This 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.
> 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.
Conversely, nvcc often stops working with major upgrades of gcc/clang. Fun times, indeed.
This is why a lot of people just use NVIDIA's containers even for local solo dev. It's a hassle to set up initially (docker/podman hell) but all the tools are there and they work fine.
> This is why a lot of people just use NVIDIA's containers even for local solo dev. It's a hassle to set up initially (docker/podman hell) but all the tools are there and they work fine.
Yeah, which I feel like is fine for one project, or one-offs, but once you've accumulated projects, having individual 30GB images for each of them quickly adds up.
I found that most of my issues went away as I started migrating everything to `ux` for the python stuff, and nix for everything system related. Now I can finally go back to a 1 year old ML project, and be sure it'll run like before, and projects share a bit more data.
Yep, right now nvidia libs are broken with clang-21 and recent glibc due to stuff like rsqrt() having throw() in the declaration and not in the definition
This article has good info, but is the overloading premise slightly contrived? Maybe I don’t talk to enough CUDA beginners. I work with CUDA a lot but I’m not exactly a CUDA expert, and from my perspective, in practice there are default assumptions one can safely make for the base terms, and people do qualify the alternatives almost always. For example, if someone says “CUDA version”, they always mean the toolkit, and never mean compute capability, runtime, or language. The term “driver” when used without qualification always means the display driver, and never means the driver API, there really is no overload there.
Oh yeah, knowing the difference between the runtime API and driver API is definitely an issue, and there is common confusion around that. But that’s not an overloaded word problem, right? I wasn’t trying to say there’s no confusion, and I do think understanding the terms in the article is super helpful. To your point, I think there’s justification for needing a codex like this article has without framing it as an overloaded terminology problem.
Wondering 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.
I'm wondering how a $4T company got away with shipping the absolute state of the toolchain to begin with. They have total and complete sovereignty on everything on the outside of the OS and PCIe boundaries with a bottomless pool of top class labor. There's no reason it has to be cruftier or more fragile than any other low latency networked computation... and yet here we are. AMD isn't any better. I'm almost interested to see if Intel has done any better with L0, but I highly suspect it suffers from the exact same ecosystem hell problems that plague the other two.
The idea that getting a PCIe FPGA board to crunch numbers is less headache prone than a GPU is laughable, but that's the absurd reality we live in.
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.
That's not the case, CUDA containers user space does not have to match the host drivers CUDA capability. The container needs to be the same major version or lower. So a system with a CUDA 13 capable driver should be able to run all previous versions.
For some versions there's even sometimes compat layers built into the container to allow forward version compatibility.
I wish GPU vendors would stick to a standard terminology, at least for common parts. It's really confusing having to deal with warps vs wavefronts vs simd groups, thread block vs workgroup, streaming multiprocessor vs compute unit vs execution unit, etc...
Great 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.
That 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!
> CUDA Runtime: The runtime library (libcudart) that applications link against.
That library is actually a rather poor idea. If you're writing a CUDA application, I strongly recommend avoiding the "runtime API". It provides partial access to the actual CUDA driver and its API, which is 'simpler' in the sense that you don't explicitly create "contexts", but:
* It hides or limits a lot of the functionality.
* Its actual behavior vis-a-vis contexts is not at all simple and is likely to make your life more difficult down the road.
* It's not some clean interface that's much more convenient to use.
So, either go with the driver, or consider my CUDA API wrappers library [1], which _does_ offer a clean, unified, modern (well, C++11'ish) RAII/CADRe interface. And it covers much more than the runtime API, to boot: JIT compilation of CUDA (nvrtc) and PTX (nvptx_compiler), profiling (nvtx), etc.
> Driver API ... provides direct access to GPU functionality.
Well, I wouldn't go that far, it's not that direct. Let's call it: "Less indirect"...
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.
I am not a layer (IANAL), but here is what Gemini 3 Pro says: "You generally do not need to use the trademark symbol for CUDA in a blog post, unless you have a specific commercial relationship with NVIDIA."
Now direct from actual sources... From [1]
> Intended users of this Brand Guideline are members of the NVIDIA Partner Network (NPN), including original equipment manufacturers (OEMs), solution advisors, cloud partners, solution providers, distributors, solutions integrators, and service delivery partners.
From [2]:
> Always include the correct trademark (™ vs ®) by referring to the content documents provided or using the list of common NVIDIA products and technologies. After the first mention of the NVIDIA product or technology, which includes the appropriate trademarks, the trademark does not need to be included in future mentions within the same document, article, etc.
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.
Conversely, nvcc often stops working with major upgrades of gcc/clang. Fun times, indeed.
This is why a lot of people just use NVIDIA's containers even for local solo dev. It's a hassle to set up initially (docker/podman hell) but all the tools are there and they work fine.
Yeah, which I feel like is fine for one project, or one-offs, but once you've accumulated projects, having individual 30GB images for each of them quickly adds up.
I found that most of my issues went away as I started migrating everything to `ux` for the python stuff, and nix for everything system related. Now I can finally go back to a 1 year old ML project, and be sure it'll run like before, and projects share a bit more data.
"The CUDA "driver version" looks like the CUDA runtime version - so what's the difference?" https://stackoverflow.com/q/40589814/1593077
or consider the version you get when you run nvidia-smi, versus the version you get when you run nvcc --version. Those are very different numbers...
The compatibility between different versions of the driver and the toolkit is also a cause for some headaches in my experience.
The idea that getting a PCIe FPGA board to crunch numbers is less headache prone than a GPU is laughable, but that's the absurd reality we live in.
There were other problems, such as the research cluster of my university not having Docker, but that is a different issue.
We were on AWS when we used this so setting up seemed easy enough - AWS gave you the driver, and a matching docker image was easy enough to find.
For some versions there's even sometimes compat layers built into the container to allow forward version compatibility.
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.
That library is actually a rather poor idea. If you're writing a CUDA application, I strongly recommend avoiding the "runtime API". It provides partial access to the actual CUDA driver and its API, which is 'simpler' in the sense that you don't explicitly create "contexts", but:
* It hides or limits a lot of the functionality.
* Its actual behavior vis-a-vis contexts is not at all simple and is likely to make your life more difficult down the road.
* It's not some clean interface that's much more convenient to use.
So, either go with the driver, or consider my CUDA API wrappers library [1], which _does_ offer a clean, unified, modern (well, C++11'ish) RAII/CADRe interface. And it covers much more than the runtime API, to boot: JIT compilation of CUDA (nvrtc) and PTX (nvptx_compiler), profiling (nvtx), etc.
> Driver API ... provides direct access to GPU functionality.
Well, I wouldn't go that far, it's not that direct. Let's call it: "Less indirect"...
[1] : https://github.com/eyalroz/cuda-api-wrappers/
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.
Now direct from actual sources... From [1]
> Intended users of this Brand Guideline are members of the NVIDIA Partner Network (NPN), including original equipment manufacturers (OEMs), solution advisors, cloud partners, solution providers, distributors, solutions integrators, and service delivery partners.
From [2]:
> Always include the correct trademark (™ vs ®) by referring to the content documents provided or using the list of common NVIDIA products and technologies. After the first mention of the NVIDIA product or technology, which includes the appropriate trademarks, the trademark does not need to be included in future mentions within the same document, article, etc.
> CUDA®
[1]: https://brand.nvidia.com/d/wGtgoY2mtYYM/nvidia-partner-netwo...
[2]: https://brand.nvidia.com/d/wGtgoY2mtYYM/nvidia-partner-netwo...