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diff --git a/tensorflow/docs_src/guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md deleted file mode 100644 index 8cb9b354c7..0000000000 --- a/tensorflow/docs_src/guide/using_gpu.md +++ /dev/null @@ -1,215 +0,0 @@ -# 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. -* `"/device:GPU:0"`: The GPU of your machine, if you have one. -* `"/device: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/device:GPU:0 -> device: 0, name: Tesla K40c, pci bus -id: 0000:05:00.0 -b: /job:localhost/replica:0/task:0/device:GPU:0 -a: /job:localhost/replica:0/task:0/device:GPU:0 -MatMul: /job:localhost/replica:0/task:0/device: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`. 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. - -``` -Device mapping: -/job:localhost/replica:0/task:0/device: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/device:GPU:0 -[[ 22. 28.] - [ 49. 64.]] -``` - -## Allowing GPU memory growth - -By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to -[`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars)) -visible to the process. This is done to more efficiently use the relatively -precious GPU memory resources on the devices by reducing [memory -fragmentation](https://en.wikipedia.org/wiki/Fragmentation_\(computing\)). - -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 Config options on the Session to control this. - -The first is the `allow_growth` option, which attempts to allocate only as much -GPU memory based on runtime allocations: it starts out allocating very little -memory, and as Sessions get run and more GPU memory is needed, we extend the GPU -memory region needed by the TensorFlow process. Note that we do not release -memory, since that can lead to even worse memory fragmentation. To turn this -option on, set the option in the ConfigProto by: - -```python -config = tf.ConfigProto() -config.gpu_options.allow_growth = True -session = tf.Session(config=config, ...) -``` - -The second method is the `per_process_gpu_memory_fraction` option, which -determines the fraction of the overall amount of memory that each visible GPU -should be allocated. For example, you can tell TensorFlow to only allocate 40% -of the total memory of each GPU by: - -```python -config = tf.ConfigProto() -config.gpu_options.per_process_gpu_memory_fraction = 0.4 -session = tf.Session(config=config, ...) -``` - -This is useful if you want to truly bound the amount of GPU memory available to -the TensorFlow process. - -## 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('/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 '/device:GPU:2' - [[{{node b}} = Const[dtype=DT_FLOAT, value=Tensor<type: float shape: [3,2] - values: 1 2 3...>, _device="/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('/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: - -``` python -# Creates a graph. -c = [] -for d in ['/device:GPU:2', '/device: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/device:GPU:0 -> device: 0, name: Tesla K20m, pci bus -id: 0000:02:00.0 -/job:localhost/replica:0/task:0/device:GPU:1 -> device: 1, name: Tesla K20m, pci bus -id: 0000:03:00.0 -/job:localhost/replica:0/task:0/device:GPU:2 -> device: 2, name: Tesla K20m, pci bus -id: 0000:83:00.0 -/job:localhost/replica:0/task:0/device:GPU:3 -> device: 3, name: Tesla K20m, pci bus -id: 0000:84:00.0 -Const_3: /job:localhost/replica:0/task:0/device:GPU:3 -Const_2: /job:localhost/replica:0/task:0/device:GPU:3 -MatMul_1: /job:localhost/replica:0/task:0/device:GPU:3 -Const_1: /job:localhost/replica:0/task:0/device:GPU:2 -Const: /job:localhost/replica:0/task:0/device:GPU:2 -MatMul: /job:localhost/replica:0/task:0/device:GPU:2 -AddN: /job:localhost/replica:0/task:0/cpu:0 -[[ 44. 56.] - [ 98. 128.]] -``` - -The [cifar10 tutorial](../tutorials/images/deep_cnn.md) is a good example -demonstrating how to do training with multiple GPUs. |