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[batch infer] Update batch inference template to use RayLLMBatch #346
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c85a6b8
update ipython
bac0d25
update readme
d2c62a5
update
1927120
update
01c0475
Update templates/batch-llm/README.ipynb
rickyyx 76c7439
Update with comments and fix GCP compute config (#352)
rickyyx 9725f93
Merge branch 'main' into raysummit-batchllm
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,84 +1,4 @@ | ||
| head_node_type: | ||
| name: head-node | ||
| instance_type: m5.2xlarge | ||
| resources: | ||
| cpu: 0 | ||
| worker_node_types: | ||
| - name: worker-g5-xlarge-nvidia-a10-1 | ||
| instance_type: g5.xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 4 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-2xlarge-nvidia-a10-1 | ||
| instance_type: g5.2xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 4 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-4xlarge-nvidia-a10-1 | ||
| instance_type: g5.4xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 4 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-8xlarge-nvidia-a10-1 | ||
| instance_type: g5.8xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 4 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-12xlarge-nvidia-a10-4 | ||
| instance_type: g5.12xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 1 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-16xlarge-nvidia-a10-1 | ||
| instance_type: g5.16xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 4 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-24xlarge-nvidia-a10-4 | ||
| instance_type: g5.24xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 1 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g5-48xlarge-nvidia-a10-8 | ||
| instance_type: g5.48xlarge | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:A10G": 1 | ||
| min_workers: 0 | ||
| max_workers: 1 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| aws: | ||
| TagSpecifications: | ||
| - ResourceType: instance | ||
| Tags: | ||
| - Key: as-feature-multi-zone | ||
| Value: "true" | ||
| name: head | ||
| # TODO(ricky): We need head node to have CUDA due to eager import from rayllm_batch now. | ||
| instance_type: g5.xlarge | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,55 +1,4 @@ | ||
| head_node_type: | ||
| name: head-node | ||
| instance_type: n2-standard-8 | ||
| resources: | ||
| cpu: 0 | ||
| worker_node_types: | ||
| - name: worker-g2standard4-nvidia-l4-1 | ||
| instance_type: g2-standard-4-nvidia-l4-1 | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:L4": 1 | ||
| min_workers: 0 | ||
| max_workers: 50 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g2standard8-nvidia-l4-1 | ||
| instance_type: g2-standard-8-nvidia-l4-1 | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:L4": 1 | ||
| min_workers: 0 | ||
| max_workers: 50 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g2standard12-nvidia-l4-1 | ||
| instance_type: g2-standard-12-nvidia-l4-1 | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:L4": 1 | ||
| min_workers: 0 | ||
| max_workers: 50 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g2standard16-nvidia-l4-1 | ||
| instance_type: g2-standard-16-nvidia-l4-1 | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:L4": 1 | ||
| min_workers: 0 | ||
| max_workers: 50 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| - name: worker-g2standard24-nvidia-l4-2 | ||
| instance_type: g2-standard-24-nvidia-l4-2 | ||
| resources: | ||
| custom_resources: | ||
| "accelerator_type:L4": 1 | ||
| min_workers: 0 | ||
| max_workers: 25 | ||
| use_spot: true | ||
| fallback_to_ondemand: true | ||
| gcp_advanced_configurations_json: | ||
| instance_properties: | ||
| labels: | ||
| as-feature-multi-zone: "true" | ||
| name: head | ||
| # TODO(ricky): We need head node to have CUDA due to eager import from rayllm_batch now. | ||
| instance_type: g2-standard-4-nvidia-l4-1 |
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we generally don't want to encourage the pattern of using GPU head nodes --> since it leads to people running code on head nodes that take down the workload due to oom (antipattern)
can you use a CPU head node instead with scheduling disabled (see basic serverless config that existing templates use) and wrap your code in an actor or something that runs on workers?
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This makes sense - instead of temp fixing this by wrapping in actor, I think we will fix this by addressing the root cause soon, that is to make this code runnable on CPU itself (which it should be, it's just we are not lazy importing vllm as of now).