Apex with windows, anaconda and jupyter noptebook
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 9k
- Forks
- 1.5k
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 3
Description
! pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ../nvidiaapex/repository/NVIDIA-apex-39e153a
import apex
from apex.normalization.fused_layer_norm import FusedLayerNorm
from pytorch_pretrained_bert import convert_tf_checkpoint_to_pytorch
this works fine but then, I still have:
ModuleNotFoundError: No module named 'fused_layer_norm_cuda'
When trying to do:
convert_tf_checkpoint_to_pytorch.convert_tf_checkpoint_to_pytorch(
BERT_MODEL_PATH + 'bert_model.ckpt',
BERT_MODEL_PATH + 'bert_config.json',
WORK_DIR + 'pytorch_model.bin')
because it tries to do :
import importlib
importlib.import_module("fused_layer_norm_cuda")
any idea ?
I have:
nvcc --version giving release 10.1 and torch.version giving 1.0.1
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Reproduce the installation in the Anaconda/Jupyter environment using the supplied pip command, then check the import of apex.normalization.fused_layer_norm and importlib.import_module("fused_layer_norm_cuda"). Compare the CUDA and PyTorch versions shown in the issue with the resulting build and import behavior; done means the conversion call runs without the ModuleNotFoundError.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- anaconda, jupyter-notebook, python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100