Merge branch 'master' of https://github.com/PyTorchLightning/pytorch-…

…lightning into add-typing

CHANGELOG.md

@@ -10,11 +10,15 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 
+- Added argument `trainer.predict(ckpt_path)` ([#7430](https://github.com/PyTorchLightning/pytorch-lightning/pull/7430))
+
+
 - Added `clip_grad_by_value` support for TPUs ([#7025](https://github.com/PyTorchLightning/pytorch-lightning/pull/7025))
 
 
 ### Changed
 
+
 - Log epoch metrics before the `on_evaluation_end` hook ([#7272](https://github.com/PyTorchLightning/pytorch-lightning/pull/7272))
 
 
@@ -25,6 +29,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Refactored Loops
     * Moved attributes `global_step`, `current_epoch`, `max/min_steps`, `max/min_epochs`, `batch_idx`, and `total_batch_idx` to TrainLoop ([#7437](https://github.com/PyTorchLightning/pytorch-lightning/pull/7025))
+    * Refactored result handling in training loop ([#7506](https://github.com/PyTorchLightning/pytorch-lightning/pull/7506))
 
 - `DataModule`s now avoid duplicate `{setup,teardown,prepare_data}` calls for the same stage ([#7238](https://github.com/PyTorchLightning/pytorch-lightning/pull/7238))
 
@@ -35,10 +40,16 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the behaviour when logging evaluation step metrics to no longer append `/epoch_*` to the metric name ([#7351](https://github.com/PyTorchLightning/pytorch-lightning/pull/7351))
 
 
-- Changed `resolve_training_type_plugins` to allow setting `num_nodes` and `sync_batchnorm` from `Trainer` setting ([7026](https://github.com/PyTorchLightning/pytorch-lightning/pull/7026))
+- Changed `resolve_training_type_plugins` to allow setting `num_nodes` and `sync_batchnorm` from `Trainer` setting ([#7026](https://github.com/PyTorchLightning/pytorch-lightning/pull/7026))
+
+
+- Default `seed_everything(workers=True)` in the `LightningCLI` ([#7504](https://github.com/PyTorchLightning/pytorch-lightning/pull/7504))
 
 
-- Changed `model.state_dict()` in `CheckpointConnector` to allow `training_type_plugin` to customize the model's `state_dict()` ([7474](https://github.com/PyTorchLightning/pytorch-lightning/pull/7474))
+- Changed `model.state_dict()` in `CheckpointConnector` to allow `training_type_plugin` to customize the model's `state_dict()` ([#7474](https://github.com/PyTorchLightning/pytorch-lightning/pull/7474))
+
+
+- MLflowLogger now uses the env variable `MLFLOW_TRACKING_URI` as default tracking uri ([#7457](https://github.com/PyTorchLightning/pytorch-lightning/pull/7457))
 
 
 ### Deprecated
@@ -52,14 +63,31 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Prune deprecated classif. metrics from `pytorch_lightning.metrics.functional.classification` ([7499](https://github.com/PyTorchLightning/pytorch-lightning/pull/7499))
+
+
+- Removed deprecated data parallel classes `LightningDataParallel` and `LightningDistributedDataParallel` from `pytorch_lightning.overrides.data_parallel` ([7510](https://github.com/PyTorchLightning/pytorch-lightning/pull/7510))
+
+
+- Removed deprecated trainer attributes - `get_model` and `accelerator_backend` ([7502](https://github.com/PyTorchLightning/pytorch-lightning/pull/7502))
+
+
+- Removed deprecated utils modules `model_utils`, `warning_utils`, `xla_device_utils` and partially `argparse_utils` ([7503](https://github.com/PyTorchLightning/pytorch-lightning/pull/7503))
+
+
+- Removed deprecated trainer attributes - `on_cpu`, `on_tpu`, `use_tpu`, `on_gpu`, `use_dp`, `use_ddp`, `use_ddp2`, `use_horovod`, `use_single_gpu` ([#7501](https://github.com/PyTorchLightning/pytorch-lightning/pull/7501))
+
 
 ### Fixed
 
+
 - Fixed parsing of multiple training dataloaders ([#7433](https://github.com/PyTorchLightning/pytorch-lightning/pull/7433))
 
+
 - Fixed recursive passing of `wrong_type` keyword argument in `pytorch_lightning.utilities.apply_to_collection` ([#7433](https://github.com/PyTorchLightning/pytorch-lightning/pull/7433))
 
 
+
 ## [1.3.1] - 2021-05-11
 
 ### Fixed

README.md

@@ -118,22 +118,22 @@ pip install pytorch-lightning
   conda install pytorch-lightning -c conda-forge
   ```
 
-  #### Install stable 1.2.x
+  #### Install stable 1.3.x
 
-  the actual status of 1.2 [stable] is following:
+  the actual status of 1.3 [stable] is following:
 
-  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.2.x&event=push)
-  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.2.x&event=push)
-  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.2.x&event=push)
-  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.2.x&event=push)
-  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.2.x&event=push)
+  ![CI base testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20base%20testing/badge.svg?branch=release%2F1.3.x&event=push)
+  ![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?branch=release%2F1.3.x&event=push)
+  ![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?branch=release%2F1.3.x&event=push)
+  ![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?branch=release%2F1.3.x&event=push)
+  ![Docs check](https://github.com/PyTorchLightning/pytorch-lightning/workflows/Docs%20check/badge.svg?branch=release%2F1.3.x&event=push)
 
   Install future release from the source
   ```bash
-  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.2.x --upgrade
+  pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@release/1.3.x --upgrade
   ```
 
-  #### Install bleeding-edge - future 1.3
+  #### Install bleeding-edge - future 1.4
 
   Install nightly from the source (no guarantees)
   ```bash

