentication

Why ModelChimp?

The idea for ModelChimp came up when I was building a recommendation algorithm for a large retail company based in India. Along with my 6 member team, we would store the meta information related to each experiment in an excel sheet. Two of the biggest problems we encountered while using this approach were:

  1. Sometimes, we would miss out on logging the details while fine-tuning and analysing the model
  2. Sharing these excel sheets over email amongst the team members and the client was a cumbersome process

ModelChimp is a solution to this problem faced by data scientists and machine learning engineers/enthusiasts. They can spend more time on experiments and not on managing the data related to the experiments.

Installation

Choose either Docker based installation or the manual approach.

  • Docker
  • Production Deployment

Docker

  1. Docker is a prerequisite. You can download it from here - https://docs.docker.com/install/
$ git clone https://github.com/ModelChimp/modelchimp
$ cd modelchimp
$ bash docker.sh
  1. After starting ModelChimp server, you can access it at http://localhost:8000

  2. Use the following credentials to log in

username: [email protected]
password: modelchimp123
  1. (Optional) If you are using modelchimp on a remote server then add the hostname or ip address in the .env file for the following variables
DOMAIN=<hostname/ip>
ALLOWED_HOSTS=.localhost,127.0.0.1,<hostname/ip>
  1. (Optional) For inviting team members, email credentials have to be added for the following variables in .env file
EMAIL_HOST=
EMAIL_HOST_USER=
EMAIL_HOST_PASSWORD=
EMAIL_PORT=587
DEFAULT_FROM_EMAIL="[email protected]"

GitHub