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- #Stat transfer tf2 not working how to
- #Stat transfer tf2 not working update
- #Stat transfer tf2 not working full
- #Stat transfer tf2 not working code
Refer to code at bottom of my other answer for an example benchmarking setup.
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The cost of some of these, however, is speed. Benefits: greatly expanded processing, distribution, debug, and deployment capabilities. Scott Zhu, TF2 focused development on Eager execution & tight integration w/ Keras, which involved sweeping changes in TF source - including at graph-level. ISSUE SUMMARY: as confirmed by a TensorFlow developer, Q. I'll be updating my answer(s) w/ more info if I learn any - can bookmark / "star" this question for reference. For a detailed, low-level description, which includes all benchmarking results + code used, see my other answer.
#Stat transfer tf2 not working how to
THIS ANSWER: aims to provide a high-level description of the issue, as well as guidelines for how to decide on the training configuration specific to your needs. But if you don't, it could cost you, lots - by a few GPU upgrades on average, and by multiple GPUs worst-case. VERDICT: it isn't, IF you know what you're doing.
![stat transfer tf2 not working stat transfer tf2 not working](https://i.imgur.com/0D5ekWI.png)
#Stat transfer tf2 not working update
I'll update the answer(s) once progress is made. Haven't opened a Git issue on these yet, but I did comment on the original - no response yet. I can't currently present reproducible code for these claims per time constraints, so instead I strongly recommend testing this for your own models. in Graph execution ( 1.6x to 2.5x slower).įurthermore, there are extreme reproducibility differences between Graph and Eager for a large model I tested - one not explainable via randomness/compute-parallelism. The one that's slower, and slower dramatically, is Large-Large - esp. All but one configs (model & data size) are as fast as or much faster than the best of TF2 & TF1. UPDATE : I've benched 2.1 and 2.1-nightly the results are mixed. I did, however, open an Issue to get devs' feedback. Unsure I'll debug this further, as I'm considering switching to Pytorch per TensorFlow's poor support for custom / low-level functionality. Per above, Graph and Eager are 1.56x and 1.97x slower than their TF1 counterparts, respectively.
#Stat transfer tf2 not working full
Plots for Large-Large Numpy train_on_batch case below, x-axis is successive fit iterations my GPU isn't near its full capacity, so doubt it's throttling, but iterations do get slower over time. UPDATE : TF 2.2, using same tests: only a minor improvement in Eager speed. The true stats on your model's speed can only be found by you, on your device. This might be my last update on this answer. Lastly, see a dev's note on Eager vs Graph. If you see a rising stem plot of iteration times, it's a reliable symptom.
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UPDATE 8/ 1730/2020: TF 2.3 has finally done it: all cases run as fast, or notably faster, than any previous version.įurther, my previous update was unfair to TF my GPU was to blame, has been overheating lately.