From 91f23e6bad37870f95035150cb17d5731d59fc58 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Sun, 23 Oct 2022 11:14:23 -0700 Subject: [PATCH 01/11] Initial commit --- doc/source/data/api/input_output.rst | 8 +- doc/source/data/creating-datasets.rst | 13 ++-- .../examples/torch_image_example.ipynb | 52 +++++-------- .../examples/torch_incremental_learning.ipynb | 34 +++------ python/ray/data/__init__.py | 2 + python/ray/data/datasource/__init__.py | 2 - .../ray/data/datasource/torch_datasource.py | 73 ------------------- python/ray/data/read_api.py | 36 +++++++++ python/ray/data/tests/test_dataset_formats.py | 35 ++------- 9 files changed, 80 insertions(+), 175 deletions(-) delete mode 100644 python/ray/data/datasource/torch_datasource.py diff --git a/doc/source/data/api/input_output.rst b/doc/source/data/api/input_output.rst index 64f851231e4b..5fd835f345b3 100644 --- a/doc/source/data/api/input_output.rst +++ b/doc/source/data/api/input_output.rst @@ -134,6 +134,11 @@ Mars .. automethod:: ray.data.Dataset.to_mars :noindex: +Torch +----- + +.. autofunction:: ray.data.from_torch + HuggingFace ------------ @@ -190,9 +195,6 @@ Built-in Datasources .. autoclass:: ray.data.datasource.SimpleTensorFlowDatasource :members: -.. autoclass:: ray.data.datasource.SimpleTorchDatasource - :members: - .. autoclass:: ray.data.datasource.TFRecordDatasource :members: diff --git a/doc/source/data/creating-datasets.rst b/doc/source/data/creating-datasets.rst index ec0836b17c1c..b8b7b2b999b6 100644 --- a/doc/source/data/creating-datasets.rst +++ b/doc/source/data/creating-datasets.rst @@ -476,23 +476,20 @@ From Torch and TensorFlow .. tabbed:: PyTorch If you already have a Torch dataset available, you can create a Ray Dataset using - :py:class:`~ray.data.datasource.SimpleTorchDatasource`. + :class:`~ray.data.from_torch`. .. warning:: - :py:class:`~ray.data.datasource.SimpleTorchDatasource` doesn't support parallel + :py:class:`~ray.data.datasource.from_torch` doesn't support parallel reads. You should only use this datasource for small datasets like MNIST or CIFAR. .. code-block:: python - import ray.data - from ray.data.datasource import SimpleTorchDatasource + import ray import torchvision - dataset_factory = lambda: torchvision.datasets.MNIST("data", download=True) - dataset = ray.data.read_datasource( - SimpleTorchDatasource(), parallelism=1, dataset_factory=dataset_factory - ) + dataset = torchvision.datasets.MNIST("data", download=True) + dataset = ray.data.from_torch(dataset) dataset.take(1) # (, 5) diff --git a/doc/source/ray-air/examples/torch_image_example.ipynb b/doc/source/ray-air/examples/torch_image_example.ipynb index dca03ffd2377..3f3a01c68346 100644 --- a/doc/source/ray-air/examples/torch_image_example.ipynb +++ b/doc/source/ray-air/examples/torch_image_example.ipynb @@ -63,41 +63,37 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2022-08-30 15:30:36,678\tINFO worker.py:1510 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "2022-08-30 15:30:37,791\tWARNING read_api.py:291 -- ⚠️ The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n", - "\u001b[2m\u001b[36m(_get_read_tasks pid=3958)\u001b[0m 2022-08-30 15:30:37,789\tWARNING torch_datasource.py:55 -- `SimpleTorchDatasource` doesn't support parallel reads. The `parallelism` argument will be ignored.\n" + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\u001b[2m\u001b[36m(_execute_read_task pid=3958)\u001b[0m Using downloaded and verified file: ./data/cifar-10-python.tar.gz\n", - "\u001b[2m\u001b[36m(_execute_read_task pid=3958)\u001b[0m Extracting ./data/cifar-10-python.tar.gz to ./data\n" + "100%|██████████| 170498071/170498071 [00:21<00:00, 7792736.24it/s]\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2022-08-30 15:30:44,508\tWARNING read_api.py:291 -- ⚠️ The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n", - "\u001b[2m\u001b[36m(_get_read_tasks pid=3958)\u001b[0m 2022-08-30 15:30:44,507\tWARNING torch_datasource.py:55 -- `SimpleTorchDatasource` doesn't support parallel reads. The `parallelism` argument will be ignored.