Rocm vs cuda reddit

Rocm vs cuda reddit. I would assume ROCm would be faster since ZLUDA uses ROCm to translate things to CUDA so you can run CUDA programs on modern hardware. CUDA also works with either Windows and Linux. AMD's ROCm vs NVidia's CUDA for reinforcement learning ? I know CUDA is more mature and by far the most used, im just curious if there is any comparation between them available, or if anyone knows about how do they stack ? I’m curious if the support is there for ROCm vs CUDA now on a 7900XTX and 7950X vs a 4080 Super and 14700K to do some light AI/ML(computer vision and forecasting). 8M subscribers in the Amd community. 1 but it's very early days yet. It is only the most popular projects that are gradually acquiring ROCm support. Much has changed. But is a little more complicated, needs to be more general. 1 models from Hugging Face, along with the newer SDXL. Sep 12, 2024 · While NVIDIA's dominance is bolstered by its proprietary advantages and developer lock-in, emerging competitors like AMD and innovations such as AMD's ROCm, OpenAI's Triton, and PyTorch 2. 516 subscribers in the hypeurls community. New comments cannot be ROCm only really works properly on MI series because HPC customers pay for that, and “works” is a pretty generous term for what ROCm does there. Dec 2, 2022 · As with CUDA, ROCm is an ideal solution for AI applications, as some deep-learning frameworks already support a ROCm backend (e. [13] Hardware support It translates CUDA to HIP and reverse, either using a Clang-based tool, I Don't know about windows but here on linux vega is supported on rocm/hip & rocm/opencl and for polaris support rocm/hip , but needs to be compiled from source with additional settings to support rocm/opencl , ROCM devs says that it is supported but not tested or validated its kinda of an "un-official" official support , but blender still doesn't support HIP on linux at All in Any GPU so we 755 subscribers in the ROCm community. Welcome to r/patient_hackernews!Remember that in this subreddit, commenting requires a special process: Declare your intention of commenting by posting a pre-comment containing only the single letter R. I have tried to find benchmarks comparing the two, as I’ve seen PyTorch and Tensorflow support ROCm, but people still just seem to do everything on CUDA. Aug 18, 2022 · The Pros and Cons of Tensorflow ROCM vs CUDA. I work with TensorFlow for deep learning and can safely say that Nvidia is definitely the way to go with running networks on GPUs right now. Just make sure to have the lastest drivers and run this command: pip install tensorflow-directml Boom, you now have tensorflow powered by AMD GPUs, although the performance needs to improve DML is a huge step forward in ML. AMD ROCm only available on certain kernel version and also doesn't work in Windows. 0, and v2. hipSYCL is written against the HIP API. , TensorFlow, PyTorch, MXNet, ONNX, CuPy, and more). Answering this question is a bit tricky though. ROCm is fundamentally flawed in some key areas, primarily it's too hardware specific and doesn't provide an intermediate interopable layer like CUDA does. He asserts that AMD's ROCM has "achieved software parity" with CUDA for LLMs. The main issue is the confusion on what interface I should be using. The rocRAND/hipRAND woes in this article are if anything showing ROCm in a better light than it really is; here it at least worked and performed within the same ballpark as CUDA. AMD Supports pretty much nothing for AI stuff. To be fair CUDA is more like 15 years old but that just goes to show how long Nvidia has been dumping resources into GPGPU before it was even remotely "a thing". I would like to know assuming the same memory and bandwidth, how much slower AMD ROCm is when we run inference for a llm such as We would like to show you a description here but the site won’t allow us. Linux has ROCm. CUDA: really the standard, but only works on Nvidia GPUs HIP: extremely similar to CUDA, made by AMD, works on AMD and Nvidia GPUs (source code compatible) OpenCL: works on all GPUs as far as I know. 7. Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon… There are numerous blog posts about C++ AMP, guides on C++ AMP vs OpenCL or CUDA, and more! As such, early versions of AMD's ROCm / HCC was based on top of the C++ AMP 1. I've seen on Reddit some user enabling it successfully on GCN4 (Polaris) as well with a registry tweak or smth. So AMD needs to align with Intel and together they can ensure that developers default to those API's instead of CUDA, at least on the consumer side. It was as much as 41% faster to use q4_K_M, the difference being bigger the more I was able to fit in VRAM. 04. 