Print help on run_mosaic_trainer.py, cleaned up verbosity.

README.md

@@ -49,7 +49,7 @@ from composer import trainer, algorithms, Trainer
 
 trainer_hparams = trainer.load("resnet50")
 trainer_hparams.algorithms = algorithms.load_multiple("squeeze_excite", "scale_schedule")
-trainer_hparams.set_datadir('your/dataset/path/')
+trainer_hparams.datadir = 'your/dataset/path/'
 
 learner = Trainer.create_from_hparams(hparams=trainer_hparams)
 learner.fit()

composer/cli/launcher.py

@@ -1,6 +1,7 @@
 # Copyright 2021 MosaicML. All Rights Reserved.
 
 import datetime
+import logging
 import os
 import signal
 import socket
@@ -14,6 +15,8 @@
 
 CLEANUP_TIMEOUT = datetime.timedelta(seconds=30)
 
+log = logging.getLogger(__name__)
+
 
 def get_parser():
     parser = ArgumentParser(description="Utility for launching distributed machine learning jobs.")
@@ -56,6 +59,7 @@ def get_parser():
                         "--module_mode",
                         action="store_true",
                         help="If set, run the training script as a module instead of as a script.")
+    parser.add_argument("-v", "--verbose", action="store_true", help="If set, print verbose messages")
     parser.add_argument("training_script",
                         type=str,
                         help="The path to the training script used to initialize a single training "
@@ -90,7 +94,7 @@ def parse_args():
 def launch_processes(nproc: int, world_size: int, base_rank: int, master_addr: str, master_port: Optional[int],
                      module_mode: bool, run_directory: Optional[str], training_script: str,
                      training_script_args: List[Any]) -> Set[subprocess.Popen]:
-    print(f"Starting DDP on local node for global_rank({base_rank}-{base_rank+nproc-1})")
+    log.info("Starting DDP on local node for global_rank(%s-%s)", base_rank, base_rank + nproc - 1)
     processes = []
 
     if run_directory is None:
@@ -104,7 +108,7 @@ def launch_processes(nproc: int, world_size: int, base_rank: int, master_addr: s
                       "This may lead to race conditions when launching multiple training processes simultaneously. "
                       "To eliminate this race condition, explicitely specify a port with --master_port PORT_NUMBER")
         master_port = get_free_tcp_port()
-    print(f"DDP Store: tcp://{master_addr}:{master_port}")
+    log.info("DDP Store: tcp://%s:%s", master_addr, master_port)
 
     for local_rank in range(nproc):
         global_rank = base_rank + local_rank
@@ -122,7 +126,7 @@ def launch_processes(nproc: int, world_size: int, base_rank: int, master_addr: s
         current_env["MASTER_PORT"] = str(master_port)
         current_env["RUN_DIRECTORY"] = run_directory
 
-        print(f"Launching process for local_rank({local_rank}), global_rank({global_rank})")
+        log.info("Launching process for local_rank(%s), global_rank(%s)", local_rank, global_rank)
 
         if local_rank == 0:
             process = subprocess.Popen(cmd, env=current_env, text=True)
@@ -150,12 +154,12 @@ def monitor_processes(processes: Set[subprocess.Popen]):
                 # return code of -9 implies sigkill, presumably from cleanup_processes()
                 if process.returncode not in (0, -9):
                     if process.stdout is None:
-                        output = ""
+                        output = None
                     else:
                         output = process.stdout.read()
 
