[Datasets] Add from_torch

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

doc/source/data/api/input_output.rst

@@ -137,6 +137,11 @@ Mars
 .. automethod:: ray.data.Dataset.to_mars
     :noindex:
 
+Torch
+-----
+
+.. autofunction:: ray.data.from_torch
+
 HuggingFace
 ------------
 
@@ -193,9 +198,6 @@ Built-in Datasources
 .. autoclass:: ray.data.datasource.SimpleTensorFlowDatasource
     :members:
 
-.. autoclass:: ray.data.datasource.SimpleTorchDatasource
-    :members:
-
 .. autoclass:: ray.data.datasource.TFRecordDatasource
     :members:
 

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)
         # (<PIL.Image.Image image mode=L size=28x28 at 0x1142CCA60>, 5)
 

doc/source/ray-air/examples/torch_image_example.ipynb

@@ -63,67 +63,49 @@
             "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",
                 "transform = transforms.Compose(\n",
                 "    [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 <ray.data.datasource.SimpleTorchDatasource>` doesn't parallelize reads, so you shouldn't use it with larger datasets.\n",
+                "{py:class}`from_torch <ray.data.from_torch>` doesn't parallelize reads, so you shouldn't use it with larger datasets.\n",
                 "\n",
                 "Next, let's represent our data using a dictionary of ndarrays instead of tuples. This lets us call {py:meth}`Dataset.iter_torch_batches <ray.data.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"
             }
         }
     },