Sync from v0.13
This commit is contained in:
15
docs/deployment/frameworks/anyscale.md
Normal file
15
docs/deployment/frameworks/anyscale.md
Normal file
@@ -0,0 +1,15 @@
|
||||
# Anyscale
|
||||
|
||||
[Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
|
||||
|
||||
Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
|
||||
without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like [examples/online_serving/run_cluster.sh](../../../examples/online_serving/run_cluster.sh).
|
||||
|
||||
When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
|
||||
|
||||
## Production-ready vLLM on Anyscale quickstarts
|
||||
|
||||
- [Offline batch inference](https://console.anyscale.com/template-preview/llm_batch_inference?utm_source=vllm_docs)
|
||||
- [Deploy vLLM services](https://console.anyscale.com/template-preview/llm_serving?utm_source=vllm_docs)
|
||||
- [Curate a dataset](https://console.anyscale.com/template-preview/audio-dataset-curation-llm-judge?utm_source=vllm_docs)
|
||||
- [Finetune an LLM](https://console.anyscale.com/template-preview/entity-recognition-with-llms?utm_source=vllm_docs)
|
||||
Reference in New Issue
Block a user