\n" + "Extracting data/cifar-10-python.tar.gz to data\n", + "Files already downloaded and verified\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "\u001b[2m\u001b[36m(_execute_read_task pid=3958)\u001b[0m Files already downloaded and verified\n" + "2022-10-23 10:33:48,403\tINFO worker.py:1518 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n" ] } ], "source": [ "import ray\n", - "from ray.data.datasource import SimpleTorchDatasource\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "\n", @@ -105,25 +101,11 @@ " [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n", ")\n", "\n", + "train_dataset = torchvision.datasets.CIFAR10(\"data\", download=True, train=True, transform=transform)\n", + "test_dataset = torchvision.datasets.CIFAR10(\"data\", download=True, train=False, transform=transform)\n", "\n", - "def train_dataset_factory():\n", - " return torchvision.datasets.CIFAR10(\n", - " root=\"./data\", download=True, train=True, transform=transform\n", - " )\n", - "\n", - "\n", - "def test_dataset_factory():\n", - " return torchvision.datasets.CIFAR10(\n", - " root=\"./data\", download=True, train=False, transform=transform\n", - " )\n", - "\n", - "\n", - "train_dataset: ray.data.Dataset = ray.data.read_datasource(\n", - " SimpleTorchDatasource(), dataset_factory=train_dataset_factory\n", - ")\n", - "test_dataset: ray.data.Dataset = ray.data.read_datasource(\n", - " SimpleTorchDatasource(), dataset_factory=test_dataset_factory\n", - ")" + "train_dataset: ray.data.Dataset = ray.data.from_torch(train_dataset)\n", + "test_dataset: ray.data.Dataset = ray.data.from_torch(test_dataset)" ] }, { @@ -156,7 +138,7 @@ "id": "a89b59e8", "metadata": {}, "source": [ - "{py:class}`SimpleTorchDatasource ` doesn't parallelize reads, so you shouldn't use it with larger datasets.\n", + "{py:class}`from_torch ` doesn't parallelize reads, so you shouldn't use it with larger datasets.\n", "\n", "Next, let's represent our data using pandas dataframes instead of tuples. This lets us call {py:meth}`Dataset.iter_torch_batches ` later in the tutorial." ] @@ -828,7 +810,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.12 ('.venv': venv)", + "display_name": "Python 3.10.8 ('.venv': venv)", "language": "python", "name": "python3" }, @@ -842,11 +824,11 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.8" }, "vscode": { "interpreter": { - "hash": "a658351b4133f922c5967ed6133cfc05c9f16c53a5161e5843ace3f528fccaf5" + "hash": "c704e19737f24b51bc631dadcac7a7e356bb35d1c5cd7766248d8a6946059909" } } }, diff --git a/doc/source/ray-air/examples/torch_incremental_learning.ipynb b/doc/source/ray-air/examples/torch_incremental_learning.ipynb index 1809ecb7444d..a58796248bda 100644 --- a/doc/source/ray-air/examples/torch_incremental_learning.ipynb +++ b/doc/source/ray-air/examples/torch_incremental_learning.ipynb @@ -255,7 +255,7 @@ "This will allow us to apply built-in preprocessors to the Ray Dataset and allow Ray Datasets to be used with Ray AIR Predictors.\n", " \n", "\n", - "For this example, since we are just working with MNIST dataset, which is small, we use the {py:class}`~ray.data.datasource.SimpleTorchDatasource` which just loads the full MNIST dataset into memory.\n", + "For this example, since we are just working with MNIST dataset, which is small, we use the {py:class}`~ray.data.datasource.from_torch` which just loads the full MNIST dataset into memory.\n", "\n", "For loading larger datasets in a parallel fashion, you should use [Ray Dataset's additional read APIs](https://docs.ray.io/en/master/data/dataset.html#supported-input-formats) to load data from parquet, csv, image files, and more!" ] @@ -274,7 +274,6 @@ "from torchvision.transforms import RandomCrop\n", "\n", "import ray\n", - "from ray.data.datasource.torch_datasource import SimpleTorchDatasource\n", "\n", "\n", "def get_mnist_dataset(train: bool = True) -> ray.data.Dataset:\n", @@ -283,15 +282,15 @@ " Args:\n", " train: Whether to return the train dataset or test dataset.