Distributed Computing Technologies
Advanced frameworks for scaling computation across distributed infrastructure.
Kubernetes
Kubernetes implements container orchestration with sophisticated scheduling and resource management for ML workloads. It provides advanced features like GPU scheduling, topology-aware placement, and dynamic resource allocation. The system includes custom resource definitions (CRDs) for ML primitives and automated scaling based on metrics. Features include stateful workload management with persistent volume claims and storage classes. Implements sophisticated networking with service mesh integration and multi-cluster federation capabilities.
Slurm
Slurm implements high-performance computing workload management with sophisticated job scheduling and resource allocation. It provides advanced features like topology-aware scheduling, GPU NUMA binding, and fair-share scheduling. The system includes job arrays with dependency management and preemptive scheduling capabilities. Features include power management with frequency scaling and node health monitoring. Implements sophisticated accounting with hierarchical fairshare and quality of service controls.
Ray Core & Ecosystem
Ray implements distributed computing primitives with sophisticated task scheduling and distributed object management. It provides advanced features like distributed training with parameter servers and reinforcement learning environments. The system includes automatic resource scaling and fault tolerance with object reconstruction. Features include distributed hyperparameter tuning with population-based training. Implements efficient distributed scheduling with locality-aware task placement and pipeline parallelism.
Ray Serve
Ray Serve implements high-performance model serving with sophisticated deployment patterns and scaling capabilities. It provides advanced features like multi-model serving with GPU scheduling and dynamic batching. The system includes HTTP/gRPC endpoints with automatic load balancing and fault tolerance. Features include A/B testing support, gradual rollouts, and model composition. Implements efficient request routing with query planning and adaptive batching strategies.
Ray Data
Ray Data implements distributed data processing with sophisticated execution planning and optimization. It provides advanced features like lazy evaluation, predicate pushdown, and automatic data partitioning. The system includes native integration with ML frameworks and efficient shuffle operations. Features include streaming execution with backpressure handling and fault tolerance. Implements sophisticated memory management with spill-to-disk capabilities and distributed caching.
Ray Train
Ray Train implements distributed training abstractions with sophisticated scaling and fault tolerance capabilities. It provides advanced features like elastic training with dynamic worker scaling and checkpointing. The system includes built-in support for popular ML frameworks with automatic distributed setup. Features include hyperparameter tuning with advanced search strategies and early stopping. Implements efficient parameter synchronization with optimized collective operations.
Dask
Dask implements distributed computing for Python with sophisticated task scheduling and parallel computing primitives. It provides advanced features like lazy evaluation with intelligent task fusion and automated data partitioning. The system includes distributed arrays and dataframes with automatic chunking and shuffle optimization. Features include adaptive scheduling with work stealing and locality-aware computation. Implements sophisticated memory management with spill-to-disk capabilities and distributed caching.