Tag

Prefect

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.

Machine Learning cheatsheets for Stanford's CS 229

Condenses Stanford's CS 229 into one-page visual cheatsheets spanning supervised, unsupervised, and deep learning, plus probability and linear-algebra refreshers. Available in 10+ languages, with all topics merged into one Super VIP PDF.

Triton Inference Server

Serves machine learning and deep learning models for cloud, data center, edge and embedded environments. Supports multiple frameworks and backends, dynamic and sequence batching, HTTP/gRPC APIs, Docker deployment and NVIDIA-optimized runtimes.

Dive into Deep Learning (d2l-en)

Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.

OpenVINO

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.

JAX

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.

Transformers

Provides unified model definitions and a single API for pretrained text, vision, audio, and multimodal models for both training and inference. Emphasizes cross-framework compatibility (PyTorch/TF/JAX), pipeline-based inference, and direct access to 1M+ Hub checkpoints.

Hugging Face — Transformers

Turns model definitions into a shared layer across training and inference stacks, covering text, vision, audio, video, and multimodal models. Pipelines, Trainer, and generation APIs make pretrained models usable without locking teams to one framework.

Differentiable programming framework for quantum computers: build variational circuits, compute their gradients alongside PyTorch, TensorFlow, or JAX, and run identical code on simulators or real hardware via IBM, AWS Braket, and Google plugins.

JAX: High performance array computing — JAX documentation

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.

ONNX Runtime | Home

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.

Bento: Run Inference at Scale

Turns Python ML code into production inference APIs that scale on Kubernetes or any cloud. Bundles models, dependencies, and serving logic into versioned "Bentos" with autoscaling, scale-to-zero, and multi-GPU serving for LLMs and custom models.