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diff --git a/delegate/BuildGuideNative.md b/delegate/BuildGuideNative.md index 62d6673925..932c74423a 100644 --- a/delegate/BuildGuideNative.md +++ b/delegate/BuildGuideNative.md @@ -11,6 +11,7 @@ natively (no cross-compilation required). This is to keep this guide simple. **Table of content:** - [Delegate build guide introduction](#delegate-build-guide-introduction) - [Dependencies](#dependencies) + * [Download Arm NN](#download-arm-nn) * [Build Tensorflow Lite for C++](#build-tensorflow-lite-for-c--) * [Build Flatbuffers](#build-flatbuffers) * [Build the Arm Compute Library](#build-the-arm-compute-library) @@ -23,7 +24,7 @@ natively (no cross-compilation required). This is to keep this guide simple. # Dependencies Build Dependencies: - * Tensorflow Lite: this guide uses version 2.5.1 . Other versions may work. + * Tensorflow Lite: this guide uses version 2.5.0. Other versions may work. * Flatbuffers 1.12.0 * Arm NN 21.11 or higher @@ -45,6 +46,18 @@ mkdir $BASEDIR cd $BASEDIR apt-get update && apt-get install git wget unzip zip python git cmake scons ``` + +## Download Arm NN + +First clone Arm NN using Git. + +```bash +cd $BASEDIR +git clone "https://review.mlplatform.org/ml/armnn" +cd armnn +git checkout <branch_name> # e.g. branches/armnn_21_11 +``` + ## Build Tensorflow Lite for C++ Tensorflow has a few dependencies on it's own. It requires the python packages pip3, numpy, and also Bazel or CMake which are used to compile Tensorflow. A description on how to build bazel can be @@ -65,13 +78,14 @@ sudo make install ``` ### Download and build Tensorflow Lite - +Arm NN provides a script, armnn/scripts/get_tensorflow.sh, that can be used to download the version of TensorFlow that Arm NN was tested with: ```bash cd $BASEDIR git clone https://github.com/tensorflow/tensorflow.git cd tensorflow/ -git checkout tags/v2.5.1 # Minimum version required for the delegate is v2.3.1 +git checkout $(../armnn/scripts/get_tensorflow.sh -p) # Minimum version required for the delegate is v2.3.1 ``` + Now the build process can be started. When calling "cmake", as below, you can specify a number of build flags. But if you have no need to configure your tensorflow build, you can follow the exact commands below: ```bash @@ -100,17 +114,19 @@ The Arm NN library depends on the Arm Compute Library (ACL). It provides a set o both Arm CPUs and GPUs. The Arm Compute Library is used directly by Arm NN to run machine learning workloads on Arm CPUs and GPUs. -It is important to have the right version of ACL and Arm NN to make it work. Luckily, Arm NN and ACL are developed -very closely and released together. If you would like to use the Arm NN version "20.11" you should use the same "20.11" -version for ACL too. +It is important to have the right version of ACL and Arm NN to make it work. Arm NN and ACL are developed very closely +and released together. If you would like to use the Arm NN version "21.11" you should use the same "21.11" version for +ACL too. Arm NN provides a script, armnn/scripts/get_compute_library.sh, that can be used to download the exact version +of Arm Compute Library that Arm NN was tested with. + +To build the Arm Compute Library on your platform, download the Arm Compute Library and checkout the tag that contains +the version you want to use. Build it using `scons`. -To build the Arm Compute Library on your platform, download the Arm Compute Library and checkout the tag -that contains the version you want to use. Build it using `scons`. ```bash -cd $BASEDIR +cd $HOME/armnn-devenv git clone https://review.mlplatform.org/ml/ComputeLibrary cd ComputeLibrary/ -git checkout <tag_name> # e.g. v20.11 +git checkout $(../armnn/scripts/get_compute_library.sh -p) # e.g. v21.11 # The machine used for this guide only has a Neon CPU which is why I only have "neon=1" but if # your machine has an arm Gpu you can enable that by adding `opencl=1 embed_kernels=1 to the command below scons arch=arm64-v8a neon=1 extra_cxx_flags="-fPIC" benchmark_tests=0 validation_tests=0 @@ -118,13 +134,10 @@ scons arch=arm64-v8a neon=1 extra_cxx_flags="-fPIC" benchmark_tests=0 validation ## Build the Arm NN Library -With ACL built we can now continue to building Arm NN. To do so, download the repository and checkout the matching -version as you did for ACL. Create a build directory and use `cmake` to build it. +With ACL built we can now continue to build Arm NN. Create a build directory and use `cmake` to build it. ```bash cd $BASEDIR -git clone "https://review.mlplatform.org/ml/armnn" cd armnn -git checkout <branch_name> # e.g. branches/armnn_20_11 mkdir build && cd build # if you've got an arm Gpu add `-DARMCOMPUTECL=1` to the command below cmake .. -DARMCOMPUTE_ROOT=$BASEDIR/ComputeLibrary -DARMCOMPUTENEON=1 -DBUILD_UNIT_TESTS=0 @@ -172,7 +185,7 @@ Download Arm NN if you have not already done so: cd $BASEDIR git clone "https://review.mlplatform.org/ml/armnn" cd armnn -git checkout <branch_name> # e.g. branches/armnn_20_11 +git checkout <branch_name> # e.g. branches/armnn_21_11 ``` Build Arm NN with the delegate included ```bash |