Merge branch 'master' into deprecated/remove_callback_hook

Author: awaelchli

.azure/gpu-tests.yml

@@ -123,6 +123,15 @@ jobs:
       timeoutInMinutes: "35"
       condition: eq(variables['continue'], '1')
 
+    - bash: bash run_standalone_tasks.sh
+      workingDirectory: tests/tests_pytorch
+      env:
+        PL_USE_MOCKED_MNIST: "1"
+        PL_RUN_CUDA_TESTS: "1"
+      displayName: 'Testing: PyTorch standalone tasks'
+      timeoutInMinutes: "10"
+      condition: eq(variables['continue'], '1')
+
     - bash: |
         python -m coverage report
         python -m coverage xml

.azure/hpu-tests.yml

@@ -45,7 +45,7 @@ jobs:
         pip --version
         sudo pip uninstall -y lightning pytorch-lightning
         pip install fire
-        python .actions/assistant.py requirements-prune-pkgs torch,torchvision,torchtext
+        python .actions/assistant.py requirements-prune-pkgs torch,torchvision
         pip install ".[extra,test]"
         pip list
       env:

.github/workflows/ci-pytorch-dockers.yml

@@ -75,7 +75,7 @@ jobs:
           push: ${{ env.PUSH_TO_HUB }}
           tags: pytorchlightning/pytorch_lightning:base-xla-py${{ matrix.python_version }}-torch${{ matrix.xla_version }}
         timeout-minutes: 60
-      - uses: ravsamhq/notify-slack-action@v1
+      - uses: ravsamhq/notify-slack-action@v2
         if: failure() && env.PUSH_TO_HUB == 'true'
         with:
           status: ${{ job.status }}
@@ -117,7 +117,7 @@ jobs:
           push: ${{ env.PUSH_TO_HUB }}
           tags: pytorchlightning/pytorch_lightning:base-cuda-py${{ matrix.python_version }}-torch${{ matrix.pytorch_version }}-cuda${{ matrix.cuda_version }}
         timeout-minutes: 95
-      - uses: ravsamhq/notify-slack-action@v1
+      - uses: ravsamhq/notify-slack-action@v2
         if: failure() && env.PUSH_TO_HUB == 'true'
         with:
           status: ${{ job.status }}
@@ -155,7 +155,7 @@ jobs:
           push: ${{ env.PUSH_TO_HUB }}
           tags: pytorchlightning/pytorch_lightning:base-conda-py${{ matrix.python_version }}-torch${{ matrix.pytorch_version }}
         timeout-minutes: 95
-      - uses: ravsamhq/notify-slack-action@v1
+      - uses: ravsamhq/notify-slack-action@v2
         if: failure() && env.PUSH_TO_HUB == 'true'
         with:
           status: ${{ job.status }}
@@ -199,7 +199,7 @@ jobs:
           push: ${{ env.PUSH_TO_HUB }}
           tags: pytorchlightning/pytorch_lightning:ipu-ci-runner-py${{ matrix.python_version }}
         timeout-minutes: 10
-      - uses: ravsamhq/notify-slack-action@v1
+      - uses: ravsamhq/notify-slack-action@v2
         if: failure() && env.PUSH_TO_HUB == 'true'
         with:
           status: ${{ job.status }}
@@ -235,7 +235,7 @@ jobs:
           push: ${{ env.PUSH_TO_HUB }}
           tags: pytorchlightning/pytorch_lightning:hpu-ci-runner-gaudi${{ matrix.gaudi_version }}
         timeout-minutes: 10
-      - uses: ravsamhq/notify-slack-action@v1
+      - uses: ravsamhq/notify-slack-action@v2
         if: failure() && env.PUSH_TO_HUB == 'true'
         with:
           status: ${{ job.status }}

.github/workflows/events-nightly.yml

@@ -48,7 +48,7 @@ jobs:
       # report failure to Slack
       - name: Slack notification
         if: failure() && github.event_name == 'schedule'
-        uses: ravsamhq/notify-slack-action@v1
+        uses: ravsamhq/notify-slack-action@v2
         with:
           status: ${{ job.status }}
           token: ${{ secrets.GITHUB_TOKEN }}

dockers/base-conda/Dockerfile

@@ -78,11 +78,11 @@ RUN \
     conda update -n base -c defaults conda && \
     CUDA_VERSION_MM=$(python -c "print('.'.join('$CUDA_VERSION'.split('.')[:2]))") && \
     conda create -y --name $CONDA_ENV \
-      python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision torchtext cudatoolkit=${CUDA_VERSION_MM} \
+      python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision cudatoolkit=${CUDA_VERSION_MM} \
       -c nvidia -c pytorch -c pytorch-test && \
     conda init bash && \
     # NOTE: this requires that the channel is presented in the yaml before packages \
-    printf "import re;\nfname = 'environment.yml';\nreq = open(fname).read();\nfor n in ['python', 'pytorch', 'torchtext', 'torchvision']:\n    req = re.sub(rf'- {n}[>=]+', f'# - {n}=', req);\nopen(fname, 'w').write(req)" > prune.py && \
+    printf "import re;\nfname = 'environment.yml';\nreq = open(fname).read();\nfor n in ['python', 'pytorch', 'torchvision']:\n    req = re.sub(rf'- {n}[>=]+', f'# - {n}=', req);\nopen(fname, 'w').write(req)" > prune.py && \
     python prune.py && \
     rm prune.py && \
     cat environment.yml && \
@@ -102,7 +102,7 @@ RUN \
     pip list | grep torch && \
     python -c "import torch; print(torch.__version__)" && \
     pip install -q fire && \
-    python assistant.py requirements_prune_pkgs torch,torchvision,torchtext && \
+    python assistant.py requirements_prune_pkgs torch,torchvision && \
     # Install remaining requirements
     pip install --no-cache-dir -r requirements/pytorch/base.txt \
         -r requirements/pytorch/extra.txt \

