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Diffstat (limited to 'tensorflow/g3doc/get_started/os_setup.md')
| -rw-r--r-- | tensorflow/g3doc/get_started/os_setup.md | 39 |
1 files changed, 36 insertions, 3 deletions
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md index f8113bcaec..0917f16832 100644 --- a/tensorflow/g3doc/get_started/os_setup.md +++ b/tensorflow/g3doc/get_started/os_setup.md @@ -36,7 +36,33 @@ Install TensorFlow (only CPU binary version is currently available). $ sudo pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl ``` -### Try your first TensorFlow program +## Docker-based installation + +We also support running TensorFlow via [Docker](http://docker.com/), which lets +you avoid worrying about setting up dependencies. + +First, [install Docker](http://docs.docker.com/engine/installation/). Once +Docker is up and running, you can start a container with one command: + +```sh +$ docker run -it b.gcr.io/tensorflow/tensorflow +``` + +This will start a container with TensorFlow and all its dependencies already +installed. + +### Additional images + +The default Docker image above contains just a minimal set of libraries for +getting up and running with TensorFlow. We also have several other containers, +which you can use in the `docker run` command above: + +* `b.gcr.io/tensorflow/tensorflow-full`: Contains a complete TensorFlow source + installation, including all utilities needed to build and run TensorFlow. This + makes it easy to experiment directly with the source, without needing to + install any of the dependencies described above. + +## Try your first TensorFlow program ```sh $ python @@ -133,6 +159,13 @@ $ sudo apt-get install python-numpy swig python-dev In order to build TensorFlow with GPU support, both Cuda Toolkit 7.0 and CUDNN 6.5 V2 from NVIDIA need to be installed. +TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5. Supported cards include but are not limited to: + +* NVidia Titan +* NVidia Titan X +* NVidia K20 +* NVidia K40 + ##### Download and install Cuda Toolkit 7.0 https://developer.nvidia.com/cuda-toolkit-70 @@ -227,7 +260,7 @@ Notes : You need to install Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html). -### Create the pip package and install +### Create the pip package and install {#create-pip} ```sh $ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package @@ -238,7 +271,7 @@ $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg $ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl ``` -### Train your first TensorFlow neural net model +## Train your first TensorFlow neural net model From the root of your source tree, run: |