\n", " \"\"\"\n", - "\n", - " def mnist_dataset_factory():\n", - " if train:\n", - " # Only perform random cropping on the Train dataset.\n", - " transform = RandomCrop(28, padding=4)\n", - " else:\n", - " transform = None\n", - " return torchvision.datasets.MNIST(\"./data\", download=True, train=train, transform=transform)\n", - "\n", + " if train:\n", + " # Only perform random cropping on the Train dataset.\n", + " transform = RandomCrop(28, padding=4)\n", + " else:\n", + " transform = None\n", + " \n", + " mnist_dataset = torchvision.datasets.MNIST(\"./data\", download=True, train=train, transform=transform)\n", + " mnist_dataset = ray.data.from_torch(mnist_dataset)\n", + " \n", " def convert_batch_to_pandas(batch):\n", " images = [np.array(item[0]) for item in batch]\n", " labels = [item[1] for item in batch]\n", @@ -300,9 +299,6 @@ "\n", " return df\n", "\n", - " mnist_dataset = ray.data.read_datasource(\n", - " SimpleTorchDatasource(), dataset_factory=mnist_dataset_factory\n", - " )\n", " mnist_dataset = mnist_dataset.map_batches(convert_batch_to_pandas)\n", " return mnist_dataset" ] @@ -694,15 +690,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-09-23 16:31:20,898\tWARNING read_api.py:293 -- ⚠️ The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n", - "\u001b[2m\u001b[36m(_get_read_tasks pid=855942)\u001b[0m 2022-09-23 16:31:20,896\tWARNING torch_datasource.py:55 -- `SimpleTorchDatasource` doesn't support parallel reads. The `parallelism` argument will be ignored.\n", "Read->Map_Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00, 3.42s/it]\n", - "2022-09-23 16:31:28,089\tWARNING read_api.py:293 -- ⚠️ The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n", - "\u001b[2m\u001b[36m(_get_read_tasks pid=855942)\u001b[0m 2022-09-23 16:31:28,087\tWARNING torch_datasource.py:55 -- `SimpleTorchDatasource` doesn't support parallel reads. The `parallelism` argument will be ignored.\n", "Read->Map_Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5.27it/s]\n", "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.40it/s]\n", - "2022-09-23 16:31:29,255\tWARNING read_api.py:293 -- ⚠️ The number of blocks in this dataset (1) limits its parallelism to 1 concurrent tasks. This is much less than the number of available CPU slots in the cluster. Use `.repartition(n)` to increase the number of dataset blocks.\n", - "\u001b[2m\u001b[36m(_get_read_tasks pid=855942)\u001b[0m 2022-09-23 16:31:29,253\tWARNING torch_datasource.py:55 -- `SimpleTorchDatasource` doesn't support parallel reads. The `parallelism` argument will be ignored.\n", "Read->Map_Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4.17it/s]\n", "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.78it/s]" ] @@ -921,8 +911,6 @@ "Map_Batches: 0%| | 0/1 [00:00Map_Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 4.26it/s]\n", "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.72it/s]" ] @@ -1143,8 +1131,6 @@ "Map_Batches: 0%| | 0/1 [00:00Map_Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 5.31it/s]\n", "Map Progress (1 actors 1 pending): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.76it/s]" ] diff --git a/python/ray/data/__init__.py b/python/ray/data/__init__.py index a35a22702ef6..527711745f65 100644 --- a/python/ray/data/__init__.py +++ b/python/ray/data/__init__.py @@ -22,6 +22,7 @@ from_pandas, from_pandas_refs, from_spark, + from_torch, range, range_arrow, range_table, @@ -64,6 +65,7 @@ "from_pandas", "from_pandas_refs", "from_spark", + "from_torch", "from_huggingface", "range", "range_table", diff --git a/python/ray/data/datasource/__init__.py b/python/ray/data/datasource/__init__.py index efa9d78c0d84..68eb7c4ceff6 100644 --- a/python/ray/data/datasource/__init__.py +++ b/python/ray/data/datasource/__init__.py @@ -39,7 +39,6 @@ from ray.data.datasource.tfrecords_datasource import