Call any trainer function from the LightningCLI

CHANGELOG.md

@@ -44,7 +44,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `log_graph` argument for `watch` method of `WandbLogger` ([#8662](https://github.com/PyTorchLightning/pytorch-lightning/pull/8662))
 
 
-- Added `LightningCLI(run=False|True)` to choose whether to run a `Trainer` subcommand ([#8751](https://github.com/PyTorchLightning/pytorch-lightning/pull/8751))
+- `LightningCLI` additions:
+  * Added `LightningCLI(run=False|True)` to choose whether to run a `Trainer` subcommand ([#8751](https://github.com/PyTorchLightning/pytorch-lightning/pull/8751))
+  * Added support to call any trainer function from the `LightningCLI` via subcommands ([#7508](https://github.com/PyTorchLightning/pytorch-lightning/pull/7508))
 
 
 - Fault-tolerant training:

pl_examples/basic_examples/autoencoder.py

@@ -109,9 +109,12 @@ def predict_dataloader(self):
 
 
 def cli_main():
-    cli = LightningCLI(LitAutoEncoder, MyDataModule, seed_everything_default=1234, save_config_overwrite=True)
-    cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    predictions = cli.trainer.predict(cli.model, datamodule=cli.datamodule)
+    cli = LightningCLI(
+        LitAutoEncoder, MyDataModule, seed_everything_default=1234, save_config_overwrite=True, run=False
+    )
+    cli.trainer.fit(cli.model, datamodule=cli.datamodule)
+    cli.trainer.test(ckpt_path="best")
+    predictions = cli.trainer.predict(ckpt_path="best")
     print(predictions[0])
 
 

pl_examples/basic_examples/backbone_image_classifier.py

@@ -124,9 +124,10 @@ def predict_dataloader(self):
 
 
 def cli_main():
-    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234, save_config_overwrite=True)
-    cli.trainer.test(cli.model, datamodule=cli.datamodule)
-    predictions = cli.trainer.predict(cli.model, datamodule=cli.datamodule)
+    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234, save_config_overwrite=True, run=False)
+    cli.trainer.fit(cli.model, datamodule=cli.datamodule)
+    cli.trainer.test(ckpt_path="best")
+    predictions = cli.trainer.predict(ckpt_path="best")
     print(predictions[0])
 
 

pl_examples/basic_examples/dali_image_classifier.py

@@ -198,8 +198,9 @@ def cli_main():
     if not _DALI_AVAILABLE:
         return
 
-    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234, save_config_overwrite=True)
-    cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234, save_config_overwrite=True, run=False)
+    cli.trainer.fit(cli.model, datamodule=cli.datamodule)
+    cli.trainer.test(ckpt_path="best")
 
 
 if __name__ == "__main__":

pl_examples/basic_examples/simple_image_classifier.py

@@ -72,8 +72,11 @@ def configure_optimizers(self):
 
 
 def cli_main():
-    cli = LightningCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234, save_config_overwrite=True)
-    cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    cli = LightningCLI(
+        LitClassifier, MNISTDataModule, seed_everything_default=1234, save_config_overwrite=True, run=False
+    )
+    cli.trainer.fit(cli.model, datamodule=cli.datamodule)
+    cli.trainer.test(ckpt_path="best")
 
 
 if __name__ == "__main__":

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -277,10 +277,10 @@ def add_arguments_to_parser(self, parser):
             }
         )
 
-    def instantiate_trainer(self):
-        finetuning_callback = MilestonesFinetuning(**self.config_init["finetuning"])
+    def instantiate_trainer(self, *args):
+        finetuning_callback = MilestonesFinetuning(**self._get(self.config_init, "finetuning"))
         self.trainer_defaults["callbacks"] = [finetuning_callback]
-        super().instantiate_trainer()
+        return super().instantiate_trainer(*args)
 
 
 def cli_main():

pl_examples/run_examples.sh

@@ -2,7 +2,14 @@
 set -ex
 
 dir_path=$(dirname "${BASH_SOURCE[0]}")
-args="--trainer.max_epochs=1 --data.batch_size=32 --trainer.limit_train_batches=2 --trainer.limit_val_batches=2"
+args="
+  --data.batch_size=32
+  --trainer.max_epochs=1
+  --trainer.limit_train_batches=2
+  --trainer.limit_val_batches=2
+  --trainer.limit_test_batches=2
+  --trainer.limit_predict_batches=2
+"
 
 python "${dir_path}/basic_examples/simple_image_classifier.py" ${args} "$@"
 python "${dir_path}/basic_examples/backbone_image_classifier.py" ${args} "$@"

requirements/extra.txt

@@ -7,5 +7,5 @@ torchtext>=0.7
 onnx>=1.7.0
 onnxruntime>=1.3.0
 hydra-core>=1.0
-jsonargparse[signatures]>=3.17.0
+jsonargparse[signatures]>=3.19.0
 gcsfs>=2021.5.0

tests/special_tests.sh

@@ -87,7 +87,7 @@ fi
 # report+="Ran\ttests/plugins/environments/torch_elastic_deadlock.py\n"
 
 # test that a user can manually launch individual processes
-args="--trainer.gpus 2 --trainer.accelerator ddp --trainer.fast_dev_run 1"
+args="--trainer.gpus 2 --trainer.accelerator ddp --trainer.max_epochs=1 --trainer.limit_train_batches=1 --trainer.limit_val_batches=1 --trainer.limit_test_batches=1"
 MASTER_ADDR="localhost" MASTER_PORT=1234 LOCAL_RANK=1 python pl_examples/basic_examples/simple_image_classifier.py ${args} &
 MASTER_ADDR="localhost" MASTER_PORT=1234 LOCAL_RANK=0 python pl_examples/basic_examples/simple_image_classifier.py ${args}
 report+="Ran\tmanual ddp launch test\n"