Description
System information.
TensorFlow version (you are using): 2.9
Are you willing to contribute it (Yes/No) : No
Describe the feature and the current behavior/state
In PyTorch-Lightning, you can identify bottlenecks in your code using Profiler API. And I've found it super useful and easy to inspect the pros and cons of the code. This API offers 3 type of inferface, namely simple, advance and pytorch. I mostly use simple that summarize the execution time of medium to high level training + validation + inference API. The saved log files look something as follows.
# by passing a string
trainer = Trainer(..., profiler="simple")
# or by passing an instance
from pytorch_lightning.profiler import SimpleProfiler
profiler = SimpleProfiler()
trainer = Trainer(..., profiler=profiler)
I don't know, in keras, if we have anything like this already. By having such high-level functionality in keras would really great. It will help to inspect the bottleneck of dataloader, model, callbacks etc, by reporting the time and memory consumption in the system.
I looked for it before, discussion.
Will this change the current api? How?
This can be considered as a Callback, I think.
tf.keras.callbacks.Profiler
or
tf.keras.utils.ProfilerWho will benefit from this feature?
The keras community. Engineers and researchers, both. Mostly engineers in production phase for code optimization.
- Do you want to contribute a PR? (yes/no):
- If yes, please read this page for instructions
- Briefly describe your candidate solution(if contributing):