Ray is a distributed compute framework that unifies task/actor execution, data processing, model training, hyperparameter tuning, RL, and model serving under a single runtime. The core is written in C++ (raylet, GCS, object store, scheduling) with a Python frontend, communicating via gRPC and Arrow-compatible plasma object storage. Production entry points include `ray.init()` for cluster bootstrap, `@ray.remote` for task/actor offload, Ray Serve for model deployment (FastAPI/DAG/gang-scheduling patterns), Ray Data for streaming ETL, Ray Train/Tune for distributed training and hyperparameter search, and RLlib for reinforcement learning. The architecture is a hybrid: a distributed systems platform (scheduler, object store, autoscaler) layered with high-level AI libraries, all built via Bazel with extensive CI (Docker images, build matrices, release tests). It is not a data research repo, though Ray Data and RLlib contain research-grade data pipeline and RL primitives; the dominant character is a production-grade application framework.
Seldon Core v2 is a production-grade, Kubernetes-native MLOps platform for deploying, scaling, and managing machine learning inference at scale. The system is architected as a set of microservices: a Kubernetes operator manages CRDs (Model, Pipeline, Server, Experiment), a centralized scheduler coordinates model placement and scaling across server replicas, an agent sidecar runs on each inference server to handle model loading/unloading, caching, and reverse proxying, and a Kafka Streams-based dataflow engine executes multi-step inference pipelines with joins, batching, and triggers. Inference requests enter via an Envoy proxy that routes to model servers or pipeline gateways, with Kafka as the underlying data plane for pipeline flows. The platform supports the V2 inference protocol, A/B experiments, explainability, drift detection, and OpenAI API translation. It integrates with MLServer, Triton Inference Server, and custom runtimes, and provides a CLI for lifecycle management.
This repository is a curated collection of research papers on machine learning systems, organized by topic into directories such as Checkpoint&Memory, Cluster Scheduling, Serving, and Training. Each directory contains PDF files of relevant papers, often accompanied by README.md files providing context or summaries. The repository serves as a reference literature review for researchers and practitioners in the field of ML systems, covering topics like checkpointing, cluster scheduling, model serving, training optimizations, and more. It does not contain any source code or executable components, focusing solely on disseminating academic research.