Graph optimizations on a tensorflow serveable created using tfEstimator

Optimizing Your TensorFlow Serveable: Unleashing the Power of Graph Optimization

TensorFlow Serving, a powerful framework for deploying trained TensorFlow models, offers various mechanisms to optimize your serveable for performance. This article delves into the realm of graph optimization, focusing on how to leverage these techniques within the context of tf.Estimator models.

Understanding Graph Optimization

Think of a TensorFlow model as a complex network of operations, represented as a computational graph. This graph, constructed during training, dictates how data flows through the model. Optimization aims to restructure this graph, improving its efficiency for inference. This can involve:

• Constant Folding: Eliminating redundant computations by pre-calculating constant values.
• Redundancy Elimination: Identifying and removing duplicate operations, streamlining the workflow.
• Shape Inference: Analyzing input and output shapes to determine optimized data paths.
• Operator Fusion: Combining multiple operations into a single, more efficient one.

Graph Optimization with tf.Estimator

tf.Estimator provides a high-level API for defining and training machine learning models in TensorFlow. The Estimator framework itself doesn’t directly handle graph optimization, but it offers several avenues to achieve it:

1. Using tf.contrib.graph_editor: This powerful library allows you to manually manipulate the computational graph, applying techniques like constant folding and operator fusion. This provides granular control, but requires deeper knowledge of TensorFlow’s internals.

2. Leveraging the tf.saved_model API: tf.saved_model provides a standardized format for exporting trained models. When exporting, you can specify optimization_level to control the degree of optimization applied. This allows you to fine-tune the trade-off between efficiency and model accuracy.

3. Employing TensorFlow’s Built-in Optimizers: TensorFlow comes equipped with various optimization passes that are automatically applied during graph construction. You can further customize these passes by setting the tf.compat.v1.disable_eager_execution flag, giving you fine-grained control over the optimization process.

Practical Example: Optimizing a Simple tf.Estimator Model

Let’s consider a basic image classification model using tf.Estimator:

import tensorflow as tf

def build_model():
    """Defines the model."""
    input_layer = tf.keras.layers.Input(shape=(28, 28, 1))
    # ... Model layers ...
    output_layer = tf.keras.layers.Dense(10, activation='softmax')(...)
    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
    return model

def model_fn(features, labels, mode):
    """Model function for tf.Estimator."""
    model = build_model()
    predictions = model(features)
    # ... Loss, evaluation metrics ...
    return tf.estimator.EstimatorSpec(mode=mode,
                                    predictions=predictions,
                                    loss=loss,
                                    train_op=train_op)

estimator = tf.estimator.Estimator(model_fn=model_fn)
# ... Train the estimator ...

To optimize this model for serving, we can use tf.saved_model:

# ... Train the estimator ...

tf.saved_model.save(estimator,
                    export_dir='exported_model',
                    signatures=tf.saved_model.signature_def_utils.predict_signature_def(
                        inputs={'input': tf.saved_model.utils.build_tensor_info(features)},
                        outputs={'output': tf.saved_model.utils.build_tensor_info(predictions)})

The optimization_level parameter can be set within the tf.saved_model.save function to control the degree of optimization applied.

Conclusion

Graph optimization plays a crucial role in boosting the performance of your TensorFlow serveable. By understanding the available techniques and leveraging tf.Estimator‘s features, you can optimize your models for efficient inference, maximizing the speed and efficiency of your TensorFlow-based applications. Remember to carefully balance optimization efforts with accuracy and complexity considerations to find the sweet spot for your specific use case.