                     if process.stderr is None:
-                        stderr = ""
+                        stderr = None
                     else:
                         stderr = process.stderr.read()
                     exc = subprocess.CalledProcessError(
@@ -164,15 +168,20 @@ def monitor_processes(processes: Set[subprocess.Popen]):
                         output=output,
                         stderr=stderr,
                     )
-                    error_msg = [
-                        "Error in subprocess",
-                        "----------Subprocess STDOUT----------",
-                        exc.output,
-                        "----------Subprocess STDERR----------",
-                        exc.stderr,
-                    ]
+                    error_msg = [f"Process {process.pid} excited with code {process.returncode}"]
+                    if output is not None:
+                        error_msg.extend([
+                            "----------Begin subprocess STDOUT----------",
+                            output,
+                            "----------End subprocess STDOUT----------",
+                        ])
+                    if stderr is not None:
+                        error_msg.extend([
+                            "----------Begin subprocess STDERR----------",
+                            exc.stderr,
+                            "----------End subprocess STDERR----------",
+                        ])
                     print("\n".join(error_msg))
-                    print(exc)
                     sys.exit(process.returncode)
                 else:
                     # exited cleanly
@@ -185,14 +194,14 @@ def cleanup_processes(processes: Set[subprocess.Popen]):
     for process in processes:
         process.poll()
         if process.returncode is None:
-            print(f"Killing subprocess {process.pid} with SIGTERM")
+            log.info("Killing subprocess %s with SIGTERM", process.pid)
             try:
                 os.killpg(process.pid, signal.SIGTERM)
             except ProcessLookupError:
                 pass
 
     current_time = datetime.datetime.now()
-    print(f"Waiting {CLEANUP_TIMEOUT.seconds} seconds for processes to terminate...")
+    print(f"Waiting up to {CLEANUP_TIMEOUT.seconds} seconds for all training processes to terminate...")
     while datetime.datetime.now() - current_time < CLEANUP_TIMEOUT:
         for process in processes:
             process.poll()
@@ -203,7 +212,7 @@ def cleanup_processes(processes: Set[subprocess.Popen]):
     for process in processes:
         process.poll()
         if process.returncode is None:
-            print(f"Failed to kill subprocess {process.pid} with SIGTERM; using SIGKILL instead")
+            log.warn("Failed to kill subprocess %s with SIGTERM; using SIGKILL instead", process.pid)
             try:
                 os.killpg(process.pid, signal.SIGKILL)
             except ProcessLookupError:
@@ -214,7 +223,7 @@ def aggregate_process_returncode(processes: Set[subprocess.Popen]) -> int:
     for process in processes:
         process.poll()
         if process.returncode is None:
-            print(f"Subprocess {process.pid} has still not exited; return exit code 1.")
+            log.warn("Subprocess %s has still not exited; return exit code 1.", process.pid)
             return 1
         if process.returncode != 0:
             return process.returncode
@@ -225,6 +234,9 @@ def aggregate_process_returncode(processes: Set[subprocess.Popen]) -> int:
 def main():
     args = parse_args()
 
+    logging.basicConfig()
+    log.setLevel(logging.INFO if args.verbose else logging.WARN)
+
     processes = launch_processes(nproc=args.nproc,
                                  world_size=args.world_size,
                                  base_rank=args.base_rank,
@@ -238,7 +250,7 @@ def main():
     try:
         monitor_processes(processes)
     except KeyboardInterrupt:
-        print("Caught Ctrl+C; killing processes")
+        print("Caught Ctrl+C; killing training processes")
         raise
     finally:
         cleanup_processes(processes)

composer/trainer/trainer.py

@@ -321,6 +321,8 @@ def create_from_hparams(cls, hparams: TrainerHparams) -> Trainer:
         """
 
         hparams.validate()
+        import composer
+        logging.getLogger(composer.__name__).setLevel(hparams.log_level)
 
         # devices and systems
         device = hparams.device.initialize_object()
@@ -337,6 +339,10 @@ def create_from_hparams(cls, hparams: TrainerHparams) -> Trainer:
         dict_config = hparams.to_dict()
         log_destinations = [x.initialize_object(config=dict_config) for x in hparams.loggers]
 
