2019 DS / ML digest 12

Highlights of the week(s)
- Cool STT papers;
- End of AI hype?
- How to download tons of images from Google;

https://spark-in.me/post/2019_ds_ml_digest_12

#digest
#deep_learning

Yeah, scraping image labels from Google / other social networks is a really cool idea ...

Installing apex ... in style )

Sometimes you just need to try fp16 training (GANs, large networks, rare cases).
There is no better way to do this than use Nvidia's APEX library.

Luckily - they have very nice examples:
- https://github.com/NVIDIA/apex/tree/master/examples/docker

Well ... it installs on a clean machine, but I want my environment to work with this always)
So, I ploughed through all the conda / environment setup mumbo-jumbo and created a version of our deep-learning / ds dockerfile, but now instlalling from pytorch image (pytorch GPU / CUDA / CUDNN + APEX).
- https://github.com/snakers4/gpu-box-setup/blob/master/dockerfile/Dockerfile_apex

It was kind of painful, because PyTorch images already contain conda / pip and it was not apparent at first, causing all sorts of problems with my miniconda instalation.

So use it and please report if it is still buggy.

#deep_learning
#pytorch

Logging your hardware, with logs, charts and alers - in style

TLDR - we have been looking for THE software to do this easily, with charts / alerts / easy install.

We found prometheus. Configuring alerts was a bit of a problem, but enjoy:
- https://github.com/snakers4/gpu-box-setup#prometheus

#deep_learning
#hardware

Trying to migrate to JupyterLab from Jupyter Notebook?

Some time ago I noticed that the Jupyter extensions project was more or less frozen => JupyterLab obviously is trying to shift community attention to npm / nodejs plugins.

Again (like 6-12 months ago) I tried to do this.

This time Lab is more mature:
- Now at version >1;
- Now they have built-in package manager;
- They have some of the most necessary extensions (i.e. git, toc, google drive, etc);
- UI got polished a bit, but window in a window still produces a bit of mental friction. Only the most popular file formats are supported. Text editor inherited the best features, but it is still a bit rudimentary;
- Full screen width by default;
- Some useful things (like codefolding) are now turned on in settings json file;
- Using these extensions is a bit of a chore in edge cases (i.e. some user permission problems / you have to re-build an app each time you add an extensions);

But I could not switch mostly for one reason - this one
- https://github.com/jupyterlab/jupyterlab/issues/2275#issuecomment-498323475

If you have a Jupyter environment it is very easy to switch. For me, before it was:

# 5.6 because otherwise I have a bug with installing extensions
RUN conda install notebook=5.6

RUN pip install git+https://github.com/ipython-contrib/jupyter_contrib_nbextensions && \
    jupyter contrib nbextension install --user

CMD jupyter notebook --port=8888 --ip=0.0.0.0 --no-browser

And it just became:

RUN conda install -c conda-forge jupyterlab

CMD jupyter lab --port=8888 --ip=0.0.0.0 --no-browser

#data_science

Full IDE in a browser?

Almost)

You all know all the pros and cons of:
- IDEs (PyCharm);
- Advanced text editors (Atom, Sublime Text);
- Interactive environments (notebook / lab, Atom + Hydrogen);

I personally dislike local IDEs - not because connecting to a remote / remote kernel / remote interpreter is a bit of a chore. Setting up is easy, but always thinking about what is synced and what is not - is just pain. Also when your daily driver machine is on Windows, using Linux subsystem all the time with Windows paths is just pain. (Also I diskile bulky interfaces, but this is just a habit and it depends).

But what if I told you there is a third option? =)
If you work as a team on a remote machine / set of machines?

TLDR - you can run a modern web "IDE" (it is something between Atom and real IDE - less bulky, but less functions) in a browser.
Now you can just run it with one command.

Pros:
- It is open source (though shipped as a part of some enterprise packages like Eclipse Che);
- Pre-built images available;
- It is extendible - new modules get released - you can build yourself or just find a build;
- It has extensive linting, python language server (just a standard library though);
- It has full text search ... kind of;
- Follow definition in your code;
- Docstrings and auto-complete work for your modules and standard library (not for you packages);

Looks cool af!
If they ship a build with a remote python kernel, then it will be a perfect option for teams!

I hope it will not follow a path taken by another crowd favourite similar web editor (it was purhcased by Amazon).

Links
- Website;
- Pre-built apps for python;
- Language server they are using;

#data_science

If you know how to add your python kernel to Theia - please ping me)

An ideal remote IDE?

Joking?
No, looks like VScode recently got its remote development extensions (it was only in insiders build a couple of months ago) working just right.

I tried remote-ssh extension and it looks quite polished. No syncing your large data folders and loading all python dependencies locally for hours.

The problem? It took me an hour just to open ssh session under Windows properly (permissions and Linux folder path substitution is hell on Windows). When I opened it - it worked like a charm.

So for now (it is personal) - best tools are in my opinion:
- Notebooks - for exploration and testing;
- VScode for codebase;
- Atom - for local scripts;

#data_science

2019 DS / ML digest 13

Link

Highlights of the week(s):
- x10 faster STT network?
- Train on 1/2 of test resolution - new down-to-earth SOTA approach to image classification? Old news!;
- New workhorse light-weight network - MixNet?

#digest
#deep_learning

Managing your DS / ML environment neatly and in style

If you have a sophisticated environment that you need to do DS / ML / DL, then using a set of Docker images may be a good idea.
You can also tap into a vast community of amazing and well-maintained Dockerhub repositories (e.g. nvidia, pytorch).

