Life & fun · alternatives · 2026
24 alternatives to Encrypted Deep Learning in PyTorch
Perform data science on data that remains in someone else's server - OpenMined/PySyft
Encrypted Deep Learning in PyTorch is a tool that enables machine learning model training and inference while keeping data encrypted. It integrates with PyTorch, a popular deep learning framework, allowing developers… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Encrypted Deep Learning in PyTorch launched in 2018; newer entries below may have overtaken it.
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Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older version of PySyft are…
2019 · blog.openmined.org · its alternatives →
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The light PyTorch wrapper for high-performance AI research
2020 · pytorchlightning.ai · its alternatives →
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TensorFlow 1.0▲823Open-source machine learning library by Google
2017 · tensorflow.org · its alternatives →
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Research Framework for easy and efficient training of GANs based on Pytorch - torchgan/torchgan
2018 · github.com · its alternatives →
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2018 · youtube.com · its alternatives →
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Netmind Power▲272Hi HN, We just opened public access to Qwen3‑Coder‑480B‑A35B‑Instruct via a REST/SDK API. Why it might be interesting: - Fully open-source (Apache‑2.0): https://github.com/QwenLM/Qwen3-Coder - 480B params (35B active MoE), long context up to 1M tokens with YaRN - Matches Claude Sonnet/GPT‑4o on SWE-Bench Verified (81.6%) - Free tier: 100K tokens/month
2023 · netmind.ai · its alternatives →
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Weights & Biases▲110Developer tools for deep learning & machine learning
2019 · wandb.ai · its alternatives →
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Hello HN, We are pleased to introduce you graphlearn-for-pytorch (https://github.com/alibaba/graphlearn-for-pytorch), an open-source distributed graph neural network library based on PyTorch and compatible with PyG. Our library is designed to make it easy for developers to build and train large-scale graph models in a distributed environment. With graphlearn-for-pytorch, you can leverage GPUs to accelerate graph sampling and utilize UVA to reduce the overheads of feature collection. Following a scalable design, graphlearn-for-pytorch supports training GNN models on…
2023 · github.com · its alternatives →
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Tool which allow you to detect and translate text. - s3nh/text-detector
2019 · github.com · its alternatives →
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Hello HN! For the past 6 months I've been working on an open source python library that implements differentiable geometric optics in PyTorch. It's very experimental still, but eventually the goal is to use it to design optical systems with a state of the art optimization framework and a beautiful code based API. Think OpenSCAD, but for optical systems. Not only is PyTorch's autograd an amazing general purpose optimizer, but torch.nn (the neural network building blocks) can be used pretty much out of the box to model an optical system. This is because there is a strong analogy to be made…
2025 · victorpoughon.github.io · its alternatives →
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Pytorch script hot swap: Change code without unloading your LLM from VRAM - valine/training-hot-swap
2025 · github.com · its alternatives →
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Learn PyTorch from scratch with simple examples.
Aug 2026 · khayyamshah2007.blogspot.com · its alternatives →
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Hey! This is something i have been working on. A tiny neural networking lib to learn how something like pytorch works, and to improve my own coding standards.
2025 · github.com · its alternatives →
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We're a group of engineers, AI/ML enthusiasts, and author of this paper https://openreview.net/forum?id=0pxiMpCyBtr who saw a closed door in AI/ML and decided to open it. This project is a PyTorch reimagining of Google's TensorFlow Lattice models, which despite being labeled open-source, were previously open in name only (you have to be a Googler to contribute). Also, side point…TensorFlow is dying https://thenextweb.com/news/why-tensorflow-for-python-is-dyi... Here's the deal: Lattice models excel in making AI interpretable—key for sectors where…
2023 · github.com · its alternatives →
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built a tiny pytorch clone in c after going through prof. vijay janapa reddi's mlsys book: mlsysbook.ai/tinytorch/ perfect for learning how ml frameworks work under the hood :)
Dec 2025 · github.com · its alternatives →
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A uniform interface to run deep learning models from multiple frameworks - uber/neuropod
2020 · github.com · its alternatives →
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An on-premises, bare-metal solution for deploying GPU-powered applications in containers - emergingstack/es-dev-stack
2016 · github.com · its alternatives →
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We needed a side project to give agentic coding a try, and created tensorspy.com together with Junie and ChatGPT 5.2. Tensor Spy lets you quickly inspect the contents of numpy & pytorch tensors locally (your tensors are not uploaded to any servers). This is useful to validate your deep learning data pipelines, to check which layers in your diverging model are actually going haywire, and just because it's kind of cool & a lot more convenient for one-off inspections than loading things up in python. If you work with diffusion models, inspecting the latent space can be quite informative: you…
Mar 2026 · tensorspy.com · its alternatives →
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Supporting Rapid Prototyping with a PyTorch Toolkit
2018 · github.com · its alternatives →
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Deep-ML▲13We just launched Deep-ML Labs, a new section on Deep-ML Each Lab is a hands-on challenge where you implement parts of real models, activations, layers, optimizers, normalization, even small transformers — directly in PyTorch (and sometimes NumPy). You can test your implementations on actual datasets, see how your results compare to others, and understand what’s really happening under the hood of deep learning frameworks. let me know what you guys think and if we should work on making more of these questions
2025 · deep-ml.com · its alternatives →
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We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →
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I built a small, self-contained K-Means implementation in pure PyTorch: https://gitlab.com/hassonofer/pt_kmeans I was working on dataset sampling and approximate nearest neighbor search, and tried several existing libraries for large-scale K-Means. I couldn't find something that was fast, simple, and would run comfortably on my own workstation without hitting memory limits. Maybe I missed an existing solution, but I ended up writing one that fit my needs. The core insight: Keep your data on CPU (where you have more RAM) and intelligently move only the necessary chunks to…
2025 · its alternatives →
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A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq
2017 · github.com · its alternatives →
Also compare
- Encrypted Deep Learning Classification with PyTorch and PySyft alternatives
- PyTorch Lightning alternatives
- TensorFlow 1.0 alternatives
- TorchGAN – Research Framework for Modelling GANs Based on Pytorch alternatives
- Peer-To-Peer Federated Deep Learning Using IPFS and PyTorch alternatives
- Netmind Power alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →