Installation and setup
For monitoring experiments using TrueFoundry you need to setup mlfoundry:
mlfoundrypython library
Step 1: Signup or Login on TrueFoundry
Sign up on TrueFoundry using email, Google account, or GitHub account.
Step 2: Setup MLFoundry
mlfoundry is the python library from TrueFoundry used for experiment tracking.
Install ServiceFoundry client library
On your machine, run (preferably within a virtual environment or conda environment):
pip install mlfoundry
Login from CLI
From the CLI, run the following and follow the link to authenticate:
mlfoundry login
Login to managed or self-hosted
If you are running the Truefoundry platform on your own infrastructure or isolated from our public cloud offering, you can login using the
--hostparametermlfoundry login --host https://app.example.yourdomain.com
You will be prompted to complete the authentication by clicking on the link.
Output:
Opening:- https://app.truefoundry.com/authorize/device?userCode=XXXX
Please click on the above link if it is not automatically opened in a browser window.
Successfully logged in to 'https://app.truefoundry.com' as 'abc-tfy'
([email protected])
Troubleshooting
How can I log in to mlfoundry on a device in a non-interactive mode?
If you are running automated training jobs, you can use the TFY_HOST and TFY_API_KEY environment variable to set the API Key. You do not need to use the mlfoundry login command if you are using environment variables.
The API key passed via the environment variable takes precedence over the API key passed via the login command.
export TFY_HOST=https://app.example.yourdomain.com # e.g. https://app.truefoundry.com
export TFY_API_KEY="your-api-key"
Troubleshooting for Mac OS users with M1 processors
Users can face issues installing mlfoundry packages on Mac OS computers with M1 chip. Here are a few links that will help you troubleshoot.
Updated 24 days ago