Nano Known Issues#

PyTorch Issues#

AttributeError: module ‘distutils’ has no attribute ‘version’#

This usually is because the latest setuptools does not compatible with PyTorch 1.9.

You can downgrade setuptools to 58.0.4 to solve this problem.

For example, if your setuptools is installed by conda, you can run:

conda install setuptools==58.0.4

error while loading shared libraries: libunwind.so.8#

You may see this error message when running source bigdl-nano-init

 Sed: error while loading shared libraries: libunwind.so.8: cannot open shared object file: No such file or directory.

You can use the following command to fix this issue.

  • apt-get install libunwind8-dev

Bus error (core dumped) in multi-instance training with spawn distributed backend#

This usually is because you did not set enough shared memory size in your docker container.

You can increase --shm-size to a larger value, e.g. a few GB, to your docker run command, or use --ipc=host.

If you are running in k8s, you can mount larger storage in /dev/shm. For example, you can add the following volume and volumeMount in your pod and container definition.

spec:
  containers:
    ...
    volumeMounts:
    - mountPath: /dev/shm
      name: cache-volume
  volumes:
  - emptyDir:
    medium: Memory
    sizeLimit: 8Gi
    name: cache-volume

TensorFlow Issues#

Nano keras multi-instance training currently does not suport tensorflow dataset.from_generators, numpy_function, py_function#

Nano keras multi-instance training will serialize TensorFlow dataset object into a graph.pb file, which does not work with dataset.from_generators, dataset.numpy_function, dataset.py_function due to limitations in TensorFlow.

RuntimeError: Inter op parallelism cannot be modified after initialization#

If you meet this error when import bigdl.nano.tf, it could be that you have already run some TensorFlow code that set the inter/intra op parallelism, such as tfds.load. You can try to workaround this issue by trying to import bigdl.nano.tf first before running TensorFlow code. See https://github.com/tensorflow/tensorflow/issues/57812 for more information.

Ray Issues#

protobuf version error#

Now pip install ray[default]==1.11.0 will install google-api-core==2.10.0, which depends on protobuf>=3.20.1. However, nano depends on protobuf==3.19.4, so if we install ray after installing bigdl-nano, pip will reinstall protobuf==4.21.5, which causes error.