MLFlow

MLFlow#

MLFlow is an open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, model versioning, and deployment. With ArchiTXT’s integration, you can effortlessly log experiment executions, traces, and key metrics directly to MLFlow for streamlined monitoring and analysis.

ArchiTXT creates a dedicated MLFlow experiment for logging and every simplification process is recorded as a separate run in this experiment. This detailed logging allows you to:

Track the progress and performance of each simplification step.
Compare different runs to identify the most effective parameters.
Easily navigate through experiment histories using the MLFlow UI.

Configure MLFlow#

Tip

By default, MLFlow logs experiments to a local directory. It is the recommended solution if you just want to try MLFlow.

To connect ArchiTXT to a remote MLFlow tracking server, set the environment variable MLFLOW_TRACKING_URI:

$ export MLFLOW_TRACKING_URI=http://127.0.0.1:5000  # Replace with your remote host

You can also set the tracking URI in your Python code:

import mlflow

mlflow.set_tracking_uri("http://127.0.0.1:5000")  # Replace with your remote host

Run Experiments#

ArchiTXT can log experiments to MLFlow if it is executed within an active MLFlow run. In Python, you can create a run as follows:

import mlflow

with mlflow.start_run():
    ... # <- Your code here

Once the run is started, execute your experiments as usual, and ArchiTXT will automatically handle the logging.

You can also enable MLFlow logging whe using the CLI by using the –log option.

Explore Data#

Visualize your logged data using the MLFlow web interface. If running locally, you can start the MLFlow UI by running:

$ mlflow ui

Open your browser and navigate to the default URL (usually http://127.0.0.1:5000). In the web UI, you can review your experiment details and performance metrics.