Trainer¶

The Trainer defines the model and training loop within the TrainingPipeline.

Standard trainers¶

A standard TFFeedForwardTrainer step is provided in the source code, which defines a simple feed-forward neural network in Tensorflow.

Create custom trainers¶

In the case of the Trainer, the built-in methods are just convenience to access popular model types. Most of the times, custom model code is required. This is how to create custom trainer steps:

Base Trainer¶

ZenML comes equipped with a BaseTrainer that all trainers should inherit from. This is how the interface looks like:

    def run_fn(self):
        pass

    def input_fn(self,
                 file_pattern: List[Text],
                 tf_transform_output: tft.TFTransformOutput):
        pass

    def model_fn(train_dataset: tf.data.Dataset,
                 eval_dataset: tf.data.Dataset):
        pass

This doc section is incomplete. Please refer to the docstrings in source code while you wait to complete this section.

Tensorflow-based Trainers¶

PyTorch-based Trainers¶

Coming soon.

Other libraries¶