specter119 opened a new pull request #20431:
URL: https://github.com/apache/incubator-mxnet/pull/20431
## Description ##
For people who use conda create python env, install `cudatoolkit` is the
most efficient way to get Cuda support.
However, the package will not implement the system variables. Then mxnet
will not find the corresponding cuda dlls.
By adding `$CONDA_PREFIX/Library/bin` can solve this problem.
for the people who always working in jupyter, adding "CUDA_PREFIX" to env of
jupyter kernelspec file will be necessary.
BTW, I still write an other more brute way:
```python
import pathlib
_ = list(
map(
lambda x: os.add_dll_directory(str(x)),
(p for p in map(pathlib.Path, set(map(str.lower,
os.environ['path'].split(';')))) if next(p.glob('*.dll'), None) is not None),
)
)
```
This way add all the dir has dll in `$PATH` to dll search path. But, I not
sure too long dll dir path will harm to some performance. By the slice test on
my machine. It has a mirror influence to the import modules.
I'm a beginner of mxnet that just finish the installation, Sorry for the
inconvenience this PR may bring to you.
## Checklist ##
### Essentials ###
- [x] PR's title starts with a category (e.g. [BUGFIX], [MODEL], [TUTORIAL],
[FEATURE], [DOC], etc)
- [x] Changes are complete (i.e. I finished coding on this PR)
- [ ] All changes have test coverage
- [x] Code is well-documented
### Changes ###
- [ ] Feature1, tests, (and when applicable, API doc)
- [ ] Feature2, tests, (and when applicable, API doc)
## Comments ##
- If this change is a backward incompatible change, why must this change be
made.
- Interesting edge cases to note here
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