+        if hparams.datadir is not None:
+            hparams.train_dataset.datadir = hparams.datadir
+            hparams.val_dataset.datadir = hparams.datadir
+
         train_device_batch_size = hparams.train_batch_size // ddp.get_world_size()
         if hparams.train_dataset.shuffle and hparams.train_subset_num_batches:
             warnings.warn(

composer/trainer/trainer_hparams.py

@@ -195,7 +195,11 @@ class TrainerHparams(hp.Hparams):
                                            default=False)
 
     compute_training_metrics: bool = hp.optional(doc="Log validation metrics on training data", default=False)
-    log_level: str = hp.optional(doc="Python loglevel to use composer", default="INFO")
+    log_level: str = hp.optional(doc="Python loglevel to use composer", default="WARNING")
+    datadir: Optional[str] = hp.optional(doc=textwrap.dedent("""
+        Datadir to apply for both the training and validation datasets. If specified,
+        it will override train_dataset.datadir and val_dataset.datadir"""),
+                                         default=None)
 
     def validate(self):
         super().validate()
@@ -226,15 +230,6 @@ def initialize_object(self) -> Trainer:
         from composer.trainer.trainer import Trainer
         return Trainer.create_from_hparams(hparams=self)
 
-    def set_datadir(self, datadir: str) -> None:
-        """Override the ``datadir`` property in the :attr:`train_dataset` and :attr:`val_dataset`.
-
-        Args:
-            datadir (str): The datadir
-        """
-        self.train_dataset.datadir = datadir
-        self.val_dataset.datadir = datadir
-
     @classmethod
     def load(cls, model: str) -> TrainerHparams:
         model_hparams_file = os.path.join(

docs/source/getting_started/using_composer.rst

@@ -73,7 +73,7 @@ Here are several ways to use the trainer:
        # edit other properties in the hparams object
        hparams.precision = Precision.FP32
        hparams.grad_accum = 2
-       hparams.set_datadir("~/datasets")
+       hparams.datadir = "~/datasets"
 
        trainer = Trainer.create_from_hparams(hparams)
        trainer.fit()
@@ -97,7 +97,7 @@ Here are several ways to use the trainer:
         from composer.trainer import TrainerHparams, Trainer
 
         hparams = TrainerHparams.create('composer/yamls/models/classify_mnist_cpu.yaml')
-        hparams.set_datadir("~/datasets")
+        hparams.datadir = "~/datasets"
         trainer = Trainer.create_from_hparams(hparams)
 
         trainer.fit()

examples/composer.ipynb

@@ -46,7 +46,7 @@
     "trainer_hparams.algorithms = algorithms.load_multiple(\n",
     "  \"blurpool\",\n",
     "  \"scale_schedule\")\n",
-    "trainer_hparams.set_datadir(\"~/datasets\")"
+    "trainer_hparams.datadir = \"~/datasets\""
    ]
   },
   {

examples/run_mosaic_trainer.py

@@ -12,33 +12,26 @@
     --algorithms label_smoothing --alpha 0.1
     --datadir ~/datasets
 """
-import argparse
-import logging
+import sys
+import warnings
+from typing import Type
 
-import composer
 from composer.trainer.trainer import Trainer
 from composer.trainer.trainer_hparams import TrainerHparams
 
-logger = logging.getLogger(__name__)
+
+def warning_on_one_line(message: str, category: Type[Warning], filename: str, lineno: int, file=None, line=None):
+    # From https://stackoverflow.com/questions/26430861/make-pythons-warnings-warn-not-mention-itself
+    return f'{category.__name__}: {message} (source: {filename}:{lineno})\n'
 
 
 def main() -> None:
-    logging.basicConfig()
-    logging.captureWarnings(True)
+    warnings.formatwarning = warning_on_one_line
 
-    parser = argparse.ArgumentParser(parents=[TrainerHparams.get_argparse(cli_args=True)])
-    parser.add_argument(
-        '--datadir',
-        default=None,
-        help='set the datadir for both train and eval datasets',
-    )
+    if len(sys.argv) == 1:
+        sys.argv = [sys.argv[0], "--help"]
 
-    args, _ = parser.parse_known_args()
     hparams = TrainerHparams.create(cli_args=True)  # reads cli args from sys.argv
-    logging.getLogger(composer.__name__).setLevel(hparams.log_level)
-    if args.datadir is not None:
-        hparams.set_datadir(args.datadir)
-        logger.info(f'Set dataset dirs in hparams to: {args.datadir}')
     trainer = Trainer.create_from_hparams(hparams=hparams)
     trainer.fit()