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# Using GPUs
## Supported devices
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
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
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
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
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.
|
