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Added dvclive.get_step
#142
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1363e18
Added get_step to test_initialization_error
daavoo 649fbc8
Added test_get_step
daavoo 3019405
Added get_step. Remove step from public API
daavoo 4bde17d
pre-commit
daavoo c33ed90
Added _lazy_init to get_step
daavoo 2ac9b6d
pre-commit
daavoo bbab7c8
Added test suggestions
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This method returns int.
Also, I think we shoul change
MetricLogger'sstepproperty intoget_step()method to maintain consistency with with API. @daavoo what do you think?Uh oh!
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Not sure, tbh.
get_Xmethod instead of@propertykind of feels strange and butstepdoesn't sound good for a public method neitherThere was a problem hiding this comment.
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Well, my POV is that I presume that at some point one might want to, parallelize their code, and in that case do something like:
dvclive = MetricsLogger()in that case,dvclive.get_stepstops working.Now that I mention that, it would probably be good to mention that dvclive is not thread-safe, and one needs to initialize their own Loggers in case of parallel jobs.
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I see your point.
However, assuming we are focusing on "integrations first" (iterative/example-repos-dev#77 (comment)), the parallelization would happen at the ML Framework level and most ML Frameworks already take care of properly calling the callbacks/loggers.
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What's the downside to having the public API match
MetricsLogger?I don't think I understand the point about ML framework integrations. Even if ML frameworks spawn a separate process for each model training, dvclive would try to read/write using the same file by default, right? Users might need to specify a different path for each one, which isn't supported yet in the callbacks.
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I would need to investigate each case but, at least in the Deep Learning Frameworks I'm familiar with, parallelism usually occurs:
At the Data Loader level
Which doesn't affect DVCLive callbacks.
In Distributed training strategy
Where ML Framework usually provide some decorator like rank_zero_only / master_only which is (should be) used in the DVCLive callback.