But what you have to do this for several people? And use it with a proper IDE via ssh?
A well-known features of Docker include copy on write and user "forwarding". If you approach naively, each user will store his own images, which take quite some space.
And also you have to make your ssh daemon works inside of a container as a second service.

So I solved these "challenges" and created 2 public layers so far:
- Basic DS / ML layer - FROM aveysov/ml_images:layer-0 - from dockerfile;
- DS / ML libraries - FROM aveysov/ml_images:layer-0- from dockerfile;

Your final dockerfile may look something like this just pulling from any of those layers.
Note that when building this, you will need to pass your UID as a variable, e.g.:

docker build --build-arg NB_UID=1000 -t av_final_layer -f Layer_final.dockerfile .

When launched, this launched a notebook with extensions. You can just exec into the machine itself to run scripts or use an ssh daemon inside (do not forget to add your ssh key and service ssh start).


#deep_learning
#data_science

Using public Dockerhub account for your private small scale deploy

Also a lifehack - you can just use Dockerhub for your private stuff, just separate the public part and the private part.
Push the public part (i.e. libraries and frameworks) to Dockerhub/

You private Dockerfile will be then something like:

FROM your_user/your_repo:latest

COPY your_app_folder your_app_folder
COPY app.py app.py

EXPOSE 8000

CMD ["python3", "app.py"]

PyTorch 1.2 release

Link

Key features:
- Tensorboard logging in now out of beta;
- They continue improving JIT and ONNX;
- NN.Transformer is a layer now;
- Looks like SyncBn is also more or less stable;
- nn.Embedding: support float16 embeddings on CUDA;
- AdamW;
- Numpy compatibility;

#deep_learning

Extreme NLP network miniaturization

Tried some plain RNNs on a custom in the wild NER task.
The dataset is huge - literally infinite, but manually generated to mimick in-the-wild data.

I use EmbeddingBag + 1m n-grams (an optimal cut-off). Yeah, on NER / classification it is a handy trick that makes your pipeline totally misprint / error / OOV agnostic. Also FAIR themselves just guessed this too. Very cool! Just add PyTorch and you are golden.

What is interesting:
- Model works with embedding sizes 300, 100, 50 and even 5! 5 is dangerously close to OHE, but doing OHE on 1m n-grams kind-of does not make sense;
- Model works with various hidden sizes
- Naturally all of the models run on CPU very fast, but the smallest model also is very light in terms of its weights;
- The only difference is - convergence time. It kind of scales as a log of model size, i.e. model with 5 takes 5-7x more time to converge compared to model with 50. I wonder what if I use embedding size of 1?;

As added bonus - you can just store such miniature model in git w/o lfs.
What is with training transformers on US$250k worth of compute credits you say?)

#nlp
#data_science
#deep_learning

My foray into the STT Dark Forest

My tongue-in-cheek article on ML in general, and how to make your STT model train 3-4x faster with 4-5x less weights with the same quality

https://spark-in.me/post/stt-dark-forest

#data_science
#deep_learning
#stt

Sampler - visualization for any shell command

A cool mix between glances and prometheus
https://github.com/sqshq/sampler

#linux

2019 DS / ML digest 14

Link

Highlights of the week(s):

- FAIR embraces embedding bags for misspellings;
- New version of Adam - RAdam. But on the only real test author has concluded (Imagenet) - SGD is better;
- Yet another LSTM replacement - SRU. Similar to QRRN - it requires additional dependencies;

#digest
#deep_learning

Our STT Dark Forest post on TDS

https://towardsdatascience.com/navigating-the-speech-to-text-dark-forest-b511e6e7aa88?source=friends_link&sk=d46e5b96aaa7e94f337cc6e2e540545b

Please 👏x50 if you have an account

#deep_learning

How to solve an arbitrary CV task ...

- W/o annotation
- W/o GPUs in production
- And make your model work in real life and help people

https://spark-in.me/post/deploy_classifier

https://medium.com/@slizhikova.a.v/how-to-get-your-own-image-classifier-region-labelling-model-without-annotation-d95aabbd8599?sk=fd844bf2a6f48171f02cbbda7bc493a6

#deep_learning
#computer_vision

Poor man's ensembling techniques

So you want to improve your model's performance a bit.
Ensembling helps. But as is ... it's useful only on Kaggle competitions, where people stack over9000 networks trained on 100MB of data.

But for real life usage / production, there exist ensembling techniques, that do not require significant computation cost increase (!).
All of this is not mainstream yet, but it may work on you dataset!
Especially if your task is easy and the dataset is small.

- SWA (proven to work, usually used as a last stage when training a model);
- Lookahead optimizer (kind of new, not thoroughly tested);
- Multi-Sample Dropout (seems like a cheap ensemble, should work for classification);

Applicability will vary with your task.
Plain vanilla classification can use all of these, s2s networks probably only partially.

#data_science
#deep_learning

ML without train / val split

Yeah, I am not crazy. But probably this applies only to NLP.
Sometimes you just need your pipeline to be flexible enough to work with any possible "in the wild" data.

A cool and weird trick - if you can make your dataset so large that your model just MUST generalize to work on it, then you do not need a validation set.

If you sample data randomly and your data generator is good enough, each new batch is just random and can serve as validation.

#deep_learning