TensorFlow 2.0 Beta is available Learn more

Using GPUs

View on TensorFlow.org View source on GitHub Download notebook

TensorFlow code, and tf.keras models will transparently run on a single GPU with no code changes required.

The simplest way to run on multiple GPUs, on one or many machines, is using Distribution Strategies

This guide is for users who have tried these approaches and found that they need find-grained control of how TensorFlow uses the GPU.

Setup

Ensure you have the latest TensorFlow gpu release installed.

from __future__ import absolute_import, division, print_function, unicode_literals

try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass
import tensorflow as tf
print("GPU Available: ", tf.test.is_gpu_available())
GPU Available:  True

Overview

TensorFlow supports running computations on a variety of types of devices, including CPU and GPU. They are represented with string identifiers for example:

  • "/device:CPU:0": The CPU of your machine.
  • "/GPU:0": Short-hand notation for the first GPU of your machine that is visible to TensorFlow
  • "/job:localhost/replica:0/task:0/device:GPU:1": Fully qualified name of the second GPU of your machine that is visible to TensorFlow.

If a TensorFlow operation has both CPU and GPU implementations, by default the GPU devices will be given priority when the operation is assigned to a device. For example, tf.matmul has both CPU and GPU kernels. On a system with devices CPU:0 and GPU:0, the GPU:0 device will be selected to run tf.matmul unless you explicitly request running it on another device.

Logging device placement

To find out which devices your operations and tensors are assigned to, put tf.debugging.set_log_device_placement(True) as the first statement of your program. Enabling device placement logging causes any Tensor allocations or operations to be printed.

tf.debugging.set_log_device_placement(True)

# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

The above code will print an indication the MatMul op was executed on GPU:0.

Manual device placement

If you would like a particular operation to run on a device of your choice instead of what's automatically selected for you, you can use with tf.device to create a device context, and all the operations within that context will run on the same designated device.

tf.debugging.set_log_device_placement(True)

# Place tensors on the CPU
with tf.device('/CPU:0'):
  a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])

c = tf.matmul(a, b)
print(c)
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

You will see that now a and b are assigned to CPU:0. Since a device was not explicitly specified for the MatMul operation, the TensorFlow runtime will choose one based on the operation and available devices (GPU:0 in this example) and automatically copy tensors between devices if required.

Limiting GPU memory growth

By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmentation. To limit TensorFlow to a specific set of GPUs we use the tf.config.experimental.set_visible_devices method.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  # Restrict TensorFlow to only use the first GPU
  try:
    tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
  except RuntimeError as e:
    # Visible devices must be set before GPUs have been initialized
    print(e)
Visible devices must be set at program startup

In some cases it is desirable for the process to only allocate a subset of the available memory, or to only grow the memory usage as is needed by the process. TensorFlow provides two methods to control this.

The first option is to turn on memory growth by calling tf.config.experimental.set_memory_growth, which attempts to allocate only as much GPU memory in needed for the runtime allocations: it starts out allocating very little memory, and as the program gets run and more GPU memory is needed, we extend the GPU memory region allocated to the TensorFlow process. Note we do not release memory, since it can lead to memory fragmentation. To turn on memory growth for a specific GPU, use the following code prior to allocating any tensors or executing any ops.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    # Currently, memory growth needs to be the same across GPUs
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    # Memory growth must be set before GPUs have been initialized
    print(e)
Memory growth must be set at program startup

Another way to enable this option is to set the environmental variable TF_FORCE_GPU_ALLOW_GROWTH to true. This configuration is platform specific.

The second method is to configure a virtual GPU device with tf.config.experimental.set_virtual_device_configuration and set a hard limit on the total memory to allocate on the GPU.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  # Restrict TensorFlow to only allocate 1GB of memory on the first GPU
  try:
    tf.config.experimental.set_virtual_device_configuration(
        gpus[0],
        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    # Virtual devices must be set before GPUs have been initialized
    print(e)
Virtual devices must be set at program startup

This is useful if you want to truly bound the amount of GPU memory available to the TensorFlow process. This is common practise for local development when the GPU is shared with other applications such as a workstation GUI.

Using a single GPU on a multi-GPU system

If you have more than one GPU in your system, the GPU with the lowest ID will be selected by default. If you would like to run on a different GPU, you will need to specify the preference explicitly:

tf.debugging.set_log_device_placement(True)

try:
  # Specify an invalid GPU device
  with tf.device('/device:GPU:2'):
    a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
    b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
    c = tf.matmul(a, b)
except RuntimeError as e:
  print(e)
Error copying tensor to device: /job:localhost/replica:0/task:0/device:GPU:2. /job:localhost/replica:0/task:0/device:GPU:2 unknown device.

If the device you have specified does not exist, you will get a RuntimeError:

If you would like TensorFlow to automatically choose an existing and supported device to run the operations in case the specified one doesn't exist, you can call tf.config.set_soft_device_placement(True).

tf.config.set_soft_device_placement(True)
tf.debugging.set_log_device_placement(True)

# Creates some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

Using multiple GPUs

Developing for multiple GPUs will allow a model to scale with the additional resources. If developing on a system with a single GPU, we can simulate multiple GPUs with virtual devices. This enables easy testing of multi-GPU setups without requiring additional resources.

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  # Create 2 virtual GPUs with 1GB memory each
  try:
    tf.config.experimental.set_virtual_device_configuration(
        gpus[0],
        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),
         tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    # Virtual devices must be set before GPUs have been initialized
    print(e)
Virtual devices must be set at program startup

Once we have multiple logical GPUs available to the runtime, we can utilize the multiple GPUs with tf.distribute.Strategy or with manual placement.

With tf.distribute.Strategy

The best practice for using multiple GPUs is to use tf.distribute.Strategy. Here is a simple example:

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
  inputs = tf.keras.layers.Input(shape=(1,))
  predictions = tf.keras.layers.Dense(1)(inputs)
  model = tf.keras.models.Model(inputs=inputs, outputs=predictions)
  model.compile(loss='mse',
                optimizer=tf.keras.optimizers.SGD(learning_rate=0.2))
Executing op RandomUniform in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Add in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarIsInitializedOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op LogicalNot in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Assert in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Reshape in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0

This program will run a copy of your model on each GPU, splitting the input data between them, also known as "data parallelism".

For more information about distribution strategies, check out the guide here.

Manual placement

tf.distribute.Strategy works under the hood by replicating computation across devices. You can manually implement replication by constructing your model on each GPU. For example:

tf.debugging.set_log_device_placement(True)

gpus = tf.config.experimental.list_logical_devices('GPU')
if gpus:
  # Replicate your computation on multiple GPUs
  c = []
  for gpu in gpus:
    with tf.device(gpu.name):
      a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
      b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
      c.append(tf.matmul(a, b))

  with tf.device('/CPU:0'):
    matmul_sum = tf.add_n(c)

  print(matmul_sum)
Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)