Bazel/Bazelisk WORKSPACE Configuration for TensorFlow C++ on Jetson Orin Nano

Issue Overview

Users are experiencing difficulties setting up a Bazel/Bazelisk WORKSPACE file for compiling TensorFlow C++ programs on the Nvidia Jetson Orin Nano development board. The primary issue involves an error message during compilation:

in cc_toolchain_alias rule @bazel_tools//tools/cpp:current_cc_toolchain: Unable to find a CC toolchain using toolchain resolution. Did you properly set --platforms?

This error suggests problems with the toolchain configuration in the WORKSPACE file. The issue persists even when using pre-built TensorFlow packages and Docker containers, indicating a more complex underlying problem.

Possible Causes

1. Incompatible Bazel/Bazelisk version: The version of Bazel or Bazelisk being used may not be compatible with the TensorFlow version or the Jetson Orin Nano’s architecture.

2. Incorrect WORKSPACE configuration: The WORKSPACE file may be missing necessary dependencies or have incorrect toolchain specifications for the Jetson platform.

3. Protobuf version mismatch: An error mentioning that the protoc version was too new for TensorFlow 2.11.0 suggests a compatibility issue between TensorFlow and the installed protobuf library.

4. GCC version incompatibility: The GCC version installed may not be compatible with CUDA or TensorFlow requirements.

5. File system corruption: In some cases, file system corruption led to errors during the build process, causing issues with Bazel cache and Docker containers.

Troubleshooting Steps, Solutions & Fixes

1. Use pre-built TensorFlow package:

   • For users who don’t need to compile TensorFlow from source, use the pre-built package provided by NVIDIA:

     sudo apt-get update
     sudo apt-get install libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran
     sudo pip3 install -U testresources setuptools==65.5.0 numpy==1.21.1 future==0.18.2 mock==3.0.5 keras_preprocessing==1.1.2 keras_applications==1.0.8 gast==0.4.0 protobuf pybind11 cython pkgconfig
     sudo env H5PY_SETUP_REQUIRES=0 pip3 install -U h5py==3.1.0
     sudo pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v51 tensorflow
     

2. Update GCC version:

   • Install GCC 11, which is the highest version allowed by CUDA:

     sudo apt-get update
     sudo apt-get install gcc-11 g++-11
     sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110 --slave /usr/bin/g++ g++ /usr/bin/g++-11
     

3. Resolve protobuf version mismatch:

   • Install a compatible version of protobuf:

     sudo pip3 uninstall protobuf
     sudo pip3 install protobuf==3.20.3
     

4. Clean Bazel cache:

   • If encountering file system-related errors, try cleaning the Bazel cache:

     bazel clean --expunge
     

5. Use NVIDIA Docker runtime:

   • When using Docker containers, ensure to add the --runtime nvidia flag:

     docker run --runtime nvidia -it <container_name>
     

6. Compile TensorFlow from source:

   • If pre-built packages don’t work, compile TensorFlow from source:

     git clone https://github.com/tensorflow/tensorflow.git
     cd tensorflow
     ./configure
     bazel build --config=cuda //tensorflow/tools/pip_package:build_pip_package
     

7. Check file system integrity:

   • If encountering "Structure needs cleaning" errors, check the file system:

     sudo umount /dev/mmcblk0p1
     sudo e2fsck -f /dev/mmcblk0p1
     

8. Use dustynv/ml Docker container:

   • As a workaround, use the dustynv/ml Docker container which has TensorFlow pre-installed:

     docker pull dustynv/ml:r35.2.1
     docker run --runtime nvidia -it dustynv/ml:r35.2.1
     

9. Reinstall L4T:

   • In case of persistent file system corruption, consider reimaging and reinstalling the NVIDIA L4T (Linux for Tegra) operating system on your Jetson Orin Nano.

If the issue persists after trying these solutions, consider reaching out to NVIDIA developer forums or TensorFlow community for more specific assistance tailored to your exact setup and requirements.