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Beyond basic logging and monitoring, implement detailed analytics and performance monitoring to provide insights into API usage patterns, performance bottlenecks, and potential areas for optimization.
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Rexster provides powerful [monitoring capabilities](https://github.com/tinkerpop/rexster/wiki/Monitoring), which can be sent to monitoring tools like Graphite. RexConnect could also collect/send vari…
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* Error counter
* Time of query execution
* Query counter
* Time of races synchronously
#159
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* [ ] how effective are different compression? This will require passing some argument from train.py
* [ ] given the same number of bytes communicated, which coding is most effective? This will requi…
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The code lacks a logging mechanism to track important events, such as when the model is trained, saved, or when a prediction is made. Logging would help in debugging and monitoring model performance.
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Currently the unit tests produce a very large number of performance samples. To actually catch performance regressions this should be reduced to a smaller set of metrics that can be stored and charte…
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Hi, thanks for your nice work. I have a question about reproducing the driving score shown in the paper. I run the evaluation with the following configurations:
```
preception_model = 'memfuser_…
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Hi! I really love how well put together this all looks - from the merging driver to even describing with clear code examples how to wire this up to zsh-autosuggestions, I must say, this looks very wel…