Train and Evaluate a Model at the Same Time

Configure the training pipeline as follows:

• In the Pipeline type field, select Full Pipeline run.
• In the Choose package field, select the package you want to train and evaluate.
• In the Choose package major version field, select a major version for your package.
• In the Choose package minor version field, select a minor version for your package. It is strongly recommended to always use minor version 0 (zero).
• In the Choose input dataset field, select a representative training dataset.
• In the Choose evaluation dataset field, select a representative evaluation dataset.
• In the Enter parameters section, enter any environment variables defined, and used by your pipeline, if any. For most use cases, no parameter needs to be specified; the model is using advanced techniques to find a performant configuration. However, here are some environment variables you could use:
• auto_retraining which allows you to complete the Auto-retraining Loop; if the variable is set to True, then the input dataset needs to be the export folder associated with the labeling session where the data is tagged; if the variable remains set to False, then the input dataset needs to correspond to the dataset format.
• model.epochs which customizes the number of epochs for the Training Pipeline (the default value is 100).
• Select whether to train the pipeline on GPU or on CPU. The Enable GPU slider is disabled by default, in which case the pipeline is trained on CPU. Using a GPU for training is at least 10 times faster than using a CPU. Moreover, training on CPU is supported for datasets up to 1000 images in size only. For larger datasets, you need to train using GPU.
• Select one of the options when the pipeline should run: Run now, Time based or Recurring. In case you are using the auto_retraining variable, select Recurring.
  
  

  

  
  
• After you configure all the fields, click Create. The pipeline is created.