Merge branch 'master' into fix/enabling-multiple-checkpoints-mlflow

Author: HarryAnkers

examples/fabric/reinforcement_learning/train_fabric_decoupled.py

@@ -274,7 +274,7 @@ def trainer(
         if group_rank == 0:
             metrics = {}
 
-        # Lerning rate annealing
+        # Learning rate annealing
         if args.anneal_lr:
             linear_annealing(optimizer, update, num_updates, args.learning_rate)
         if group_rank == 0:

src/lightning/__setup__.py

@@ -98,7 +98,6 @@ def _setup_args() -> dict[str, Any]:
         "entry_points": {
             "console_scripts": [
                 "fabric = lightning.fabric.cli:_main",
-                "lightning = lightning.fabric.cli:_legacy_main",
             ],
         },
         "setup_requires": [],

src/lightning/fabric/CHANGELOG.md

@@ -4,6 +4,12 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [unreleased] - YYYY-MM-DD
+
+### Removed
+
+Removed legacy supoport for `lightning run model`. Use `fabric run` instead. ([#20588](https://github.com/Lightning-AI/pytorch-lightning/pull/20588))
+
 ## [2.5.0] - 2024-12-19
 
 ### Added

src/lightning/fabric/cli.py

@@ -14,8 +14,6 @@
 import logging
 import os
 import re
-import subprocess
-import sys
 from argparse import Namespace
 from typing import Any, Optional
 
@@ -50,25 +48,6 @@ def _get_supported_strategies() -> list[str]:
 if _CLICK_AVAILABLE:
     import click
 
-    def _legacy_main() -> None:
-        """Legacy CLI handler for fabric.
-
-        Raises deprecation warning and runs through fabric cli if necessary, else runs the entrypoint directly
-
-        """
-        hparams = sys.argv[1:]
-        if len(hparams) >= 2 and hparams[0] == "run" and hparams[1] == "model":
-            print(
-                "`lightning run model` is deprecated and will be removed in future versions."
-                " Please call `fabric run` instead."
-            )
-            _main()
-            return
-
-        if _LIGHTNING_SDK_AVAILABLE:
-            subprocess.run([sys.executable, "-m", "lightning_sdk.cli.entrypoint"] + hparams)
-            return
-
     @click.group()
     def _main() -> None:
         pass

tests/tests_fabric/loggers/test_csv.py

@@ -148,7 +148,7 @@ def test_automatic_step_tracking(tmp_path):
 
 
 @mock.patch(
-    # Mock the existance check, so we can simulate appending to the metrics file
+    # Mock the existence check, so we can simulate appending to the metrics file
     "lightning.fabric.loggers.csv_logs._ExperimentWriter._check_log_dir_exists"
 )
 def test_append_metrics_file(_, tmp_path):
@@ -189,7 +189,7 @@ def test_append_columns(tmp_path):
 
 
 @mock.patch(
-    # Mock the existance check, so we can simulate appending to the metrics file
+    # Mock the existence check, so we can simulate appending to the metrics file
     "lightning.fabric.loggers.csv_logs._ExperimentWriter._check_log_dir_exists"
 )
 def test_rewrite_with_new_header(_, tmp_path):

tests/tests_fabric/test_cli.py

@@ -179,18 +179,6 @@ def test_run_through_fabric_entry_point():
     assert message in result.stdout or message in result.stderr
 
 
-@pytest.mark.skipif("lightning.fabric" == "lightning_fabric", reason="standalone package")
-def test_run_through_lightning_entry_point():
-    result = subprocess.run("lightning run model --help", capture_output=True, text=True, shell=True)
-
-    deprecation_message = (
-        "`lightning run model` is deprecated and will be removed in future versions. Please call `fabric run` instead"
-    )
-    message = "Usage: lightning run [OPTIONS] SCRIPT [SCRIPT_ARGS]"
-    assert deprecation_message in result.stdout
-    assert message in result.stdout or message in result.stderr
-
-
 @mock.patch("lightning.fabric.cli._process_cli_args")
 @mock.patch("lightning.fabric.cli._load_distributed_checkpoint")
 @mock.patch("lightning.fabric.cli.torch.save")

tests/tests_pytorch/accelerators/test_xla.py

@@ -148,7 +148,7 @@ def on_train_start(self):
 
     def on_train_end(self):
         # this might fail if run in an environment with too many ranks, as the total
-        # length of the dataloader will be distrbuted among them and then each rank might not do 3 steps
+        # length of the dataloader will be distributed among them and then each rank might not do 3 steps
         assert self.called["training_step"] == 3
         assert self.called["on_train_batch_start"] == 3
         assert self.called["on_train_batch_end"] == 3