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
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
huggingface_hub
The official Python client and hf CLI for the Hugging Face Hub, for downloading, uploading, and managing models, datasets, and Spaces.
qdrant-client
Official Python SDK for the Qdrant vector search engine
PEFT
Hugging Face's library of state-of-the-art parameter-efficient fine-tuning methods for large models.
ONNX Runtime
Cross-platform, high-performance ML inference and training accelerator for ONNX models
spaCy
Industrial-strength Natural Language Processing library for Python
google-cloud-aiplatform
Google's official Python SDK for Vertex AI model training, deployment, and generative AI workflows
onnxruntime-node
Microsoft's official Node.js binding for running ONNX model inference in production JavaScript apps
Transformers.js
Run Hugging Face Transformers models directly in the browser or Node.js with no server required.
einops
A readable, framework-independent notation for tensor rearrangement, reduction, and repetition.
ort
A safe Rust wrapper for ONNX Runtime — fast ML inference and training
XGBoost
Scalable, portable gradient boosting library for Python, R, Java, Scala, and C++
unstructured
Open-source Python library for partitioning and preprocessing unstructured documents for LLM pipelines
