TensorFlow for AMD ROCm GPU: Difference between revisions

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== TensorFlow for AMD ROCm GPU ==
{{Status
|status=Draft
|owner=Knowledge Agent
|last_update=2026-07-16
|review=Pending
}}
 
{{TOC}}
 
== Overview ==
 
TensorFlow for AMD ROCm GPU에 대한 기술 문서입니다.
 
=== Summary ===
 
* 무엇인가? - TensorFlow for AMD ROCm GPU
* 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
 
 
== Purpose ==
 
이 문서가 존재하는 이유
 
* Goal: TensorFlow for AMD ROCm GPU에 대한 기술 정보 제공
* Scope: TensorFlow for AMD ROCm GPU의 개념, 사용법, 설정
* Non-goals: 다른 주제로의 확장
 
 
== Key Concepts ==
 
{| class="wikitable"
! Concept
! Description
! Related
|-
| TensorFlow for AMD ROCm GPU
| HPC/서버 환경에서 중요한 기술 개념
| [[Linux]], [[Server]]
|}
 
 
== Detailed Explanation ==
 
* [[AMD GPUs]]
[[file : AMD_Radeon.png | center | 500px]]
[[file : AMD_Radeon.png | center | 500px]]
In brief : ROCm - The Radeon Open Ecosystem - Google Tensorflow announced (on Aug 27, 2018) the release of TensorFlow v1.8 for ROCm-enabled GPUs including the Radeon Instinct™ MI25 GPU by leveraging [https://github.com/ROCmSoftwarePlatform/MIOpen MIOpen ], a library of highly optimized GPU routines for deep learning. This is a major milestone in AMD’s ongoing work to accelerate deep learning. ROCm is an open-source software foundation for GPU computing on Linux.  
In brief : ROCm - The Radeon Open Ecosystem - Google Tensorflow announced (on Aug 27, 2018) the release of TensorFlow v1.8 for ROCm-enabled GPUs including the Radeon Instinct™ MI25 GPU by leveraging [https://github.com/ROCmSoftwarePlatform/MIOpen MIOpen ], a [[library]] of highly optimized GPU routines for deep learning. This is a major milestone in AMD’s ongoing work to accelerate deep learning. ROCm is an open-source software foundation for GPU computing on [[Linux]].  
 