TFRecordDatasource from ray.data.datasource.tensorflow_datasource import SimpleTensorFlowDatasource from ray.data.datasource.text_datasource import TextDatasource -from ray.data.datasource.torch_datasource import SimpleTorchDatasource __all__ = [ "BaseFileMetadataProvider", @@ -71,7 +70,6 @@ "ReadTask", "Reader", "SimpleTensorFlowDatasource", - "SimpleTorchDatasource", "TextDatasource", "TFRecordDatasource", "WriteResult", diff --git a/python/ray/data/datasource/torch_datasource.py b/python/ray/data/datasource/torch_datasource.py deleted file mode 100644 index a6de55fad34a..000000000000 --- a/python/ray/data/datasource/torch_datasource.py +++ /dev/null @@ -1,73 +0,0 @@ -import logging -from typing import TYPE_CHECKING, Callable, Iterator, List - -from ray.data.block import Block, BlockMetadata, T -from ray.data.datasource import Datasource, ReadTask -from ray.util.annotations import PublicAPI - -if TYPE_CHECKING: - import torch.utils.data - -logger = logging.getLogger(__name__) - - -@PublicAPI -class SimpleTorchDatasource(Datasource[T]): - """A datasource that let's you use Torch datasets with Ray Data. - - .. warning:: - ``SimpleTorchDatasource`` doesn't support parallel reads. You should only use - this datasource for small datasets like MNIST or CIFAR. - - Example: - >>> import ray - >>> from ray.data.datasource import SimpleTorchDatasource - >>> - >>> dataset_factory = lambda: torchvision.datasets.MNIST("data", download=True) - >>> dataset = ray.data.read_datasource( # doctest: +SKIP - ... SimpleTorchDatasource(), parallelism=1, dataset_factory=dataset_factory - ... ) - >>> dataset.take(1) # doctest: +SKIP - (, 5) - """ - - def prepare_read( - self, - parallelism: int, - dataset_factory: Callable[[], "torch.utils.data.Dataset"], - ) -> List[ReadTask]: - """Return a read task that loads a Torch dataset. - - Arguments: - parallelism: This argument isn't used. - dataset_factory: A no-argument function that returns the Torch dataset to - be read. - """ - import torch.utils.data - - if isinstance(dataset_factory, torch.utils.data.Dataset): - raise ValueError( - "Expected a function that returns a Torch dataset, but got a " - "`torch.utils.data.Dataset` instead." - ) - - if parallelism > 1: - logger.warn( - "`SimpleTorchDatasource` doesn't support parallel reads. The " - "`parallelism` argument will be ignored." - ) - - def read_fn() -> Iterator[Block]: - # Load the entire dataset into memory. - block = list(dataset_factory()) - # Store the data in a single block. - yield block - - metadata = BlockMetadata( - num_rows=None, - size_bytes=None, - schema=None, - input_files=None, - exec_stats=None, - ) - return [ReadTask(read_fn, metadata)] diff --git a/python/ray/data/read_api.py b/python/ray/data/read_api.py index 763d18a8fe41..c06808bae4f7 100644 --- a/python/ray/data/read_api.py +++ b/python/ray/data/read_api.py @@ -57,6 +57,7 @@ import pandas import pyarrow import pyspark + import torch T = TypeVar("T") @@ -1326,6 +1327,41 @@ def convert(ds: "datasets.Dataset") -> Dataset[ArrowRow]: ) +@PublicAPI +def from_torch( + dataset: "torch.utils.data.Dataset", +) -> Dataset: + """Create a dataset from a Torch dataset. + + This function is intended for small datasets like MNIST or CIFAR-10. + + .. warning:: + If your dataset is large, this function may execute slowly or raise an + out-of-memory error. To avoid issues, read the underyling data with a function + like :meth:`~ray.data.read_images`. + + .. note:: + This function isn't paralellized. It loads the entire dataset into the head + node's memory before moving the data to the distributed object store. + + Examples: + >>> import ray + >>> from torchvision import datasets + >>> dataset = datasets.MNIST("data", download=True) + >>> dataset = ray.data.from_torch(dataset) + Dataset(num_blocks=200, num_rows=60000, schema=) + >>> dataset.take(1) + [(, 5)] + + Args: + dataset: A Torch dataset. + + Returns: + A :class:`Dataset` that contains the samples stored in the Torch