81 lines
2.0 KiB
ReStructuredText
81 lines
2.0 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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16 bit precision can cut your memory footprint by half.
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If using volta architecture GPUs it can give a dramatic training speed-up as well.
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.. note:: PyTorch 1.6+ is recommended for 16-bit
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Native torch
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^^^^^^^^^^^^
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When using PyTorch 1.6+ Lightning uses the native amp implementation to support 16-bit.
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.. testcode::
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# turn on 16-bit
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trainer = Trainer(precision=16)
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Apex 16-bit
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^^^^^^^^^^^
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If you are using an earlier version of PyTorch Lightning uses Apex to support 16-bit.
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Follow these instructions to install Apex.
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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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.. 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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.. warning:: NVIDIA Apex and DDP have instability problems. We recommend native 16-bit in PyTorch 1.6+
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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='O2', 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(tpu_cores=8, precision=32)
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# turn on 16-bit
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trainer = Trainer(tpu_cores=8, precision=16)
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