0-dev. Actually you can tensorflow-directml on native Windows. HIP then can compile to rocm for amd, or CUDA for nvidia. The ROCm Platform brings a rich foundation to advanced computing by seamlessly integrating the CPU and GPU with the goal of solving real-world problems. This release allows accelerated machine learning training for PyTorch on any DirectX12 GPU and WSL, unlocking new potential in computing with mixed reality. HIP is a tool for porting CUDA-Code to OpenCL-Hardware. There's news going around that the next Nvidia driver will have up to 2x improved SD performance with these new DirectML Olive models on RTX cards, but it doesn't seem like AMD's being noticed for adopting Olive as well. Now we get higher. Interesting to see a developer's critique of the comparison: We recommend using r/SpaceX with Old Reddit. It was originally developed by researchers at Google Brain and is now used by a large number of organizations, including Twitter, Uber, and Airbnb. On AMD, hipSYCL is therefore not running on top of CUDA, but directly on top of the AMD ROCm compute platform. cuDNN is Nvidias gem für AI-Programmers. ROCm is a huge package containing tons of different tools, runtimes and libraries. ROCm will never be able to beat CUDA, not unless AMD magically surpasses Nvidia in market share and AI performance. If you are on Linux, you can use AMD's ROCm. These were the lower level approaches. Locked post. Is there an evaluation done by a respectable third party? My use case is running LLMs, such as llama2 70B. They use HIP which is almost identical to CUDA in syntax and language. I had basically the same choice a month ago and went with AMD. Simply because everything relies heavily on CUDA, and AMD just doesnt have CUDA. github. TLDR - rocm/amd works well, but it's basic usage is confusing relative to cuda. Or Intel's oneAPI, although I find their website and github a lot more cryptic. Even in a basic 2D Brownian dynamics simulation, rocRAND showed a 48% slowdown compared to cuRAND. ROCm [3] is an Advanced Micro to date, to be found on Reddit. . It's not ROCM news as such but an overlapping circle of interest - plenty of ppl use ROCM on Linux for speed for Stable Diffusion (ie not cabbage nailed to the floor speeds on Windows with DirectML). It's 2022, and amd is a leader in DL market share right now. I have seen some people say that the directML processes images faster than the CUDA model. Absolutely not. It is a bridge designed to neuter Nvidia's hold on datacenter compute. This software enables the high-performance operation of AMD GPUs for computationally-oriented tasks in the Linux operating system. CUDA vs ROCM . g. The information in this comment thread is from about 5 years ago, when cuda and opencl were the only options. The ROCm Platform brings a rich foundation to advanced computing by seamlessly integrating the CPU and GPU… Ok so I have been questioning a few things to do with codeproject. But on the other hand AMD treats ROCm both like an unwanted child (documentation and Ressources aren't that extensive compared to CUDA or oneAPI) and something super special that is officially only available for Workstation grade cards or Radeon instinct cards. There will definitely still be times though when you wish you had CUDA. In many ways, AMD's ROCm / HCC is the spiritual successor to Microsoft AMP. Windows will have full ROCm soon maybe but already has mlc-llm(Vulkan), onnx, directml, openblas and opencl for LLMs. hipSYCL has supported that since 2018, even before Intel even announced oneAPI. Most productivity users already bought this generation cards if they ever going to buy them and amd productivity users are probably utilizing their gpus outside of cuda exclusive ways. ROCm can apparently support CUDA using HIP code on Windows now, and this allows me to use a AMD GPU with Nvidias accelerated software. Often it simply does not work at all, or if it works it's behind by a lot more. I think you need to get expectations in check. There are VERY FEW libraries that kinda work with ADM, but youre not gonna be able to run any proper Program with a AMD card. I would like to look into this option seriously. AMD Quietly Funded A Drop-In CUDA Implementation Built On ROCm: It's Now Open-Source Dec 15, 2023 · Deciding which version of Stable Generation to run is a factor in testing. I have 2x 1070 gpu's in my BI rig. 5, v2. Nvidia 4070 Ti is slightly cheaper than an RX 7900 XTX, but the XTX is way better in general, but is beaten by 