Experiment tracking for machine and deep learning projects
ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.
ModelChimp provides the following features:
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:
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.
Choose either Docker based installation or the manual approach.
$ git clone https://github.com/ModelChimp/modelchimp
$ cd modelchimp
$ bash docker.sh
After starting ModelChimp server, you can access it at http://localhost:8000
Use the following credentials to log in
username: [email protected]
password: modelchimp123
DOMAIN=<hostname/ip>
ALLOWED_HOSTS=.localhost,127.0.0.1,<hostname/ip>
EMAIL_HOST=
EMAIL_HOST_USER=
EMAIL_HOST_PASSWORD=
EMAIL_PORT=587
DEFAULT_FROM_EMAIL="[email protected]"
docker-compose -f docker-compose.local.yml up --build -d
This will start the containers in daemon mode on the machine where Modelchimp resides. Modelchimp can be accessed from port 8000
DB_HOST=<DB_HOST>
DB_NAME=<DB_NAME>
DB_USER=<DB_USER>
DB_PASSWORD=<DB_PASSWORD>
DBPORT=
AWS_STORAGE_FLAG=True
AWS_ACCESS_KEY_ID=<ID>
AWS_SECRET_ACCESS_KEY=<KEY>
AWS_STORAGE_BUCKET_NAME=<bucket_name>
EMAIL_HOST=
EMAIL_HOST_USER=
EMAIL_HOST_PASSWORD=
EMAIL_PORT=587
DEFAULT_FROM_EMAIL="[email protected]"