script to continuously evaluate elser#2670
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| parser.add_argument('--output_file', default='out.json') | ||
| parser.add_argument('--log_file', default='log.txt') | ||
| parser.add_argument('--num_threads_per_allocation', type=int, help='The number of inference threads used by LibTorch. Defaults to 1.') | ||
| parser.add_argument('--num_allocations', type=int, help='The number of allocations for parallel forwarding. Defaults to 1') |
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We should probably include setting the cache size here. Does it default to 0 if --cacheMemorylimitBytes isn't passed?
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Does it default to 0 if --cacheMemorylimitBytes isn't passed?
Yes, the default cache value is 0:
--cacheMemorylimitBytes arg Optional memory in bytes that the
inference cache can use - default is 0
which disables caching
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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.jsonwill show that data.Run with
--num_threads_per_allocationand--num_allocationsare the parameters to tweak. Increasing either of those will make inference faster and changes in memory should be seen sooner.