AMD GPU正式支援深度學習框架tensorflow - 3C

Table of Contents


消息來源:
https://gpuopen.com/rocm-tensorflow-1-8-release/
https://rocm.github.io/ROCmInstall.html

對應於Nvidia的cuda SDK的AMD ROCm終於正式支援新版tensorflow (1.8版)

不過目前只支援Ubuntu /CentOS等Linux作業系統

GPU方面也有限制。VEGA應無大礙,至於舊版的架構如Polaris以及VEGA APU似乎還沒。



VEGA的賣點是有高速的HBM2

加上可以拿系統RAM當GPU RAM的HBCC技術

不過AMD的優化...



有空的話,我會拿我的VEGA 56來和1070ti PK看看深度學習的運算效能

看看AMD是到底是優化效能還是優化笑能


以下是官方的支援訊息:

Supported CPUs

Starting with ROCm 1.8 we have relaxed the use of PCIe Atomics and also PCIe
lane choice for Vega10/GFX9 class GPU. So now you can support CPU without
PCIe Atomics and also use Gen2 x1 lanes.

Currently our GFX8 GPU’s (Fiji & Polaris family) still need to use PCIe Gen
3 and PCIe Atomics, but are looking at relaxing this in a future release,
once we have fully tested firmware.

Current CPUs which support PCIe Gen3 + PCIe Atomics are:

AMD Ryzen CPUs;
AMD EPYC CPUs;
Intel Xeon E7 V3 or newer CPUs;
Intel Xeon E5 v3 or newer CPUs;
Intel Xeon E3 v3 or newer CPUs;
Intel Core i7 v4, Core i5 v4, Core i3 v4 or newer CPUs (i.e. Haswell
family or newer).

For Fiji and Polaris GPU’s the ROCm platform leverages PCIe Atomics (Fetch
and Add, Compare and Swap, Unconditional Swap, AtomicsOp Completion). PCIe
Atomics are only supported on PCIe Gen3 enabled CPUs and PCIe Gen3 switches
like Broadcom PLX. When you install your GPUs make sure you install them in a
fully PCIe Gen3 x16 or x8, x4 or x1 slot attached either directly to the CPU’
s Root I/O controller or via a PCIe switch directly attached to the CPU’s
Root I/O controller. In our experience many issues stem from trying to use
consumer motherboards which provide physical x16 connectors that are
electrically connected as e.g. PCIe Gen2 x4 connected via the Southbridge
PCIe I/O controller.

Experimental support for our GFX7 GPUs Radeon R9 290, R9 390, AMD FirePro
S9150, S9170 note they do not support or take advantage of PCIe Atomics.
However, we still recommend that you use a CPU from the list provided above.
Not supported or very limited support under ROCm
Limited support

With ROCm 1.8 and Vega10 it should support PCIe Gen2 enabled CPUs such as
the AMD Opteron, Phenom, Phenom II, Athlon, Athlon X2, Athlon II and older
Intel Xeon and Intel Core Architecture and Pentium CPUs. But we have done
very limited testing. Since our test farm today has been catering to CPU
listed above. This is where we need community support.
Thunderbolt 1,2 and 3 enabled breakout boxes GPU’s should now be able to
work with ROCm. Thunderbolt 1 and 2 are PCIe Gen2 based. But we have done no
testing on this config and would need comunity support do limited access to
this type of equipment

Not supported

We also do not support AMD Carrizo and Kaveri APU as host for compliant
dGPU attachments.
Thunderbolt 1 and 2 enabled GPU’s are not supported by ROCm. Thunderbolt
1 & 2 are PCIe Gen2 based.
AMD Carrizo based APUs have limited support due to OEM & ODM’s choices
when it comes to some key configuration parameters. On point, we have
observed that Carrizo laptops, AIOs and desktop systems showed
inconsistencies in exposing and enabling the System BIOS parameters required
by the ROCm stack. Before purchasing a Carrizo system for ROCm, please verify
that the BIOS provides an option for enabling IOMMUv2. If this is the case,
the final requirement is associated with correct CRAT table support - please
inquire with the OEM about the latter.
AMD Merlin/Falcon Embedded System is also not currently supported by the
public repo.
AMD Raven Ridge APU are currently not supported


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All Comments

Jacob avatarJacob2018-09-02
坐等測試
Elvira avatarElvira2018-09-06
Jake avatarJake2018-09-10
看起來GPU限制只是限搭新一點的CPU?PCIE GEN3+Atomi
cs
Olive avatarOlive2018-09-15
千呼萬喚終於有人做出來...等樓主測試就可以放心買
跳水Vega了
Olive avatarOlive2018-09-17
這玩意如果真的夠好 amd的股價可能就追nv了
畢竟nvda的股價這麼高也是這幾個當年在炒作
Michael avatarMichael2018-09-18
有夠慢的 離發表都四五個月了
Linda avatarLinda2018-09-21
真的是格了好一陣子 以為當初發表會就有了
Genevieve avatarGenevieve2018-09-22
不過有了還是好事 至少真正踏入門檻了
Daph Bay avatarDaph Bay2018-09-26
讚讚讚 還好我卡還在糾結沒買下去
Tom avatarTom2018-09-26
不可質疑你的AMD
Rebecca avatarRebecca2018-09-27
可以來個Pytorch嗎…
Frederica avatarFrederica2018-10-02
等好久了
Necoo avatarNecoo2018-10-05
1.3 benchmark表現還不差,但是現在很多人用
pytorch
Dorothy avatarDorothy2018-10-06
沒記錯是400系列以後都能用
Selena avatarSelena2018-10-08
機器學習本來就大多是Linux系統上跑吧
Anthony avatarAnthony2018-10-09
這從去年Raja跑路之前喊到現在才生出來啊......
Gilbert avatarGilbert2018-10-10
不過其實beta很久了 現在是正式Release
Edith avatarEdith2018-10-12
沾水居多
Iris avatarIris2018-10-15
期待測試
Gilbert avatarGilbert2018-10-16
tf 不是用 cuda 寫的嗎??
Todd Johnson avatarTodd Johnson2018-10-18
mac勒?
Rebecca avatarRebecca2018-10-22
TensorFlow 是GPU的不分之前只支援CUDA
Ingrid avatarIngrid2018-10-26
沾水個頭 tensorflow要沾水幹嘛.......
Leila avatarLeila2018-10-29
目前來說跟股東交代的成分居多
賣得如何要看Datc Center綁樁的成功度
Elma avatarElma2018-11-03
不是支援OpenCL嗎?
Xanthe avatarXanthe2018-11-04
連 pytorch 也支援那就跟 nv可以打了
Genevieve avatarGenevieve2018-11-05
會有O廠小妹跳出來說AMD都不給支援 壞壞
Emily avatarEmily2018-11-09
XD
Olga avatarOlga2018-11-12
不過AMD的支援度真的差很多 買回家要自己debug
Google大概沒差啦 不過其他中小型單位就有差了
Faithe avatarFaithe2018-11-12
幹好忙@@
Steve avatarSteve2018-11-17
好忙出現了!!
Olivia avatarOlivia2018-11-20
好忙就不要再上PTT了,快去寫程式!!!
Steve avatarSteve2018-11-23
樓上還不快去debug!!!
Olga avatarOlga2018-11-27
哈哈好忙
Sandy avatarSandy2018-11-30
所以快嗎
Zora avatarZora2018-12-01
caffe opencl 我有比過,390 比1080 強七倍
380 比 1080 強兩倍
Wallis avatarWallis2018-12-02
編譯的時候的OpenCL SYCL選項毫無用途?
Freda avatarFreda2018-12-06
compile時要選ROCm path