[TensorFlow] How to build your C++ program or application with TensorFlow library using CMake

When you want to build your  C++ program or application using TensorFlow library or functions, you probably will encounter some header file missed issues or linking problems. Here is the step list that I have verified and it works well.

1. Prepare TensorFlow ( v1.10) and its third party's library

$ git clone --recursive https://github.com/tensorflow/tensorflow
$ cd tensorflow/contrib/makefile
$ ./build_all_linux.sh

2. Modify .tf_.tf_configure.bazelrc

$ cd tensorflow/
$ vim .tf_configure.bazelrc
  append this line in the bottom of the file
  ==>
  build --define=grpc_no_ares=true

[XLA JIT] How to turn on XLA JIT compilation at multiple GPUs training

Before I discuss this question, let's recall how to turn on  XLA JIT compilation and use it in TensorFlow python API.

1. Session
Turning on JIT compilation at the session level will result in all possible operators being greedily compiled into XLA computations. Each XLA computation will be compiled into one or more kernels for the underlying device.

[PCIe] How to read/write PCIe Switch Configuration Space?

Here is a question, how to read/write PCIe Switch Configuration Space? We can see this picture first.

[TensorFlow] How to get CPU configuration flags (such as SSE4.1, SSE4.2, and AVX...) in a bash script for building TensorFlow from source

The AVX and SSE4.2 and others are offered by Intel CPU. (AVX and SSE4.2 are CPU infrastructures for faster matrix computations) Did you wonder what CPU configuration flags (such as SSE4.1, SSE4.2, and AVX...) you should use on your machine when building Tensorflow from source? If so, here is a quick solution for you.

[TensorFlow 記憶體優化實驗] Compare the memory options in Grappler Memory Optimizer

As we know that in Tensorflow, there is an optimization module called "Grappler". It provides many kinds of optimization functionalities, such as: Layout, Memory, ModelPruner, and so on... In this experiment, we can see the effect of some memory options enabled in a simple CNN model using MNIST dataset.

[XLA 研究] How to use XLA AOT compilation in TensorFlow

This document is going to explain how to use AOT compilation in TensorFlow. We will use the tool: tfcompile, which is a standalone tool that ahead-of-time (AOT) compiles TensorFlow graphs into executable code. It can reduce the total binary size, and also avoid some runtime overheads. A typical use-case of tfcompile is to compile an inference graph into executable code for mobile devices. The following steps are as follows:

1. Build tool: tfcompile

> bazel build --config=opt --config=cuda //tensorflow/compiler/aot:tfcompile

[XLA 研究] Take a glance to see the graph changes in XLA JIT compilation

In the preamble of this article, to understand XLA JIT is pretty hard because you probably need to understand TensorFlow Graph, Executor,  LLVM, and math... I have been through this painful study work somehow so that I hope my experience can help for those who are interested in XLA but have not get understood yet.

[TX2 研究] My first try on Jetson TX2

I got a Jetson TX2 several days ago from my friend and it looks like following pictures. I setup it using Nivida's installing tool: JetPack-L4T-3.2 version (JetPack-L4T-3.2-linux-x64_b196.run). During the installation, I indeed encounter some issues with not abling to setup IP address on TX2, and I resolved it. If anyone still has this issue, let me know and I will post another article to explain the resolving steps.