docs/source-pytorch/common/checkpointing_intermediate.rst

@@ -120,7 +120,7 @@ What
 Where
 =====
 
-- It gives you the ability to specify the ``dirpath`` and ``filename`` for your checkpoints. Filename can also be dynamic so you can inject the metrics that are being logged using :meth:`~pytorch_lightning.core.module.LightningModule.log`.
+- By default, the ``ModelCheckpoint`` will save files into the ``Trainer.log_dir``. It gives you the ability to specify the ``dirpath`` and ``filename`` for your checkpoints. Filename can also be dynamic so you can inject the metrics that are being logged using :meth:`~pytorch_lightning.core.module.LightningModule.log`.
 
 |
 

docs/source-pytorch/common/lightning_module.rst

@@ -1345,18 +1345,6 @@ load_from_checkpoint
 .. automethod:: pytorch_lightning.core.module.LightningModule.load_from_checkpoint
     :noindex:
 
-on_hpc_save
-~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.module.LightningModule.on_hpc_save
-    :noindex:
-
-on_hpc_load
-~~~~~~~~~~~
-
-.. automethod:: pytorch_lightning.core.module.LightningModule.on_hpc_load
-    :noindex:
-
 on_train_start
 ~~~~~~~~~~~~~~
 

docs/source-pytorch/common/trainer.rst

@@ -1745,3 +1745,63 @@ execution within that function, and the status of the Trainer.
     trainer.state.status
     # stage in ("train", "sanity_check", "validate", "test", "predict", "tune")
     trainer.state.stage
+
+should_stop
+***********
+
+If you want to terminate the training during ``.fit``, you can set ``trainer.should_stop=True`` to terminate the training
+as soon as possible. Note that, it will respect the arguments ``min_steps`` and ``min_epochs`` to check whether to stop. If these
+arguments are set and the ``current_epoch`` or ``global_step`` don't meet these minimum conditions, training will continue until
+both conditions are met. If any of these arguments is not set, it won't be considered for the final decision.
+
+
+.. code-block:: python
+
+    # setting `trainer.should_stop` at any point of training will terminate it
+    class LitModel(LightningModule):
+        def training_step(self, *args, **kwargs):
+            self.trainer.should_stop = True
+
+
+    trainer = Trainer()
+    model = LitModel()
+    trainer.fit(model)
+
+.. code-block:: python
+
+    # setting `trainer.should_stop` will stop training only after at least 5 epochs have run
+    class LitModel(LightningModule):
+        def training_step(self, *args, **kwargs):
+            if self.current_epoch == 2:
+                self.trainer.should_stop = True
+
+
+    trainer = Trainer(min_epochs=5, max_epochs=100)
+    model = LitModel()
+    trainer.fit(model)
+
+.. code-block:: python
+
+    # setting `trainer.should_stop` will stop training only after at least 5 steps have run
+    class LitModel(LightningModule):
+        def training_step(self, *args, **kwargs):
+            if self.global_step == 2:
+                self.trainer.should_stop = True
+
+
+    trainer = Trainer(min_steps=5, max_epochs=100)
+    model = LitModel()
+    trainer.fit(model)
+
+.. code-block:: python
+
+    # setting `trainer.should_stop` at any until both min_steps and min_epochs are satisfied
+    class LitModel(LightningModule):
+        def training_step(self, *args, **kwargs):
+            if self.global_step == 7:
+                self.trainer.should_stop = True
+
+
+    trainer = Trainer(min_steps=5, min_epochs=5, max_epochs=100)
+    model = LitModel()
+    trainer.fit(model)

environment.yml

@@ -41,7 +41,6 @@ dependencies:
     - scikit-learn>=0.20.0
     - matplotlib>=3.1.1
     - omegaconf>=2.0.5
-    - torchtext>=0.10.*
 
     # Examples
     - torchvision>=0.10.*

pyproject.toml

@@ -50,11 +50,8 @@ warn_no_return = "False"
 # mypy --no-error-summary 2>&1 | tr ':' ' ' | awk '{print $1}' | sort | uniq | sed 's/\.py//g; s|src/||g;  s|\/|\.|g' | xargs -I {} echo '"{}",'
 module = [
     "pytorch_lightning.callbacks.progress.rich_progress",
-    "pytorch_lightning.core.datamodule",
     "pytorch_lightning.profilers.base",
     "pytorch_lightning.profilers.pytorch",
-    "pytorch_lightning.strategies.sharded",
-    "pytorch_lightning.trainer.connectors.data_connector",
     "pytorch_lightning.trainer.supporters",
     "pytorch_lightning.trainer.trainer",
     "pytorch_lightning.tuner.batch_size_scaling",