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- Documentation
Torch Object StorageTool GuideTOS PyTorch ConnectorUsing TOS PyTorch ConnectorTosMapDataset
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TosMapDataset
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This topic explains how to construct a dataset using TosMapDataset.
Prerequisites
You have installed and configured TOS PyTorch Connector. For instructions, see Installing TOS PyTorch Connector and Configuring TOS PyTorch Connector.
Constructing a dataset
How to construct
There are two ways to construct a dataset using TosMapDataset.
- TOS_URI prefix: This option is ideal for scenarios where TOS paths follow a consistent pattern.
- List of TOS URIs: This option is ideal for scenarios where TOS paths are specific but scattered.
Constructing a dataset using a TOS_URI prefix
The following example demonstrates how to construct a dataset using the from_prefix() method of TosMapDataset.
class TosMapDataset(torch.utils.data.Dataset):
# Construct a dataset using from_prefix
@classmethod
def from_prefix(cls, prefix: str, *, region: str, endpoint: Optional[str] = None,
transform: Callable[[TosObjectReader], Any] = None,
cred: Optional[CredentialProvider] = None,
client_conf: Optional[TosClientConfig] = None,
use_native_client: bool = True,
reader_type: Optional[ReaderType] = None,
buffer_size: Optional[int] = None,
enable_crc: bool = True,
prefetch_concurrency: int = 0):
...
Constructing a dataset from a list of TOS URIs
The following example demonstrates how to construct a dataset using the from_urls() method of TosMapDataset.
class TosMapDataset(torch.utils.data.Dataset):
# Construct a dataset using from_urls
@classmethod
def from_urls(cls, urls: Union[str, Iterator[str]], *, region: str, endpoint: Optional[str] = None,
transform: Callable[[TosObjectReader], Any] = None,
cred: Optional[CredentialProvider] = None,
client_conf: Optional[TosClientConfig] = None,
use_native_client: bool = True,
reader_type: Optional[ReaderType] = None,
buffer_size: Optional[int] = None,
enable_crc: bool = True,
prefetch_concurrency: int = 0)
...
Supported parameters
Parameter | Required | Type | Example value | Description |
Parameter | Required | Type | Example value | Description |
urls | Yes | str ｜ Iterator[str] | ['tos://bucket/key1', 'tos://bucket/key2'] | Format for a single URL: tos://{bucketname}/{objectname}. |
prefix | Yes | str | tos://bucket/prefix | Format: tos://{bucketname}/{prefix}. |
region | Yes | str | cn-beijing | TOS region ID. |
endpoint | No | str | http://tos-cn-beijing.ivolces.com | TOS endpoint. |
transform | No | Callable[[TosObjectReader], Any] | def transform(item): data = item.read() return item.key, item.size | Transformation function. |
cred | No | CredentialProvider | CredentialProvider('ak', 'sk') | Used to pass credentials. |
client_conf | No | TosClientConfig | - | Used to configure the connector. |
reader_type | No | ReaderType | ReaderType.RANGED | The reader type for TosObjectReader. Possible values:
|
buffer_size | No | int | 8388608 | The size of a cache block. This parameter is only valid when ReaderType is ReaderType.RANGED. |
enable_crc | No | bool | True | Specify whether to check data integrity for uploads and sequential reads. |
prefetch_concurrency | No | int | 32 | The number of concurrent reads when preloading the dataset. |
Code example
The following example demonstrates how to create a DataLoader in PyTorch. Replace the parameters, such as region, endpoint, AK, and SK, with the values you need.
from tostorchconnector import CredentialProvider
from tostorchconnector import TosMapDataset
from torch.utils.data import DataLoader
# Dataset path
dataset_uri = 'tos://bucket/prefix'
region = 'cn-beijing'
# TOS endpoint
endpoint = 'http://tos-cn-beijing.ivolces.com'
ak = '<your-access-key>'
sk = '<your-secret-key>'
# Dataset batch size
BATCH_SIZE = 256
# Number of worker processes
NUM_WORKERS = 32
# Data transformation function
def transform(item):
data = item.read()
return item.key, item.size
# Build a dataset object from the specified dataset prefix using TosMapDataset.from_prefix
with TosMapDataset.from_prefix(dataset_uri,
region=region, endpoint=endpoint,
transform=transform,
cred=CredentialProvider(
ak=ak,
sk=sk)) as dataset:
# Create a DataLoader based on the constructed dataset
train_loader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, shuffle=True)
# Iterate for training/reading using the DataLoader
for data in train_loader:
print(data)
Last updated: 2026.03.23 11:37:42
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