script to continuously evaluate elser #2670
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First download the elser model locally. Either
The script runs pytorch_inference, loads the model then continuously runs inference on it. Logging is to std out, the model output is written to a json file. Every 100 request the script asks the pytorch_inference how much memory it is using and this is written to the same json file.
grep mem out.json
will show that data.Run with
--num_threads_per_allocation
and--num_allocations
are the parameters to tweak. Increasing either of those will make inference faster and changes in memory should be seen sooner.