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PyTorch Integration with Tensor Comprehensions
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| FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu14.04 | ||
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| ENV DEBIAN_FRONTEND noninteractive | ||
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| RUN apt-get update | ||
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| RUN apt-get install -y --no-install-recommends make git ssh realpath wget unzip cmake3 vim | ||
| RUN apt-get install -y --no-install-recommends libgoogle-glog-dev libyaml-dev | ||
| RUN apt-get install -y --no-install-recommends libgtest-dev libz-dev libgmp3-dev | ||
| RUN apt-get install -y --no-install-recommends automake libtool valgrind subversion | ||
| RUN apt-get install -y --no-install-recommends ca-certificates software-properties-common | ||
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| RUN cmake --version | ||
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| # GCC 4.8.* | ||
| RUN add-apt-repository ppa:ubuntu-toolchain-r/test | ||
| RUN apt-get update | ||
| RUN apt-get install -y --no-install-recommends libcilkrts5 gcc-4.8 g++-4.8 | ||
| RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 50 | ||
| RUN update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50 | ||
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| ENV CC /usr/bin/gcc | ||
| ENV CXX /usr/bin/g++ | ||
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| # LLVM+Clang | ||
| ENV LLVM_SOURCES /tmp/llvm_sources-tapir5.0 | ||
| WORKDIR $LLVM_SOURCES | ||
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| ENV CLANG_PREFIX /usr/local/clang+llvm-tapir5.0 | ||
| ENV CMAKE_VERSION cmake | ||
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| RUN git clone --recursive https://github.com/wsmoses/Tapir-LLVM llvm && \ | ||
| mkdir -p ${LLVM_SOURCES}/llvm_build && cd ${LLVM_SOURCES}/llvm_build && \ | ||
| ${CMAKE_VERSION} -DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_OCAMLDOC=OFF -DLLVM_INSTALL_OCAMLDOC_HTML_DIR=/tmp -DLLVM_OCAML_INSTALL_PATH=/tmp -DCMAKE_INSTALL_PREFIX=${CLANG_PREFIX} -DLLVM_TARGETS_TO_BUILD=X86 -DCOMPILER_RT_BUILD_CILKTOOLS=OFF -DLLVM_ENABLE_CXX1Y=ON -DLLVM_ENABLE_TERMINFO=OFF -DLLVM_BUILD_TESTS=OFF -DLLVM_ENABLE_ASSERTIONS=ON -DCMAKE_BUILD_TYPE=Release -DLLVM_BUILD_LLVM_DYLIB=ON -DLLVM_ENABLE_RTTI=ON ../llvm/ && \ | ||
| make -j"$(nproc)" -s && make install -j"$(nproc)" -s | ||
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| RUN rm -Rf ${LLVM_SOURCES} | ||
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| # Anaconda3 | ||
| ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/targets/x86_64-linux/lib/stubs/:$LD_LIBRARY_PATH | ||
| ENV PATH /usr/local/bin:/usr/local/cuda/bin:$PATH | ||
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| WORKDIR /conda-install | ||
| RUN echo 'export PATH=/opt/conda/bin:$PATH' > /etc/profile.d/conda.sh &&\ | ||
| wget --quiet https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh -O anaconda.sh && \ | ||
| chmod +x anaconda.sh && \ | ||
| ./anaconda.sh -b -p /opt/conda && \ | ||
| rm anaconda.sh | ||
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| ENV PATH /opt/conda/bin:$PATH | ||
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| RUN conda install numpy decorator six future cmake pyyaml | ||
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| # Protobuf 3.4* | ||
| WORKDIR /proto-install | ||
| RUN wget --quiet https://github.com/google/protobuf/archive/v3.4.0.zip -O proto.zip && unzip -qq proto.zip -d / | ||
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| RUN cd /protobuf-3.4.0 && ./autogen.sh && ./configure && make -j 8 | ||
| RUN cd /protobuf-3.4.0 && make install && ldconfig | ||
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| RUN which conda | ||
| RUN conda --version | ||
| RUN which protoc | ||
| RUN protoc --version | ||
| RUN which python | ||
| RUN python --version | ||
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| CMD ["bash"] |
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| # How to build conda package for Tensor Comprehensions and its dependencies | ||
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| ## Building Conda Packages inside TC nvidia-docker image | ||
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| We have a runtime nvidia docker image for TC to build conda packages and run all | ||
| tests including gpu ones. To build the packages, follow the below commands: | ||
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| ```Shell | ||
| # Build a new nvidia docker image | ||
| cd TensorComprehensions/conda_recipes | ||
| docker build -t trusty-gcc4.8-py3-conda-cuda:2 . | ||
| ``` | ||
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| Now, we have the docker image built, we will run it by following command: | ||
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| ```Shell | ||
| nvidia-docker run --rm -i -t trusty-gcc4.8-py3-conda-cuda:2 | ||
| ``` | ||
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| Once you are inside the docker image, run `nvidia-smi` and verify that you have | ||
| gpus access. Now, you are good to go and build conda packages. For that run the | ||
| following command: | ||
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| ```Shell | ||
| cd TensorComprehensions/conda_recipes | ||
| ./conda_build_tc.sh | ||
| ``` | ||
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| Sit back, relax and let the packages build. If you see any errors in packages, fix them. | ||
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| ## Building conda packages outside docker image | ||
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| Steps: | ||
| 1. You need to have conda installed. Follow the instructions in [dockerfile](https://github.com/facebookresearch/TensorComprehensions/tree/master/docker/linux-trusty-gcc4.8-cuda8-cudnn6-py3-conda) for how to install conda | ||
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| *NOTE*: Make sure that conda get download things from web and you might need some | ||
