github.com/NVIDIA/aistore@v1.3.23-0.20240517131212-7df6609be51d/python/examples/aisio-pytorch/dataset_example.ipynb (about)

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     7      "# PyTorch: Creating Datasets from AIS Backend\n",
     8      "In the rapidly evolving field of machine learning, efficient data handling is crucial for training models effectively. This guide explores how to leverage AIStore (AIS), a scalable object storage solution, to create and manage datasets directly within PyTorch. We'll cover the integration of AIS with PyTorch through two custom dataset classes: AISDataset for map-style datasets and AISIterDataset for iterable datasets. These classes facilitate seamless access to data stored in AIS, supporting a variety of machine learning workflows. For details refer to [README](https://github.com/NVIDIA/aistore/tree/main/python/aistore/pytorch).\n"
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    16     "source": [
    17      "# Imports\n",
    18      "import os\n",
    19      "import torch\n",
    20      "from torch.utils.data import DataLoader\n",
    21      "from aistore.pytorch.dataset import AISDataset, AISIterDataset\n",
    22      "from aistore.sdk import Client"
    23     ]
    24    },
    25    {
    26     "cell_type": "markdown",
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    28     "source": [
    29      "## Setup client and necessary buckets "
    30     ]
    31    },
    32    {
    33     "cell_type": "code",
    34     "execution_count": 4,
    35     "metadata": {},
    36     "outputs": [],
    37     "source": [
    38      "ais_url = os.getenv(\"AIS_ENDPOINT\", \"http://localhost:8080\")\n",
    39      "client = Client(ais_url)\n",
    40      "bucket = client.bucket(\"my-bck\").create(exist_ok=True)"
    41     ]
    42    },
    43    {
    44     "cell_type": "markdown",
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    46     "source": [
    47      "### Create some objects in the bucket"
    48     ]
    49    },
    50    {
    51     "cell_type": "code",
    52     "execution_count": 7,
    53     "metadata": {},
    54     "outputs": [],
    55     "source": [
    56      "object_names = [f\"example_obj_{i}\" for i in range(10)]\n",
    57      "for name in object_names:\n",
    58      "    bucket.object(name).put_content(f\"{name} - object content\".encode(\"utf-8\"))"
    59     ]
    60    },
    61    {
    62     "cell_type": "markdown",
    63     "metadata": {},
    64     "source": [
    65      "### Creating a Map-Style Dataset"
    66     ]
    67    },
    68    {
    69     "cell_type": "code",
    70     "execution_count": null,
    71     "metadata": {},
    72     "outputs": [],
    73     "source": [
    74      "map_dataset = AISDataset(client_url=ais_url, urls_list='ais://my-bck')\n",
    75      "\n",
    76      "for i in range(len(map_dataset)): # calculate length of all items present using len() function\n",
    77      "    print(map_dataset[i]) # get object url and byte array of the object\n",
    78      "\n",
    79      "# Create a DataLoader from the dataset\n",
    80      "map_data_loader = DataLoader(map_dataset, batch_size=10, num_workers=2)\n"
    81     ]
    82    },
    83    {
    84     "cell_type": "markdown",
    85     "metadata": {},
    86     "source": [
    87      "### Creating a Iterable-Style Dataset"
    88     ]
    89    },
    90    {
    91     "cell_type": "code",
    92     "execution_count": null,
    93     "metadata": {},
    94     "outputs": [],
    95     "source": [
    96      "iter_dataset = AISIterDataset(client_url=ais_url, ais_source_list=bucket)\n",
    97      "for sample in iter_dataset:\n",
    98      "    print(sample) # get object url and byte array of the object\n",
    99      "\n",
   100      "# Create a DataLoader from the dataset\n",
   101      "iter_data_loader = DataLoader(iter_dataset, batch_size=10, num_workers=2)"
   102     ]
   103    },
   104    {
   105     "cell_type": "markdown",
   106     "metadata": {},
   107     "source": [
   108      "**Note:** We can also provide an etl_name (which is present in our cluster) to the Dataset to apply an etl to the objects. For example -  `AISDataset(client_url=ais_url, urls_list='ais://my-bck', etl_name=your_etl_name)`"
   109     ]
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