Efficient TensorFlow Workflows: How GPUs Can Help You Scale

TensorFlow can be configured to use multiple GPUs to accelerate the training of machine learning models. There are several ways to do this, depending on the hardware and software environment you are using.

If you have multiple GPUs on a single machine, you can use the tf.device context manager to specify which GPU to use for a particular operation. 

For example:

import tensorflow as tf with tf.device('/gpu:0'): # All operations in this block will be placed on GPU 0 a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # Creates a session with allow_soft_placement=True. sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # Runs the op. print(sess.run(c))




import tensorflow as tf

with tf.device('/gpu:0'):

# All operations in this block will be placed on GPU 0 
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)
# Creates a session with allow_soft_placement=True.
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
# Runs the op.
print(sess.run(c))

Alternatively, you can use the tf.distribute.Strategy API to distribute training across multiple GPUs. This API provides a high-level interface for distributing training across multiple devices and servers. It is designed in a way to be easy to use and to work with a variety of hardware configurations.

To use tf.distribute.Strategy, you will need to modify your model and training code to use the tf.distribute.Strategy API. For example:

import tensorflow as tf

# Define a simple convolutional neural networ
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
 
# Define a loss function and an optimizer 
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)

accuracy_metric = tf.keras.metrics.SparseCategoricalAccuracy()

# Define the test dataset
x_val = x_train[:1000]
y_val = y_train[:1000]
val_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
val_dataset = val_dataset.batch(64)

# Define the training dataset
x_train_2 = x_train[1000:]
y_train_2 = y_train[1000:]
train_dataset = tf.data.Dataset.from_tensor_slices((x_train_2, y_train_2))
train_dataset = train_dataset.batch(64)

To use a single GPU with TensorFlow, you can simply run your code on a machine with a GPU. TensorFlow will automatically detect the GPU and use it to accelerate computations.

To use multiple GPUs with TensorFlow, you can use the tf.distribute.Strategy API which allows you to distribute the computation across multiple GPUs. There are several strategies available for distribution, including:

• tf.distribute.MirroredStrategy: This strategy creates copies of all variables in the model on each GPU. The gradients are calculated on each GPU independently and then synchronized across all GPUs.
• tf.distribute.MultiWorkerMirroredStrategy: This strategy is used for distributed training across multiple machines, each with potentially multiple GPUs.
• tf.distribute.experimental.MultiDeviceStrategy: This strategy allows you to specify which device to place each variable on, which can be useful in some advanced cases.

To use these strategies, you need to wrap your model and optimizer with the appropriate strategy and then call the .compile() and .fit() method on the wrapped model.

Additionally, TensorFlow also provides a way to use specific GPUs by specifying the GPU ID by using tf.config.experimental.set_visible_devices() before creating the model.

It is worth noting that using multiple GPUs can also cause an increase in memory usage, so it’s important to monitor the GPU memory usage during training.

TensorFlow provides functions like tf.debugging.check_numerics() and tf.debugging.set_log_device_placement() to help monitor the GPU usage and debug issues that may arise.