4070 Ti if it uses CUDA in machine learning. That headline makes it sound like NVIDIA and AMD was not a thing for SYCL/oneAPI before. Interested in hearing your opinions. MIOpen is a GPU-accelerated library for machine learning algorithms, that is in large parts source code compatible to cuDNN. ROCm probably does hit parity with CUDA, but CUDA has been so ubiquitous in almost every industry that it's what everyone learns to use and what every business is set up for. but the reason ZLUDA was needed was because somehow many people still develop/developed for that legacy software CUDA instead of it's newer alternatives, meaning much stuf was optimized for cuda. I did want to use AMD ROCm because I’m lowkey an AMD fanboy but also I really don’t mind learning a whole lot of the coding language. shihab-shahriar. Syntax and usage wise, CUDA code looks like weird C/C++ code, while Vulkan "kernels" using the CUDA nomenclature are separate shaders compiled to SPIR-V and aren't integrated with host code the way CUDA is, you communicate between the two primarily with buffer objects. Reply reply But on the other hand AMD treats ROCm both like an unwanted child (documentation and Ressources aren't that extensive compared to CUDA or oneAPI) and something super special that is officially only available for Workstation grade cards or Radeon instinct cards. just as ı said automatic 1111 for cuda example they probably get comfy in their shark aı tools. Only two RDNA3 cards have been released and it's only been a few months. 1. Get the Reddit app Scan this QR code to download the app now CUDA vs. [N] CUDA Architect and Cofounder of MLPerf: AMD's ROCM has achieved software parity with CUDA Greg Diamos, the CTO of startup Lamini, was an early CUDA architect at NVIDIA and later cofounded MLPerf. The main problem, in my opinion, is awful documentation and packaging. Dec 5, 2023 · How far along is AMD’s ROCm in catching up to Cuda? AMD has been on this race for a while now, with ROCm debuting 7 years ago. ROCm does not guarantee backward or forward compatibility which means it's very hard to make code that would run on all current and future hardware without having to maintain it, and AMD CUDA Support ist leider ungeschlagen, AMD versucht schon lange bei ML Fuß zu fassen und bei extra dafür gebauter Software funktioniert das auch einige maßen, aber gerade die "Standard" Dinger wie Tensorflow etc, da ist es immer einfacher und zuverlässiger einfach CUDA zu nutzen, nicht weil AMD scheiße ist, sondern weil der CUDA Support und Dokumentation einfach viel zu gut ist. OFFICIAL COMMUNITY OF HYPEURLS. You allocate some memory for the host and some for the device, schedule the function Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. Start with ubuntu 22. ROCm is far from perfect but it is far better than the hit peice you posted would lead some people to believe. io Open. Just to start, focus on implementing a kernel, which typically requires you to write a function in a specific way to notify the compioer it is a device function, not a host function. 0 and “should” (see note at the end) work best with the 7900xtx. CUDA Support ist leider ungeschlagen, AMD versucht schon lange bei ML Fuß zu fassen und bei extra dafür gebauter Software funktioniert das auch einige maßen, aber gerade die "Standard" Dinger wie Tensorflow etc, da ist es immer einfacher und zuverlässiger einfach CUDA zu nutzen, nicht weil AMD scheiße ist, sondern weil der CUDA Support und Dokumentation einfach viel zu gut ist. These things take a bit of time but it is coming. CUDA isn’t a single piece of software—it’s an entire ecosystem spanning compilers, libraries, tools, documentation, Stack Overflow/forum answers, etc. People need to understand that ROCm is not targeted at DIY coders. COM: r/hypeurls is a Reddit community for sharing and discussing new tech… I wish AMD would just drop ROCm at this stage, and focus on SYCL. Yes, ROCm (or HIP better said) is AMD's equivalent stack to Nvidia's CUDA. Well it is and it isn't ROCm is six years old, so it's been around a while. Let’s settle this once in for all, which one do you prefer and why? I see that ROCm has come a long way in the past years, though CUDA still appears to be the default choice. But in reality, it's not like NVIDIA/AMD support with SYCL (or even oneAPI code bases) is a new thing. 4, v1. DirectML goes off of DX12 so much wider support for future setups etc. Making SD with Automatic1111 work was INSANELY painful given the 'super helpful' documentation of ROCm. Haven't tested with Torch 2. I wish Codeplay/Intel communicated more clearly. 