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Dockerfile install #1
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Hi @brian-dellabetta. Thanks for your interest in cuQuantum!
All samples require an Nvidia GPU to run. Specifically, a GPU with compute capability 7.0+. Here's a useful table.
I'm guessing this is the issue. The import statements will fail without a valid driver installation. Without seeing the full error output, I cannot confirm.
For cuQuantum, as long as your CUDA toolkit version is 11.2+, and CuPy's version is 9.5+, you should be fine. If you have a more specific concern, please include it in your response.
Unfortunately, we aren't accepting code contributions at this time. I'm wondering why you're using
(e.g.)
It is also true that all of the samples are hosted in this repository. Let us know if you're still having trouble or if you have other questions! |
One more thing:
Yes, we have become aware of this issue for building cuQuantum Python from source. We'll push a fix shortly. Thanks for bringing it up, Brian. |
@mtjrider I'm just trying to make sure the image is valid and has all dependencies before attempting to run on an nvidia GPU. This requires an nvidia V100 or higher for compute capability 7.0+, corresponding to a Here's the error I'm seeing:
This seems to me more related to the versions of cupy and libcuda than an actual runtime error from lack of gpu. I might be mistaken though that the driver won't live in the docker image, that it will need to be installed on host and mounted into the image? I hope to try on a VM with a GPU later this week, will post updates here. If not a Dockerfile, will an image be made available at some point on the NGC catalog or elsewhere? I'm sure it would be useful to others |
Also @mtjrider the wget on the repo is just to pull in the code samples. i didn't see them in the installed directories Also, thanks for all the help! |
Makes perfect sense. Thanks for this clarification. To be clear, I've tested your Dockerfile on a system with GPUs to compile and run the tests, and it works without issue. When you deploy, please take care to confirm that the driver and compilation toolchain are compatible. The CUDA driver and kernel mode driver compatibility is documented here. The following error indicates that the CUDA driver is missing. This is not installed in the container. Here is an architecture overview.
I meant that you may also clone the samples because they are hosted in this repository:
Note: per this comment, I had to modify the Makefile to rename
Here, I should note that I removed any |
@mtjrider thank you! The architecture diagram is what I was missing, this is super helpful. I appreciate your help in sanity checking the image in a working environment, we'll try to reproduce on our end. I will close and re-open the issue if we have further questions. Thanks again for the help |
Hi,
I am trying to build an image with cuquantum and the code samples installed. Here is what I have so far, compiled from the README here and in the documentation :
The image has
cupy-cuda115
, the conda install ofcuquantum-python
installs another version of cupy as a dependency so I uninstall the old one (it will complain during import if both are available).make all
builds successfully (though the lib64->lib symlink is needed for it to work), but I am unable to run the python samples without hitting import errors.I am running on an intel-chip mac, just trying to clear up the import errors before we run this on a cloud instance with an nvidia GPU mounted in.
Before posting any stacktraces, am I on the right track here? Maybe I should use a different base image that has an equivalent version of cupy. I'm also not sure if the cuda version is incompatible.
I am happy to submit a PR with the working Dockerfile once we figure this all out :)
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