Data processing
Multimodal data curation
Build and run scalable pipelines to curate and prepare multimodal datasets for foundation model training with Ray on Anyscale.
Build and deploy multimodal data pipelines at scale
Run end-to-end multimodal pipelines with unified CPU and GPU processing with Ray on Anyscale.
Process any modality
Scale processing from raw unstructured data to tensors for model training.
Fast CPU + GPU pipelines
Eliminate I/O in between steps and keep CPUs and GPUs busy with streaming execution
Reliability at petabyte scale
Scale from one machine to thousands of nodes with elastic, fault-tolerant managed Ray clusters.
With Anyscale, our researchers can just write code without worrying about the underlying infrastructure.”
Ray scheduling heterogeneous workloads is something we couldn’t really do easily before. We see much lower idle time and much better utilization. ”
The fact that we don’t have to dedicate a person to make all of the plumbing and infrastructure work has been really valuable.”
With Anyscale, our researchers can just write code without worrying about the underlying infrastructure.”
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Researchers building pipelines feeding VLA model training runs
End-to-end multimodal AI pipelines that scale
Ray on Anyscale abstracts distributed AI infra complexity so you can focus on development
Streaming execution
Maximize throughput with continuous processing across different stages vs. batch execution in traditional systems
Native GPU support
Support for different accelerators and topologies, multi-node inference, and integration with vLLM and SGLang
Job-level checkpointing
Resume from previous state without reprocessing already completed data after pause or failure
Advanced observability
Use tree and DAG dashboard views pinpoint bottlenecks and errors for faster debugging and optimization
Fast autoscaling
Scale resources dynamically based on workload and gracefully handle node failures without job interruption
Spot instances
Run reliably on discounted spot instances with built-in preemption recovery and on-demand fallback
Build. Run. Scale. Repeat.
Scale multimodal pipelines without growing operational complexity with Ray on Anyscale.
Explore more on Anyscale
Distributed training, fine-tuning
Scale existing training code from one machine to thousands of GPUs with intuitive scaling configs
Composite AI serving
Serve one or many models and Python applications working together as a single API endpoint
Embedding generation
Process large-scale multimodal datasets for AI and applications with your model of choice