Add forward to training servicer #227
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- Supported operations: start, resume, pause, shutdown - pytorch-3dunet package is used as the framework to create the models
… failed I caught an edge case, where events are blocked, because we have exited the training, and the tasks of the queue would remain unprocessed.
Creating and closing processes and threads can be quite time consuming resulting to test timeouts if the tests performs a lot of actions.
Applying monkeypatch to a parent process won't propagated to a child process if the start method is spawn (macos) instead of fork (linux)
- To fix test on windows, convert label data to float64
The should stop callbacks are boolean, so we need to aggregate their return value. Previously the return value wasn't taken into account, and the callbacks were returning none
The enum is used for validation check before triggering one of them. Previously I was checking if the queue was alive, but that won't be enough, for example if you want to perform resume, while you are resumed, the queue is operational, but the action shouldn't be valid.
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Move NamedInt and Tensor proto to a separate file so training proto can use as well
- The inference servicer had a procedure to list the available devices. This is needed or the training servicer as well. So list devices is decoupled to be shared.
If the training is running or paused, the forward, will retain the state after completion. But it requires to pause so we can release memory and do the forward pass.
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #227 +/- ##
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- Coverage 64.60% 63.93% -0.67%
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Files 40 47 +7
Lines 2195 2745 +550
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+ Hits 1418 1755 +337
- Misses 777 990 +213 ☔ View full report in Codecov by Sentry. |
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Since both inference and training servicers have common the concept of id, the training session id was replaced with the model session one used for inference. This model session protobuf interfaced moved to a separate utils proto file. The PredictRequest being common, can be leveraged for abstraction.
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It builds on top of the #225
I have implemented the forward method. The training is paused, we do the forward pass, and then we resume.