TensorFlow Installation

pip install tensorflow Install TensorFlow pip install tensorflow-gpu Install TensorFlow with GPU support python -c 'import tensorflow as tf; print(tf.__version__)' Check TensorFlow version python -c 'import tensorflow as tf; print(tf.config.list_physical_devices("GPU"))' Check available GPUs

TensorFlow Basics

tf.constant(value) Create a constant tensor tf.Variable(value) Create a variable tensor tf.placeholder(dtype, shape=None, name=None) Create a placeholder for input tf.keras.Model() Create a model tf.keras.layers.Dense(units) Add a dense layer to your model

Model Training

model.compile(optimizer, loss, metrics) Configure the model for training model.fit(x, y, epochs, batch_size) Train the model model.evaluate(x_test, y_test) Evaluate the model model.predict(x) Generate predictions

TensorFlow Profiling

tf.profiler.experimental.start('logdir') Start profiling tf.profiler.experimental.stop() Stop profiling tensorboard --logdir=path/to/logs Launch TensorBoard to visualize profiling data

TensorFlow Debugging

tf.debugging.assert_equal(x, y) Assert tensors are equal tf.debugging.check_numerics(tensor, message) Check tensor for NaN or Inf tf.print(tensor) Print tensor values during execution tf.debugging.set_log_device_placement(True) Log device placement

TensorBoard

writer = tf.summary.create_file_writer('logs/') Create a file writer for logs with writer.as_default(): tf.summary.scalar('loss', loss, step=step) Log scalar values tensorboard --logdir=logs/ Launch TensorBoard server

Model Saving/Loading

model.save('path/to/model') Save entire model model = tf.keras.models.load_model('path/to/model') Load entire model model.save_weights('path/to/weights') Save model weights model.load_weights('path/to/weights') Load model weights

Distributed Training

strategy = tf.distribute.MirroredStrategy() Create distribution strategy for multiple GPUs with strategy.scope(): model = tf.keras.Model() Define model within strategy scope

TensorFlow Serving

docker pull tensorflow/serving Pull TensorFlow Serving Docker image docker run -p 8501:8501 --mount type=bind,source=/path/to/model,target=/models/my_model -e MODEL_NAME=my_model tensorflow/serving Run TensorFlow Serving container curl -d '{"instances": [[1.0, 2.0, 3.0]]}' -X POST http://localhost:8501/v1/models/my_model:predict Make prediction request to TF Serving