CUDA Ontology
jamesakl.com
CUDA Ontology
1–10 of 44 posts
Re: CUDA Ontology
#2That is a great reference, explains a lot of small inaccuracies between various tutorials when you're trying to debug some of these issues. Saved and printed, thanks a lot!
Re: CUDA Ontology
#3Wondering why a $4T company can't afford a smart installation assistant that can auto-detect problems and apply fixes as needed. I wasted too many days chasing driver and torch versions. It's probably the worst part of working in ML. Combine this with Python's horrible package management and you got a perfect combo - like the cough and the stitch.
Re: CUDA Ontology
#4Wondering why a $4T company can't afford a smart installation assistant that can auto-detect problems and apply fixes as needed. I wasted too many days chasing driver and torch versions. It's probably the worst part of working in ML. Combine this with Python's horrible package management and you got a perfect combo - like the cough and the stitch.
They provide containers to cater to those needs: https://catalog.ngc.nvidia.com/search
Re: CUDA Ontology
#5This is a good resource. But for the computer vision and machine learning practitioner most of the fun can start where this article ends.
nvcc from the CUDA toolkit has a compatibility range with the underlying host compilers like gcc. If you install a newer CUDA toolkit on an older machine, likely you'll need to upgrade your compiler toolchain as well, and fix the paths.
While orchestration in many (research) projects happens from Python, some depend on building CUDA extensions. An innocently looking Python project may not ship the compiled kernels and may require a CUDA toolkit to work correctly. Some package management solutions provide the ability to install CUDA toolkits (conda/mamba, pixi), the pure-Python ones do not (pip, uv). This leaves you to match the correct CUDA toolkit to your Python environment for a project. conda specifically provides different channels (default/nvidia/pytorch/conda-forge), from conda 4.6 defaulting to a strict channel priority, meaning "if a name exists in a higher-priority channel, lower ones aren't considered". The default strict priority can make your requirements unsatisfiable, even though there would be a version of each required package in the collection of channels. uv is neat and fast and awesome, but leaves you alone in dealing with the CUDA toolkit.
Also, code that compiles with older CUDA toolkit versions may not compile with newer CUDA toolkit versions. Newer hardware may require a CUDA toolkit version that is newer than what the project maintainer intended. PyTorch ships with a specific CUDA runtime version. If you have additional code in your project that also is using CUDA extensions, you need to match the CUDA runtime version of your installed PyTorch for it to work. Trying to bring up a project from a couple of years ago to run on latest hardware may thus blow up on you on multiple fronts.
Re: CUDA Ontology
#6This is a good resource. But for the computer vision and machine learning practitioner most of the fun can start where this article ends. nvcc from the CUDA toolkit has a compatibility range with the underlying host compilers like gcc. If you install a newer CUDA toolkit on an older machine, likely you'll need to upgrade your compiler toolchain as well, and fix the paths. While orchestration in many (research) projec…
Yes, this is the actual lived reality. Thank you for outlining it so well.
Re: CUDA Ontology
#7Great explanation!
It should probably also add that everything CUDA is owned by NVIDIA, and "CUDA" itself is a registered trademark. The official way to refer to it is that the first time you spell it out as "NVIDIA® CUDA®" and then subsequently refer to just CUDA.
Re: CUDA Ontology
#8Great overview, with lots of effort place into it.
However, it misses the polyglot part (Fortran, Python GPU JIT, all the backends that support PTX), the library ecosystem (writing CUDA kernels should be the exception not the rule), the graphical debugging tools and IDE integration.
Re: CUDA Ontology
#9Wondering why a $4T company can't afford a smart installation assistant that can auto-detect problems and apply fixes as needed. I wasted too many days chasing driver and torch versions. It's probably the worst part of working in ML. Combine this with Python's horrible package management and you got a perfect combo - like the cough and the stitch.
They provide containers to cater to those needs: https://catalog.ngc.nvidia.com/search
After being once again frustrated by the CUDA installation experience, I thought that I should give those containers a try. Unfortunately, my computer did not boot anymore after following the installation instructions for the NVIDIA container toolkit as outlined on the NVIDIA website. Reinstalling everything and following the instructions from some random blog post made it work, but I then found that the container with the CUDA version that I needed had been deprecated.
There were other problems, such as the research cluster of my university not having Docker, but that is a different issue.
Re: CUDA Ontology
#10Great explanation! It should probably also add that everything CUDA is owned by NVIDIA, and "CUDA" itself is a registered trademark. The official way to refer to it is that the first time you spell it out as "NVIDIA® CUDA®" and then subsequently refer to just CUDA.
Why should the author use the registered trademark symbol?