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ROCm
tensorflow-upstream
C++2018–2020
ROCm / tensorflow-upstream

tensorflow-upstream

TensorFlow ROCm port

AI & Machine Learning
Explore on GitHubRead Hacker News discussions
13Distinct people
17Mentions
0Explicit recommenders
707GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

TensorFlow ROCm port

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month17 dated mentions

Observed mentions: 2018-06-10 01:44:36 UTC to 2020-11-16 17:58:36 UTC.

01232018-06-01 00:00:00 UTC to 2018-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 20182018-07-01 00:00:00 UTC to 2018-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-01 00:00:00 UTC to 2018-09-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2018-09-01 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 20192019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-06-01 00:00:00 UTC to 2020-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-07-01 00:00:00 UTC to 2020-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-01 00:00:00 UTC to 2020-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-01 00:00:00 UTC to 2020-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20182018-06-01 00:00:00 UTC2018-07-01 00:00:00 UTC101
Jul 20182018-07-01 00:00:00 UTC2018-08-01 00:00:00 UTC000
Aug 20182018-08-01 00:00:00 UTC2018-09-01 00:00:00 UTC202
Sep 20182018-09-01 00:00:00 UTC2018-10-01 00:00:00 UTC000
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC101
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC202
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC000
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC101
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC211
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC101
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC101
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC312
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC101
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC101
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC000
Jun 20202020-06-01 00:00:00 UTC2020-07-01 00:00:00 UTC000
Jul 20202020-07-01 00:00:00 UTC2020-08-01 00:00:00 UTC000
Aug 20202020-08-01 00:00:00 UTC2020-09-01 00:00:00 UTC000
Sep 20202020-09-01 00:00:00 UTC2020-10-01 00:00:00 UTC000
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC000
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC101