dockers/README.md

@@ -71,14 +71,14 @@ Inspiration comes from https://u.group/thinking/how-to-put-jupyter-notebooks-in-
 1. Build the docker image:
     ```bash
     docker image build \
-        -t pytorch-lightning:v1.2.9 \
+        -t pytorch-lightning:v1.3.1 \
         -f dockers/nvidia/Dockerfile \
-        --build-arg LIGHTNING_VERSION=1.2.9 \
+        --build-arg LIGHTNING_VERSION=1.3.1 \
         .
     ```
 2. start the server and map ports:
     ```bash
-    docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.2.9
+    docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.3.1
     ```
 3. Connect in local browser:
     - copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6`

dockers/tpu-tests/tpu_test_cases.jsonnet

@@ -22,7 +22,7 @@ local tputests = base.BaseTest {
     |||
       cd pytorch-lightning
       coverage run --source=pytorch_lightning -m pytest -v --capture=no \
-          pytorch_lightning/utilities/xla_device_utils.py \
+          pytorch_lightning/utilities/xla_device.py \
           tests/accelerators/test_tpu_backend.py \
           tests/models/test_tpu.py
       test_exit_code=$?

docs/source/common/lightning_cli.rst

@@ -91,8 +91,8 @@ practice to create a configuration file and provide this to the tool. A way to d
 
 The instantiation of the :class:`~pytorch_lightning.utilities.cli.LightningCLI` class takes care of parsing command line
 and config file options, instantiating the classes, setting up a callback to save the config in the log directory and
-finally running :func:`trainer.fit`. The resulting object :code:`cli` can be used for instance to get the result of fit,
-i.e., :code:`cli.fit_result`.
+finally running the trainer. The resulting object :code:`cli` can be used for example to get the instance of the
+model, (:code:`cli.model`).
 
 After multiple trainings with different configurations, each run will have in its respective log directory a
 :code:`config.yaml` file. This file can be used for reference to know in detail all the settings that were used for each

pl_examples/basic_examples/autoencoder.py

@@ -116,8 +116,7 @@ def test_dataloader(self):
 
 def cli_main():
     cli = LightningCLI(LitAutoEncoder, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pl_examples/basic_examples/backbone_image_classifier.py

@@ -129,8 +129,7 @@ def test_dataloader(self):
 
 def cli_main():
     cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pl_examples/basic_examples/dali_image_classifier.py

@@ -222,8 +222,7 @@ def cli_main():
         return
 
     cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == "__main__":

pl_examples/basic_examples/simple_image_classifier.py

@@ -77,8 +77,7 @@ def configure_optimizers(self):
 
 def cli_main():
     cli = LightningCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234)
-    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    print(result)
+    cli.trainer.test(cli.model, datamodule=cli.datamodule)
 
 
 if __name__ == '__main__':

pytorch_lightning/core/datamodule.py

@@ -37,7 +37,7 @@ def __init__(self):
             def prepare_data(self):
                 # download, split, etc...
                 # only called on 1 GPU/TPU in distributed
-            def setup(self):
+            def setup(self, stage):
                 # make assignments here (val/train/test split)
                 # called on every process in DDP
             def train_dataloader(self):

pytorch_lightning/loggers/mlflow.py

@@ -16,6 +16,7 @@
 -------------
 """
 import logging
+import os
 import re
 from argparse import Namespace
 from time import time
@@ -85,7 +86,8 @@ def any_lightning_module_function_or_hook(self):
     Args:
         experiment_name: The name of the experiment
         tracking_uri: Address of local or remote tracking server.
-            If not provided, defaults to `file:<save_dir>`.
+            If not provided, defaults to `MLFLOW_TRACKING_URI` environment variable if set, otherwise it falls
+            back to `file:<save_dir>`.
         tags: A dictionary tags for the experiment.
         save_dir: A path to a local directory where the MLflow runs get saved.
             Defaults to `./mlflow` if `tracking_uri` is not provided.
@@ -104,7 +106,7 @@ def any_lightning_module_function_or_hook(self):
     def __init__(
         self,
         experiment_name: str = 'default',
-        tracking_uri: Optional[str] = None,
+        tracking_uri: Optional[str] = os.getenv('MLFLOW_TRACKING_URI'),
         tags: Optional[Dict[str, Any]] = None,
         save_dir: Optional[str] = './mlruns',
         prefix: str = '',

pytorch_lightning/metrics/functional/__init__.py

@@ -15,13 +15,6 @@
 from pytorch_lightning.metrics.functional.auc import auc  # noqa: F401
 from pytorch_lightning.metrics.functional.auroc import auroc  # noqa: F401
 from pytorch_lightning.metrics.functional.average_precision import average_precision  # noqa: F401
-from pytorch_lightning.metrics.functional.classification import (  # noqa: F401
-    dice_score,
-    get_num_classes,
-    multiclass_auroc,
-    stat_scores_multiple_classes,
-    to_categorical,
-)
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix  # noqa: F401
 from pytorch_lightning.metrics.functional.explained_variance import explained_variance  # noqa: F401
 from pytorch_lightning.metrics.functional.f_beta import f1, fbeta  # noqa: F401