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Adrian WälchliandJirka Borovec
a6de1b8d75
* add doctest to circleci * Revert "add doctest to circleci" This reverts commit c45b34ea911a81f87989f6c3a832b1e8d8c471c6. * Revert "Revert "add doctest to circleci"" This reverts commit 41fca97fdcfe1cf4f6bdb3bbba75d25fa3b11f70. * doctest docs rst files * Revert "doctest docs rst files" This reverts commit b4a2e83e3da5ed1909de500ec14b6b614527c07f. * doctest only rst * doctest debugging.rst * doctest apex * doctest callbacks * doctest early stopping * doctest for child modules * doctest experiment reporting * indentation * doctest fast training * doctest for hyperparams * doctests for lr_finder * doctests multi-gpu * more doctest * make doctest drone * fix label build error * update fast training * update invalid imports * fix problem with int device count * rebase stuff * wip * wip * wip * intro guide * add missing code block * circleci * logger import for doctest * test if doctest runs on drone * fix mnist download * also run install deps for building docs * install cmake * try sudo * hide output * try pip stuff * try to mock horovod * Tranfer -> Transfer * add torchvision to extras * revert pip stuff * mlflow file location * do not mock torch * torchvision * drone extra req. * try higher sphinx version * Revert "try higher sphinx version" This reverts commit 490ac28e46d6fd52352640dfdf0d765befa56988. * try coverage command * try coverage command * try undoc flag * newline * undo drone * report coverage * review Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * remove torchvision from extras * skip tests only if torchvision not available * fix testoutput torchvision Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
65 lines
1.5 KiB
ReStructuredText
65 lines
1.5 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.trainer.trainer import Trainer
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16-bit training
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=================
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Lightning offers 16-bit training for CPUs, GPUs and TPUs.
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GPU 16-bit
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-----------
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Lightning uses NVIDIA apex to handle 16-bit precision training.
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To use 16-bit precision, do two things:
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1. Install Apex
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2. Set the "precision" trainer flag.
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Install apex
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^^^^^^^^^^^^
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.. code-block:: bash
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$ git clone https://github.com/NVIDIA/apex
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$ cd apex
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# ------------------------
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# OPTIONAL: on your cluster you might need to load cuda 10 or 9
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# depending on how you installed PyTorch
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# see available modules
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module avail
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# load correct cuda before install
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module load cuda-10.0
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# ------------------------
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# make sure you've loaded a cuda version > 4.0 and < 7.0
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module load gcc-6.1.0
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$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
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Enable 16-bit
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^^^^^^^^^^^^^
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.. testcode::
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# turn on 16-bit
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trainer = Trainer(amp_level='O1', precision=16)
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If you need to configure the apex init for your particular use case or want to use a different way of doing
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16-bit training, override :meth:`pytorch_lightning.core.LightningModule.configure_apex`.
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TPU 16-bit
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----------
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16-bit on TPus is much simpler. To use 16-bit with TPUs set precision to 16 when using the tpu flag
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.. testcode::
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# DEFAULT
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trainer = Trainer(num_tpu_cores=8, precision=32)
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# turn on 16-bit
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trainer = Trainer(num_tpu_cores=8, precision=16)
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