Deployment on Azure ML with Tensor Parallelism Fails – IndexError & Initialization Issues

Question

Deployment on Azure ML with Tensor Parallelism Fails – IndexError & Initialization Issues

Sebastian Buzdugan 40

Hello,

I am facing deployment issues with my fine-tuned model on Azure ML using DeepSpeed and vLLM. Despite configuring tensor parallelism, the deployment fails when calling the endpoint.

Deployment Details

Model: Auto-deployed through Azure ML (MLFlow format) - FineTuned Phi3.5MoE
Inference Engine: vLLM (auto-configured).
VM SKU: Standard_NC24ads_A100_v4.
Instance Count: 3 for GPU parallelism.

Errors in Logs

Main Error:

IndexError: list index out of range
File "/azureml-envs/default/lib/python3.10/site-packages/llm/optimized/inference/replica_manager.py", line 215, in get_replica
    replica = self.engine_replicas[self._replica_index]

Replica Manager Initialization:

2024-12-16 13:21:00,587 [replica_manager] initialize 136: INFO Lock acquired by worker with pid: 7. Loading model. Using tensor parallel of 2 GPUs per replica.
2024-12-16 13:21:00,974 [replica_manager] initialize 168: INFO 0
Initialized 0 replicas.

Warnings:

• async_io requires the dev libaio .so object and headers but these were not found.

• sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3.

• using untested triton version (2.3.1), only 1.0.0 is known to be compatible.

Questions

Why does the replica manager fail to initialize replicas (Initialized 0 replicas)?
How can I resolve the list index out of range issue in the inference server?
Are the warnings related to missing libaio headers, the Torch version mismatch, or Triton compatibility causing this failure?
Since the deployment was auto-configured, is there a way to adjust configuration files (DeepSpeed, vLLM) without needing custom Python modifications?

Accepted answer

0 additional answers

Your answer

Answer 1

Azar 29,520 MVP Volunteer Moderator

Hi there Sebastian Buzdugan

Thanks for using QandA platform

Thiss maybe due to mismatched tensor parallelism settings or missing dependencies. The list index out of range error suggests issues with tensor slicing or incomplete model shards. Compatibility warnings about PyTorch, Triton, and missing libaio could also disrupt initialization, as vLLM and DeepSpeed require specific versions.

Try, updating configuration files eg., DeepSpeed, vLLM to align tensor parallelism with the 3 GPUs, specify compatible PyTorch (e.g., 1.14) and Triton (e.g., 1.0.0), and install libaio. You can extract and customize these settings from deployment logs without reprogramming.\

If this helps kindly accept the answer thanks much.

Sebastian Buzdugan 40 Reputation points

2024-12-16T14:25:30.78+00:00

Hi, thanks for the suggestions!

Is there a way I can update the DeepSpeed, vLLM, and dependency settings directly in Azure ML UI or through code (e.g., Azure ML SDK) as I can’t manually edit the artifacts? Can I pass these configs when deploying or creating the endpoint in the code? I just hope this can be done without needing to run the finetuning process again

Would love some guidance on how to do this.

Thanks!
Azar 29,520 Reputation points MVP Volunteer Moderator

2024-12-16T14:32:20.2466667+00:00

Hi again Sebastian Buzdugan

I think you can update DeepSpeed, vLLM, and dependency settings directly in Azure ML without re-fine-tuning by modifying the environment and deployment configurations. In the Azure ML UI, edit the environment YAML to compatible dependencies eg-0 PyTorch 1.14, etc and redeploy.

Alsos, use the Azure ML SDK to create a custom environment with updated packages and define an inference configuration, including tensor parallelism settings.

Dont forget to mark the answer as accepted if it helped.
Sebastian Buzdugan 40 Reputation points

2024-12-16T15:19:37.0433333+00:00

Great, thank you! So all this can be done in the UI? That is great, I will investigate further on how to!

Share via

Deployment on Azure ML with Tensor Parallelism Fails – IndexError & Initialization Issues

0 additional answers

Your answer