Promote the CLI out of utilities

docs/source-pytorch/cli/lightning_cli_advanced_2.rst

@@ -15,7 +15,7 @@
             pass
 
 
-    class LightningCLI(pl.utilities.cli.LightningCLI):
+    class LightningCLI(pl.cli.LightningCLI):
         def __init__(self, *args, trainer_class=NoFitTrainer, run=False, **kwargs):
             super().__init__(*args, trainer_class=trainer_class, run=run, **kwargs)
 

docs/source-pytorch/cli/lightning_cli_advanced_3.rst

@@ -15,7 +15,7 @@
             pass
 
 
-    class LightningCLI(pl.utilities.cli.LightningCLI):
+    class LightningCLI(pl.cli.LightningCLI):
         def __init__(self, *args, trainer_class=NoFitTrainer, run=False, **kwargs):
             super().__init__(*args, trainer_class=trainer_class, run=run, **kwargs)
 
@@ -88,7 +88,7 @@ Similar to the callbacks, any parameter in :class:`~pytorch_lightning.trainer.tr
 :class:`~pytorch_lightning.core.module.LightningModule` and
 :class:`~pytorch_lightning.core.datamodule.LightningDataModule` classes that have as type hint a class, can be
 configured the same way using :code:`class_path` and :code:`init_args`. If the package that defines a subclass is
-imported before the :class:`~pytorch_lightning.utilities.cli.LightningCLI` class is run, the name can be used instead of
+imported before the :class:`~pytorch_lightning.cli.LightningCLI` class is run, the name can be used instead of
 the full import path.
 
 From command line the syntax is the following:
@@ -117,7 +117,7 @@ callback appended. Here is an example:
 
 .. note::
 
-    Serialized config files (e.g. ``--print_config`` or :class:`~pytorch_lightning.utilities.cli.SaveConfigCallback`)
+    Serialized config files (e.g. ``--print_config`` or :class:`~pytorch_lightning.cli.SaveConfigCallback`)
     always have the full ``class_path``'s, even when class name shorthand notation is used in command line or in input
     config files.
 
@@ -306,7 +306,7 @@ example can be when one wants to add support for multiple optimizers:
 
 .. code-block:: python
 
-    from pytorch_lightning.utilities.cli import instantiate_class
+    from pytorch_lightning.cli import instantiate_class
 
 
     class MyModel(LightningModule):
@@ -330,7 +330,7 @@ example can be when one wants to add support for multiple optimizers:
     cli = MyLightningCLI(MyModel)
 
 The value given to :code:`optimizer*_init` will always be a dictionary including :code:`class_path` and
-:code:`init_args` entries. The function :func:`~pytorch_lightning.utilities.cli.instantiate_class`
+:code:`init_args` entries. The function :func:`~pytorch_lightning.cli.instantiate_class`
 takes care of importing the class defined in :code:`class_path` and instantiating it using some positional arguments,
 in this case :code:`self.parameters()`, and the :code:`init_args`.
 Any number of optimizers and learning rate schedulers can be added when using :code:`link_to`.

docs/source-pytorch/cli/lightning_cli_expert.rst

@@ -15,7 +15,7 @@
             pass
 
 
-    class LightningCLI(pl.utilities.cli.LightningCLI):
+    class LightningCLI(pl.cli.LightningCLI):
         def __init__(self, *args, trainer_class=NoFitTrainer, run=False, **kwargs):
             super().__init__(*args, trainer_class=trainer_class, run=run, **kwargs)
 
@@ -62,23 +62,23 @@ Eliminate config boilerplate (Advanced)
 Customize the LightningCLI
 **************************
 
-The init parameters of the :class:`~pytorch_lightning.utilities.cli.LightningCLI` class can be used to customize some
+The init parameters of the :class:`~pytorch_lightning.cli.LightningCLI` class can be used to customize some
 things, namely: the description of the tool, enabling parsing of environment variables and additional arguments to
 instantiate the trainer and configuration parser.
 
 Nevertheless the init arguments are not enough for many use cases. For this reason the class is designed so that can be
 extended to customize different parts of the command line tool. The argument parser class used by
-:class:`~pytorch_lightning.utilities.cli.LightningCLI` is
-:class:`~pytorch_lightning.utilities.cli.LightningArgumentParser` which is an extension of python's argparse, thus
+:class:`~pytorch_lightning.cli.LightningCLI` is
+:class:`~pytorch_lightning.cli.LightningArgumentParser` which is an extension of python's argparse, thus
 adding arguments can be done using the :func:`add_argument` method. In contrast to argparse it has additional methods to
 add arguments, for example :func:`add_class_arguments` adds all arguments from the init of a class, though requiring
 parameters to have type hints. For more details about this please refer to the `respective documentation
 <https://jsonargparse.readthedocs.io/en/stable/#classes-methods-and-functions>`_.
 
