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release 1.3.0 (#7404)
* v1.3.0 * ci event * chlog * badge * formatting
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.github/workflows/ci_test-conda.yml

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matrix:
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# os: [ubuntu-20.04]
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python-version: [3.7]
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pytorch-version: [1.4, 1.5, 1.6, 1.7, 1.8, 1.9]
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pytorch-version: [1.4, 1.5, 1.6, 1.7, 1.8] # todo: add back 1.9
2020

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# Timeout: https://stackoverflow.com/a/59076067/4521646
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timeout-minutes: 35

.github/workflows/release-docker.yml

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push:
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branches: [master, "release/*"]
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release:
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types: [created, published]
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types: [published]
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jobs:
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cuda-PL:

.github/workflows/release-pypi.yml

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@@ -5,7 +5,7 @@ on: # Trigger the workflow on push or pull request, but only for the master bra
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push:
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branches: [master, "release/*"]
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release:
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types: [created, published]
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types: [published]
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jobs:

CHANGELOG.md

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pl_examples/basic_examples/dali_image_classifier.py

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self.pipe_train,
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size=len(self.mnist_train),
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auto_reset=True,
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fill_last_batch=True
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fill_last_batch=True,
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)
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def val_dataloader(self):
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return DALIClassificationLoader(
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self.pipe_val,
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size=len(self.mnist_val),
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auto_reset=True,
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fill_last_batch=False
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fill_last_batch=False,
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)
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def test_dataloader(self):
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return DALIClassificationLoader(
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self.pipe_test,
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size=len(self.mnist_test),
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auto_reset=True,
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fill_last_batch=False
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fill_last_batch=False,
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)
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pl_examples/basic_examples/profiler_example.py

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class ModelToProfile(LightningModule):
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def __init__(
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self,
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name: str = "resnet50"
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):
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def __init__(self, name: str = "resnet50"):
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super().__init__()
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self.model = getattr(models, name)(pretrained=True)
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self.criterion = torch.nn.CrossEntropyLoss()

pl_examples/bug_report_model.py

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import os
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import torch
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from torch.utils.data import Dataset, DataLoader
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from torch.utils.data import DataLoader, Dataset
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from pytorch_lightning import LightningModule, Trainer
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pytorch_lightning/__about__.py

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import time
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_this_year = time.strftime("%Y")
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__version__ = '1.3.0rc3'
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__version__ = '1.3.0'
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__author__ = 'William Falcon et al.'
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__author_email__ = '[email protected]'
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__license__ = 'Apache-2.0'

pytorch_lightning/accelerators/cpu.py

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)
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if "cpu" not in str(self.root_device):
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raise MisconfigurationException(
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f"Device should be CPU, got {self.root_device} instead."
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)
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raise MisconfigurationException(f"Device should be CPU, got {self.root_device} instead.")
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return super().setup(trainer, model)

pytorch_lightning/utilities/device_dtype_mixin.py

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# limitations under the License.
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from typing import Optional, Union
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import torch
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from torch.nn import Module
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