FCCV Docs

docs/source-pytorch/data/custom_data_iterables.rst

@@ -0,0 +1,122 @@
+.. _dataiters:
+
+##################################
+Injecting 3rd Party Data Iterables
+##################################
+
+When training a model on a specific task, data loading and preprocessing might become a bottleneck.
+Lightning does not enforce a specific data loading approach nor does it try to control it.
+The only assumption Lightning makes is that the data is returned as an iterable of batches.
+
+For PyTorch-based programs, these iterables are typically instances of :class:`~torch.utils.data.DataLoader`.
+
+However, Lightning also supports other data types such as plain list of batches, generators or other custom iterables.
+
+.. code-block:: python
+
+    # random list of batches
+    data = [(torch.rand(32, 3, 32, 32), torch.randint(0, 10, (32,))) for _ in range(100)]
+    model = LitClassifier()
+    trainer = Trainer()
+    trainer.fit(model, data)
+
+Examples for custom iterables include `NVIDIA DALI <https://github.com/NVIDIA/DALI>`__ or `FFCV <https://github.com/libffcv/ffcv>`__ for computer vision.
+Both libraries offer support for custom data loading and preprocessing (also hardware accelerated) and can be used with Lightning.
+
+
+For example, taking the example from FFCV's readme, we can use it with Lightning by just removing the hardcoded ``ToDevice(0)``
+as Lightning takes care of GPU placement. In case you want to use some data transformations on GPUs, change the
+``ToDevice(0)`` to ``ToDevice(self.trainer.local_rank)`` to correctly map to the desired GPU in your pipeline.
+
+.. code-block:: python
+
+    from ffcv.loader import Loader, OrderOption
+    from ffcv.transforms import ToTensor, ToDevice, ToTorchImage, Cutout
+    from ffcv.fields.decoders import IntDecoder, RandomResizedCropRGBImageDecoder
+
+
+    class CustomClassifier(LitClassifier):
+        def train_dataloader(self):
+
+            # Random resized crop
+            decoder = RandomResizedCropRGBImageDecoder((224, 224))
+
+            # Data decoding and augmentation
+            image_pipeline = [decoder, Cutout(), ToTensor(), ToTorchImage()]
+            label_pipeline = [IntDecoder(), ToTensor()]
+
+            # Pipeline for each data field
+            pipelines = {"image": image_pipeline, "label": label_pipeline}
+
+            # Replaces PyTorch data loader (`torch.utils.data.Dataloader`)
+            loader = Loader(
+                write_path, batch_size=bs, num_workers=num_workers, order=OrderOption.RANDOM, pipelines=pipelines
+            )
+
+            return loader
+
+When moving data to a specific device, you can always refer to ``self.trainer.local_rank`` to get the accelerator
+used by the current process.
+
+By just changing ``device_id=0`` to ``device_id=self.trainer.local_rank`` we can also leverage DALI's GPU decoding:
+
+.. code-block:: python
+
+        from nvidia.dali.pipeline import pipeline_def
+        import nvidia.dali.types as types
+        import nvidia.dali.fn as fn
+        from nvidia.dali.plugin.pytorch import DALIGenericIterator
+        import os
+
+
+        class CustomLitClassifier(LitClassifier):
+            def train_dataloader(self):
+
+                # To run with different data, see documentation of nvidia.dali.fn.readers.file
+                # points to https://github.com/NVIDIA/DALI_extra
+                data_root_dir = os.environ["DALI_EXTRA_PATH"]
+                images_dir = os.path.join(data_root_dir, "db", "single", "jpeg")
+
+                @pipeline_def(num_threads=4, device_id=self.trainer.local_rank)
+                def get_dali_pipeline():
+                    images, labels = fn.readers.file(file_root=images_dir, random_shuffle=True, name="Reader")
+                    # decode data on the GPU
+                    images = fn.decoders.image_random_crop(images, device="mixed", output_type=types.RGB)
+                    # the rest of processing happens on the GPU as well
+                    images = fn.resize(images, resize_x=256, resize_y=256)
+                    images = fn.crop_mirror_normalize(
+                        images,
+                        crop_h=224,
+                        crop_w=224,
+                        mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+                        std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
+                        mirror=fn.random.coin_flip(),
+                    )
+                    return images, labels
+
+                train_data = DALIGenericIterator(
+                    [get_dali_pipeline(batch_size=16)],
+                    ["data", "label"],
+                    reader_name="Reader",
+                )
+
+                return train_data
+
+
+Limitations
+------------
+Lightning works with all kinds of custom data iterables as shown above. There are, however, a few features that cannot
+be supported this way. These restrictions come from the fact that for their support,
+Lightning needs to know a lot on the internals of these iterables.
+
+- In a distributed multi-GPU setting (ddp),
+  Lightning automatically replaces the DataLoader's sampler with its distributed counterpart.
+  This makes sure that each GPU sees a different part of the dataset.
+  As sampling can be implemented in arbitrary ways with custom iterables,
+  there is no way for Lightning to know, how to replace the sampler.
+
+- When training fails for some reason, Lightning is able to extract all of the relevant data from the model,
+  optimizers, trainer and dataloader to resume it at the exact same batch it crashed.
+  This feature is called fault-tolerance and is limited to PyTorch DataLoaders.
+  Lighning needs to know a lot about sampling, fast forwarding and random number handling to enable fault tolerance,
+  meaning that it cannot be supported for arbitrary iterables.

docs/source-pytorch/index.rst

@@ -206,6 +206,7 @@ Current Lightning Users
    Train on single or multiple TPUs <accelerators/tpu>
    Train on MPS <accelerators/mps>
    Use a pretrained model <advanced/pretrained>
+   Inject Custom Data Iterables <data/custom_data_iterables>
    model/own_your_loop
 
 .. toctree::