Anyscale

Provisions and manages Ray clusters on Anyscale's hosted infrastructure or user-owned cloud environments (AWS, Azure, GCP, Kubernetes, on-prem VMs) with automatic node scaling based on workload demands. Clusters are initialized via Python SDK with ScalingConfig specifications (num_workers, GPU allocation, memory per worker) and managed through Ray's actor/task scheduling system, which distributes work across nodes with automatic fault tolerance and task re-execution on node failure.

Executes distributed PyTorch training across multiple GPU workers using Ray's TorchTrainer abstraction, which handles distributed data loading, gradient synchronization (via torch.distributed.launch), and automatic checkpoint/recovery on worker failure. Training code is written as a standard PyTorch training loop function, passed to TorchTrainer with ScalingConfig specifying worker count and GPU allocation; Ray automatically distributes the function across workers and manages inter-worker communication via NCCL.

Provides automatic fault tolerance for distributed jobs via Ray's actor model and task retry mechanism. On worker failure, Ray automatically restarts failed tasks (up to max_failures retries) and resumes from the last checkpoint. Checkpoints are user-defined (e.g., model weights saved to disk) and Ray handles recovery by reloading checkpoints and resuming execution. Fault tolerance is transparent to user code.

Provides a web-based dashboard (Ray Dashboard) for monitoring distributed jobs, including task execution timeline, worker resource utilization (CPU, GPU, memory), actor state, and error logs. Dashboard is accessible via browser at cluster's IP:8265 and shows real-time metrics for all running tasks and actors. Users can inspect task dependencies, identify bottlenecks, and debug failures via the dashboard.

Enables deployment of Anyscale clusters on user-owned cloud infrastructure (AWS, Azure, GCP, Kubernetes, on-prem VMs) via BYOC (Bring Your Own Cloud) tier. Users provide cloud credentials (AWS IAM role, Azure service principal, GCP service account) and Anyscale provisions Ray clusters on their infrastructure. BYOC eliminates vendor lock-in and enables compliance with data residency requirements.

Processes large datasets (Parquet, CSV, images, multimodal data) across distributed GPU workers using Ray Data's functional API (map_batches, filter, select, write_parquet). Data is partitioned across workers, and GPU-accelerated transformations (e.g., embedding generation, image resizing) are applied in parallel via map_batches with batch_size parameter. Ray Data handles data shuffling, repartitioning, and spilling to disk for datasets larger than cluster memory.

Executes batch inference on large language models using vLLM (a high-throughput LLM inference engine) deployed as Ray remote actors across multiple GPU workers. vLLM handles KV-cache optimization, continuous batching, and tensor parallelism for large models; Ray orchestrates actor placement, load balancing, and result aggregation. Inference requests are submitted to Ray actors, which return generated text or embeddings.

Executes post-training workflows (supervised fine-tuning, DPO, PPO) and reinforcement learning on language models using SkyRL and veRL frameworks, which are natively built on Ray. These frameworks handle distributed reward computation, policy gradient updates, and model checkpointing across multiple GPU workers. Users define training objectives (e.g., DPO loss, PPO reward) and Anyscale/Ray orchestrates distributed execution.

Deploys open-source language models (e.g., Llama 2, Mistral, Phi) as serverless endpoints with automatic scaling based on request volume. Endpoints are backed by vLLM for high-throughput inference and Ray's actor model for horizontal scaling. Users submit inference requests via HTTP API; Anyscale handles model loading, request queuing, and worker scaling without requiring manual cluster management.

Orchestrates end-to-end fine-tuning workflows combining data preparation (Ray Data), distributed training (Ray Train), and model evaluation across multiple GPU workers. Pipelines are defined as Ray DAGs (directed acyclic graphs) or Python functions that compose Ray Data transformations, TorchTrainer jobs, and custom evaluation logic. Ray handles task scheduling, fault recovery, and resource allocation across the pipeline.

Distributes large language models across multiple GPUs using tensor parallelism, where model layers are sharded across GPUs and forward/backward passes are parallelized. vLLM (integrated with Anyscale) handles tensor parallelism automatically; users specify num_gpus in ScalingConfig and vLLM partitions the model across available GPUs. Communication between GPUs is via NCCL for low-latency gradient synchronization.

Integrates Ray Data with S3 for reading/writing large datasets with automatic partitioning and caching. Ray Data reads Parquet/CSV from S3 paths, partitions data across workers for parallel processing, and writes results back to S3. Anyscale optimizes S3 access patterns (e.g., batching requests, caching metadata) to reduce latency and egress costs. Data locality is managed automatically; workers read from S3 buckets in the same region.

Allocates GPU resources to Ray tasks and actors with fine-grained control via ScalingConfig and @ray.remote decorators. Users specify num_gpus per worker or per task, and Ray's scheduler ensures GPU availability before task execution. GPU allocation is per-worker granularity (e.g., 1 GPU per worker, 2 GPUs per worker); no fractional GPU sharing. Ray tracks GPU utilization and prevents overallocation.