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.github/workflows/ci_test-base.yml

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with:
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name: pytest-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}
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path: junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
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# Use always() to always run this step to publish test results when there are test failures
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if: always()
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if: failure()
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- name: Statistics
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if: success()

.github/workflows/ci_test-conda.yml

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with:
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name: pytest-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}
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path: junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
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# Use always() to always run this step to publish test results when there are test failures
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if: always()
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if: failure()

.github/workflows/ci_test-full.yml

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with:
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name: pytest-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}
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path: junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
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# Use always() to always run this step to publish test results when there are test failures
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if: always()
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if: failure()
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- name: Statistics
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if: success()

.mergify.yml

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# See the License for the specific language governing permissions and
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# limitations under the License.
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pull_request_rules:
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- name: Automatic merge on approval
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conditions:
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- base=master
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# number of review approvals
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- "#approved-reviews-by>=3"
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# no waiting or assigned review
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- "#review-requested=0"
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# no requested chnages from any reviewer
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- "#changes-requested-reviews-by=0"
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# this serves as ALL check has to pass as we have actually around 40 tests in total
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- "#status-success>=54"
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# this is just in case since we rely on GPU tests (note: redundand to the above)
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- status-success=continuous-integration/drone/pr
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- "status-success=ci/circleci: TPU-tests"
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# this is patter-like, unofrunatly serves as `any(...)` (note: redundand to the above)
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#- "status-success~=^ci/circleci:"
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# no conflict with master branch
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- -conflict
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# was not closed yet
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- -closed
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# filter-out GH draft PRs
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- -draft
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actions:
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delete_head_branch: {}
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merge:
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# https://doc.mergify.io/merge-action.html#strict-merge
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# (on head branch) $ git merge --no-ff base
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# (on head branch) # Wait for CI to go green
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# (on head branch) # Squash all commits
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# (on base branch) $ git merge --ff head
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strict: true
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method: squash
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comment:
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message: Great job! =)
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- name: warn on conflicts
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conditions:
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- conflict
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# filter-out GH draft PRs
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- -draft
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actions:
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comment:
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message: This pull request is now in conflict... :(
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- name: add core reviewer
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conditions:
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# filter-out GH draft PRs
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- -draft
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# number of review approvals
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- "#approved-reviews-by<3"
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actions:
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request_reviews:
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teams:
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- core-contributors
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#pull_request_rules:
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#
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# - name: Automatic merge on approval
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# conditions:
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# - base=master
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# # number of review approvals
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# - "#approved-reviews-by>=3"
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# # no waiting or assigned review
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# - "#review-requested=0"
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# # no requested chnages from any reviewer
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# - "#changes-requested-reviews-by=0"
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# # this serves as ALL check has to pass as we have actually around 40 tests in total
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# - "#status-success>=54"
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# # this is just in case since we rely on GPU tests (note: redundand to the above)
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# - status-success=continuous-integration/drone/pr
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# - "status-success=ci/circleci: TPU-tests"
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# # this is patter-like, unofrunatly serves as `any(...)` (note: redundand to the above)
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# #- "status-success~=^ci/circleci:"
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# # no conflict with master branch
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# - -conflict
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# # was not closed yet
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# - -closed
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# # filter-out GH draft PRs
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# - -draft
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# actions:
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# delete_head_branch: {}
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# merge:
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# # https://doc.mergify.io/merge-action.html#strict-merge
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# # (on head branch) $ git merge --no-ff base
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# # (on head branch) # Wait for CI to go green
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# # (on head branch) # Squash all commits
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# # (on base branch) $ git merge --ff head
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# strict: true
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# method: squash
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# comment:
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# message: Great job! =)
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#
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# - name: warn on conflicts
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# conditions:
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# - conflict
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# # filter-out GH draft PRs
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# - -draft
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# actions:
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# comment:
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# message: This pull request is now in conflict... :(
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#
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# - name: add core reviewer
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# conditions:
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# # filter-out GH draft PRs
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# - -draft
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# # number of review approvals
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# - "#approved-reviews-by<3"
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# actions:
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# request_reviews:
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# teams:
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# - core-contributors

