I have many big deep learning tasks in python 3.6 ahead and wanted to build tensorflow (CPU only) from source, as my MacBook Pro with Touchbar 13" noted that tensorflow would run faster if it were build with SSE4.1 SSE4.2 AVX AVX2 and FMA support. There are quite a lot questions on StackOverflow and GitHub regarding that topic and I read them all. None of which is addressing why it is not working for me.
I strictly followed the instructions provided by https://www.tensorflow.org/install/install_sources
my configure looks like this
./configure
Please specify the location of python. [Default is /anaconda/bin/python]: /anaconda/python.app/Contents/MacOS/python
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] n
No Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] n
No XLA JIT support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N] n
No VERBS support will be enabled for TensorFlow
Found possible Python library paths:
/anaconda/python.app/Contents/lib/python3.6/site-packages
Please input the desired Python library path to use. Default is [/anaconda/python.app/Contents/lib/python3.6/site-packages]
Using python library path: /anaconda/python.app/Contents/lib/python3.6/site-packages
Do you wish to build TensorFlow with OpenCL support? [y/N] n
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] n
No CUDA support will be enabled for TensorFlow
INFO: Starting clean (this may take a while). Consider using --async if the clean takes more than several minutes.
Configuration finished
with bazel 0.4.5 I then try to do the build as in the instructions
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
This is executed without error but it gives literally hundreds of warnings. I can provide such as an example, but there hardly any snippets that go on without warning.
I appreciate ever help, thank you all very much.
Unfortunately compiler warnings are a fact of life. However, many of these come from external libraries which are pulled into the build. These can be filtered out with the "output_filter" argument to Bazel:
bazel build --config=opt --output_filter='^//tensorflow' //tensorflow/tools/pip_package:build_pip_package
This limits output to warnings generated by TensorFlow code (you can also turn warnings off entirely this way, but that takes all the fun out of compiling). Since the tooling used to build matches what TensorFlow is developed with more closely, there are fewer warnings (I get some about multi-line comment continuations, a bunch of signed/unsigned integer comparisons, and some about variables which "may" be uninitialized).
None of these indicate definite bugs, just patterns of code which are sometimes bug-prone. If the compiler knew something was wrong, it would emit an error instead. Which is a long way of saying there's nothing to worry about.
Related
The TFLite team recently announced XNNPack support in TF v2.3 (https://blog.tensorflow.org/2020/07/accelerating-tensorflow-lite-xnnpack-integration.html). This should provide some pretty impressive speedups on float operations on ARM v8 cores.
Does anyone know how to enable XNNPack for ARM64 builds of TFLite? The benchmarking application in particular would be a good place to test out this new functionality on target hardware. iOS and Android support is enabled by passing a flag to Bazel when compiling. Unfortunately, no guidance is given for building for ARM64 boards. The build instructions (see below) don't provide any updated guidance, and inspecting download_dependencies.sh doesn't show XNNPack being downloaded from anywhere.
https://www.tensorflow.org/lite/guide/build_arm64
XNNPACK is not yet supported via Makefile-based builds. We have recently added experimental support for cross-compilation to ARM64 (via --config=elinux_aarch64 in the bazel build command), which should allow build-time opt-in to XNNPACK by also adding --define tflite_with_xnnpack=true in your build command. Expect some improvements in documentation for cross-compilation to ARM64 in the next TF 2.4 release, where we'll also be looking into enabling XNNPACK by default for as many platforms as possible.
The above said modules are not build into the site-packages. I am using Python3.5 for it, and followed all the steps for the building from the source, that are given on the website.
I did search on the Internet, but there is no apparent solution found.
The following is the configuration used during ./configure:
./configure
..................
You have bazel 0.5.2 installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3
Found possible Python library paths:
/usr/lib/python3/dist-packages
/usr/local/lib/python3.5/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
Using python library path: /usr/lib/python3/dist-packages
Do you wish to build TensorFlow with MKL support? [y/N] y
MKL support will be enabled for TensorFlow
Do you wish to download MKL LIB from the web? [Y/n] Y
mklml_lnx_2018.0.20170425.tgz
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Do you wish to use jemalloc as the malloc implementation? [Y/n] y
jemalloc enabled
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] y
Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with Hadoop File System support? [y/N] y
Hadoop File System support will be enabled for TensorFlow
Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] y
XLA JIT support will be enabled for TensorFlow
Do you wish to build TensorFlow with VERBS support? [y/N] y
VERBS support will be enabled for TensorFlow
Do you wish to build TensorFlow with OpenCL support? [y/N] n
No OpenCL support will be enabled for TensorFlow
Do you wish to build TensorFlow with CUDA support? [y/N] n
No CUDA support will be enabled for TensorFlow
Do you wish to build TensorFlow with MPI support? [y/N] n
MPI support will not be enabled for TensorFlow
Configuration finished
After which I proceed with the steps given in the following website: https://www.tensorflow.org/install/install_sources#ConfigureInstallation
I am using Ubuntu 16.04 LTS 64 bit.
