Joined August 2017
18 Photos and videos
Taras Savchyn retweeted
May 13
PyTorch 2.12 introduces major updates across compilation, export, distributed training, and accelerator support. Highlights include up to 100x faster batched linalg.eigh on CUDA, the new torch.accelerator.Graph API, Microscaling quantization support in torch .export.save, and fused Adagrad. The release includes 2,926 commits from 457 contributors since PyTorch 2.11. Have questions? Join @AndreyTalman (@Meta), @albanDesmaison (@Meta), and @joespeez (@reflection_ai), moderated by @Chris_AI_HPC (@Meta), on May 20 at 10:00 AM PT for a live Q&A covering the release and answering questions from the community. ๐Ÿ”— Read the release blog and register for the webinar: pytorch.org/blog/pytorch-2-1โ€ฆ #PyTorch #OpenSourceAI #MachineLearning #AIInfrastructure
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Taras Savchyn retweeted
Bacteria move around using a molecular machine called the flagellar motor that rotates faster than the flywheel of a race car engine and switches directions in an instant. After 50 yrs, scientists have finally figured out how it works. โ€œMy lifelong quest is now fulfilled.โ€ Linkโคต๏ธ
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Taras Savchyn retweeted
My new trillion-dollar startup provides security for AI agents. It's called Codebusters.
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Taras Savchyn retweeted
Jan 21
PyTorch 2.10 is now available, with updates focused on performance, determinism, and numerical debugging for modern training and post-training workflows. Highlights include Python 3.14 support for torch.compile(), reduced kernel launch overhead in TorchInductor, a new varlen_attn() op for variable-length sequences, and improved tools for tracking numerical divergence. ๐Ÿ–‡๏ธ ๐Ÿ”ฅ Read the PyTorch 2.10 release blog and release notes: hubs.la/Q03_NHfT0 #PyTorch #OpenSourceAI #AIInfrastructure
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Taras Savchyn retweeted
15 Oct 2025
PyTorch 2.9 is now available, introducing key updates to performance, portability, and the developer experience. This release includes a stable libtorch ABI for C /CUDA extensions, symmetric memory for multi-GPU kernels, expanded wheel support to include ROCm, XPU, and CUDA 13, and enhancements for Intel, Arm, and x86 platforms. With 3,216 commits from 452 contributors, PyTorch 2.9 continues to advance open source AI for developers worldwide. ๐Ÿ”— Read the full release blog: hubs.la/Q03NNKqW0 #PyTorch #OpenSourceAI #AI #Performance
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Taras Savchyn retweeted
๐Ÿ Python 3.14 is here! ๐ŸŽ‰ โœจ Template strings (t-strings) ๐Ÿš€ Free-threaded Python officially supported ๐ŸŽจ Syntax highlighting in the REPL ๐Ÿ“ฆ Zstandard compression in stdlib ๐Ÿ” Remote PDB debugging Full release notes: docs.python.org/3.14/whatsneโ€ฆ
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Taras Savchyn retweeted
6 Oct 2025
Resolute Raccoon ๐Ÿฆ Ubuntu 26.04 LTS
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Taras Savchyn retweeted
18 Sep 2025
Compiling large #PyTorch models at Meta could take an hour . Engineers cut PT2 compile time by 80% with parallel Triton compilation, dynamic shape marking, autotuning config pruning, and cache improvements now integrated into the stack. ๐Ÿ”— hubs.la/Q03J-6P20
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Taras Savchyn retweeted
11 Sep 2025
As #training jobs grow, failures like preemptions and crashes cause costly delays. Efficient distributed #checkpointing is key. #PyTorch @Google built a local checkpointing solution using DCP to cut overhead, reduce rollbacks, and boost training goodput. ๐Ÿ”— hubs.la/Q03J1b110 ๐Ÿ–‹๏ธ @meta & @Google
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Taras Savchyn retweeted
14 Aug 2025
Introducing DINOv3: a state-of-the-art computer vision model trained with self-supervised learning (SSL) that produces powerful, high-resolution image features. For the first time, a single frozen vision backbone outperforms specialized solutions on multiple long-standing dense prediction tasks. Learn more about DINOv3 here: ai.meta.com/blog/dinov3-selfโ€ฆ
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Taras Savchyn retweeted
6 Aug 2025
Update from #PyTorch maintainers: 2.8 is out now. ๐Ÿ”นA limited stable libtorch ABI for third-party C /CUDA extensions ๐Ÿ”น High-performance quantized LLM inference on Intel CPUs with native PyTorch & more! ๐Ÿ“„ Release notes: hubs.la/Q03BDn_40 ๐Ÿ”— Blog: hubs.la/Q03BDmT50
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Taras Savchyn retweeted
Ozzy Forever!
