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 :
1
from picsellia import Client
2
3
api_token = '4a54b5d45e45f4c454b54dee5b54bac4dd4'
4
project_token = '9a7d45b4c-691d-4c3a-9972-6a22b1dcd6f'
5
6
experiment = Client.Experiment(
7
api_token=api_token,
8
project_token=project_token
9
)
10
experiment.checkout(
11
name='my_new_experiment'
12
)
Copied!
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.

Arguments

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)

Tree

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.

with_data

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

with_files

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 modified 9mo ago