AI Infra2016
Meta (Facebook AI Research), PyTorch Foundation (Linux Foundation)PyTorch Foundation (Linux Foundation)
Lets researchers and engineers build neural networks as regular Python programs, with GPU-backed tensors, autograd, distributed training, and production paths through TorchScript and related tooling.
AI Infra2016
Provides a NumPy/SciPy-compatible GPU array library for Python, enabling existing NumPy/SciPy numerical code to run on NVIDIA CUDA and AMD ROCm with minimal changes. Exposes low-level CUDA features (RawKernels, Streams) and offers prebuilt binaries for multiple CUDA/ROCm versions.
MLOps2017
Tracks every ML run — hyperparameters, metrics, checkpoints, dataset versions — into one dashboard you share as a live report, with Sweeps for tuning and a model registry. Weave extends it to LLM apps: tracing, evals, and production monitoring.
AI Train2017
Trains gradient-boosted decision trees with native categorical-feature handling, GPU acceleration, and production-ready prediction APIs. A strong fit for tabular ML when preprocessing categories into numeric features would add noise or leakage.
AI Train2017
Sequence modeling toolkit for training custom models for translation, summarization, and language modeling. Reference implementation behind RoBERTa, BART, mBART, XLM-R, and wav2vec 2.0, with multi-GPU and mixed-precision training.
MLOps2017
Expresses data quality checks as reusable, declarative "expectations" and auto-generates human-readable validation reports and docs; integrates with Python data stacks to enforce and monitor data reliability in ML and analytics pipelines.
AI Infra2017
Provides reusable PyTorch modules and utilities to build, train and scale Graph Neural Networks — includes many implemented GNN layers, benchmark datasets, minibatch/sampling loaders, and support for large-scale, heterogeneous, temporal and point-cloud graphs.
AI Infra2017
Scales any Python or ML workload across CPUs and GPUs with a few decorators, instead of rewriting code for Spark or MPI. Bundles libraries for distributed training, hyperparameter tuning, RL, batch inference, and online model serving on one cluster.
AI Image2017
Swaps faces in images and videos using deep learning, offering tools to extract faces, train generative models, and convert media via CLI or GUI for research, VFX, and ethical experimentation.
Curates step-by-step, hands-on tutorials for reimplementing technologies from scratch—covering everything from OSs and compilers to neural networks, LLMs, and vision systems—so learners learn by rebuilding real systems across languages.
Provides 150+ executed Jupyter notebooks and code that reproduce the book 'Machine Learning for Algorithmic Trading (2nd ed.)' — covers feature engineering, alternative-data signal extraction, backtesting, NLP, deep learning and reinforcement learning for trading; best for quant researchers and practitioners.
AI Infra2018
Rust-native, event-driven trading platform for backtesting and live execution across crypto, forex, equities, and futures on 27+ venues. The same strategy code runs in nanosecond backtests and in production, giving true research-to-live parity.