Environmental Variable for Evaluation Pipeline

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1

Where can I get the list of Environmental variable parameters for Evaluation or Full pipeline run?
Any link which has the list please?

2

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3

Hi @anto.santhosh
It actually depends of the model you want to use, all models are described in documentation.

4

Hello Mr Jeremy Tederry. Can you please explain why do we use environment variables in pipelines in UiPath AI Center. Thanks in advance

5

Hi @Jothi_prasanna_B

Please find the Details on "environment variables "

  • In the Enter parameters section, enter the environment variables defined and used by your pipeline, if any. The environment variables are:
    training_data_directory , with default value dataset/training : Defines where the training data is accessible locally for the pipeline. This directory is used as input for the train() function. Most users will never have to override this through the UI and can just write data into os.environ['training_data_directory'] in the process_data function and can just expect that the argument data_directory in train(self, data_directory will be called with os.environ['training_data_directory'] .
    test_data_directory with default value dataset/test : Defines where the test data is accessible locally for the pipeline. This directory is used as input to the evaluate() function. Most users will never have to override this through the UI and can just write data into os.environ['test_data_directory'] in the process_data function and can just expect that the argument data_directory in evaluate(self, data_directory will be called with os.environ['test_data_directory'] .
    artifacts_directory , with default value artifacts : This defines the path to a directory that will be persisted as ancillary data related to this pipeline. Most, if not all users, will never have the need to override this through the UI. Anything can be saved during pipeline execution including images, pdfs, and subfolders. Concretely, any data your code writes in the directory specified by the path os.environ['artifacts_directory'] will be uploaded at the end of the pipeline run and will be viewable from the Pipeline details page.
    save_training_data , with default value true : If set to true , training_data_directory folder will be uploaded at the end of the pipeline run as an output of the pipeline under directory training_data_directory .
    save_test_data , with default value true : If set to true , test_data_directory folder will be uploaded at the end of the pipeline run as an output of the pipeline under directory test_data_directory .
6

I am also curious on the environment variable section. So, is this creating a new variable with a value or is it assigning a value to a variable?

For example, I have this evaluate method
def evaluate(self, test_directory):
content = pd.read_csv(os.path.join(test_directory, ‘Test_case.csv’))
Based on some research, once called the ‘test_directory’ will have an assigned value of os.environ[‘test_data_directory’] (./data/dataset) but here its hardcoded “.csv” so I can’t change it in the UI.
However, if I do
def evaluate(self, test_data_directory, test_file):
content = pd.read_csv(os.path.join(test_directory, test_file))
and assign in the environment variable test_file = ‘Other_test.csv’
Will it work if not any suggestions? I want to know if that is the function of environment variables.