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