akarsh3007 / modelchimp

Experiment tracking for machine and deep learning projects

What is ModelChimp?

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

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

2. After starting ModelChimp server, you can access it at http://localhost:8000

3. Use the following credentials to log in

username: [e-mail]
password: modelchimp123

4. (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>

5. (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="[e-mail]"

Production Deployment

For production deployment, contact Karthik at [e-mail]

Documentation

• Getting Started - https://docs.modelchimp.com/#installation
• Sample Projects
  • Scikit: https://docs.modelchimp.com/scikit/
  • Tensorflow: https://docs.modelchimp.com/tensorflow/
  • Pytorch: https://docs.modelchimp.com/pytorch/
  • Keras: https://docs.modelchimp.com/keras/
  • PySpark: https://docs.modelchimp.com/pyspark/