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Describe the bug
Inference of PyTorch Model when mapped to Elastic Inference Accelarator is 15 times slow as compared to the CPU inference
To reproduce
I am loading CLIP model in Elasitic Accelerator. I had to make some changes in CLIP code, and I am getting same exact output with EIA call as I am getting on CPU call but EIA inference is 15 times slow
Expected behavior
EIA Call should be faster than CPU call
System information
Framework name (eg. PyTorch) or algorithm (eg. KMeans): PyTorch
Framework version: 1.5.1
Python version: 3.6
CPU or GPU: eia2.xlarge
Custom Docker image (Y/N): N using 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference-eia
Additional context
I am running the model using Docker Container