AI · alternatives · 2026
24 alternatives to Machine Learning on Spatiotemporal Data – PyTorch Geometric Temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021) - benedekrozemberczki/pytorch_geometric_temporal
PyTorch Geometric Temporal is a machine learning library for processing spatiotemporal data using PyTorch. It is designed for researchers and developers working with data that has both spatial and temporal dimensions… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Machine Learning on Spatiotemporal Data – PyTorch Geometric Temporal launched in 2021; newer entries below may have overtaken it.
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min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch - kuprel/min-dalle
2022 · github.com · its alternatives →
- 2
PyPOTS▲67a Python lib for data mining on PartiallyObserved TimeSeries
2023 · pypots.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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The light PyTorch wrapper for high-performance AI research
2020 · pytorchlightning.ai · its alternatives →
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Save 30% of dev time - generate tests without writing code
2023 · github.com · its alternatives →
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An online course on how to detect objects in real time
2019 · lode88vn.skin · 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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I'm a machine learning engineer and researcher. I got fed up with how difficult it is to understand why neural networks behave the way they do, so i wrote a library to help with it. Comgra (computation graph analysis) is a library you can use with pytorch to extract all the tensor data you care about and visualize it graphically in a browser. This allows for a much more detailed analysis of what is happening than the usual approach of using tensorboard. You can go investigate tensors as training proceeds, drill down into individual neurons, inspect single data sets that are of special…
2023 · github.com · its alternatives →
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Weights & Biases▲110Developer tools for deep learning & machine learning
2019 · wandb.ai · 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 →
- 12BA
2019 · brancher.org · 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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Open-source machine learning library by Google
2018 · tensorflow.org · 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 →
- 16
Vibe profile your ML models to get max performance.
2025 · github.com · its alternatives →
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Cartwright is a data profiler that identifies and categorizes spatial and temporal features. Cartwright uses deep learning, natural language processing, and a variety of heuristics to determine whether a column in a dataset contains spatial or temporal information and, if so, what is specifically contained. Cartwright was built to automate complex data pipelines for heterogenous climate and geopolitical data that are generally oriented around geospatial and temporal features (think maps and time series). The challenge that Cartwright solves is automatically detecting those features so they…
2022 · github.com · its alternatives →
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I forked a PyTorch DeepDream implementation and added video support with temporal consistency. It produces smooth DeepDream videos with minimal flickering, and is highly flexible including many parameters and supports multiple pretrained image classifiers including GoogLeNet. Check out the repo for sample videos! Features: - Optical flow warps previous hallucinations into the current frame - Occlusion masking prevents ghosting and hallucination transfer when objects move - Advanced parameters (layers, octaves, iterations) still work - Works on GPU, CPU, and Apple Silicon
Jan 2026 · github.com · its alternatives →
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A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019) - benedekrozemberczki/GraphWaveletNeuralNetwork
2019 · github.com · its alternatives →
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Perform data science on data that remains in someone else's server - OpenMined/PySyft
2018 · github.com · its alternatives →
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Fresh and (I think) clean implementations of Faster R-CNN in PyTorch and TensorFlow 2/Keras. I wanted to learn about object detectors and decided to understand and implement a foundational model in the field, Faster R-CNN (elements of which are still used in modern models to this day), using the paper alone. That proved to be more difficult than expected and I had to relent and take a peak at existing implementations to fill in some important gaps. I've documented my struggles and learnings in the README for others to benefit from. I also wanted to solidify my understanding of…
2022 · github.com · its alternatives →
- 24
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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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 →