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AMD Demos 7nm Vega GPU
- bitL 8y agoIf they get TensorFlow/Torch/MXNet running with no changes in the code, 32GB HBM2 for training models would be awesome! These days hitting memory limitations is biting more than general slowness of GPUs during Deep Learning training.
- bcatanzaro 8y ago32GB HBM2 Nvidia Volta GPUs are available now, just in case you hadn’t heard.
- c2h5oh 8y agoHm? not close enough? https://github.com/ROCmSoftwarePlatform/tensorflow https://github.com/ROCmSoftwarePlatform/tensorflow https://github.com/ROCmSoftwarePlatform/cutorch_hip https://github.com/ROCmSoftwarePlatform/cutorch_hip https://github.com/ROCmSoftwarePlatform/mxnet https://github.com/ROCmSoftwarePlatform/mxnet https://github.com/ROCmSoftwarePlatform/hipCaffe https://github.com/ROCmSoftwarePlatform/hipCaffe
- infocollector 8y agoIs TF running on AMD Vega's? Keras?
- c2h5oh 8y ago> Is TF running on AMD Vega's? Not with mainline kernel (which supports up to Polaris), but it it already got merged into 4.18 that is going to be released in a couple of weeks. Until then it requires a bit extra effort https://github.com/RadeonOpenCompute/ROCm#dkms-driver-installation https://github.com/RadeonOpenCompute/ROCm#dkms-driver-instal... > Keras? over opencl with https://github.com/plaidml/plaidml https://github.com/plaidml/plaidml
- nl 8y agoThe problem is that it’s TF 1.3. TF is up to v1.8, and if you want to use any recent model you really need 1.4+. And even ignoring that, you still can’t use PyTorch.
- symisc_devel 8y agoWell, you can train your own models on the CPU for real-time object detection tasks using the SOD ML library we just released recently https://sod.pixlab.io/c_api/sod_realnet_train_start.html https://sod.pixlab.io/c_api/sod_realnet_train_start.html https://sod.pixlab.io https://sod.pixlab.io
- p1esk 8y agoHow much time does it take, compared to 1080Ti?
- symisc_devel 8y agoIt depends on your dataset. For example, a single class (face, pedestrian or car) object detection model with ten of thousands of positive samples should take at least 18 hours to complete on a modern Intel CPU with a target FPR (false positive rate) set to 0.00001. The lower the fpr is, the more training time is required. https://sod.pixlab.io/samples.html https://sod.pixlab.io/samples.html for some code samples.
- p1esk 8y agoI mean standard dataset and standard model, for example YOLO v2 on COCO. Say 1950X vs 1080Ti.
- WhitneyLand 8y agoSorry, I’m sure you didn’t mean to ignore the question. The commenter asked how the performance compares to an NVidia 1080Ti GPU. A precise benchmark may not be necessary but in general, it will take for example how many Skylake cores at 3.5 Ghz to approximate the GPU example?
- andbberger 8y agoOrders of magnitudes slower....
- ipsum2 8y agoIs anyone doing machine learning on AMD hardware right now? What's the preferred pipeline to go from Tensorflow to GPU?
- bitL 8y agoOnly adventurous people (i.e. framework developers). Everybody else runs on NVidia and TPU.
- currymj 8y agoAMD is scrambling to build a CUDA-esque thing called ROCm and get various frameworks including TF ported over, but it’s not even in “early adopter” state yet, so i doubt anyone is using it. However they seem to be making progress.
- singhrac 8y agoIt's not that far away. PyTorch merged a build script semi-recently, though I don't know if it's fully tested or supported. https://github.com/pytorch/pytorch/blob/master/tools/amd_build/build_pytorch_amd.py https://github.com/pytorch/pytorch/blob/master/tools/amd_bui...
- nl 8y agoThat “disabled_feautures.json” file indicates it will be a while before it is useful though.
- rdlecler1 8y agoCould it really be that hard? They’ve had at least a few years heads up and this feels like something that could be accomplished relatively quickly with 5-10 10x engineers. As a high profile project that shouldn’t have difficulty finding the talent.
- maeln 8y agoAs I remember, AMD was supporting OpenCL (and now SPIR-V Computer Shader) fairly well. Are those technologies not well regarded in ML ?
- jimnotgym 8y agoI wonder if this will get the Crypto-mining people interested too? I am really looking forward to them having more specific hardware to waste their money on, so we can return to buying GPU's for gaming at a sensible price!
- acrossthepond10 8y agoIdeally the increased demand from to crypto-mania would be channeled to fund R&D for Graphics & AI chips but AFAIK its mostly the middlemen (distributors and retailers) who pocket these premiums.
- swerveonem 8y agoThe more difficult the hash the stronger the given blockchain. Faster chips is not what users of crypto benefit from, we want larger networks of chips no more powerful than those imagined by the blockchain creators.
- sanxiyn 8y agoMost GPU mining blockchains can be obliterated by hash power moving from Ethereum. It is ASICs that are protecting smaller chains.
- deleted 8y ago[deleted]
- gzu 8y agoThey would be good for Monero and Aeon mining
- icelancer 8y ago>> I am really looking forward to them having more specific hardware to waste their money on That's not how it works. You won't make GPUs that suck up all the demand while you get good gaming GPUs. If it can process stuff fast - either in computation or in memory speeds - then cryptominers want it, and so do you. The card you wish to exist simply doesn't, and if the cards are good, then they are profitable, which means they buy more of everything. Most intelligent GPU miners have a diversified farm of Nvidia and AMD cards in some proportion to cover the specific algos that are good on each platform. Wouldn't hold your breath.
- jimnotgym 8y agoBeing pedantic but shouldn't the graphic for the 'Roadmap' say "Next-Gen <7nm" or "7nm-"? That looks like things will begin getting bigger again in a few years?
- bedros 8y agobasically 7nm+ means tweaks to the process with improved speed and power consumption, stays 7nm from [0] """ TSMC plans to introduce a second improved process called 7nm+ a year later, which will introduce some layers processed with EUVL. This will improve yields and reduce fab cycle times. The 7nm+ process will deliver improved power consumption and between 15-20% area scaling over their first generation 7nm process. """ [0] https://en.wikichip.org/wiki/7_nm_lithography_process https://en.wikichip.org/wiki/7_nm_lithography_process update: link
- kingosticks 8y agoWhere do wikichip get their info? Is it rumours since I don't see any sources listed?That's not what I heard about 7nm+.
- deleted 8y ago[deleted]
- c2h5oh 8y agoTo add to other answers, at both GlobalFoundries and TSMC: - 7nm has have very limited extreme ultraviolet patterning on larger features of the chip - 7nm+ will have significantly more EUV patterning which will allow shrinking some stuff that wasn't for 1st process iteration because yields would be too low - should have slightly higher density, possibly higher clocks/lower power draw.
- shmerl 8y agoSo, are gaming 7nm GPUs from AMD coming this year or next?
- all_blue_chucks 8y agoOdds are we will see overclocked 14nm Vegas with an X suffix before Christmas this year, and 7nm Vegas next year.
- jamesblonde 8y agoHere's a benchmark comparing the latest ROCm software with Vega cards from 2017 (with tensorflow 1.3). TLDR; - Vega's are nearly as good as V100s for CIFAR-10! http://blog.gpueater.com/en/2018/04/23/00011_tech_cifar10_bench_on_tf13/ http://blog.gpueater.com/en/2018/04/23/00011_tech_cifar10_be...
- tntn 8y agoThat post doesn't specify whether the training was fine with single or half precision floating point. Given that it's on the website of an AMD GPU cloud provider, I tend to think they probably used fp32 since it will make vega look better.