tensorflow

name	tensorflow
description	TensorFlow for production ML pipelines and deep learning

Core Concepts

• Keras API: High-level tf.keras.Sequential and Model (Functional API) for model building
• tf.data: High-performance input pipeline with Dataset.from_tensor_slices, map, batch, prefetch
• Eager Execution: Imperative execution by default in TF2 with tf.function for graph compilation
• SavedModel: Universal serialization format for serving with model.save("path")
• TF Serving: gRPC/REST server for production model deployment with version management
• TensorBoard: Visualization toolkit via tf.keras.callbacks.TensorBoard logging

Common Patterns

• tf.keras.Sequential([layers.Dense(128, activation="relu"), layers.Dropout(0.2), layers.Dense(10, "softmax")])
• model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
• model.fit(train_ds, validation_data=val_ds, epochs=10, callbacks=[early_stopping])
• tf.data.Dataset.from_tensor_slices((x, y)).batch(32).prefetch(tf.data.AUTOTUNE)
• @tf.function decorator to trace and optimize Python functions into graphs
• Distribution strategy: with MirroredStrategy().scope(): for multi-GPU training

Best Practices

• Chain .cache(), .shuffle(), .batch(), .prefetch() for optimal input pipeline
• Use tf.keras.mixed_precision.set_global_policy("mixed_float16") for speedup
• Prefer tf.keras.Model subclassing for research, Functional API for production
• Validate SavedModel with saved_model_cli show --dir exported/path
• Set TF_CPP_MIN_LOG_LEVEL=3 to suppress verbose INFO logs in production