Play with TensorFlow on a real GPU. This is a fork of TensorFlow with support for AWS G2 instances using CUDA 7.0. The NVIDIA GRID K520 cards these instances have use CUDA Compute 3.0 which is not currently supported by TensorFlow, but as far as I can tell they still work.
We recommend using our precompiled public AMI ami-7b503e1b. Launch a g2.2xlarge instance now!
You can run the hello_world.py example like so:
$ python hello_world.py
This is a fork of TensorFlow with support for AWS G2 instances using CUDA 7.0. To install on a blank Ubuntu 14.04 g2.2xlarge instance:
$ curl https://raw.githubusercontent.com/pavlovml/tensorflow/master/aws/bootstrap.sh | sh
When prompted, select Keep the local version currently installed. Upon reboot, run:
$ ./install.sh
When prompted, scroll to the bottom of the EULA, and select accept > y > y > y > default (/usr/local/cuda-7.0) > y > n. Bazel will take a long time to install. To see if it worked, run hello_world.py and check if a device for gpu:0 was created:
$ curl -O https://raw.githubusercontent.com/pavlovml/tensorflow/master/aws/hello_world.py
$ source .bash_profile
$ python hello_world.py
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
Note: Currently we do not accept pull requests on github -- see CONTRIBUTING.md for information on how to contribute code changes to TensorFlow through tensorflow.googlesource.com
We use github issues for tracking requests and bugs, but please see Community for general questions and discussion.
To install the CPU version of TensorFlow using a binary package, see the instructions below. For more detailed installation instructions, including installing from source, GPU-enabled support, etc., see here.
The TensorFlow Python API currently requires Python 2.7: we are working on adding support for Python 3.
The simplest way to install TensorFlow is using pip for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for more detailed installation instructions, see here.
# For CPU-only version
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print sess.run(a+b)
42
>>>
##For more information