PyTorch Native. PyTorch 1.6 release introduced mixed precision functionality into their core as the AMP package, torch.cuda.amp. It is more flexible and intuitive compared to NVIDIA APEX . Since computation happens in FP16, there is a chance of numerical instability during training.
19.02.2021 · I am using PyTorch==1.6.0 and pytorch-lightning==1.2.5, pytorch-lightning-bolts==0.3.0 with 8 Titan Xp GPUs.. UPDATE-20210330 In version 1.1.6 There is no problem of using apex, because amp.initialize is properly called. However, there is a warning shown in the command line LightningOptimizer doesn't support Apex, but the program runs without errors.
To use a different key set a string instead of True with the key name. auto_scale_batch_size: If set to True, will `initially` run a batch size finder trying to find the largest batch size that fits into memory. The result will be stored in self.batch_size in the LightningModule. Additionally, can be set to either `power` that estimates the ...
13.08.2020 · In CUDA/Apex AMP, you set the optimization level: model, optimizer = amp.initialize(model, optimizer, opt_level="O1") In the examples I read on PyTorch’s website, I don’t see anything analogous to this. How is this ac…
In this video, we give a short intro to Lightning's flag 'amp_level.'To learn more about Lightning, please visit the official website: https://pytorchlightni...
pytorch_lightning.utilities.exceptions.MisconfigurationException: You have asked for amp_level='O2' but it's only ... tchaton. Dec 6, 2021. Maintainer Hey @RuixiangZhao, There are currently 2 precision backends. AMP and APEX. level are supported only with apex and you need to provide Trainer(amp_backend='apex') to activate it as native is ...
25.11.2020 · Traniner code import pytorch_lightning as pl traine... Problem I encounter some questions when using Trainer. Because I used precision=16 and amp_backend='apex' and amp_level='O2' in Trainer class.
PyTorch-Lightning Documentation, Release 0.6.0 configure_apex(amp, model, optimizers, amp_level) Override to init AMP your own way Must return a model and list of optimizers Parameters • amp(object) – pointer to amp library object • model(LightningModule) – pointer to current lightningModule
21.06.2021 · In this video, we give a short intro to Lightning's flag 'amp_level.'To learn more about Lightning, please visit the official website: https://pytorchlightni...
When using PyTorch 1.6+, Lightning uses the native AMP implementation to support 16-bit precision. 16-bit precision with PyTorch < 1.6 is supported by NVIDIA Apex library. NVIDIA Apex and DDP have instability problems.
NVIDIA Apex and DDP have instability problems. We recommend upgrading to PyTorch 1.6+ in order to use the native AMP 16-bit precision with multiple GPUs. If you are using an earlier version of PyTorch (before 1.6), Lightning uses Apex to support 16-bit training. To use Apex 16-bit training: Install Apex
Hey @RuixiangZhao,. There are currently 2 precision backends. AMP and APEX. level are supported only with apex and you need to provide Trainer(amp_backend='apex') to activate it as native is the default.
This video gives a short intro to Lightning's flag called 'precision', allowing you to switch between 32 and 16-bit precision.To learn more about Lightning, ...
pytorch-lightning/pytorch_lightning/trainer/trainer.py ... amp_level: The optimization level to use (O1, O2, etc...). By default it will be set to "O2".