※ 本文為 JackLee5566.bbs. 轉寄自 ptt.cc 更新時間: 2018-08-30 21:32:21
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作者 標題 [情報] AMD GPU正式支援深度學習框架tensorflow
時間 Thu Aug 30 13:52:49 2018
消息來源:
https://gpuopen.com/rocm-tensorflow-1-8-release/
ROCm Tensorflow 1.8 Release - GPUOpen
We are excited to announce the release of ROCm enabled TensorFlow v1.8 for AMD GPUs. This post demonstrates the steps to install and use ... ...
We are excited to announce the release of ROCm enabled TensorFlow v1.8 for AMD GPUs. This post demonstrates the steps to install and use ... ...
ROCm Install
ROCm, a New Era in Open GPU Computing : Platform for GPU Enabled HPC and UltraScale Computing ...
ROCm, a New Era in Open GPU Computing : Platform for GPU Enabled HPC and UltraScale Computing ...
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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推 : 坐等測試1F 08/30 13:57
推 : !2F 08/30 13:57
推 : 看起來GPU限制只是限搭新一點的CPU?PCIE GEN3+Atomi3F 08/30 14:13
→ : cs
→ : 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
→ : cs
→ : 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
推 : 這玩意如果真的夠好 amd的股價可能就追nv了7F 08/30 14:18
→ : 畢竟nvda的股價這麼高也是這幾個當年在炒作
→ : 畢竟nvda的股價這麼高也是這幾個當年在炒作
推 : 有夠慢的 離發表都四五個月了9F 08/30 14:19
→ : 真的是格了好一陣子 以為當初發表會就有了10F 08/30 14:30
→ : 不過有了還是好事 至少真正踏入門檻了
→ : 不過有了還是好事 至少真正踏入門檻了
推 : 讚讚讚 還好我卡還在糾結沒買下去12F 08/30 14:32
噓 : 不可質疑你的AMD13F 08/30 14:32
推 : 可以來個Pytorch嗎…14F 08/30 14:39
官網是說正在做推 : 等好久了15F 08/30 14:52
推 : 1.3 benchmark表現還不差,但是現在很多人用16F 08/30 14:53
→ : pytorch
→ : 沒記錯是400系列以後都能用
→ : pytorch
→ : 沒記錯是400系列以後都能用
推 : 機器學習本來就大多是Linux系統上跑吧19F 08/30 14:58
現在裝VEGA的電腦大部分都是拿來打電動或挖礦所以是裝在windows上面 我現在就是這樣
要馬灌ubuntu 或是把卡拿去Linux主機插 很不方便
推 : 這從去年Raja跑路之前喊到現在才生出來啊......20F 08/30 15:19
→ : 不過其實beta很久了 現在是正式Release21F 08/30 15:24
推 : 沾水居多22F 08/30 15:50
推 : 期待測試23F 08/30 16:13
推 : tf 不是用 cuda 寫的嗎??24F 08/30 16:19
推 : mac勒?25F 08/30 16:30
mac應該也會出吧→ : TensorFlow 是GPU的不分之前只支援CUDA26F 08/30 16:36
推 : 沾水個頭 tensorflow要沾水幹嘛.......27F 08/30 16:55
推 : 目前來說跟股東交代的成分居多28F 08/30 17:01
→ : 賣得如何要看Datc Center綁樁的成功度
→ : 賣得如何要看Datc Center綁樁的成功度
我倒是覺得是Google為了防止Nvidia坐地起價刻意扶植AMD
Nvidia超靠杯 資料中心條款 做雲端服務只能用運算卡
偏偏運算卡是遊戲卡四倍貴以上 然後效能差不多
反正AMD沒人買就賣便宜一點就好了 一堆資料中心搶著要
推 : 不是支援OpenCL嗎?30F 08/30 17:47
caffe才可以用OpenCL推 : 連 pytorch 也支援那就跟 nv可以打了31F 08/30 18:23
→ : 會有O廠小妹跳出來說AMD都不給支援 壞壞32F 08/30 19:10
推 : XD33F 08/30 20:12
※ 編輯: exeex (123.195.47.208), 08/30/2018 20:48:23→ : 不過AMD的支援度真的差很多 買回家要自己debug34F 08/30 20:53
→ : Google大概沒差啦 不過其他中小型單位就有差了
→ : Google大概沒差啦 不過其他中小型單位就有差了
推 : 幹好忙@@36F 08/30 21:30
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