PR feedback

Signed-off-by: James Bornholt <[email protected]>

examples/pytorch/resnet.py

@@ -1,5 +1,5 @@
 """This is a simple example of how to use Mountpoint as a PyTorch data loader via the torchdata
-library. By default, it trains a ResNet-18 model for a few epochs using PyTorch Lightning, with
+library. By default, it trains a ResNet-50 model for a few epochs using PyTorch Lightning, with
 synthetic ImageNet-sized training data, stored in S3 as shards in WebDataset format or as individual
 images.
 
@@ -200,16 +200,16 @@ def make_s3_datapipe(args: argparse.Namespace) -> (torchdata.datapipes.iter.Iter
     the local path of Mountpoint if args.source_kind == mountpoint, or None otherwise."""
     if args.source_kind == "mountpoint" or args.source_kind == "local":
         if args.source_kind == "mountpoint":
-            local_path = make_mountpoint(args.s3url, args.mountpoint_path, args.mountpoint_args)
+            local_path = make_mountpoint(args.path, args.mountpoint_path, args.mountpoint_args)
             sentinel_path = local_path
         elif args.source_kind == "local":
-            local_path, sentinel_path = args.s3url, None
+            local_path, sentinel_path = args.path, None
         lister = torchdata.datapipes.iter.FileLister([local_path], recursive=True)
         lister = lister.sharding_filter()
         return torchdata.datapipes.iter.FileOpener(lister, mode="rb"), sentinel_path
     elif args.source_kind == "fsspec":
         # Load from S3 using the FSSpec/S3FS libraries
-        lister = torchdata.datapipes.iter.FSSpecFileLister([args.s3url])
+        lister = torchdata.datapipes.iter.FSSpecFileLister([args.path])
         if args.dataset_format == "single":
             # fsspec lists directories rather than recursively, so need a second-level list
             lister = torchdata.datapipes.iter.FSSpecFileLister(lister)
@@ -219,7 +219,7 @@ def make_s3_datapipe(args: argparse.Namespace) -> (torchdata.datapipes.iter.Iter
         # Load from S3 using the S3-specific IO datapipe (requires a BUILD_S3=1 version of torchdata)
         if args.region is None:
             raise Exception("region must be specified for s3io")
-        lister = torchdata.datapipes.iter.S3FileLister([args.s3url], region=args.region)
+        lister = torchdata.datapipes.iter.S3FileLister([args.path], region=args.region)
         lister = lister.sharding_filter()
         return torchdata.datapipes.iter.S3FileLoader(lister, region=args.region), None
     else:
@@ -231,18 +231,18 @@ def make_dataset(args: argparse.Namespace) -> (torch.utils.data.Dataset, Optiona
     args.source_kind == mountpoint, or None otherwise."""
     if args.dataset_format == "imagefolder":
         if args.source_kind == "mountpoint":
-            local_path = make_mountpoint(args.s3url, args.mountpoint_path, args.mountpoint_args)
+            local_path = make_mountpoint(args.path, args.mountpoint_path, args.mountpoint_args)
             return torchvision.datasets.ImageFolder(local_path), local_path
         elif args.source_kind == "local":
-            return torchvision.datasets.ImageFolder(args.s3url), None
+            return torchvision.datasets.ImageFolder(args.path), None
         else:
             raise Exception(f"imagefolder dataset only supports mountpoint and local sources")
 
     pipe, local_path = make_s3_datapipe(args)
     if args.dataset_format == "webdataset":
         return pipe.load_from_tar().webdataset().map(load_image), local_path
     elif args.dataset_format == "single":
-        return pipe.map(lambda x: {".jpg": x[1], ".cls": extract_class(x[0], args.s3url)}).map(load_image), local_path
+        return pipe.map(lambda x: {".jpg": x[1], ".cls": extract_class(x[0], args.path)}).map(load_image), local_path
     else:
         raise Exception(f"unknown dataset format {args.dataset_format}")
 
@@ -298,7 +298,7 @@ def run_training(dataset: torch.utils.data.Dataset, args: argparse.Namespace, lo
         "--model", default="resnet50", help="model name to train (must be a model from `torchvision.models`)"
     )
     p_train.add_argument(
-        "s3url", help="S3 URL for sharded training data directory (starts with 's3://', ends with '/')"
+        "path", help="path to sharded training data directory (can be an S3 URI like 's3://bucket/prefix/' or a local folder)"
     )
     p_train.add_argument(
         "--source-kind",