3 and pytorch 1. Takes me at least a day to get a trivial vector addition program actually working properly. 3, it has support for ROCm 5. But if you compile it with the NVIDIA compiler, the HIP headers turn all HIP calls into CUDA calls, and so it runs without additional overhead on CUDA. Currently, you can find v1. The article is more or less talking about PyTorch+Triton stack. And it currently officially supports RDNA2, RDNA1 and GCN5. 2 standard. With PyTorch now supporting ROCm, will we see it easy support with Pop like cuda? Discussion From my understanding one of the user-friendly aspects of PopOS is how easy it is to set up for AI development on Nvidia cards, it's even been a selling point on the system76 webpage for years now With AMD, their listing of supported cards isn't even complete, it has many various versions that are or aren't supported by the incomplete list of cards, and the 'stack' isn't as simple as just installing cuda-drivers or cuda (meta-package). Earlier this week ZLuda was released to the AMD world, across this same week, the SDNext team have beavered away implementing it into their Stable Made my PC dual boot for this. If you really hate Out-of-Tree kernel modules and have to run deep learning workload on your desktop like me, you can consider ROCm option. They're just not officially supported by ROCm as a whole. It’s not ROCm/etc this article is talking about. Yeah and it seems pointless except showing the "goodwill" of amd. TensorFlow is an open source software library for data analysis and machine learning. For example, ROCm officially supports the WX6800 now, no consumer 6xxx or 5xxx cards - except most or even all of them do actually work. Without knowing too much details of Triton, I suppose it’s not too hard to integrate it with the current TF/Keras ecosystem (probably zero extra work compared to integrating with PyTorch even) but still, need support and 322 votes, 124 comments. ROCm is still in early development by AMD. If you want to run random AI stuff from papers as it gets released, you need CUDA. I wouldn’t touch windows with a 10ft pole for ROCm. The AMD equivalents of CUDA and cuDNN (processes for running computations and computational graphs on the GPU) simply perform worse overall and have worse support with TensorFlow, PyTorch, and I assume most other frameworks. upstream ROCm has no support for RDNA3 Works with the latest ROCm 5. (currently running ROCm on 6900XT) However, for the general experience-wise, especially when you don't have to render the desktop, pick CUDA. 4. Aug 12, 2024 · This article provides a comprehensive comparison of ROCm and CUDA, focusing on key factors like deployment, cost, usability, code compatibility, and support for AI frameworks, helping you make an informed decision for your next project. Honestly, I'm pretty surprised by how big the speed difference is between q5_K_M vs q4_K_M, I expected it to be much smaller. The only way AMD could potentially take market share in this regard is if they become a loss leader for a while and essentially reach out to businesses themselves to help ROCm also doesn’t support the full CUDA API, like there’s no support for texture unit access (which in GPGPU isn’t about graphics, it just provides 2d/3d locality to 1D memory). 0 are beginning to challenge this stronghold by offering open-source alternatives and reducing reliance on CUDA. CUDA being tied directly to NVIDIA makes it more limiting. In a case study comparing CUDA and ROCm using random number generation libraries in a ray tracing application, the version using rocRAND (ROCm) was found to be 37% slower than the one using cuRAND (CUDA). rocm-smi is no supported on wsl2, as AMD state « Due to WSL architectural limitations for native Linux User Kernel Interface (UKI), rocm-smi is not supported. ROCm: A case study . 13. The Microsoft Windows AI team has announced the f irst preview of DirectML as a backend to PyTorch for training ML models. Not every features in CUDA implemented in ROCm, you may encounter some problem with ROCm Documentation relating ROCm is very limited, so don't expect so much support. Look into Oakridge for example. Tried a ton of solutions posted by the community and I honesty do not know the finalized steps to make this work. This board is not an official outlet for Someone told me that AMD ROCm has been gradually catching up. I do know that CUDA is practically used everywhere and that is like a big bonus. If you’re running llama 2, mlc is great and runs really well on the 7900 xtx. jfls zwdjz dzs lzu tlwt axrzv jsi vajph ttjvkx kmsywm