Unable to Import Modules Despite Successfull Install inside Conda Env

[DaveBGld]

Describe the Bug After a successful install with recommended settings for full package, none of the modules can be imported.

Minimal Steps/Code to Reproduce the Bug a) Downloaded source from Github via zip file b) Unpacked c) conda activate tspp d) Run the recommended command: pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ Complete install log attached. There is a warning...

WARNING: : due to the presence of --build-option / --global-option / --install-option. Consider using --config-settings for more flexibility. DEPRECATION: --no-binary currently disables reading from the cache of locally built wheels. In the future --no-binary will not influence the wheel cache. pip 23.1 will enforce this behaviour change. A possible replacement is to use the --no-cache-dir option. You can use the flag --use-feature=no-binary-enable-wheel-cache to test the upcoming behaviour. Discussion can be found at https://github.com/pypa/pip/issues/11453

Complete Install Log HERE

e) In a Notebook,

import types
import apex
modules = [name for name, obj in vars(apex).items() if isinstance(obj, types.ModuleType)]
print(modules)
print(dir(apex))

Produce no errors but empty module lists:

[] ['doc', 'file', 'loader', 'name', 'package', 'path', 'spec']

And any of these

from apex.fp16_utils import *
from apex import amp, optimizers
from apex.multi_tensor_apply import multi_tensor_applier
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from apex.parallel import DistributedDataParallel as DDP

will result in either

ImportError: cannot import name 'xxx' from 'apex' (unknown location) or ModuleNotFoundError: (for example, ) No module named 'apex.fp16_utils'

Expected Behavior Normal import of modules from the package

Environment

PyTorch version: 1.13.1+cu116 Is debug build: False CUDA used to build PyTorch: 11.6 ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0 Clang version: Could not collect CMake version: version 3.16.3 Libc version: glibc-2.31

Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 11.6.124 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 Nvidia driver version: 510.47.03 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.0 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.4.0 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

Versions of relevant libraries: [pip3] numpy==1.21.6 [pip3] torch==1.13.1+cu116 [pip3] torchaudio==0.13.1+cu116 [pip3] torchvision==0.14.1+cu116 [conda] blas 1.0 mkl
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py39h7f8727e_0
[conda] mkl_fft 1.3.1 py39hd3c417c_0
[conda] mkl_random 1.2.2 py39h51133e4_0
[conda] numpy 1.21.6 pypi_0 pypi [conda] torch 1.13.1+cu116 pypi_0 pypi [conda] torchaudio 0.13.1+cu116 pypi_0 pypi [conda] torchvision 0.14.1+cu116 pypi_0 pypi