For a long time, AMD has avoided competing with Nvidia. with AMD products in 2019. Specifically, Computex 2019 should be the place and time when we become familiar with [[AMD GPU]] products that are competitive with Nvidia RTX.
=== ROCm - Radeon Open Ecosystem ===
For a long time, AMD has avoided competing with Nvidia. with AMD products in 2019. Specifically, Computex 2019 should be the place and time when we become familiar with AMD GPU products that are competitive with Nvidia RTX.
ROCm is a place where AMD is trying to get in the GPU computing domain. AMD start to provide a pre-built whl package, allowing a simple install akin to the installation of generic TensorFlow for Linux. AMD published [https://gpuopen.com/rocm-tensorflow-1-8-release/ installation instructions] and also a [https://hub.docker.com/r/rocm/tensorflow pre-built Docker image] including upstreaming all the ROCm-specific enhancements to the TensorFlow master repository.
ROCm is a place where AMD is trying to get in the GPU computing domain. AMD start to provide a pre-built whl package, allowing a simple install akin to the installation of generic TensorFlow for Linux. AMD published [https://gpuopen.com/rocm-tensorflow-1-8-release/ installation instructions] and also a [https://hub.docker.com/r/rocm/tensorflow pre-built Docker image] including upstreaming all the ROCm-specific enhancements to the TensorFlow master repository.
AMD's the open source effrots has been also highlighted by selection as a Google [[Stadia]]’s Building Blocks. Google has chosen Linux as a base for Stadia. Moreover, it has used the open source Vulkan API for 3D graphics and computing. Some of the other critical open source components being used for Stadia are LLVM, DirectX Shaper Compiler, GAPID, Radeon GPU Profiler, And RenderDoc. Google has also mentioned that a big reason for choosing AMD as its GPU partner is the availability of open source drivers and tools. The other specs of the AMD GPU are 16GB of video memory and 9.5 MB of L2 + L3 cache.
AMD's the open source effrots has been also highlighted by selection as a Google [[Stadia]]’s Building Blocks. Google has chosen Linux as a base for Stadia. Moreover, it has used the open source Vulkan API for 3D graphics and computing. Some of the other critical open source components being used for Stadia are LLVM, DirectX Shaper Compiler, GAPID, Radeon GPU Profiler, And RenderDoc. Google has also mentioned that a big reason for choosing AMD as its GPU partner is the availability of open source drivers and tools. The other specs of the AMD GPU are 16GB of video memory and 9.5 MB of L2 + L3 cache.
<p align="center"> <youtube>https://youtu.be/alhEgNvzv50</youtube> </p>
<p align="center"> <youtube>https://youtu.be/alhEgNvzv50</youtube> </p>
Linus tech tips reviewed it so yeah, i would say it is compting with NVIDIA especially if you look compared to the 1080 ti (it outperforms the 2080 by about 2 percent)
Linus tech tips reviewed it so yeah, i would say it is compting with NVIDIA especially if you look compared to the 1080 ti (it outperforms the 2080 by about 2 percent)
=== ROCm Platform and Features ===
ROCm is the first open-source HPC/Hyperscale-class platform for GPU computing that’s also programming-language independent. The ROCr System Runtime is language independent and makes heavy use of the Heterogeneous System Architecture (HSA) Runtime API. This approach provides a rich foundation to exectute programming languages such as HCC C++ and HIP, the Khronos Group’s OpenCL, and Continuum’s Anaconda Python.
ROCm is the first open-source HPC/Hyperscale-class platform for GPU computing that’s also programming-language independent. The ROCr System Runtime is language independent and makes heavy use of the Heterogeneous System Architecture (HSA) Runtime API. This approach provides a rich foundation to exectute programming languages such as HCC C++ and HIP, the Khronos Group’s OpenCL, and Continuum’s Anaconda Python.
[[file : ROCr.png | center | 500px]]
[[file : ROCr.png | center | 500px]]
=== ROCm features ===
* Multi-GPU coarse-grain shared virtual memory
* Multi-GPU coarse-grain shared virtual memory
* Process concurrency and preemption
* Process concurrency and preemption
Line 24: Line 59:
* User-mode queues and DMA
* User-mode queues and DMA
* Standardized loader and code-object format
* Standardized loader and code-object format
* Dynamic and offline-compilation support
* Dynamic and offline-compilation [[support]]
* Peer-to-peer multi-GPU operation with RDMA support
* Peer-to-peer multi-GPU operation with [[RDMA]] support
* Profiler trace and event-collection API
* Profiler trace and event-collection API
* Systems-management API and tools
* Systems-management API and tools


[[file : ROCr_stack.png | center | 300px]]


More detailed technical document about ROMc is available at https://rocm-documentation.readthedocs.io/en/latest/index.html
== Best Practices ==
 
* 최신 버전 사용 권장
* 공식 문서 참고
* 테스트 환경에서 먼저 검증
 
 
== References ==
 
* [https://wiki.hpcmate.com TensorFlow for AMD ROCm GPU]
 
 
== Related Pages ==
 
* [[Linux]]
* [[Server]]
* [[Hardware]]
* [[Network]]
 
 
[[Category:GPU]]
== Knowledge Graph ==
 
Related


=== ROCm oppertunities ===
→ [[CUDA]]
Nvidia has Geforce Now, with "big" data center partners like Softbank and LG. Google, Microsoft, and AWS better watch out.
→ [[GPU]]
Based on usages, some customers are definitely demanding CPU+GPU interconnection and coherency. Nvidia's AI plans seems to be failing as Google makes its own tensor processing units and Tesla as well. Even Amazon is developing its own ASIC optimized for its workload. Nvidia's data center GPUs are way too expensive and not suited for these customers' specific needs. Althogh NVIDIA's proprietary CUDA ecosystem is great if your products are dominant, otherwise it may becomes a minus when AMD GPUs are equal/better. And '''for customers on the exascale, open source is always best way to go'''. by entering AI era, accelerating growth that looks basically guaranteed for AMD.
→ [[NVIDIA GPU]]
→ [[NVLink]]
→ [[AMD GPUs]]
→ [[MIG]]
→ [[NVIDIA GPUs]]
→ [[Tensor Core]]