-The :class:`~pytorch_lightning.utilities.cli.LightningCLI` class has the
-:meth:`~pytorch_lightning.utilities.cli.LightningCLI.add_arguments_to_parser` method which can be implemented to include
+The :class:`~pytorch_lightning.cli.LightningCLI` class has the
+:meth:`~pytorch_lightning.cli.LightningCLI.add_arguments_to_parser` method which can be implemented to include
 more arguments. After parsing, the configuration is stored in the :code:`config` attribute of the class instance. The
-:class:`~pytorch_lightning.utilities.cli.LightningCLI` class also has two methods that can be used to run code before
+:class:`~pytorch_lightning.cli.LightningCLI` class also has two methods that can be used to run code before
 and after the trainer runs: :code:`before_<subcommand>` and :code:`after_<subcommand>`.
 A realistic example for these would be to send an email before and after the execution.
 The code for the :code:`fit` subcommand would be something like:
@@ -104,7 +104,7 @@ instantiating the trainer class can be found in :code:`self.config['fit']['train
 
 .. tip::
 
-    Have a look at the :class:`~pytorch_lightning.utilities.cli.LightningCLI` class API reference to learn about other
+    Have a look at the :class:`~pytorch_lightning.cli.LightningCLI` class API reference to learn about other
     methods that can be extended to customize a CLI.
 
 ----
@@ -211,7 +211,7 @@ A more compact version that avoids writing a dictionary would be:
 ************************
 Connect two config files
 ************************
-Another case in which it might be desired to extend :class:`~pytorch_lightning.utilities.cli.LightningCLI` is that the
+Another case in which it might be desired to extend :class:`~pytorch_lightning.cli.LightningCLI` is that the
 model and data module depend on a common parameter. For example in some cases both classes require to know the
 :code:`batch_size`. It is a burden and error prone giving the same value twice in a config file. To avoid this the
 parser can be configured so that a value is only given once and then propagated accordingly. With a tool implemented

docs/source-pytorch/cli/lightning_cli_faq.rst

@@ -15,7 +15,7 @@
             pass
 
 
-    class LightningCLI(pl.utilities.cli.LightningCLI):
+    class LightningCLI(pl.cli.LightningCLI):
         def __init__(self, *args, trainer_class=NoFitTrainer, run=False, **kwargs):
             super().__init__(*args, trainer_class=trainer_class, run=run, **kwargs)
 
@@ -65,7 +65,7 @@ there is a failure an exception is raised and the full stack trace printed.
 Reproducibility with the LightningCLI
 *************************************
 The topic of reproducibility is complex and it is impossible to guarantee reproducibility by just providing a class that
-people can use in unexpected ways. Nevertheless, the :class:`~pytorch_lightning.utilities.cli.LightningCLI` tries to
+people can use in unexpected ways. Nevertheless, the :class:`~pytorch_lightning.cli.LightningCLI` tries to
 give a framework and recommendations to make reproducibility simpler.
 
 When an experiment is run, it is good practice to use a stable version of the source code, either being a released
@@ -85,7 +85,7 @@ For every CLI implemented, users are encouraged to learn how to run it by readin
 :code:`--help` option and use the :code:`--print_config` option to guide the writing of config files. A few more details
 that might not be clear by only reading the help are the following.
 
-:class:`~pytorch_lightning.utilities.cli.LightningCLI` is based on argparse and as such follows the same arguments style
+:class:`~pytorch_lightning.cli.LightningCLI` is based on argparse and as such follows the same arguments style
 as many POSIX command line tools. Long options are prefixed with two dashes and its corresponding values should be
 provided with an empty space or an equal sign, as :code:`--option value` or :code:`--option=value`. Command line options
 are parsed from left to right, therefore if a setting appears multiple times the value most to the right will override

docs/source-pytorch/cli/lightning_cli_intermediate.rst

@@ -82,7 +82,7 @@ The simplest way to control a model with the CLI is to wrap it in the LightningC
 
     # main.py
     import torch
-    from pytorch_lightning.utilities.cli import LightningCLI
+    from pytorch_lightning.cli import LightningCLI
 
