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🐍 Python stuff
A clean customizable documentation theme for Sphinx
An extremely fast Python linter and code formatter, written in Rust.
⚡VoltaML is a lightweight library to convert and run your ML/DL deep learning models in high performance inference runtimes like TensorRT, TorchScript, ONNX and TVM.
A Visual Studio Code extension with support for the Ruff linter.
Install and Run Python Applications in Isolated Environments
Google Drive Public File Downloader when Curl/Wget Fails
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
A tutorial for writing a systemd service in Python
On-Device Training Under 256KB Memory [NeurIPS'22]
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Easily share permanent links to ChatGPT conversations with your friends
📈 Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ.
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Nvidia GPU exporter for prometheus using nvidia-smi binary
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
A Collection of Variational Autoencoders (VAE) in PyTorch.
A set of tools to keep your pinned Python dependencies fresh.
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data (NeurIPS 2023 Spotlight) / / / / When Does Perceptual Alignment Benefit Vision Representations? (NeurIPS 2024)
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Convert tensorflow model to pytorch model via [MMdnn](https://github.com/microsoft/MMdnn) for adversarial attacks.