Quickstart

Start logging your experiments with mlop in 4 simple steps:

1. Get an account at app.mlop.ai
2. Install our Python SDK. Within a Python environment, open a Terminal window and paste in the following,

pip install "mlop[full]"

For the latest version, you can install directly from the repository,

pip install "mlop[full] @git+https://github.com/mlop-ai/mlop.git"

3. Log in to your mlop.ai account from within the Python client,

import mlop
mlop.login()

4. Start logging your experiments by integrating mlop to the scripts, as an example,

import mlop
 
config = {'lr': 0.001, 'epochs': 1000}
run = mlop.init(project="mlop", name="experiment", config=config)
 
# insert custom model training code
for i in range(config['epochs']):
    run.log({"val/loss": 0})
 
run.finish()

And... profit! The script will redirect you to the webpage where you can view and interact with the run. The web dashboard allows you to easily compare time series data and can provide actionable insights for your training.

These steps are described in further detail in our introductory tutorial.
You may also learn more about mlop by checking out our demo.