# Using GPUs <a class="md-anchor" id="AUTOGENERATED-using-gpus"></a>

## Supported devices <a class="md-anchor" id="AUTOGENERATED-supported-devices"></a>

On a typical system, there are multiple computing devices. In TensorFlow, the
supported device types are `CPU` and `GPU`.  They are represented as
`strings`. For example:

*  `"/cpu:0"`: The CPU of your machine.
*  `"/gpu:0"`: The GPU of your machine, if you have one.
*  `"/gpu:1"`: The second GPU of your machine, etc.

If a TensorFlow operation has both CPU and GPU implementations, the
GPU devices will be given priority when the operation is assigned to
a device. For example, `matmul` has both CPU and GPU kernels.  On a
system with devices `cpu:0` and `gpu:0`, `gpu:0` will be selected to run
`matmul`.

## Logging Device placement <a class="md-anchor" id="AUTOGENERATED-logging-device-placement"></a>

To find out which devices your operations and tensors are assigned to, create
the session with `log_device_placement` configuration option set to `True`.

```python
# Creates a graph.
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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(c)
```

You should see the following output:

```
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/gpu:0
a: /job:localhost/replica:0/task:0/gpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]

```

## Manual device placement <a class="md-anchor" id="AUTOGENERATED-manual-device-placement"></a>

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 such that all the operations
within that context will have the same device assignment.

```python
# Creates a graph.
with tf.device('/cpu: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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(c)
```

You will see that now `a` and `b` are assigned to `cpu:0`.

```
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/cpu:0
a: /job:localhost/replica:0/task:0/cpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22.  28.]
 [ 49.  64.]]
```

## Using a single GPU on a multi-GPU system <a class="md-anchor" id="AUTOGENERATED-using-a-single-gpu-on-a-multi-gpu-system"></a>

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:

```python
# Creates a graph.
with tf.device('/gpu:2'):
  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 log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(c)
```

If the device you have specified does not exist, you will get
`InvalidArgumentError`:

```
InvalidArgumentError: Invalid argument: Cannot assign a device to node 'b':
Could not satisfy explicit device specification '/gpu:2'
   [[Node: b = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [3,2]
   values: 1 2 3...>, _device="/gpu:2"]()]]
```

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 set `allow_soft_placement` to `True` in the configuration
option when creating the session.

```python
# Creates a graph.
with tf.device('/gpu:2'):
  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 and log_device_placement set
# to True.
sess = tf.Session(config=tf.ConfigProto(
      allow_soft_placement=True, log_device_placement=True))
# Runs the op.
print sess.run(c)
```

## Using multiple GPUs <a class="md-anchor" id="AUTOGENERATED-using-multiple-gpus"></a>

If you would like to run TensorFlow on multiple GPUs, you can construct your
model in a multi-tower fashion where each tower is assigned to a different GPU.
For example:

```
# Creates a graph.
c = []
for d in ['/gpu:2', '/gpu:3']:
  with tf.device(d):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2])
    c.append(tf.matmul(a, b))
with tf.device('/cpu:0'):
  sum = tf.add_n(c)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print sess.run(sum)
```

You will see the following output.

```
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K20m, pci bus
id: 0000:02:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: Tesla K20m, pci bus
id: 0000:03:00.0
/job:localhost/replica:0/task:0/gpu:2 -> device: 2, name: Tesla K20m, pci bus
id: 0000:83:00.0
/job:localhost/replica:0/task:0/gpu:3 -> device: 3, name: Tesla K20m, pci bus
id: 0000:84:00.0
Const_3: /job:localhost/replica:0/task:0/gpu:3
Const_2: /job:localhost/replica:0/task:0/gpu:3
MatMul_1: /job:localhost/replica:0/task:0/gpu:3
Const_1: /job:localhost/replica:0/task:0/gpu:2
Const: /job:localhost/replica:0/task:0/gpu:2
MatMul: /job:localhost/replica:0/task:0/gpu:2
AddN: /job:localhost/replica:0/task:0/cpu:0
[[  44.   56.]
 [  98.  128.]]
```

The [cifar10 tutorial](../../tutorials/deep_cnn/index.md) is a good example
demonstrating how to do training with multiple GPUs.