     # simple demo classes for your convenience
     from pytorch_lightning.demos.boring_classes import DemoModel, BoringDataModule

examples/convert_from_pt_to_pl/image_classifier_4_lightning_module.py

@@ -23,9 +23,9 @@
 from torchmetrics import Accuracy
 
 from pytorch_lightning import cli_lightning_logo, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.boring_classes import Net
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.utilities.cli import LightningCLI
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
 

examples/convert_from_pt_to_pl/image_classifier_5_lightning_datamodule.py

@@ -23,9 +23,9 @@
 from torchmetrics import Accuracy
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.boring_classes import Net
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.utilities.cli import LightningCLI
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
 

examples/pl_basics/autoencoder.py

@@ -24,8 +24,8 @@
 from torch.utils.data import DataLoader, random_split
 
 from pytorch_lightning import callbacks, cli_lightning_logo, LightningDataModule, LightningModule, Trainer
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.utilities.cli import LightningCLI
 from pytorch_lightning.utilities.imports import _TORCHVISION_AVAILABLE
 from pytorch_lightning.utilities.rank_zero import rank_zero_only
 

examples/pl_basics/backbone_image_classifier.py

@@ -23,8 +23,8 @@
 from torch.utils.data import DataLoader, random_split
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.utilities.cli import LightningCLI
 from pytorch_lightning.utilities.imports import _TORCHVISION_AVAILABLE
 
 if _TORCHVISION_AVAILABLE:

examples/pl_basics/profiler_example.py

@@ -31,8 +31,8 @@
 import torchvision.transforms as T
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.profilers.pytorch import PyTorchProfiler
-from pytorch_lightning.utilities.cli import LightningCLI
 
 DEFAULT_CMD_LINE = (
     "fit",

examples/pl_domain_templates/computer_vision_fine_tuning.py

@@ -56,7 +56,7 @@
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
 from pytorch_lightning.callbacks.finetuning import BaseFinetuning
-from pytorch_lightning.utilities.cli import LightningCLI
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.utilities.rank_zero import rank_zero_info
 
 log = logging.getLogger(__name__)

examples/pl_domain_templates/imagenet.py

@@ -47,8 +47,8 @@
 
 from pytorch_lightning import LightningModule
 from pytorch_lightning.callbacks import ModelCheckpoint, TQDMProgressBar
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.strategies import ParallelStrategy
-from pytorch_lightning.utilities.cli import LightningCLI
 
 
 class ImageNetLightningModel(LightningModule):

examples/pl_hpu/mnist_sample.py

@@ -16,9 +16,9 @@
 from torch.nn import functional as F
 
 from pytorch_lightning import LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.mnist_datamodule import MNISTDataModule
 from pytorch_lightning.plugins import HPUPrecisionPlugin
-from pytorch_lightning.utilities.cli import LightningCLI
 
 
 class LitClassifier(LightningModule):

examples/pl_integrations/dali_image_classifier.py

@@ -23,8 +23,8 @@
 from torch.utils.data import random_split
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.demos.mnist_datamodule import MNIST
-from pytorch_lightning.utilities.cli import LightningCLI
 from pytorch_lightning.utilities.imports import _DALI_AVAILABLE, _TORCHVISION_AVAILABLE
 
 if _TORCHVISION_AVAILABLE:

examples/pl_servable_module/production.py

@@ -12,8 +12,8 @@
 from PIL import Image as PILImage
 
 from pytorch_lightning import cli_lightning_logo, LightningDataModule, LightningModule
+from pytorch_lightning.cli import LightningCLI
 from pytorch_lightning.serve import ServableModule, ServableModuleValidator
-from pytorch_lightning.utilities.cli import LightningCLI
 
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "Datasets")
 

src/pytorch_lightning/CHANGELOG.md

@@ -173,6 +173,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated LightningCLI's registries in favor of importing the respective package ([#13221](https://github.com/PyTorchLightning/pytorch-lightning/pull/13221))
 
 
+- Deprecated public utilities in `pytorch_lightning.utilities.cli.LightningCLI` in favor of equivalent copies in `pytorch_lightning.cli.LightningCLI` ([#13767](https://github.com/PyTorchLightning/pytorch-lightning/pull/13767))
+
 
 - Deprecated `pytorch_lightning.profiler` in favor of `pytorch_lightning.profilers` ([#12308](https://github.com/PyTorchLightning/pytorch-lightning/pull/12308))