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You can download the code of this step here or all the steps here.

For Notebooks

The "Getting Started" Notebook is available here. In Taipy GUI, the process to execute a Jupyter Notebook is different from executing a Python Script. It is important to check the Notebook content and see the documentation.

Step 4: Pipeline Management

In Step 3, you have described your graph; let's implement it with Taipy!

Pipeline configuration

To configure your first pipeline, you need to list all the tasks you want to be done by the pipeline. This pipeline executes the cleaning (clean_data_task) and the predicting (predict_baseline_task). Note that the task_configs is a list, so you don't have to worry about the order of the tasks. Taipy does that for you and optimizes its execution.

# Create the first pipeline configuration
baseline_pipeline_cfg = Config.configure_pipeline(id="baseline",
                                                  task_configs=[clean_data_task_cfg, predict_baseline_task_cfg])

Pipeline creation and execution

Then, create your pipeline from its configuration, submit it, and print the "predictions" Data Node results.

import taipy as tp

# Create the pipeline
baseline_pipeline = tp.create_pipeline(baseline_pipeline_cfg)
# Submit the pipeline (Execution)

# Read output data from the pipeline
baseline_predictions =
print("Predictions of baseline algorithm\n", baseline_predictions)

Note that when creating the pipeline (tp.create_pipeline()), all associated Taipy objects of the pipeline (Data nodes, Tasks, etc) get automatically created (unless already present).