Python Training Reference

This page describes all the methods from our different package (picsellia_tf1, picsellia_tf2) that simplifies training for the different AI frameworks.

The checkout method

To checkout an experiment, you can use the following method :

from picsellia import Client

api_token = '4a54b5d45e45f4c454b54dee5b54bac4dd4'
project_token = '9a7d45b4c-691d-4c3a-9972-6a22b1dcd6f'

experiment = Client.Experiment(

The checkout method will return the instance of the Experiment Class, you will now have access to every method without entering the name or id again.


There are several optional arguments you can specify for this method

  • tree (Boolean, default=False)

  • with_files (Boolean, default=False)

  • with_data (Boolean, default=False)


Use the tree parameter to automatically create training-ready folders for your experiment. If set to True it will create the following folders :

  • checkpoint

  • config

  • exported_model

  • images

  • metrics

  • records

  • results

Please check the picsellia_tf documentation for more details on how this different folders are used.


Set this argument to True to access the details of every data assets stored for your experiment, this will give you access to the raw data stored in every asset


Set this argument to True to download every file asset stored for your experiment

If tree is set to True, the files will be downloaded to the folder corresponding to their type (e.g a file named 'chekpoint-index' will be stored in the 'checkpoint' folder.

If not, all the files will be stored to the path of the code you are runnning.

Last updated