github.com/NVIDIA/aistore@v1.3.23-0.20240517131212-7df6609be51d/python/aistore/pytorch/README.md (about) 1 # AIS Plugin for PyTorch 2 3 ## PyTorch Dataset and DataLoader for AIS 4 5 6 AIS plugin is a PyTorch dataset library to access datasets stored on AIStore. 7 8 PyTorch comes with powerful data loading capabilities, but loading data in PyTorch is fairly complex. One of the best ways to handle it is to start small and then add complexities as and when you need them. Below are some of the ways one can import data stored on AIS in PyTorch. 9 10 ### PyTorch DataLoader 11 12 The PyTorch DataLoader class gives you an iterable over a Dataset. It can be used to shuffle, batch and parallelize operations over your data. 13 14 ### PyTorch Dataset 15 16 But, to create a DataLoader you have to first create a Dataset, which is a class to read samples into memory. Most of the logic of the DataLoader resides on the Dataset. 17 18 PyTorch offers two styles of Dataset class: Map-style and Iterable-style. 19 20 **Note:** Both datasets can be initialized with a urls_list parameter and/or an ais_source_list parameter that defines which objects to reference in AIS. 21 ```urls_list``` can be a single prefix url or a list of prefixes. Eg. ```"ais://bucket1/file-"``` or ```["aws://bucket2/train/", "ais://bucket3/train/"]```. 22 Likewise ```ais_source_list``` can be a single [AISSource](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/ais_source.py) object or a list of [AISSource](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/ais_source.py) objects. Eg. ```"Client.bucket()``` or ```[Client.bucket.objects(), Client.bucket()]```. 23 24 #### ***Map-style Dataset*** 25 26 A map-style dataset in PyTorch implements the `__getitem__()` and `__len__()` functions and provides the user a map from indices/keys to data samples. 27 28 For example, we can access the i-th index label and its corresponding image by dataset[i] from a bucket in AIStore. 29 30 ``` 31 from aistore.pytorch.dataset import AISDataset 32 33 dataset = AISDataset(client_url="http://ais-gateway-url:8080", urls_list='ais://bucket1/') 34 35 for i in range(len(dataset)): # calculate length of all items present using len() function 36 print(dataset[i]) # get object url and byte array of the object 37 38 ``` 39 40 41 #### ***Iterable-style datasets*** 42 43 Iterable-style datasets in PyTorch are tailored for scenarios where data needs to be processed as a stream, and direct indexing is either not feasible or inefficient. Such datasets inherit from IterableDataset and override the `__iter__()` method, providing an iterator over the data samples. 44 45 These datasets are particularly useful when dealing with large data streams that cannot be loaded entirely into memory, or when data is continuously generated or fetched from external sources like databases, remote servers, or live data feeds. 46 47 We have extended support for iterable-style datasets to AIStore (AIS) backends, enabling efficient streaming of data directly from AIS buckets. This approach is ideal for training models on large datasets stored in AIS, minimizing memory overhead and facilitating seamless data ingestion from AIS's distributed object storage. 48 49 Here's how you can use an iterable-style dataset with AIStore: 50 51 ``` 52 from aistore.pytorch.dataset import AISIterDataset 53 from aistore.sdk import Client 54 55 ais_url = os.getenv("AIS_ENDPOINT", "http://localhost:8080") 56 client = Client(ais_url) 57 bucket = client.bucket("my-bck").create(exist_ok=True) 58 59 # Creating objects in our bucket 60 object_names = [f"example_obj_{i}" for i in range(10)] 61 for name in object_names: 62 bucket.object(name).put_content("object content".encode("utf-8")) 63 64 # Creating an object group 65 my_objects = bucket.objects(obj_names=object_names) 66 67 # Initialize the dataset with the AIS client URL and the data source location 68 dataset = AISIterDataset(client_url=ais_url, urls_list="ais://bucket1/", ais_source_list=my_objects) 69 70 # Iterate over the dataset to fetch data samples as a stream 71 for data_sample in dataset: 72 print(data_sample) # Each iteration fetches a data sample (object name and byte array) 73 74 ``` 75 76 77 **Creating DataLoader from AISDataset** 78 ``` 79 from aistore.pytorch.dataset import AISDataset 80 81 train_loader = torch.utils.data.DataLoader( 82 AISDataset( 83 "http://ais-gateway-url:8080", urls_list=["ais://dataset1/train/", "ais://dataset2/train/"]), 84 batch_size=args.batch_size, shuffle=True, 85 num_workers=args.workers, pin_memory=True, 86 ) 87 88 ``` 89 90 ## AIS IO Datapipe 91 92 ### AIS File Lister 93 94 Iterable Datapipe that lists files from the AIS backends with the given URL prefixes. Acceptable prefixes include but not limited to - `ais://bucket-name`, `ais://bucket-name/`, `ais://bucket-name/folder`, `ais://bucket-name/folder/`, `ais://bucket-name/prefix`. 95 96 **Note:** 97 1) This function also supports files from multiple backends (`aws://..`, `gcp://..`, etc) 98 2) Input *must* be a list and direct URLs are not supported. 99 3) `length` is -1 by default, all calls to `len()` are invalid as not all items are iterated at the start. 100 4) This internally uses [AIStore Python SDK](https://github.com/NVIDIA/aistore/tree/main/python). 101 102 ### AIS File Loader 103 104 Iterable Datapipe that loads files from the AIS backends with the given list of URLs (no prefixes allowed). Iterates all files in BytesIO format and returns a tuple (url, BytesIO). 105 **Note:** 106 1) This function also supports files from multiple backends (`aws://..`, `gcp://..`, etc) 107 2) Input *must* be a list and direct URLs are not supported. 108 3) This internally uses [AIStore Python SDK](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk). 109 110 ### Example 111 ``` 112 from aistore.pytorch.aisio import AISFileListerIterDataPipe, AISFileLoaderIterDataPipe 113 114 prefixes = ['ais://bucket1/train/', 'aws://bucket2/train/'] 115 116 list_of_files = AISFileListerIterDataPipe(url='http://ais-gateway-url:8080', source_datapipe=prefixes) 117 118 files = AISFileLoaderIterDataPipe(url='http://ais-gateway-url:8080', source_datapipe=list_of_files) 119 ``` 120 121 For a more in-depth example, see [here](https://github.com/NVIDIA/aistore/blob/main/python/examples/aisio_pytorch_example.ipynb)