CHANGELOG.md

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The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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## [unreleased.BugFix] - YYYY-MM-DD
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### Added
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### Changed
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### Deprecated
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### Removed
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### Fixed
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## [1.1.1] - 2020-12-15
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### Added
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- Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning ([#4818](https://github.com/PyTorchLightning/pytorch-lightning/pull/4818)
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### Changed
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- Simplify accelerator steps ([#5015](https://github.com/PyTorchLightning/pytorch-lightning/pull/5015)
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- Refactor load in checkpoint connector ([#4593](https://github.com/PyTorchLightning/pytorch-lightning/pull/4593)
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- Fixed the saved filename in `ModelCheckpoint` when it already exists ([#4861](https://github.com/PyTorchLightning/pytorch-lightning/pull/4861))
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=======
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### Removed
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- Drop duplicate metrics ([#5014](https://github.com/PyTorchLightning/pytorch-lightning/pull/5014)
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- Remove beta arg from F1 class and functional ([#5076](https://github.com/PyTorchLightning/pytorch-lightning/pull/5076)
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### Fixed
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- Fixed trainer by default `None` in `DDPAccelerator` ([#4915](https://github.com/PyTorchLightning/pytorch-lightning/pull/4915))
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- Fixed `LightningOptimizer` to expose optimizer attributes ([#5095](https://github.com/PyTorchLightning/pytorch-lightning/pull/5095))
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- Do not warn when the `name` key is used in the `lr_scheduler` dict ([#5057](https://github.com/PyTorchLightning/pytorch-lightning/pull/5057))
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- Check if optimizer supports closure ([#4981](https://github.com/PyTorchLightning/pytorch-lightning/pull/4981)
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- Extend LightningOptimizer to exposure underlying Optimizer attributes + update doc ([#5095](https://github.com/PyTorchLightning/pytorch-lightning/pull/5095)
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- Add deprecated metric utility functions back to functional (
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[#5067](https://github.com/PyTorchLightning/pytorch-lightning/pull/5067),
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[#5068](https://github.com/PyTorchLightning/pytorch-lightning/pull/5068))
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- Allow any input in `to_onnx` and `to_torchscript` ([#4378](https://github.com/PyTorchLightning/pytorch-lightning/pull/4378)
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- Do not warn when the name key is used in the `lr_scheduler` dict ([#5057](https://github.com/PyTorchLightning/pytorch-lightning/pull/5057)
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- Fixed `DDPHPCAccelerator` hangs in DDP construction by calling `init_device` ([#5157](https://github.com/PyTorchLightning/pytorch-lightning/pull/5157))
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## [1.1.0] - 2020-12-09
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- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
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- Tuner algorithms will be skipped if `fast_dev_run=True` ([#3903](https://github.com/PyTorchLightning/pytorch-lightning/pull/3903))
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- WandbLogger does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
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- `WandbLogger` does not force wandb `reinit` arg to True anymore and creates a run only when needed ([#4648](https://github.com/PyTorchLightning/pytorch-lightning/pull/4648))
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- Changed `automatic_optimization` to be a model attribute ([#4602](https://github.com/PyTorchLightning/pytorch-lightning/pull/4602))
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- Changed `Simple Profiler` report to order by percentage time spent + num calls ([#4880](https://github.com/PyTorchLightning/pytorch-lightning/pull/4880))
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- Simplify optimization Logic ([#4984](https://github.com/PyTorchLightning/pytorch-lightning/pull/4984))
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### Removed
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- Removed `reorder` parameter of the `auc` metric ([#5004](https://github.com/PyTorchLightning/pytorch-lightning/pull/5004))
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- Removed `multiclass_roc` and `multiclass_precision_recall_curve`, use `roc` and `precision_recall_curve` instead ([#4549](https://github.com/PyTorchLightning/pytorch-lightning/pull/4549))
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benchmarks/test_parity.py

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import pytest
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import torch
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from pytorch_lightning import seed_everything, Trainer
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import tests.base.develop_utils as tutils
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benchmarks/test_sharded_parity.py

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import pytest
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from pytorch_lightning import Trainer, seed_everything
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from pytorch_lightning import seed_everything, Trainer
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from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
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from pytorch_lightning.plugins.sharded_plugin import DDPShardedPlugin
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dockers/base-xla/Dockerfile

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python -c "fname = 'requirements.txt' ; lines = [line for line in open(fname).readlines() if not line.startswith('torch')] ; open(fname, 'w').writelines(lines)" && \
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# drop Horovod as it is not needed
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python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if not line.startswith('horovod')] ; open(fname, 'w').writelines(lines)" && \
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# drop fairscale as it is not needed
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python -c "fname = 'requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'fairscale' not in line] ; open(fname, 'w').writelines(lines)" && \
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python -c "fname = 'requirements/examples.txt' ; lines = [line for line in open(fname).readlines() if not line.startswith('torchvision')] ; open(fname, 'w').writelines(lines)" && \
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dockers/tpu-tests/Dockerfile

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RUN \
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# drop Horovod
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# drop Horovod as it is not needed
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python -c "fname = 'pytorch-lightning/requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if not line.startswith('horovod')] ; open(fname, 'w').writelines(lines)" && \
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# drop fairscale as it is not needed
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python -c "fname = 'pytorch-lightning/requirements/extra.txt' ; lines = [line for line in open(fname).readlines() if 'fairscale' not in line] ; open(fname, 'w').writelines(lines)" && \
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#RUN python -c "import pytorch_lightning as pl; print(pl.__version__)"

docs/source/introduction_guide.rst

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MNIST(os.getcwd(), train=True, download=False, transform=transform)
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MNIST(os.getcwd(), train=False, download=False, transform=transform)
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mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
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mnist_test = MNIST(os.getcwd(), train=False, download=False, transform=transform)
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mnist_train, mnist_val = random_split(mnist_train, [55000, 5000])

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