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Taras Savchyn retweeted
In 1918, the control room of a German submarine, or U-boat, represented the pinnacle of naval engineering during WWI. Packed into the compact space were a multitude of valves, gauges, levers, and wheels, each essential for the operation of the vessel. The control room served as the nerve center, where the captain and crew managed the submarineโ€™s movements, depth, and communication with other parts of the vessel. Periscopes extended through the hull, allowing for surface and aerial observation, while rudimentary sonar systems began to appear, showcasing the early strides in underwater warfare technology. The design of the control room emphasized functionality and efficiency, as space aboard a U-boat was at a premium. Crewmembers had to maneuver carefully in the cramped quarters, often working shoulder-to-shoulder during combat or emergency situations. Key instruments included the depth gauge, which monitored the submarine's position in the water, and the dive planes, used to control the ascent and descent. The ballast tanks, crucial for submerging and surfacing, were controlled from this room, requiring constant attention from the crew. The smell of oil, metal, and sea permeated the air, a testament to the harsh and demanding conditions inside. By the final year of WWI, German U-boats had become a significant threat to Allied shipping, employing advanced tactics like unrestricted submarine warfare to disrupt supply lines. However, they also faced increasing countermeasures, including depth charges and improved convoy systems. The control room was often a scene of intense activity during such encounters, as the crew worked tirelessly to evade detection and execute attacks. The ingenuity and determination within these control rooms underscored the technological race that defined much of the naval warfare during the Great War. ยฉ History Pictures #archaeohistories
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Taras Savchyn retweeted
9 Jun 2025
Update from the PyTorch ecosystem: The latest @nvidia DALI release adds DALI Proxyโ€”making it easier to accelerate parts of your PyTorch DataLoader pipeline without a full refactor. Highlights: - Better GPU use in multiprocess mode - Selective pipeline offloading - New video decoding features ๐Ÿ”— hubs.la/Q03rmCc80 #PyTorch #OpenSourceAI #DataPipelines #DeepLearning
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Taras Savchyn retweeted
23 Apr 2025
Update from the PyTorch maintainers: 2.7 is out now. ๐Ÿ”น Support for NVIDIA Blackwell (CUDA 12.8) ๐Ÿ”น Mega Cache ๐Ÿ”น torch.compile for Function Modes ๐Ÿ”น FlexAttention updates ๐Ÿ”น Intel GPU perf boost ๐Ÿ”— Blog: hubs.la/Q03jBPSL0 ๐Ÿ“„ Release notes: hubs.la/Q03jBPlW0 #PyTorch #OpenSourceAI
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Taras Savchyn retweeted
๐Ÿ‘€ 3.6 billion medical imaging tests are performed globally each year. See how @databricks Pixels 2.0 and #MONAI are reducing data labeling time by up to 75% using active learning. #NVIDIAhealthcare Get the details ๐Ÿ‘‰ nvda.ws/4hY35wq
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Taras Savchyn retweeted
AI made in ๐Ÿ‡ช๐Ÿ‡บ OpenEuroLLM, the first family of open source Large Language Models covering all EU languages, has earned the first STEP Seal for its excellence. It brings together EU startups, research labs and supercomputing hosts to train AI on European supercomputers โ†“
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BREAKING: Trump Imposes 25% Tariffs on Pandas Imports
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