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Spark in me

Логотип телеграм канала @snakers4 — Spark in me S
Логотип телеграм канала @snakers4 — Spark in me
Адрес канала: @snakers4
Категории: Технологии
Язык: Русский
Количество подписчиков: 2.68K
Описание канала:

Lost like tears in rain. DS, ML, a bit of philosophy and math. No bs or ads.

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Последние сообщения 34

2021-03-23 10:15:09 PyTorch + AMD Inference

We were benchmarking our networks on Intel vs AMD processors using out-of-the-box official build.

And Intel mostly is better (with the same number of threads and roughly the same core speed and lack of overclocking). I was wondering why this is, and then I found this thread.

To be honest I have little motivation to invest time in redoing our environment builds from scratch with OpenBLAS + CUDA (and most likely it is not worth the time since in production most likely there will be Intel CPUs).

But I wonder, does anyone in the community have dockerized dev environment builds based around CUDA + OpenBLAS? Because looks like out of the box PyTorch ships with MKL by Intel.

#deep_learning
537 viewsAlexander, 07:15
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2021-03-22 18:44:32 Silero VAD Micro 8 kHz

10k param VAD added (vs. 1.1m params) for 8 kHz audio only.

If you do not want to do any resampling.

https://github.com/snakers4/silero-vad
539 viewsAlexander, 15:44
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2021-03-19 08:08:50 TLDR Doom Eternal Review

The og game - once in a generation game. 11/10. Go play it. Beat ultra nightmare.

DLC 1 - epic, beautiful and ball crushing. Beat ultra nightmare if you have the balls.

DLC 2 - fun, beautiful, but a bit rushed and disappointing ending / boss. Does not up the ante for seasoned players at all.

#off_topic
410 viewsAlexander, edited  05:08
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2021-03-17 16:43:31 Spark in me pinned « While ThreadRipper Pro MBs are impossible to buy, this MB may be the base for our next huge server build: - https://market.yandex.ru/product--materinskaia-plata-asrock-rack-romed8-2t/705623617 Looks a bit expensive (and it uses ECC RAM + EPYC processors)…»
13:43
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2021-03-17 16:43:26 Spark in me pinned « BIFURCATED RISER X16 TO 2X8 (SET) Remember that there is a very limited number of motherboards with 5+ PCIE slots? Now there are risers like this - https://riser.maxcloudon.com/ru/bifurcated-risers/25-bifurcated-riser-x16-to-2x8-set.html Has anyone…»
13:43
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2021-03-17 16:43:08 Spark in me pinned «Building Your Own Supercomputer Cheap (RU) My guest post on ODS @ habr: - https://habr.com/ru/company/ods/blog/546808/ EDIT - some awesome comments! #deep_learning»
13:43
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2021-03-17 10:00:45

While ThreadRipper Pro MBs are impossible to buy, this MB may be the base for our next huge server build:

- https://market.yandex.ru/product--materinskaia-plata-asrock-rack-romed8-2t/705623617

Looks a bit expensive (and it uses ECC RAM + EPYC processors), but with 7 x PCIE 4.0 16x and 2x10Gbit/s Ethernet possibilities are limitless.



And another hack - buying used 100 GBit/s infiniband cards from ebay, they are cheap now in the US

#deep_learning
496 viewsAlexander, edited  07:00
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2021-03-16 17:21:14

BIFURCATED RISER X16 TO 2X8 (SET)

Remember that there is a very limited number of motherboards with 5+ PCIE slots?

Now there are risers like this - https://riser.maxcloudon.com/ru/bifurcated-risers/25-bifurcated-riser-x16-to-2x8-set.html

Has anyone tried something similar for DL?

#deep_learning
572 viewsAlexander, edited  14:21
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2021-03-16 14:01:00 Building Your Own Supercomputer Cheap (RU)


My guest post on ODS @ habr:

- https://habr.com/ru/company/ods/blog/546808/

EDIT - some awesome comments!

#deep_learning
648 viewsAlexander, edited  11:01
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2021-03-09 08:24:07 New Benchmarking Tool in PyTorch

https://pytorch.org/tutorials/recipes/recipes/benchmark.html#pytorch-benchmark

Looks a bit over-complicated at the first glance (why provide classes for random tensor generation, I have no idea), but it has a few very nice features:

- Automated num_threads handling
- Automated CUDA synchronization
- Report generation, storing the results, comparing the results

But I suppose there is nothing wrong just using %%timeit manually setting num_threads.

#deep_learning
472 viewsAlexander, 05:24
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