updated installation instructions for Tensowflow-TensorRT integration (#18135)

* updated installation instructions for Tensowflow-TensorRT integration

* Minor format changes to clean it up.

* Adding the python symlink command for devel packages too.

* Forcing the symlink creation.

* Updating the sed command for docker parameterized build.

diff --git a/tensorflow/contrib/tensorrt/README.md b/tensorflow/contrib/tensorrt/README.md
index 6eafc17..687dee0 100644 (file)
--- a/tensorflow/contrib/tensorrt/README.md
+++ b/tensorflow/contrib/tensorrt/README.md
@@ -1,59 +1,29 @@
 # Using TensorRT in TensorFlow
 
-
-This module provides necessary bindings and introduces TRT_engine_op
-operator that wraps a subgraph in TensorRT. This is still a work in progress
-but should be useable with most common graphs.
+This module provides necessary bindings and introduces TRT_engine_op operator
+that wraps a subgraph in TensorRT. This is still a work in progress but should
+be useable with most common graphs.
 
 ## Compilation
 
-
-In order to compile the module, you need to have a local TensorRT
-installation ( libnvinfer.so and respective include files ). During the
-configuration step, TensorRT should be enabled and installation path
-should be set. If installed through package managers (deb,rpm),
-configure script should find the necessary components from the system
-automatically. If installed from tar packages, user has to set path to
-location where the library is installed during configuration.
+In order to compile the module, you need to have a local TensorRT installation
+(libnvinfer.so and respective include files). During the configuration step,
+TensorRT should be enabled and installation path should be set. If installed
+through package managers (deb,rpm), configure script should find the necessary
+components from the system automatically. If installed from tar packages, user
+has to set path to location where the library is installed during configuration.
 
 ```shell
 bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
 bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
 ```
 
-After the installation of tensorflow package, TensorRT transformation
-will be available. An example use can be found in test/test_tftrt.py script
+After the installation of tensorflow package, TensorRT transformation will be
+available. An example use can be found in test/test_tftrt.py script
 
 ## Installing TensorRT 3.0.4
 
-In order to make use of TensorRT integration, you will need a local installation of TensorRT 3.0.4 from the [NVIDIA Developer website](https://developer.nvidia.com/tensorrt). Due to compiler compatibility, you will need to download and install the TensorRT 3.0.4 tarball for _Ubuntu 14.04_, i.e., **_TensorRT-3.0.4.Ubuntu-14.04.5.x86_64.cuda-9.0.cudnn7.0-tar.gz_**, even if you are using Ubuntu 16.04 or later.
-
-### Preparing TensorRT installation
-
-Once you have downloaded TensorRT-3.0.4.Ubuntu-14.04.5.x86_64.cuda-9.0.cudnn7.0-tar.gz, you will need to unpack it to an installation directory, which will be referred to as <install_dir>. Please replace <install_dir> with the full path of actual installation directory you choose in commands below.
-
-```shell
-cd <install_dir> && tar -zxf /path/to/TensorRT-3.0.4.Ubuntu-14.04.5.x86_64.cuda-9.0.cudnn7.0-tar.gz
-```
-
-After unpacking the binaries, you have several options to use them:
-
-#### To run TensorFlow as a user without superuser privileges
-
-For a regular user without any sudo rights, you should add TensorRT to your `$LD_LIBRARY_PATH`:
-
-  ```shell
-   export LD_LIBRARY_PATH=<install_dir>/TensorRT-3.0.4/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
-  ```
-
-Then you are ready to use TensorFlow-TensorRT integration. `$LD_LIBRARY_PATH` must contain the path to TensorRT installation for TensorFlow-TensorRT integration to work. If you are using a VirtualEnv-like setup, you can add the command above to your `bin/activate` script or to your `.bashrc` script.
-
-#### To run TensorFlow as a superuser
-
- When running as a superuser, such as in a container or via sudo, the `$LD_LIBRARY_PATH` approach above may not work. The following is preferred when the user has superuser privileges:
-
-  ```shell
-  echo "<install_dir>/TensorRT-3.0.4/lib" | sudo tee /etc/ld.so.conf.d/tensorrt304.conf && sudo ldconfig
-  ```
-
-  Please ensure that any existing deb package installation of TensorRT is removed before following these instructions to avoid package conflicts.
\ No newline at end of file
+In order to make use of TensorRT integration, you will need a local installation
+of TensorRT 3.0.4 from the [NVIDIA Developer website](https://developer.nvidia.com/tensorrt).
+Installation instructions for compatibility with TensorFlow are provided on the
+[TensorFlow Installation page](https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support).

diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md
index 04e4242..58f6c60 100644 (file)
--- a/tensorflow/docs_src/install/install_linux.md
+++ b/tensorflow/docs_src/install/install_linux.md
@@ -65,16 +65,38 @@ must be installed on your system:
     <pre>
     $ <b>sudo apt-get install libcupti-dev</b>
     </pre>
+
   * **[OPTIONAL]**  For optimized inferencing performance, you can also install
-    NVIDIA TensorRT 3.0. For details, see
-    [NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing-tar).
-    Only steps 1-4 in the TensorRT Tar File installation instructions are
-    required for compatibility with TensorFlow; the Python package installation
-    in steps 5 and 6 can be omitted. Detailed installation instructions can be found at [package documentataion](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#installing-tensorrt-304)
+    **NVIDIA TensorRT 3.0**. The minimal set of TensorRT runtime components needed
+    for use with the pre-built `tensorflow-gpu` package can be installed as follows:
+
+    <pre>
+    $ <b>wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64/nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb</b>
+    $ <b>sudo dpkg -i nvinfer-runtime-trt-repo-ubuntu1404-3.0.4-ga-cuda9.0_1.0-1_amd64.deb</b>
+    $ <b>sudo apt-get update</b>
+    $ <b>sudo apt-get install -y --allow-downgrades libnvinfer-dev libcudnn7-dev=7.0.5.15-1+cuda9.0 libcudnn7=7.0.5.15-1+cuda9.0</b>
+    </pre>
 
     **IMPORTANT:** For compatibility with the pre-built `tensorflow-gpu`
-    package, please use the Ubuntu **14.04** tar file package of TensorRT
-    even when installing onto an Ubuntu 16.04 system.   
+    package, please use the Ubuntu **14.04** package of TensorRT as shown above,
+    even when installing onto an Ubuntu 16.04 system.<br/>
+    <br/>
+    To build the TensorFlow-TensorRT integration module from source rather than
+    using pre-built binaries, see the [module documentation](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt#using-tensorrt-in-tensorflow).
+    For detailed TensorRT installation instructions, see [NVIDIA's TensorRT documentation](http://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html).<br/>
+    <br/>
+    To avoid cuDNN version conflicts during later system upgrades, you can hold
+    the cuDNN version at 7.0.5:
+
+    <pre>
+    $ <b> sudo apt-mark hold libcudnn7 libcudnn7-dev</b>
+    </pre>
+
+    To later allow upgrades, you can remove the hold:
+
+    <pre>
+    $ <b> sudo apt-mark unhold libcudnn7 libcudnn7-dev</b>
+    </pre>
 
 If you have an earlier version of the preceding packages, please upgrade to
 the specified versions. If upgrading is not possible, then you may still run