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End-to-end quantitative trading framework that covers feature engineering, model training, signal generation and live execution; includes vnpy.alpha for ML-driven strategy research and workflow management, aimed at production trading and research pipelines.
Unified Node.js library for web crawling and browser automation that fetches pages and files via headless browsers or raw HTTP. Provides persistent queues, proxy rotation, session management, storage, and human-like fingerprints to build scalable data pipelines (e.g., RAG/LLM datasets).
Orchestrates and schedules Python data pipelines and workflows with primitives for retries, caching, parameters, and deployments. Provides either a self-hosted server or managed Prefect Cloud for monitoring, observability, and integrations across common data tools.
Train and experiment with multi-billion to trillion-parameter transformer models on large GPU clusters using GPU-optimized building blocks and reference training scripts; offers advanced parallelism and mixed-precision support for research teams and ML engineers.
Connect LLMs to major chat platforms so teams can build, deploy, and operate multi-platform AI chatbots and agents. Provides multi-platform adapters, a plugin marketplace, an MCP server and built-in RAG plus production features like access control, rate limiting and monitoring.
Lets you write compositional Python programs that compile into self‑improving LLM pipelines — replacing brittle prompt engineering with a declarative, programmatic approach and built‑in algorithms to optimize prompts and weights for RAG, multi‑stage pipelines, and agent loops.
Turns documents, web pages and audio into a private, searchable knowledge base and builder for AI assistants and agents — supports wide-format ingestion, multi-model (cloud or local) execution, retrieval-augmented responses, and enterprise deployment features like RBAC and SSO.
Provides a lightweight build platform for HIP and ROCm that supports building ROCm, PyTorch, and JAX from source, multi-architecture nightly releases, and integrated CI/CD and developer tooling for Linux and Windows.
Applies quantization, pruning, distillation, speculative decoding and sparsity to compress and accelerate deep learning models, producing exportable checkpoints ready for deployment on TensorRT‑LLM, TensorRT, vLLM and similar inference runtimes.
Runs a self-hosted meeting bot and transcription API that joins Google Meet, Teams and Zoom and streams speaker-attributed transcripts in real time. Compiles meetings into a git-backed Markdown workspace and runs sandboxed agents on your infrastructure; Apache-2.0 and air-gap capable.
An asynchronous, high-throughput framework for large-scale reinforcement learning and agentic training that scales to 1T+ MoE models and 1000+ GPUs, with native verifiers integration, end-to-end SFT/RL/evals, and Slurm/Kubernetes deployment; requires NVIDIA GPUs.