Deploying LLama2 on Inf2.xlarge

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Every time I try to deploy my llama2 7B model on an inf2.xlarge instance I get Shard process was signaled to shutdown with signal 9 error. I know that my instance is running out of memory because on an inf2.8xlarge it deploys successfully. Now I have seen people deploy a llama2 7B model on inf2.xlarge and it is crucial for me that it is deployed on this instance type for price related issues. Can somebody explain how I can mitigate this error without upgrading to a larger instance?

asked 2 months ago104 views
1 Answer
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Hi Lars Jacobs,

Please look at this solution it will be helpful for you.

If you follow these five steps, you will reduce the size of the instance.

To mitigate the "Shard process was signaled to shutdown with signal 9" error when deploying your LLama2 7B model on an inf2.xlarge instance without upgrading to a larger instance, you can try this step.

Optimize Memory Usage: Review your LLama2 model and your deployment setup to identify any memory-intensive operations or inefficiencies. Optimize your code and configurations to reduce memory usage where possible.

Batch Processing: If your LLama2 model processes large amounts of data in a single batch, consider breaking down the workload into smaller batches. This can help reduce memory consumption per batch and alleviate the strain on the inf2.xlarge instance.

Reduce Model Size: If feasible, consider reducing the size or complexity of your LLama2 model. Smaller models typically require less memory to deploy and run, making them more suitable for resource-constrained environments like the inf2.xlarge instance.

Instance Swap Configuration: Check if your inf2.xlarge instance has swap space configured. Adding swap space allows the system to use disk space as virtual memory, which can help mitigate memory limitations. However, note that swapping can impact performance, so it should be used judiciously.

Resource Limits: Adjust resource limits for your LLama2 deployment to prevent memory exhaustion. Set limits on memory usage to ensure that the deployment stays within the available memory capacity of the inf2.xlarge instance.

Model Parallelism: Explore options for model parallelism, where different parts of your LLama2 model are processed on separate devices or nodes simultaneously. This can distribute the memory load more evenly across resources and may improve performance on smaller instances.

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answered 2 months ago
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