Files
pytorch-lightning/pytorch_lightning/trainer/auto_mix_precision.py
T
d4a31f02e0 Enable TPU support (#868)
* added tpu docs

* added tpu flags

* add tpu docs + init training call

* amp

* amp

* amp

* amp

* optimizer step

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* fix test pkg create (#873)

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* Update pytorch_lightning/trainer/trainer.py

Co-Authored-By: Luis Capelo <luiscape@gmail.com>

* Fix segmentation example (#876)

* removed torchvision model and added custom model

* minor fix

* Fixed relative imports issue

* Fix/typo (#880)

* Update greetings.yml

* Update greetings.yml

* Changelog (#869)

* Create CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update PULL_REQUEST_TEMPLATE.md

* Update PULL_REQUEST_TEMPLATE.md

* Add PR links to Version 0.6.0 in CHANGELOG.md

* Add PR links for Unreleased in CHANGELOG.md

* Update PULL_REQUEST_TEMPLATE.md

* Fixing Function Signatures (#871)

* added tpu docs

* added tpu flags

* add tpu docs + init training call

* amp

* amp

* amp

* amp

* optimizer step

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Luis Capelo <luiscape@gmail.com>
Co-authored-by: Akshay Kulkarni <akshayk.vnit@gmail.com>
Co-authored-by: Ethan Harris <ewah1g13@soton.ac.uk>
Co-authored-by: Shikhar Chauhan <xssChauhan@users.noreply.github.com>
2020-02-17 16:01:20 -05:00

32 lines
777 B
Python

from abc import ABC
try:
from apex import amp
APEX_AVAILABLE = True
except ImportError:
APEX_AVAILABLE = False
import logging as log
class TrainerAMPMixin(ABC):
def __init__(self):
self.use_amp = None
def init_amp(self, use_amp):
self.use_amp = use_amp and APEX_AVAILABLE
if self.use_amp:
log.info('Using 16bit precision.')
if use_amp and not APEX_AVAILABLE: # pragma: no cover
msg = """
You set `use_amp=True` but do not have apex installed.
Install apex first using this guide and rerun with use_amp=True:
https://github.com/NVIDIA/apex#linux
this run will NOT use 16 bit precision
"""
raise ModuleNotFoundError(msg)