dataset. + """ + return from_items(list(dataset)) + + def _df_to_block(df: "pandas.DataFrame") -> Block[ArrowRow]: stats = BlockExecStats.builder() import pyarrow as pa diff --git a/python/ray/data/tests/test_dataset_formats.py b/python/ray/data/tests/test_dataset_formats.py index d70e2f536e60..41dc01f88cfd 100644 --- a/python/ray/data/tests/test_dataset_formats.py +++ b/python/ray/data/tests/test_dataset_formats.py @@ -6,6 +6,7 @@ import pyarrow as pa import pyarrow.parquet as pq import pytest +import torchvision from ray.data.datasource.file_meta_provider import _handle_read_os_error from fsspec.implementations.local import LocalFileSystem @@ -18,7 +19,6 @@ Datasource, DummyOutputDatasource, SimpleTensorFlowDatasource, - SimpleTorchDatasource, WriteResult, ) @@ -217,41 +217,16 @@ def dataset_factory(): tf.debugging.assert_equal(expected_label, actual_label) -def test_torch_datasource(ray_start_regular_shared, local_path): - import torchvision - - # Download datasets to separate folders to prevent interference. - torch_dataset_root = os.path.join(local_path, "torch") - ray_dataset_root = os.path.join(local_path, "ray") - - torch_dataset = torchvision.datasets.MNIST(torch_dataset_root, download=True) +def test_from_torch(ray_start_regular_shared, tmp_path): + torch_dataset = torchvision.datasets.MNIST(tmp_path, download=True) expected_data = list(torch_dataset) - def dataset_factory(): - return torchvision.datasets.MNIST(ray_dataset_root, download=True) - - ray_dataset = ray.data.read_datasource( - SimpleTorchDatasource(), parallelism=1, dataset_factory=dataset_factory - ) - actual_data = list(next(ray_dataset.iter_batches(batch_size=None))) + ray_dataset = ray.data.from_torch(torch_dataset) + actual_data = list(ray_dataset.take_all()) assert actual_data == expected_data -def test_torch_datasource_value_error(shutdown_only, local_path): - import torchvision - - dataset = torchvision.datasets.MNIST(local_path, download=True) - - with pytest.raises(ValueError): - # `dataset_factory` should be a function, not a Torch dataset. - ray.data.read_datasource( - SimpleTorchDatasource(), - parallelism=1, - dataset_factory=dataset, - ) - - class NodeLoggerOutputDatasource(Datasource[Union[ArrowRow, int]]): """A writable datasource that logs node IDs of write tasks, for testing.""" From 76627c083a172b259552884008f92dc182ed967f Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Sun, 23 Oct 2022 11:26:44 -0700 Subject: [PATCH 02/11] Update wording --- python/ray/data/read_api.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/ray/data/read_api.py b/python/ray/data/read_api.py index c06808bae4f7..a8fc64022b59 100644 --- a/python/ray/data/read_api.py +++ b/python/ray/data/read_api.py @@ -1333,7 +1333,7 @@ def from_torch( ) -> Dataset: """Create a dataset from a Torch dataset. - This function is intended for small datasets like MNIST or CIFAR-10. + This function is inefficient. Use it to read small datasets or prototype. .. warning:: If your dataset is large, this function may execute slowly or raise an @@ -1342,7 +1342,7 @@ def from_torch( .. note:: This function isn't paralellized. It loads the entire dataset into the head - node's memory before moving the data to the distributed object store. + node's memory, and then moves the data to the distributed object store. Examples: >>> import ray From 1e660c1bf5587828e4c0502e5684fbdc7435fc32 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Sun, 23 Oct 2022 11:44:34 -0700 Subject: [PATCH 03/11] Update wording --- python/ray/data/read_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/ray/data/read_api.py b/python/ray/data/read_api.py index a8fc64022b59..552629369ed8 100644 --- a/python/ray/data/read_api.py +++ b/python/ray/data/read_api.py @@ -1342,7 +1342,7 @@ def from_torch( .. note:: This function isn't paralellized. It loads the entire dataset into the head - node's memory, and then moves the data to the distributed object store. + node's memory before moving the data to the distributed object store. Examples: >>> import ray From b58155f29b4aa8ee58cc8b44f9b733cd18ae6eaf Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Sun, 23 Oct 2022 11:48:55 -0700 Subject: [PATCH 04/11] Fix docstring example --- python/ray/data/read_api.