Training Python Packages

swesmith

[NeurIPS 2025 D&B Spotlight] Scaling Data for SWE-agents

torchao

PyTorch native quantization and sparsity for training and inference

optimum

🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

sagemaker-training

Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

swanlab

⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl / LLaMA Factory / ms-swift / Ultralytics / MMEngine / Keras etc.

torchft-nightly

Fault tolerance for PyTorch (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo)

dstack

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

split-folders

🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders

tokenicer

A (nicer) tokenizer you want to use for model inference and training: with all known peventable gotchas normalized or auto-fixed.

kubetorch

Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.

datasetrising

Toolchain for creating custom datasets and training Stable Diffusion (1.x, 2.x, XL) models and LoRAs

mlx-lm-lora

Train Large Language Models on MLX.

hypernix

Download and quantize the HyperNix PyTorch model to GGUF (fp32/fp16/Q8_0/Q6_K/Q4_K_M).

deadwood-sec

A self-hosted, leveled web-security range (tutorial → impossible) — the practice town for wraith & hickok. Intentionally vulnerable; localhost only.

tensorbored-nightly

TensorBored: Fixing TensorBoard's deficiencies

mlx-lattice

Sparse convolution library for MLX designed for point cloud operation.

pytorch-accelerated

A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/

neptune-sklearn

Experiment tracking for scikit-learn. 🧩 Log, organize, visualize and compare model metrics, parameters, dataset versions, and more.

awex

A high-performance RL training-inference weight synchronization framework, designed to enable second-level parameter updates from training to inference in RL workflows

avalanche-lib

Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

runhouse

Distribute and run AI workloads on Kubernetes magically in Python, like PyTorch for ML infra.

backpropagate

Headless LLM fine-tuning in 3 lines — smart defaults, VRAM-aware batch sizing, multi-run SLAO, GGUF export for Ollama.

easymodels

Easily find and view pre-trained AI models and deep learning projects through the command line :computer:

deepepochs

Pytorch模型训练工具