| proxy-setup. You can add following lines to your `~/.condarc` file | ||
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| ``` | ||
| proxy_servers: | ||
| http: <http_proxy_to_use> | ||
| https: <https_proxy_to_use> | ||
| ``` | ||
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| 2. Next install conda build | ||
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| ```Shell | ||
| conda install conda-build | ||
| ``` | ||
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| This installs a bunch of dependencies like patchelf, glob, pkginfo, conda-verify. | ||
| When prompted, say yes to install these dependencies. | ||
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| 3. While building conda packaged, we might have some third-party dependencies for which we want to install the conda package available on [conda-forge](https://github.com/conda-forge/feedstocks). In order to install packages from conda-forge, we need to add conda-forge to the conda channels by running the following command | ||
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| We will add `soumith` channel since we will get pytorch from there. Channels are prioritized in the order they were added. | ||
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| ```Shell | ||
| conda config --add channels conda-forge | ||
| conda config --add channels pytorch | ||
| ``` | ||
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| For example: this is required for the libgmp dependency of ISL and we can get | ||
| conda package of libgmp from conda-forge | ||
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| 4. Now create a Python 3.6 conda environment for packaging TC by following below | ||
| instructions: | ||
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| ```Shell | ||
| conda create --name tc_build python=3.6 | ||
| source activate tc_build | ||
| ``` | ||
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| `tc_build` is the name of environment. You may chose any name you want. | ||
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| **NOTE**: If you want to exit the conda environment, you can run | ||
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| ```Shell | ||
| source deactivate | ||
| ``` | ||
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| 5. Now, we have conda setup, we are ready to build conda package for TC. For this, | ||
| since TC has its dependencies that are linked dynamically, we will build conda | ||
| packages for them separately. In short, we need to build packages for `clang+llvm-tapir5.0`, `gflags`, `glog`, `Halide`, TC version of `isl`, TC version of `protobuf`, and finally `Tensor Comprehensions`. | ||
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| For building each package, we need to specify a `build version`, `build number` and | ||
| `git hash`. This information is used to build each package. | ||
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| If you ever want to build new packages corresponding to new git hashes for example | ||
| when submodules are bumped, you will need to build conda package for submodule | ||
| with the new git hash. For that, all you need to do is change the build version, | ||
| number and git hash. | ||
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| Now, we will go ahead and build the conda package of TC and all of its dependencies. For that, run the command below: | ||
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| ```Shell | ||
| cd $TC_DIR/conda_recipes | ||
| ./conda_build_tc.sh | ||
| ``` | ||
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| You will see that you have conda packages in `<anaconda_root>/conda-bld/linux-64` | ||
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| **NOTE**: If some of the conda packaging fails, you can clean them up by following | ||
| command: | ||
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| ```Shell | ||
| conda build purge | ||
| ``` | ||
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| Now, let's install the tensor Comprehensions package. By default, the package | ||
| will get installed to `<anaconda_root>/lib/python3.6/site-packages` | ||
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| ```Shell | ||
| conda install --use-local tensor_comprehensions | ||
| ``` | ||
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| **NOTE**: `--use-local` means that we are going to install locally built packages. | ||
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| 6. In order to uninstall a conda package, you can run | ||
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| ```Shell | ||
| conda uninstall <package_name> | ||
| ``` | ||
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| If you want to uninstall a python version of package like `tensor_comprehensions`, | ||
| run the following command **twice** | ||
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| ```Shell | ||
| pip uninstall tensor_comprehensions | ||
| ``` | ||
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| # A few helpful things | ||
| 1. When building a private repo, git clone might fail with authentication errors | ||
| even when you pass the https_proxy (especially for private repo). In that case, | ||
| generate an access token from github https://help.github.com/articles/creating-a-personal-access-token-for-the-command-line/ | ||
| and use that as your password when prompted for authentication. | ||
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| 2. Once the build finished, the packaging and un-packaging step takes long | ||
| and might seem like it's stuck but it's not. | ||
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| 3. Look at the description on top in `build.sh` script of conda-recipes for how | ||
| conda builds packages and tests the builds. | ||
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| 4. Last, [here](https://conda.io/docs/user-guide/tasks/index.html) is a very useful reference on conda packaging. |
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