Difference between revisions of "Tensorflow Guide"

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Revision as of 16:10, 9 May 2017

Tensorflow
Author Google Brain Team
Website https://www.tensorflow.org/
Source HistoryVersions
Category Machine Learning, Deep Neural Networks
Help documentation


Current Supported Versions in HPC

CPU Support Version Corresponding Python Module Dependence Released Date
v0.10.0rc0 python/2.7.6 - Jul 29, 2016
1.1.0-rc2 python/3.5.2 - Apr 21, 2017
GPU & CPU Support Version Corresponding Python Module Dependence Released Date
v0.12.1 python/2.7.6-gcc-unicode cuda, cudnn Dec 25, 2016
- - - -

Import Modules

TensorFlow v0.10.0rc0 (CPU Support Only)

module load python/2.7.6

TensorFlow v0.12.1 (Both GPU and CPU Support)

module load cuda/8.0.61 cudnn/6.0 python/2.7.6-gcc-unicode

Notice:

  • 1: This version supports both CPU and GPU.
  • 2: You should see the following auto-output, which means the gpu dependence libraries have been loaded successfully.
 I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
 I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
 I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
 I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so.6 locally
 I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
  • 3: You will get the error below if you run python in normal computing node. Please do not worry about it! This just mean the gpu feature is unavailable due to current node isn't GPU node. You can still use cpu for calculation without any problem.
 E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit: CUresult(-1)
 I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:145] kernel driver does not appear to be running on this host (cn02): /proc/driver/nvidia/version does not exist

Application Usage

Check Version Number

 $ python
 Python 2.7.6 (default, Apr 20 2016, 16:39:52) 
 [GCC 4.8.2] on linux2
 Type "help", "copyright", "credits" or "license" for more information.
 >>> import tensorflow as tf
 >>> print tf.__version__
 0.12.1
 >>>

Example: How to Build Computational Graph

$ python
Python 2.7.6 (default, Apr 20 2016, 16:39:52) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> node1 = tf.constant(3.0, tf.float32)
>>> node2 = tf.constant(4.0)
>>> node3 = tf.add(node1, node2)
>>> sess = tf.Session()
>>> sess.run(node3)
7.0
>>> a = tf.placeholder(tf.float32)
>>> b = tf.placeholder(tf.float32)
>>> adder_node = a + b
>>> print(sess.run(adder_node, {a: 3, b:4.5}))
7.5
>>> print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
[ 3.  7.]
>>> add_and_triple = adder_node * 3
>>> print(sess.run(add_and_triple, {a: 3, b:4.5}))
22.5
>>>

Example: Logging Device placement (GPU Version Guide)

hpc-xin@cn02:~$ ssh gpu01
hpc-xin@gpu01:~$ module purge
hpc-xin@gpu01:~$ module load cuda/8.0.61 cudnn/6.0 python/2.7.6-gcc-unicode
hpc-xin@gpu01:~$ python
Python 2.7.6 (default, Apr 20 2016, 16:39:52) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcudnn.so.6 locally
I tensorflow/stream_executor/dso_loader.cc:128] successfully opened CUDA library libcublas.so locally
>>> 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)
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40m, pci bus id: 0000:03:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K40m, pci bus id: 0000:82:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40m, pci bus id: 0000:03:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: Tesla K40m, pci bus id: 0000:82:00.0
I tensorflow/core/common_runtime/direct_session.cc:255] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40m, pci bus id: 0000:03:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: Tesla K40m, pci bus id: 0000:82:00.0
>>> print(sess.run(c))
MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:827] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:827] b: (Const)/job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:827] a: (Const)/job:localhost/replica:0/task:0/gpu:0
Const: (Const): /job:localhost/replica:0/task:0/cpu:0
I tensorflow/core/common_runtime/simple_placer.cc:827] Const: (Const)/job:localhost/replica:0/task:0/cpu:0
[[ 22.  28.]
 [ 49.  64.]]

More Examples

https://www.tensorflow.org/tutorials/mandelbrot