AI Infra2017
Manages polyglot monorepos by caching unchanged outputs and running only affected tasks. Built with Rust and extensible in TypeScript; includes integrated CI features (remote caching, task distribution) and AI-native tooling such as a CLI optimized for autonomous agents and self-healing CI.
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.
AI Infra2017
Defines a portable model format and operator set for moving trained machine learning models across frameworks, runtimes, and hardware targets without locking the model to one toolchain.
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 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.
AI Deploy2018
Converts, quantizes, and runs deep learning models from PyTorch, TensorFlow, ONNX, and PaddlePaddle across Intel CPUs, GPUs, and NPUs without the training framework. Adds a GenAI pipeline for LLMs plus Hugging Face, vLLM, and LangChain integrations.
AI Infra2018
Provides composable function transformations for Python+NumPy—automatic differentiation, JIT compilation, and vectorization—while using XLA to run and scale numerical programs on GPUs/TPUs for ML and scientific computing.
AI Infra2018
Turns NumPy-style Python into differentiable, compiled, vectorized programs for CPU, GPU, and TPU. Its leverage is composable transformations: grad, jit, vmap, and sharding combine instead of living in separate APIs.
AI Infra2018
Runs ONNX models faster on CPU, GPU, and NPU by routing graph subgraphs to backend execution providers (CUDA, TensorRT, OpenVINO, DirectML, CoreML). One engine serves the same model across cloud, browser, mobile, and edge, for both inference and training.
AI Train2019
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.
AI Train2019
Turns raw PyTorch training loops into structured modules that scale from a laptop to multi-node GPUs without rewriting model logic. It handles precision, checkpointing, logging, and distributed execution while preserving PyTorch control.