Dev tools · alternatives · 2026
24 alternatives to Install PyTorch
Your one-stop solution for easy PyTorch installation.
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Install PyTorch launched in 2024; newer entries below may have overtaken it.
- 1IS
2022 · 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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Launch you AI project with PyTorch through Bunnyshell
2023 · its alternatives →
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The (collaborative) guide to get PyTorch running on Windows
2017 · its alternatives →
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TorchTPU▲106Running PyTorch Natively on TPUs at Google Scale
Apr 2026 · cloud.google.com · its alternatives →
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I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
Apr 2026 · github.com · its alternatives →
- 8TL
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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The light PyTorch wrapper for high-performance AI research
2020 · its alternatives →
- 10LF
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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Forge Agent▲113Swarm Agents That Turn Slow PyTorch Into Fast GPU Kernels
Jan 2026 · rightnowai.co · its alternatives →
- 12KY
2025 · github.com · its alternatives →
- 13GA
I'm starting to implement a barebones version of pytorch in Go. The primary motivation is: 1. I want to better learn Pytorch and how it works so what better way than to just re-implement some of its core features. 2. I write mainly in Go and haven't come across a lot of ML support in Go 3. I'd rather have a Go ML service instead of spinning up additional infrastructure to just support a python ML service in my Go projects 4. Go's static typing, native concurrency (avoid GIL problem in python), efficient memory management, single binary deployment and more make it a better interface compared…
2024 · its alternatives →
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Forge CLI▲107Swarm agents optimize CUDA/Triton for any HF/PyTorch model
Jan 2026 · rightnowai.co · its alternatives →
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Lightning▲83AI code editor for PyTorch development on GPU workspaces
Oct 2025 · lightning.ai · its alternatives →
- 16PP
2025 · github.com · its alternatives →
- 17RP
I integrated a remote GPU execution backend into PyTorch through the same system that custom hardware accelerators get integrated into PyTorch. You can create a remote machine and obtain its CUDA device whenever you want to create or move tensors onto the remote GPU. machine = mycelya_torch.RemoteMachine("modal", "A100") cuda_device = machine.device("cuda") x = torch.randn(1000, 1000, device=cuda_device) y = torch.randn(1000, 1000).to(cuda_device) I made it reasonably performant by having most operations dispatch asynchronously whenever possible. For cases where slow performance is…
Oct 2025 · github.com · its alternatives →
- 18PA
I’m sure many of you are familiar, but there’s a treacherous gap between finding (or building) a model that works in PyTorch, and getting that deployed into your application, especially in consumer-facing applications. I’ve been very interested in solving this problem with a great developer experience. Over time, I gradually realized that the highest-impact thing to have was a way to go from existing Python code to a self-contained native binary—in other words, a Python compiler. I was already pretty familiar with a successful attempt: when Apple introduced armv8 on the iPhone 5s, they…
2025 · blog.fxn.ai · its alternatives →
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Build your first neural network with PyTorch step by step.
Aug 2026 · khayyamshah2007.blogspot.com · its alternatives →
- 20OA
https://github.com/gugarosa/opytimizer Did you ever reach a bottleneck in your computational experiments? Are you tired of selecting suitable parameters for a chosen technique? If yes, Opytimizer is the real deal! This package provides an easy-to-go implementation of meta-heuristic optimizations. From agents to search space, from internal functions to external communication, we will foster all research related to optimizing stuff. Use Opytimizer if you need a library or wish to: - Create your optimization algorithm; - Design or use pre-loaded optimization tasks; -…
2021 · its alternatives →
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