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Tensors and neural networks in Python with strong hardware acceleration. Register for 2021 here:

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Joined September 2016
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  1. Pinned Tweet
    Sep 8

    is back! Enter to compete and connect with your worldwide PyTorch community across 3 project categories: - PyTorch Dev Tools - Web/Mobile Applications - PyTorch Responsible AI Dev Tools Learn more about how to win up to $5,000 here:

    2 replies 26 retweets 122 likes
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  2. 20 hours ago

    7. Full details here:

    1 reply 2 retweets 28 likes
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  3. 20 hours ago

    6. Read more about InferenceMode:

    1 reply 1 retweet 21 likes
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  4. 20 hours ago

    5.❗Note that the highest speedups are for lightweight operations that are bottlenecked by the tracking overhead. ❗If the ops are fairly complex, disabling tracking with InferenceMode doesn't provide big speedups; e.g. using InferenceMode on ResNet101 forward

    2 replies 3 retweets 20 likes
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  5. 20 hours ago

    4. ⚠️ Inference tensors can't be used outside InferenceMode for Autograd operations. ⚠️ Inference tensors can't be modified in-place outside InferenceMode. ✅ Simply clone the inference tensor and you're good to go.

    1 reply 2 retweets 23 likes
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  6. 20 hours ago

    3. ⏩ InferenceMode reduces overheads by disabling two Autograd mechanisms - version counting and metadata tracking - on all tensors created here ("inference tensors"). Disabled mechanisms mean inference tensors have some restrictions in how they can be used 👇

    1 reply 2 retweets 35 likes
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  7. 20 hours ago

    2. ⏩ inference_mode() is _grad() on steroids While NoGrad excludes operations from being tracked by Autograd, InferenceMode takes that two steps ahead, potentially speeding up your code (YMMV depending on model complexity and hardware)

    2 replies 3 retweets 50 likes
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  8. 20 hours ago

    Want to make your inference code in PyTorch run faster? Here’s a quick thread on doing exactly that. 1. Replace _grad() with the ✨torch.inference_mode()✨ context manager.

    6 replies 247 retweets 1,214 likes
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  9. 21 hours ago

    Build Scalable ML workflows using PyTorch on Pipelines & GCP Vertex AI Pipelines.NLP, CV workflows included with Ax/BoTorch for HPO,TorchServe w/ canary rollouts & autoscaling,Captum for model interpretability,PyTorch Profiler & PyTorchLightnin

    9 retweets 54 likes
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  10. Sep 13

    . chose PyTorch when they began work on developing an AI system to detect and map mitotic figures for more reliable cancer prognosis in pets. Learn how they're able to more accurately detect cancer earlier and provide better treatment.

    9 retweets 42 likes
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  11. Retweeted
    Sep 3

    Just released: 3D DL researchers can build on the latest algorithms to simplify and accelerate workflows using Kaolin PyTorch Library. Learn more:

    Visualizing Complex 3D Datasets with NVIDIA Kaolin
    88 retweets 369 likes
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  12. Retweeted
    Sep 4

    The shortest guide for pytorch training on GPUs

    17 replies 321 retweets 2,083 likes
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  13. Retweeted
    Aug 24

    If you're like me, you've written a lot of PyTorch code without ever being entirely sure what's _really_ happening under the hood. Over the last few weeks, I've been dissecting some training runs using 's trace viewer in . Read on to learn what I learned!

    6 replies 175 retweets 901 likes
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  14. Sep 7

    Join us at 9AM PT 9/8 for an interview with from on . Catalyst focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Watch live here:

    1 reply 6 retweets 19 likes
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  15. Retweeted
    Aug 26

    We are excited to share Isaac Gym tech report . Physics simulation data is directly passed to pytorch without ever going through any CPU bottlenecks in the process allowing blazing fast training on many challenging environments.

    13 replies 141 retweets 783 likes
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  16. Sep 7

    . diagnoses up to 700 cases of cancer in pets per day. By choosing PyTorch, Mars Petcare is able to seamlessly debug and inspect models, use flexible APIs and support a more reliable and objective evaluation of cancer in pets. Read more:

    3 replies 24 retweets 64 likes
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  17. Retweeted
    Aug 30

    re-implementation of DeepMind's Perceiver IO: A General Architecture for Structured Inputs & Outputs

    3 replies 37 retweets 193 likes
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  18. Retweeted
    Aug 31

    PyTorch公式ブログにPFN連載記事の続編が掲載されました。今回は、計算グラフの構築がどのように実装されているかを解説します。

    PyTorchのAutograd(自動微分)の実装について解説した連載記事を、PFNのDeep Learning Frameworks TeamがPyTorch公式ブログに寄稿しました。
    14 retweets 108 likes
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  19. Retweeted
    Aug 31

    Our second article on computational graph construction is now up on the PyTorch blog! You will learn what's happening behind the simple `tensor1 * tensor2`.

    PFN's Deep Learning Frameworks Team contributed an article on Autograd Engine to the PyTorch blog.
    9 retweets 12 likes
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  20. Sep 1

    2021 is coming soon! Stay tuned for more information.

    3 replies 19 retweets 139 likes
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