What Hacker News says

17 sources
fxtentacle·Nov 16, 2020
In case someone from AMD is reading here, you should probably gift out some of the cards to generate some traction. For example, send one to me ;) But joke aside, the ROCm documentation seems so wacky that I would not dare to purchase an expensive AMD card without recommendation by someone that I trust. For example, rocmdocs.amd.com appears to be the official documentation, but the front page doesn't mention any popular AI framework. Opening the "Deep Learning" box in the sidebar, I see that there should be a TensorFlow 2.2.0-beta1 release. But on https://github.com/ROCmSoftwarePlatform/tensorflow-upstream the featured release is "2.0.0-rocm", the docker link says it's TensorFlow 2.3 and the "Official Builds" at the bottom of the readme point to CUDA versions ... The whole GitHub repo kind of feels like a bad copy&paste job. I manage to e
Read the rest
ventually download the TensorFlow 2.3.1 + ROCm source from GitHub, only to find that it doesn't compile. I need the hip_hcc library https://github.com/ROCm-Developer-Tools/HIP/ but apparently that one has been renamed some months ago, but the ROCm TensorFlow port has not been updated yet. After spending half a day trying to install a ROCm-compatible TensorFlow, I gave up. Obviously, I wouldn't want to hope on those issues getting resolved when I buy an as-is unusable $6400 accelerator card. That said, the MI100 could be a great deal if I could get it to work. AMD hardware is a lot more affordable than comparable NVIDIA products, plus the MI100 has lots of fast RAM. I work on optical flow (think autonomous selfie drone) and that needs lots and lots of GPU RAM. Plus I'm very happy with my AMD CPU. But like I said in the beginning, I wouldn't dare buying an AMD GPU unless there was believable evidence that I can get my TF2.3 model running with ROCm in a reasonable timeframe. I'm not trying to blame AMD for having bugs here. I have run into a fair share of issues with NVIDIA GPUs, too, like for example a RAM corruption bug inside CUDA 10.0 and a reproducible kernel freeze in CUDA 10.1. But for NVIDIA, the initial setup is quick and easy. And the CUDA documentation looks like they have a dedicated team that cares about developer productivity. For AMD's ROCm, on the other hand, even the marketing material reads more like someone hopes that the open source community will fix things for free. "first open-source software development platform for HPC/Hyperscale-class GPU computing" sounds great until you notice that in this context, "open-source" apparently means "no binaries + no support".
In AMD at a Tipping Point with Instinct MI100 GPU Accelerators
adev_·Apr 27, 2020
> That's something you need to ask to your package manager. Making sure that all dependencies of a package (e.g. drivers, compilers, etc.) are installed is the package manager's job. No that's bullshit and it shows you never have done it. Tensorflow is CUDA only and does not run over AMD GPU. Because CUDA is proprietary and has a monopoly on GPGPU for now. It is such a big deal that AMD had to fork tensorflow and try to make their own version it in order to make it "partially" run over HIP. This naturally makes it behind upstream, buggy and partially updated [^1] CUDA is not portable and will never be. The sad truth is that, due to CUDA, a big majority of GPGPU programs are now vendor lock-in on Nvidia hardware. That is unacceptable, but it is like that. [^1]: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
In Khronos Announces OpenCL 3.0
jamesblonde·Mar 6, 2020
Yes, ROCm is upstreamed into Tensorflow. On our Hopsworks platform, you can run the same TensorFlow code on either ROCm or Cuda. No code changes needed: https://www.logicalclocks.com/blog/welcoming-amd-rocm-to-hop... Performance ain't so bad on AMD GPUs anymore - and none of the EULA crap. Just buy a Vega R7 for 500 bucks and get 16 GB of memory and the performance of close to a 2080 Ti (for convnets, at least) - but with no data center restrictions: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/...
In AMD is determined to gets its rightful datacenter share
mastazi·Nov 28, 2019
Of course many HN readers know that TF has been available for AMD for a while, but I still think that this Github thread is newsworthy, specifically this comment[1]: > The RADEON VII's performance is crazy with tensorflow 2.0a. In our tests, we reached close to the same speed like our 2080ti(about 10-15% less)! But the Radeon VII has more memory which was a bottleneck in our case. On this price this videocard has the best value to do machine learning we think that in our company! The comment above echoes the findings of HN user bonoboTP, see comment currently at the top of this thread[2] [1] comment link on Github: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/... [2] comment link on HN: https://news.ycombinator.com/item?id=21657173
In Tensorflow 2.0 AMD Support
jamesblonde·Sep 18, 2019
I think you underestimate the challenge in programming TensorFlow to use Tensor-cores, currently. It is a hell of a lot of fp16 and fp32 casts, and, frankly, most Python programmers / Data Scientists do not have the skills to do it. There is some work in getting TensorFlow to automatically do it for you, but i haven't seen the evidence in benchmarks, yet. From this thread, it looks like (for Resnet-50), the Radeon VII is closing in on the nvidia 2080Ti - with more memory (16 vs 11 GB), can be used in a data-center EULA, and 2/3 of the price. https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/... In any case, Google are not going the tensorcores route with TPUs. Fast general purpose matmuls. Same as AMD. I don't think tensorcores is a clear winner, yet.
In ROCm (AMD) Now Upstreamed into TensorFlow
microtonal·Jun 20, 2019
If you really want low cost to compute for Deep Learning and you needs lots of compute and don't want to pay for V100s, then the AMD Vega R7 is the card for you. 700 dollars, 16GB Ram, 1TB of GPU bandwidth (higher than the V100!), works with Tensorflow (pip install tensorflow-rocm), and about 60% of the performance on resnet-50.FP64 is not fully gimped (it is halved, i think - so still quite good). Two of my colleagues use high-end AMD GPUs to train RNNs and transformers with tensorflow-rocm. There are still some nasty bugs (e.g. [1]), so it is currently not for everyone. However, given how far they have come compared to 1-2 years ago, it is very likely that in a year or so, they are a real competitor to NVIDIA for compute. That competition was long needed. [1] https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/...
In Linode GPU Instances
bitL·Aug 27, 2018
I was looking at OpenCL support first: https://github.com/tensorflow/tensorflow/issues/22 Anyone using ROCm? Is is stable enough/does it support everything in TF 1.8? How is the performance comparing to 1080Ti? Their port of TF doesn't seem to have many users, only 6 issues in TF upstream: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream If it were comparable with 1080/Ti and working flawlessly, 32GB would be very enticing!
In AMD Announces Dual-Vega Radeon Pro V340 Card with 32GB of ECC HBM Memory

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