Commands Cheat Sheet

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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