Glow is a machine learning compiler and execution engine for hardwareaccelerators. It is designed to be used as a backend for high-level machinelearning frameworks. The compiler is designed to allow state of the artcompiler optimizations and code generation of neural network graphs. Thislibrary is in active development. The project plan is described in the Githubissues section and in theRoadmap wiki page.
Turbine Sites Guide Justin M. Public installation: Mira/Cetus. We set CC=gcc and use compiler flags to configure the MPI library. Lp is a collection of programs used to provide an easy-to-use interface for printing. Polaris compiler for Fortran Cetus for C/C++. Its plugin-based framework allows users to easily customize execution both at installation time. User Guide; Download; Contact. Ganja babe michael franti download mp3. 10 ft ganja plant shake up the place rar. Download Mekakucity Actors Episode 1. Everyone From Beachfront Media: The go-to destination for all things hp g85 scanner driver!. If you are using the basic installation to install IBM XL C for. Visual C++ 2010 SP1 Compiler Update for the Windows SDK 7.1 Please visit the Visual C++ team blog for more information or questions. Back to top Search this blog Search all blogs Share This Post Share Share Share Share. This paper provides an overview and an evaluation of the Cetus source-to-source compiler infrastructure. The original goal of the Cetus project was to create an easy-to-use compiler for research in automatic parallelization of C programs. The Cetus tool provides an infrastructure for research on multicore compiler optimizations that emphasizes automatic parallelization. The compiler infrastructure, which targets C programs.
Partners
Contributions to Glow are welcomed and encouraged! Glow is developed incollaboration with the following partners:
How does it work?
Glow lowers a traditional neural network dataflow graph into a two-phasestrongly-typed intermediate representation (IR). The high-levelIR allows the optimizer to perform domain-specific optimizations. Thelower-level instruction-based address-only IR allows the compiler to performmemory-related optimizations, such as instruction scheduling, static memoryallocation and copy elimination. At the lowest level, the optimizer performsmachine-specific code generation to take advantage of specialized hardwarefeatures. Glow features a lowering phase which enables the compiler to support ahigh number of input operators as well as a large number of hardware targets byeliminating the need to implement all operators on all targets. The loweringphase is designed to reduce the input space and allow new hardware backends tofocus on a small number of linear algebra primitives.The design philosophy is described in an arXiv paper.
Getting Started
System Requirements
Glow builds and runs on macOS and Linux. The software depends on a modern C++compiler that supports C++11, on CMake, LLVM, glog, protocol buffers, andlibpng.
Get Glow!
Submodules
Glow depends on a few submodules: googletest, onnx, and a libraryfor FP16 conversions.
To get them, from the glow directory, run:
macOS
Install the required dependencies using either Homebrew orMacPorts. If using Homebrew, run:
If using MacPorts, run:
Note that LLVM is installed in a non-default location to avoid conflicts withthe system's LLVM --Homebrew usually installs LLVM in
/usr/local/opt/llvm/
,whereas MacPorts installs it in /opt/local/libexec/llvm-7.0/
. This means thatCMake will need to be told where to find LLVM when building; instructions onthat can be found here.Finally, create a symbolic link to the Homebrew- or MacPorts-installed
clang-*
tools so that the utils/format.sh
script is able to find them lateron. For a Homebrew-managed installation, run:For MacPorts, run:
Note: On newer versions of macOS, Xcode's command line tools come with anon-traditional header layout. In order for Glow to build on newer macOSversions, you might need to install
macOS_SDK_headers_for_macOS_10.14.pkg
manually. For example, on Mojave this package is located in/Library/Developer/CommandLineTools/Packages/
.Ubuntu
[The following instructions have been tested on Ubuntu 16.04]
In order to build Glow on Ubuntu it is necessary to install a few packages. Thefollowing command should install the required dependencies:
[Note: building Glow on Ubuntu 16.04 with llvm-7 fails because llvm-7 xenial distributionuses an older c++ ABI]
It may be desirable to use
update-alternatives
to manage the version ofclang/clang++:Glow uses the system default C/C++ compiler (/usr/bin/c++), and so you may alsowant to switch your default C/C++ compiler to clang:
Glow should build just fine with gcc (e.g. gcc 5.4), but we mostly use clangand are more attentive to compatibility with clang.
