Machine Learning & Deep Learning Packages
Machine-learning and deep-learning frameworks and model libraries for training, fine-tuning, and serving models (PyTorch, scikit-learn, Transformers).
Packages in Machine Learning & Deep Learning
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PyTorch
A Python-first tensor library with GPU acceleration and a dynamic, define-by-run autograd engine for building and training deep neural networks.
Transformers
State-of-the-art pretrained models for text, vision, audio, and multimodal inference and training.
scikit-learn
Simple and efficient tools for machine learning and data analysis in Python.
Datasets
One-line loading and fast, Arrow-backed processing for thousands of ML datasets
Tokenizers
Fast, production-grade tokenizer implementations for NLP and LLMs, written in Rust
opencv-python
Pre-built, pip-installable OpenCV bindings that give Python programs full computer vision and image-processing capabilities without compiling from source.
safetensors
A safe, zero-copy binary format for storing and loading ML tensors without pickle
PEFT
Hugging Face's library of state-of-the-art parameter-efficient fine-tuning methods for large models.
torchaudio
GPU-accelerated audio processing library built on PyTorch, with differentiable transforms, Kaldi-compatible features, and pretrained speech models.
pymilvus
The official Python SDK for Milvus, the open-source vector database for embedding-based similarity search.
Transformers.js
Run Hugging Face Transformers models directly in the browser or Node.js with no server required.
Gradio
Build and share machine learning web apps and demos in pure Python.
TRL
Fine-tune and align language models with SFT, DPO, GRPO, and RLHF trainers built on Hugging Face Transformers.
PySpark
Python API for Apache Spark, the unified engine for large-scale distributed data processing
Transformers.js
Run Hugging Face Transformers models directly in the browser with no server or Python runtime required.
PyTorch Lightning
A deep learning framework that organizes PyTorch training code and automates multi-GPU, mixed-precision, and distributed training.
LightGBM
A fast, distributed, high-performance gradient boosting framework based on decision tree algorithms, used for ranking, classification, and other machine learning tasks.
ModelScope
Alibaba's open model-hub library for NLP, CV, speech, and multi-modal AI models.
PyTorch Lightning
The deep learning framework that separates PyTorch research code from engineering boilerplate, scaling from CPU to multi-node GPU clusters without code changes.
EasyOCR
Ready-to-use Python OCR supporting 80+ languages and all popular writing scripts.
kornia
A differentiable computer vision library for PyTorch, with 500+ GPU-ready ops for image processing, augmentation, and geometry.
CatBoost
A fast, high-performance gradient boosting library with best-in-class native categorical feature support
skops
Securely persist and share scikit-learn models without pickle, and auto-generate model cards for them.
fastai
A high-level deep learning framework that layers state-of-the-art training APIs on top of PyTorch.
