Is it viable to use an AMD GPU for deep learning? What frameworks are best?

Here are my concise bullet point notes:

• The webpage is titled “A Developer’s Guide to Choosing the Right GPUs for Deep Learning,” and it’s presented by Amazon Web Services and NVIDIA.
• The introduction explains the importance of using high-performance computing for deep learning tasks and the role of GPUs in accelerating this process.
• It mentions that deep learning is computationally intensive and requires specialized hardware to achieve good results.
• The article presents different NVIDIA GPUs and which ones are best for deep learning, it covers T4, A100, A30, A40, A10, P40, P100, V100, and others.
• The author explains that the new-generation NVIDIA GPUs are better suited for deep learning workloads and specifically mentions A100 is highly optimized for deep learning or AI workloads.
• The article mentions that the best GPU for deep learning depends on several factors, including the size of the models, the size of the data, and the available budget.
• The author also discusses the importance of compatibility with different machine learning frameworks, such as TensorFlow, PyTorch, and MXNet, and how different GPUs perform with different frameworks.
• The NVIDIA GPUs are separated into tiers, comprising of the different machine learning frameworks and the quality of resources (tensor cores, memory bandwidth, etc.) that they provide to the framework.
• The article explains that Tensor cores, a NVIDIA hardware, accelerate deep learning training by performing matrix multiplication within a single clock cycle.
• It mentions that NVIDIA Store and Amazon Web Services’ marketplace have an extensive selection of GPU instances available for training deep learning models in the cloud.
• It explains how instances can be utilized and the cost associated with them.
• Using the A100 instance, the author provides a step-by-step tutorial on how to set up a Deep Learning VM on Google Cloud Platform with a free trial.
• The author also includes several performance benchmark results for different NVIDIA GPUs and AWS instance configurations for deep learning benchmarks such as distributed training and inference.
• Finally, the article includes a brief recommendation for the best Nvidia GPU based on the reader’s use case. This provides a table for the best GPU for a given task such as training models, inference, and optimizing the model.
• The conclusion summarizes the main highlights of the article’s content and provides resources and links for further reading.

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Not used in article

-Relevant: True -Importance: 8

• AMD Radeon and Radeon PRO graphics cards are fully supported for GPU-accelerated training workflows using DirectML-enabled machine learning frameworks in Windows and the Windows Subsystem for Linux (WSL).
• Microsoft recently released the TensorFlow-DirectML preview for Windows and the WSL, the TensorFlow software library being one of the most widely used tools for machine learning training workflows.
• AMD and Microsoft have collaborated on co-engineering to deliver multiple improvements and performance optimizations that improve the GPU-accelerated ML training workflow experience when using TensorFlow-DirectML on DirectX 12 compatible AMD graphics hardware.
• Performance optimizations have improved both machine learning training and inference performance, with up to a 3.7x improvement in the overall AI Benchmark Alpha score when using TensorFlow-DirectML on AMD graphics.
• Inference performance has been substantially better on AMD Radeon RX 6900 XT and RX 6600 XT graphics hardware with up to a 3.1x increase in performance.

Notes:

• The webpage delves into the various specifications of GPUs that are essential for deep learning performance.
• The author explains the importance of Tensor Cores for matrix multiplication and recommends not using any GPU that does not have them.
• Memory bandwidth and cache hierarchy are discussed and explained how they relate to deep learning performance.
• The webpage discusses the new NVIDIA RTX 40 Ampere series and its unique features, such as Asynchronous Copies and Tensor Memory Accelerator (TMA) unit.
• The author also mentions that the RTX 3060 Ti is a cost-effective option compared to the more powerful RTX 3080.
• A comparative analysis is made between the RTX 3090 and the second-best option, RTX 3080.
• The author discusses the technical specifications of the AMD Radeon RX 6800 XT, explaining that it can outperform the RTX 3080 in some scenarios, particularly when using the TensorFlow with ROCm platform.
• The webpage mentions that the ROCm platform has focused on providing improved support for TensorFlow on AMD GPUs.
• A Quora answer, linked from the webpage, provides an in-depth explanation of why GPUs are well-suited to deep learning over CPUs.
• The webpage also includes a Q&A section where the author answers common questions and misconceptions.
• The author compares AMD and NVIDIA GPUs briefly in the Q&A section.
• A link is provided to an older article about using AMD GPUs for deep learning.
• The article concludes with recommendations for different deep learning scenarios based on the GPU specifications.

• The webpage is a discussion thread titled “Deep Learning/AI with AMD GPU’s” on the Reddit forum r/Amd.
• The original post asked about expectations for greater AI support with AMD GPUs.
• A user explains that ML researchers do not typically write GPU code and instead use frameworks like Tensorflow to construct neural networks.
• Another user asks about good resources to get started with ML, and is recommended to explore frameworks like Pytorch, neon, and ngraph.
• There is debate over the level of support for AMD in mainstream software like Tensorflow, with some users reporting successful usage and others reporting issues.
• One user suggests looking into AMD’s ROCm as an alternative to using mainstream software.
• The discussion expands to include the role of GPUs in ML and the importance of parallelization for certain types of problems.
• Intel’s neon and ngraph frameworks are highlighted as having hardware agnostic capabilities and being fast on x86 CPUs.
• A user asks about AVX support for Ryzen CPUs and the limitations of ngraph with non-Intel CPUs.
• The thread ends with users commenting on the state of AMD versus Nvidia in the field of ML and GPU computing.

💭  Looking into

Reviewing the best frameworks for deep learning on AMD GPUs

💭  Looking into

Comparison of performance between AMD GPUs and NVIDIA GPUs on deep learning workloads