Tricky rsync flags

Rsync is the best program ever.

I find these flags the most useful

--ignore-existing (ignores existing files)
--update (updates to newer versions of files based on ts)
--size-only (uses file-size to compare files)
-e 'ssh -p 22 -i /path/to/private/key' (use custom ssh identity)

Sometimes first three flags get confusing.

#linux

More about STT from also us ... soon)

Вторая экспериментальная гостевая лекция курса.
Один из семинаристов курса, Юрий Бабуров, расскажет о распознавании речи и работе с аудио.

1-го мая в 8:40 Мск (12:40 Нск, 10:40 вечера 30-го апреля по PST).

Deep Learning на пальцах 11 - Аудио и Speech Recognition (Юрий Бабуров)
https://www.youtube.com/watch?v=wm4H2Ym33Io

correct link
streaming STT lecture now
https://www.youtube.com/watch?v=JpS0LzEWr-4

Poor man's computing cluster

So, when I last checked, Amazon's p3.4xlarge instances cost around US$12 per hour (unless you reserve them for a year). A tower supercomputer from Nvidia costs probably US$40-50k or more (it was announced at around US$69k).

It is not difficult to crunch the numbers and see, that 1 month of renting such a machine would cost at least US$8-10k. Also there will the additional cost / problem of actually storing your large datasets. When I last used Amazon - their cheap storage was sloooooow, and fast storage was prohibitively expensive.


So, why I am saying this?


Let's assume (according to my miner friends' experience) - that consumer Nvidia GPUs can work 2-3 years non-stop given proper cooling and care (test before buying!). Also let's assume that 4xTesla V100 is roughly the same as 7-8 * 1080Ti.

Yeah, I know that you will point out at least one reason why this does not hold, but for practical purposes this is fine (yes, I know that Teslas have some cool features like Nvlink).

Now let me drop the ball - modern professional motherboards often boast 2-3 Ethernet ports. And sometimes you can even get 2x10Gbit/s ports (!!!).

It means, that you actually can connect at least 2 (or maybe you can daisy chain them?) machines into a computing cluster.

Now let's crunch the numbers

According to quotes I collected through the years, you can build a cluster roughly equivalent to Amazon's p3.4xlarge for US$10k (but with storage!) with used GPUs (miners sell them like crazy now). If you buy second market drives, motherboards, CPUs and processors you can lower the cost to US$5k or less.

So, a cluster, that would serve you at least one year (if you test everything properly and take care of it) costing US$10k is roughly equivalent to:
- 20-25% of DGX desktop;
- 1 month of renting on Amazon;

Assuming that all the hardware will just break in a year:
- It is 4-5x cheaper than buying from Nvidia;
- It is 10x cheaper than renting;

If you buy everything used, then it is 10x and 20x cheaper!

I would buy that for a dollar!
Ofc you have to invest your free time.

See my calculations here:
http://bit.ly/spark00001

#deep_learning
#hardware

Russian Open Speech To Text (STT/ASR) Dataset
4000 hours of STT data in Russian

Made by us. Yes, really. I am not joking.
It was a lot of work.

The dataset:
https://github.com/snakers4/open_stt/

Accompanying post:
https://spark-in.me/post/russian-open-stt-part1

TLDR:
- On third release, we have ~4000 hours;
- Contributors and help wanted;
- Let's bring the Imagenet moment in STT closer together!;

Please repost this as much as you can.

#stt
#asr
#data_science
#deep_learning

PyTorch

PyTorch 1.1
https://github.com/pytorch/pytorch/releases/tag/v1.1.0

- Tensorboard (beta);
- DistributedDataParallel new functionality and tutorials;
- Multi-headed attention;
- EmbeddingBag enhancements;
- Other cool, but more niche features:
- nn.SyncBatchNorm;
- optim.lr_scheduler.CyclicLR;

#deep_learning

PyTorch DP / DDP / model parallel

Finally they made proper tutorials:
- https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html
- https://pytorch.org/tutorials/intermediate/model_parallel_tutorial.html
- https://pytorch.org/tutorials/intermediate/ddp_tutorial.html

Model parallel = have parts of the same model on different devices
Data Parallel (DP) = wrapper to use multi-GPU withing a single parent process
Distributed Data Parallel = multiple processes are spawned across cluster / on the same machine

#deep_learning

The State of ML, eof 2018 in Russian

Quite down-to-earth and clever lecture
https://www.youtube.com/watch?v=l6djLCYnOKw

Some nice examples for TTS and some interesting forecasts (some of them happened already).

#deep_learning

Habr.com / TowardsDataScience post for our dataset

In addition to a github release and a medium post, we also made habr.com post:
- https://habr.com/ru/post/450760/

Also our post was accepted to an editor's pick part of TDS:
- http://bit.ly/ru_open_stt

Share / give us a star / clap if you have not already!

Original release
https://github.com/snakers4/open_stt/

#deep_learning
#data_science
#dataset

2019 DS / ML digest 10

Highlights of the week(s)
- New MobileNet;
- New PyTorch release;
- Practical GANs?;

https://spark-in.me/post/2019_ds_ml_digest_10

#digest
#deep_learning

SWA in contrib repo of pytorch )

New in our Open STT dataset

https://github.com/snakers4/open_stt#updates

- An mp3 version of the dataset;
- A torrent for mp3 dataset;
- A torrent for the original wav dataset;
- Benchmarks on the public dataset / files with "poor" annotation marked;

#deep_learning
#data_science
#dataset

Really working in the wild audio noise reduction libraries

Spectral gating
https://github.com/timsainb/noisereduce

It works. But you need a sample of your noise.
Will work well out of box for larger files / files with gaps where you can pay attention to each file and select a part of file that would act as noise example.

RNNoise: Learning Noise Suppression

Works with any arbitrary noise. Just feed your file.
It works more like adative equalizer.
It filters noise when there is no speech.
But it mostly does not change audio when speech is present.
As authors explain, it improves snr overall and makes sound less "tiring" to listen.

Description / blog posts
- https://people.xiph.org/~jm/demo/rnnoise/
- https://github.com/xiph/rnnoise

Step-by-step instructions in python
- https://github.com/xiph/rnnoise/issues/69

#audio
#deep_learning

Do not use AllenNLP though

​​Have finished building demo and landing page for my project on mention classification. The idea of this project is to create a model which can assign some labels to objects based on their mentions in context. Right now it works only for people mentions, but if I find interest in this work, I will extend the model to other types like organizations or events. For now, you can check out the online demo of the neural network.

The current implementation can take account of several mentions at a time, so it can distinguish relevant parts of the context, not just averaging prediction.
It's also open sourced, and built with AllenNLP framework from training to serving. Take a look at it.
More technical details of implementation coming later.

2019 DS / ML digest 11

Highlights of the week(s)
- New attention block for CV;
- Reducing the amount of data for CV 10x?;
- Brain-to-CNN interfaces start popping up in the mainstream;

https://spark-in.me/post/2019_ds_ml_digest_11

#digest
#deep_learning