Finally, in order to support the ONNX net serialization format, Glow requires
protobuf >= 2.6.1
, but the above command may install olderversion on older Ubuntu (e.g. 14.04). If this is the case, we suggest to lookat utils/install_protobuf.sh
to install a newer version from source.For details on installing OpenCL on Ubuntu please seethese instructions.
Configure and Build
To build the compiler, create a build directory and run cmake on the sourcedirectory. It's a good idea to build two configurations (Release and Debug)because some programs take a really long time to run in Debug mode. It's also agood idea to build the project outside of the source directory.
It's possible to configure and build the compiler with any CMake generator,like GNU Makefiles, Ninja and Xcode build.
For platform-specific build instructions and advanced options, such asbuilding with Address-Sanitizers refer to this guide:Building the Compiler.
If you're running Mac OS v10.14 (Mojave) and
ninja all
fails because it can'tfind headers (e.g. string.h
), run this command to fix it, and try again.More information is available hereunder 'Command Line Tools'.Building with dependencies (LLVM)
![Free Free](https://cdn3.ljustema.se/20355-thickbox_default/cetus-takflaekt-nickelfaergad-motor.jpg)
Dev C++ Online Compiler Installation
By default, Glow will use a system provided LLVM. Note that Glow requires LLVM7.0 or later. If you have LLVM installed in a non-default location (forexample, if you installed it using Homebrew on macOS), you need to tell CMakewhere to find llvm using
-DLLVM_DIR
. For example, if LLVM wereinstalled in /usr/local/opt
:If LLVM is not available on your system you'll need to build it manually. Runthe script '
/utils/build_llvm.sh
to clone, build and install LLVM in a localdirectory. You will need to configure Glow with the flag -DLLVM_DIR
to tellthe build system where to find LLVM given the local directory you installed itin (e.g. -DLLVM_DIR=/path/to/llvm_install/lib/cmake/llvm
if usingbuild_llvm.sh
).Testing and Running
Unit tests
The project has a few unit tests in the tests/unittests subdirectory. To run allof them, simply run
ninja test
.C++ API examples
A few test programs that use Glow's C++ API are found under the
examples/
subdirectory. The mnist
, cifar10
, fr2en
and ptb
programs train and run digitrecognition, image classification and language modeling benchmarks,respectively.To run these programs, build Glow in Release mode, then run the following commandsto download the cifar10, mnist and ptb databases.
Now run the examples. Note that the databases should be in the current workingdirectory.
If everything goes well you should see:
mnist
: pictures from the mnist digits databasecifar10
: image classifications that steadily improvefr2en
: an interactive French-to-English translatorptb
: decreasing perplexity on the dataset as the network trainschar-rnn
: generates random text based on some document
Note that the default build mode is
Debug
, which means that the compileritself is easy to debug because the binary contains debug info, lots ofassertions, and the optimizations are disabled. It also means that the compilerand runtime are very slow, and the execution time can be hundreds of timesslower than that of release builds. If you wish to benchmark the compiler, runlong benchmarks, or release the product then you should compile the compiler inRelease mode. Check the main CMake file for more details.More details on testing and running Glow can be found in:Testing the Glow Compiler.
Ahead-of-time Compilation
Glow can be used to compile neural networks into object files containing nativecode. We provide resnet50 (both quantized and non-quantized versions) as anexample of this capability in
examples/bundles/resnet50
. See CreatingStandalone Executable Bundles for more detail.Contributing
To get started contributing, please refer to the following guides:
Communication
- Forums: discuss implementations, research, etc: https://discuss.pytorch.org/c/glow.Make sure to label topic with the 'glow' category.
- GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
License
Glow is licensed under the Apache 2.0 License.