This is the next step in dropping the dependency on protobuf.
The simple logger produces an output consisting of lines of json
strings. Tensor values - which should constitute the bulk of the data -
are serialized as raw byte buffers. This allows for light-weight reading
of the values.
The next step is to switch the training logic to the new logging format,
following which the protobuf-based logger will be dropped, together with
the training dependency on protobuf.
Subsequent changes will also stop buffering and stream, instead - the
buffering model is just as a convenient point-in-time.
Differential Revision: https://reviews.llvm.org/D139370
This is in preparation for dropping the dependency on protobuf. This
first step allows us to subsequently introduce the non-protobuf
implementation behind a flag. After that we can update the training side
to ingest the new format, after which we can drop the protobuf
implementation and de-virtualize everything.
Differential Revision: https://reviews.llvm.org/D139062
It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.
Differential Revision: https://reviews.llvm.org/D139031
This just shuffles implementations and declarations around. Now the
logger and the TF C API-based model evaluator are separate.
Differential Revision: https://reviews.llvm.org/D131116