There are no error messages, just the modules are not getting built for using! This: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/learn
I want to understand the text_classification code by running it, so I had to install tensorflow from source, but in the site-packages/tensorflow, these modules are not there.
I want to understand why such thing happened, even when I did the steps right.
It seems you only finished the configure step, but you didn't actually build anything yet. Try running e.g. bazel build //tensorflow/examples/learn:boston.
Whenever I use tensorflow, it displays the message "The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations" and 2 more similar messages.
So I decided to build tensorflow from source to get rid of these messages. I'm using python 3.5 on Debian, and followed the instructions at https://www.tensorflow.org/install/install_sources (CPU only, no GPU).
It asked during the build if the build should be for the machine it's doing the build on, I selected that, it included -march=native in some compiler option.
Everything seemed to work, but when I ran python3 to test the build, it still gives the messages about "The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available ..." etc. How to I make the build use the hardware that it's running on?
There have been similar questions, and most of the answers to them are wrong. They say it's necessary to specify options like "--copt=-msse4.1 --copt=-msse4.2" in the build; it isn't. With the default option "-march=native", the GNU compiler will use SSE4.1 and SSE4.2 instructions if they are available.
The real problem is that if you build tensorflow from source, after installing the default build with pip, pip won't replace the old build with the new build. Everything will seem to work, but your old build remains in place in a directory under ~/.local.
The solution is simply to uninstall the old tensorflow with pip ('pip uninstall tensorflow' or 'pip3 uninstall tensorflow'), and then rebuild from source. If you have already done a build, and wondered why nothing seemed to change, you needn't repeat the build but can just execute the last couple of steps (https://www.tensorflow.org/install/install_sources), namely bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg , followed by the pip install.
I am trying to install tensorflow
Please specify the location where ComputeCpp for SYCL 1.2 is installed. [Default is /usr/local/computecpp]:
Invalid SYCL 1.2 library path. /usr/local/computecpp/lib/libComputeCpp.so cannot be found
What should I do?What is SYCL 1.2?
SYCL is a C++ abstraction layer for OpenCL. TensorFlow's experimental support for OpenCL uses SYCL, in conjunction with a SYCL-aware C++ compiler.
As Yaroslav pointed out in his comment, SYCL is only required if you are building TensorFlow with OpenCL support. The following question during the execution of ./configure asks about OpenCL support:
Do you wish to build TensorFlow with OpenCL support? [y/N]
If you answer N, you will not have to supply a SYCL path.
It is optional step so you can skip if you want.
OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators.
so if you want to intall you have to Setting Up TensorFlow With OpenCL Using SYCL bellow link provide step by step information about it
https://developer.codeplay.com/computecppce/latest/getting-started-with-tensorflow
So the documentation mentions that it is better to install from source, then build a pip package. Why is this recommended over doing a direct pip install using the wheel file provided on the downloads page? here
I tried the direct pip install and then runnning some scripts in the inception folder.This results in errors with bazel not finding some of the dependencies. I am guessing this is related to not building tensorflow from source, but I can't figure out why this is the case. Any pointers? Thanks!
Installing from pip is supported, can you provide more details on your os and the specific errors you saw?
The main reason to build from source is simply performance.
Building and installing from source
The default TensorFlow binaries target the broadest range of hardware to make TensorFlow accessible to everyone. If using CPUs for training or inference, it is recommended to compile TensorFlow with all of the optimizations available for the CPU in use. Speedups for training and inference on CPU are documented below in Comparing compiler optimizations.
To install the most optimized version of TensorFlow, build and install from source. If there is a need to build TensorFlow on a platform that has different hardware than the target, then cross-compile with the highest optimizations for the target platform. The following command is an example of using bazel to compile for a specific platform
ref: https://www.tensorflow.org/performance/performance_guide