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/ray/data/read_api.py b/python/ray/data/read_api.py index 552629369ed8..5ad9ab6e6795 100644 --- a/python/ray/data/read_api.py +++ b/python/ray/data/read_api.py @@ -1349,6 +1349,7 @@ def from_torch( >>> from torchvision import datasets >>> dataset = datasets.MNIST("data", download=True) >>> dataset = ray.data.from_torch(dataset) + >>> dataset Dataset(num_blocks=200, num_rows=60000, schema=) >>> dataset.take(1) [(, 5)] From 2ecde6fbeb5b1c9b541ea8ca4cb30f2dbbdb8d20 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Wed, 2 Nov 2022 16:01:44 -0700 Subject: [PATCH 05/11] Try fix tests --- python/ray/data/tests/test_dataset_formats.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/ray/data/tests/test_dataset_formats.py b/python/ray/data/tests/test_dataset_formats.py index 41dc01f88cfd..0488cd5d8afa 100644 --- a/python/ray/data/tests/test_dataset_formats.py +++ b/python/ray/data/tests/test_dataset_formats.py @@ -217,7 +217,7 @@ def dataset_factory(): tf.debugging.assert_equal(expected_label, actual_label) -def test_from_torch(ray_start_regular_shared, tmp_path): +def test_from_torch(shutdown_only, tmp_path): torch_dataset = torchvision.datasets.MNIST(tmp_path, download=True) expected_data = list(torch_dataset) From eaa62b414345de7382bf5d07fd42bf8758d7345a Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Wed, 2 Nov 2022 19:49:59 -0700 Subject: [PATCH 06/11] Update `requirements-doc` --- doc/requirements-doc.txt | 2 ++ python/ray/data/tests/test_dataset_formats.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/requirements-doc.txt b/doc/requirements-doc.txt index 81886727c1d5..06ba20c994ad 100644 --- a/doc/requirements-doc.txt +++ b/doc/requirements-doc.txt @@ -31,6 +31,8 @@ werkzeug wandb tensorflow; sys_platform != 'darwin' or platform_machine != 'arm64' tensorflow-macos; sys_platform == 'darwin' and platform_machine == 'arm64' +torch +torchvision transformers # Ray libraries diff --git a/python/ray/data/tests/test_dataset_formats.py b/python/ray/data/tests/test_dataset_formats.py index 5caffce4dd69..184a468f0128 100644 --- a/python/ray/data/tests/test_dataset_formats.py +++ b/python/ray/data/tests/test_dataset_formats.py @@ -216,7 +216,7 @@ def dataset_factory(): tf.debugging.assert_equal(expected_label, actual_label) -def test_from_torch(shutdown_only, tmp_path): +def test_from_torch(ray_start_regular_shared, tmp_path): torch_dataset = torchvision.datasets.MNIST(tmp_path, download=True) expected_data = list(torch_dataset) From 7d74bde200d356bef8ac303e50aa763af6edc434 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Thu, 3 Nov 2022 11:52:34 -0700 Subject: [PATCH 07/11] Update python/ray/data/tests/test_dataset_formats.py Co-authored-by: Clark Zinzow Signed-off-by: Balaji Veeramani --- python/ray/data/tests/test_dataset_formats.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/ray/data/tests/test_dataset_formats.py b/python/ray/data/tests/test_dataset_formats.py index 184a468f0128..5caffce4dd69 100644 --- a/python/ray/data/tests/test_dataset_formats.py +++ b/python/ray/data/tests/test_dataset_formats.py @@ -216,7 +216,7 @@ def dataset_factory(): tf.debugging.assert_equal(expected_label, actual_label) -def test_from_torch(ray_start_regular_shared, tmp_path): +def test_from_torch(shutdown_only, tmp_path): torch_dataset = torchvision.datasets.MNIST(tmp_path, download=True) expected_data = list(torch_dataset) From 1974c0357a89579287997b275991483e8f40292a Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Thu, 3 Nov 2022 13:57:30 -0700 Subject: [PATCH 08/11] Appease doctest --- doc/data/MNIST/raw/train-images-idx3-ubyte | Bin 0 -> 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