Photo by Mohamed Nohassi on Unsplash

During the last decade, the Deep Learning progress led to various projects and frameworks. One of the most famous among researchers became the PyTorch one. Thanks to its pure pythonic way of executing and great low-level design, it gathered a lot of attention from the research community. Nevertheless, with great power comes great responsibility: due to such low-level functions, users are likely to introduce bugs during their research and development process. Meanwhile, Deep Learning methods are now used almost everywhere: from on-site e-commerce recommendations to healthcare treatment or bank scoring, meaning that such… “bugs” could have serious consequences.

For the…


Recommender systems — a retrospective

You probably already know that recommender systems are all around you:

  • they select and rank products in marketplaces (Amazon, Yandex) and movies on Netflix/Disney to find the most relevant one to you,
  • they prepare podcasts and select the next track/video on Spotify/Youtube to suit your personal preferences,
  • they filter your feed when you scroll through Twitter/Instagram/VK to show the most important news to you.

As a result, today's recommender systems affect almost every aspect of user experience. And the current demands for personalized user experience and advances in machine learning are constantly pushing the field towards new scientific advances.


Falcon 9 SpaceX launch

Hi, I am Sergey, the author of the Catalyst — PyTorch library for deep learning research and development. In our previous blog posts, we covered an introduction to the Catalyst and our advanced pipeline for NLP on BERT distillation. In this post, I would like to share with you our development progress for the last month. Let’s check what features we have added to the framework in such a short time.

tl;dr

  • Training Flow improvements: BatchOverfitCallback, PeriodicLoaderCallback, ControlFlowCallback
  • Metric Learning features: InBatchSampler, AllTripletsSampler, HardTripletsSampler, tutorial
  • Fixes and acknowledgments
  • New integrations: MONAI & Catalyst
  • Ecosystem update — Alchemy

You can find all…


Break the cycle — use the Catalyst!

PyTorch is great framework to create deep learning models and pipelines. Nevertheless, for all its merits, it could use improvements in terms of writing training loops, validating and testing neural networks. Moreover, PyTorch users are likely to introduce more bugs during the research and development process as they mix in complicated things like mutli-GPU, mixed precision, and distributed training.

For real breakthroughs in deep learning, we need a strong foundation. In this blog post, I would like to introduce Catalyst framework, developed with focus on reproducibility, fast experimentation and code/idea reusing. …


2 weeks ago, 13 November, one of the most exiting RL competitions of this year finally end. NIPS 2017: Learning to Run was really interesting and hard to solve. Nevertheless, me and my friend Mikhail Pavlov took 3rd place in the final round and we invited to NIPS conference. For more detailed description you can read our article or PyTorch/Theano source code.

Long story short, we use DDPG as a main agent and speed up environment as fast as we can. Our final agent was trained on 36 cores for about 5 days. …

Sergey Kolesnikov

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