Torch Object Storage
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- Documentation
Torch Object StorageTool GuideTOS PyTorch ConnectorBest practices
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Best practices
Copy pageDownload PDFBest practices
This topic outlines the standard programming template and provides recommendations for performance optimization and logging configuration for TOS PyTorch Connector.
Standard programming template
The TosMapDataset, TosIterableDataset, and TosCheckpoint classes all have __enter__ and __exit__ functions. We recommend using the context manager pattern and consistently handling TosException.
from tostorchconnector import TosMapDataset, TosIterableDataset, TosCheckpoint, CredentialProvider, TosException
# Dataset path
dataset_uri = 'tos://bucket/prefix'
checkpoint_uri = 'tos://bucket/key'
region = 'cn-beijing'
# TOS endpoint
endpoint = 'http://tos-cn-beijing.ivolces.com'
ak = '<your-access-key>'
sk = '<your-secret-key>'
# Use TosMapDataset
try:
with TosMapDataset.from_prefix(dataset_uri,
region=region, endpoint=endpoint,
cred=CredentialProvider(
ak=ak,
sk=sk)) as dataset:
for item in dataset:
print(item.key)
except TosException as ex:
print(str(ex))
# Use TosIterableDataset
try:
with TosIterableDataset.from_prefix(dataset_uri,
region=region, endpoint=endpoint,
cred=CredentialProvider(
ak=ak,
sk=sk)) as dataset:
for item in dataset:
print(item.key)
except TosException as ex:
print(str(ex))
# Use TosCheckpoint
try:
with TosCheckpoint(region, endpoint, cred=CredentialProvider(ak, sk)) as checkpoint:
# Use TosObjectWriter
with checkpoint.writer(checkpoint_uri) as writer:
writer.write(b'hello')
writer.write(b'world')
# Use TosObjectReader
with checkpoint.reader(checkpoint_uri) as reader:
data = reader.read()
assert data == b'helloworld'
except TosException as ex:
print(str(ex))
Performance optimization
- Large object read/write: Performance can be improved by configuring a higher concurrency level.
- PyTorch dataset loading: Performance can be improved by configuring the preloading read concurrency.
- Repeated reads: Performance can be improved by selecting a reader with a cache.
tip
- ReaderType.SEQUENTIAL: A reader that caches all data. It is ideal for repeatedly reading the same object.
- ReaderType.RANGED: A reader that only caches data up to the block size. This type of reader is suitable for scenarios where a single object is read repeatedly in small chunks, and where memory cache size needs to be controlled.
- ReaderType.DIRECT: A reader without a cache. It is suitable for one-time read scenarios.
from tostorchconnector import CredentialProvider, TosClientConfig, ReaderType, TosIterableDataset, TosCheckpoint
region = 'cn-beijing'
# TOS endpoint
endpoint = 'http://tos-cn-beijing.ivolces.com'
ak = '<your-access-key>'
sk = '<your-secret-key>'
#1. Large object read/write
shared_prefetch_tasks = 200
shared_upload_part_tasks = 200
with TosCheckpoint(region, endpoint, cred=CredentialProvider(ak, sk),
client_conf=TosClientConfig(shared_prefetch_tasks=shared_prefetch_tasks,
shared_upload_part_tasks=shared_upload_part_tasks)) as checkpoint:
pass
#2. PyTorch dataset loading
dataset_uri = 'tos://bucket/prefix'
prefetch_concurrency = 256
with TosIterableDataset.from_prefix(dataset_uri, region=region, endpoint=endpoint, cred=CredentialProvider(ak=ak, sk=sk), prefetch_concurrency=prefetch_concurrency) as dataset:
pass
#3. Repeated reads
checkpoint_uri = 'tos://bucket/key'
with TosCheckpoint(region, endpoint, cred=CredentialProvider(ak, sk)) as checkpoint:
with checkpoint.reader(checkpoint_uri, reader_type=ReaderType.SEQUENTIAL) as reader:
pass
with TosIterableDataset.from_prefix(dataset_uri, region=region, endpoint=endpoint, cred=CredentialProvider(ak=ak, sk=sk), reader_type=ReaderType.SEQUENTIAL) as dataset:
pass
Logging configuration
TOS PyTorch Connector offers daily log rotation, which can be activated using tostorchconnector.init_log, as demonstrated below.
from tostorchconnector import init_log
# Log level
level = 'info'
# Log directory
directory = 'logs'
# Log file prefix
file_name_prefix = 'connector'
init_log(level, directory, file_name_prefix)
In multi-process data loading scenarios, logging must be initialized independently for each worker process when it starts. To do so, configure logging for each process using the worker_init_fn function, as demonstrated below.
from tostorchconnector import TosMapDataset, CredentialProvider, init_log
region = 'cn-beijing'
# TOS endpoint
endpoint = 'http://tos-cn-beijing.ivolces.com'
ak = '<your-access-key>'
sk = '<your-secret-key>'
dataset_uri = 'tos://bucket/prefix'
def transform(item):
data = item.read()
return item.key, item.size
def worker_init_fn(x):
init_log('info', 'logs', 'connector.log')
with TosMapDataset.from_prefix(dataset_uri, region=region, endpoint=endpoint,
cred=CredentialProvider(ak, sk),
transform=transform) as dataset:
train_loader = DataLoader(dataset=dataset, batch_size=256, num_workers=32,
shuffle=True, worker_init_fn=worker_init_fn)
for data in train_loader:
print(data)
Last updated: 2026.03.23 11:37:42
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