[[category : article]]
[[Category:Reference]]
[[category : tech]]
[[category : AMD]]

Latest revision as of 11:30, 17 July 2026

Template:Status

Template:TOC

Overview

TensorFlow for AMD ROCm GPU에 대한 기술 문서입니다.

Summary

  • 무엇인가? - TensorFlow for AMD ROCm GPU
  • 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
  • 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시


Purpose

이 문서가 존재하는 이유

  • Goal: TensorFlow for AMD ROCm GPU에 대한 기술 정보 제공
  • Scope: TensorFlow for AMD ROCm GPU의 개념, 사용법, 설정
  • Non-goals: 다른 주제로의 확장


Key Concepts

Concept Description Related
TensorFlow for AMD ROCm GPU HPC/서버 환경에서 중요한 기술 개념 Linux, Server


Detailed Explanation

AMD Radeon.png

In brief : ROCm - The Radeon Open Ecosystem - Google Tensorflow announced (on Aug 27, 2018) the release of TensorFlow v1.8 for ROCm-enabled GPUs including the Radeon Instinct™ MI25 GPU by leveraging MIOpen , a library of highly optimized GPU routines for deep learning. This is a major milestone in AMD’s ongoing work to accelerate deep learning. ROCm is an open-source software foundation for GPU computing on Linux. For a long time, AMD has avoided competing with Nvidia. with AMD products in 2019. Specifically, Computex 2019 should be the place and time when we become familiar with AMD GPU products that are competitive with Nvidia RTX. ROCm is a place where AMD is trying to get in the GPU computing domain. AMD start to provide a pre-built whl package, allowing a simple install akin to the installation of generic TensorFlow for Linux. AMD published installation instructions and also a pre-built Docker image including upstreaming all the ROCm-specific enhancements to the TensorFlow master repository. AMD's the open source effrots has been also highlighted by selection as a Google Stadia’s Building Blocks. Google has chosen Linux as a base for Stadia. Moreover, it has used the open source Vulkan API for 3D graphics and computing. Some of the other critical open source components being used for Stadia are LLVM, DirectX Shaper Compiler, GAPID, Radeon GPU Profiler, And RenderDoc. Google has also mentioned that a big reason for choosing AMD as its GPU partner is the availability of open source drivers and tools. The other specs of the AMD GPU are 16GB of video memory and 9.5 MB of L2 + L3 cache.

Linus tech tips reviewed it so yeah, i would say it is compting with NVIDIA especially if you look compared to the 1080 ti (it outperforms the 2080 by about 2 percent) ROCm is the first open-source HPC/Hyperscale-class platform for GPU computing that’s also programming-language independent. The ROCr System Runtime is language independent and makes heavy use of the Heterogeneous System Architecture (HSA) Runtime API. This approach provides a rich foundation to exectute programming languages such as HCC C++ and HIP, the Khronos Group’s OpenCL, and Continuum’s Anaconda Python.

ROCr.png
  • Multi-GPU coarse-grain shared virtual memory
  • Process concurrency and preemption
  • Large memory allocations
  • HSA signals and atomics
  • User-mode queues and DMA
  • Standardized loader and code-object format
  • Dynamic and offline-compilation support
  • Peer-to-peer multi-GPU operation with RDMA support
  • Profiler trace and event-collection API
  • Systems-management API and tools


Best Practices

  • 최신 버전 사용 권장
  • 공식 문서 참고
  • 테스트 환경에서 먼저 검증


References


Related Pages

Knowledge Graph

Related

→ CUDA → GPU → NVIDIA GPU → NVLink → AMD GPUs → MIG → NVIDIA GPUs → Tensor Core

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