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998f55e b277483 998f55e b277483 998f55e b277483 998f55e b277483 998f55e b277483 998f55e b277483 998f55e b277483 998f55e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | """Unified storage abstraction for dataset I/O.
This module provides a common interface for saving/loading HuggingFace datasets,
abstracting away whether we're using HuggingFace Hub, S3, or GCS.
Usage:
from .storage import get_storage
storage = get_storage()
storage.save_dataset(dataset, "my_dataset")
dataset = storage.load_dataset()
"""
from __future__ import annotations
import logging
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
from .config import env
if TYPE_CHECKING:
from datasets import Dataset
LOGGER = logging.getLogger(__name__)
class DatasetStorage(ABC):
"""Abstract base class for dataset storage backends."""
@abstractmethod
def save_dataset(self, dataset: "Dataset", name: str) -> bool:
"""Save a HuggingFace dataset to storage.
Args:
dataset: HuggingFace Dataset to save
name: Name/identifier for the dataset
Returns:
True if save succeeded
"""
pass
@abstractmethod
def load_dataset(self, split: str = "train") -> Optional["Dataset"]:
"""Load a HuggingFace dataset from storage.
Args:
split: Dataset split to load
Returns:
Loaded Dataset or None if not available
"""
pass
@property
@abstractmethod
def is_configured(self) -> bool:
"""Check if this storage backend is configured."""
pass
class HFHubStorage(DatasetStorage):
"""HuggingFace Hub storage backend."""
def __init__(
self,
repo_id: Optional[str] = None,
branch: Optional[str] = None,
commit_message: Optional[str] = None,
):
self.repo_id = repo_id or env("HF_REPO_ID")
self.branch = branch or env("HF_BRANCH")
self.commit_message = commit_message or env("HF_COMMIT_MESSAGE")
self._token = env("HF_TOKEN")
@property
def is_configured(self) -> bool:
return bool(self.repo_id)
def save_dataset(self, dataset: "Dataset", name: str) -> bool:
if not self.is_configured:
LOGGER.debug("HF Hub not configured, skipping dataset save")
return False
try:
dataset.push_to_hub(
self.repo_id,
token=self._token,
revision=self.branch,
commit_message=self.commit_message or f"Add {name}",
)
LOGGER.info("Pushed dataset to HF Hub: %s", self.repo_id)
return True
except Exception as exc:
LOGGER.exception("HF Hub dataset push failed: %s", exc)
return False
def load_dataset(self, split: str = "train") -> Optional["Dataset"]:
if not self.is_configured:
LOGGER.debug("HF Hub not configured, cannot load dataset")
return None
try:
from datasets import load_dataset
LOGGER.info("Loading dataset from HF Hub: %s", self.repo_id)
return load_dataset(self.repo_id, split=split, token=self._token)
except Exception as exc:
LOGGER.exception("HF Hub dataset load failed: %s", exc)
return None
class S3Storage(DatasetStorage):
"""Amazon S3 storage backend."""
def __init__(
self,
output_uri: Optional[str] = None,
input_uri: Optional[str] = None,
):
self.output_uri = output_uri or env("S3_OUTPUT_URI")
self.input_uri = input_uri or env("S3_INPUT_URI")
@property
def is_configured(self) -> bool:
return bool(self.output_uri or self.input_uri)
def save_dataset(self, dataset: "Dataset", name: str) -> bool:
if not self.output_uri:
LOGGER.debug("S3 output URI not configured, skipping dataset save")
return False
try:
from .sm_io import save_dataset_to_s3
save_dataset_to_s3(dataset, self.output_uri, name)
return True
except ImportError as exc:
LOGGER.warning("S3 save failed (missing dependency): %s", exc)
return False
except Exception as exc:
LOGGER.exception("S3 dataset save failed: %s", exc)
return False
def load_dataset(self, split: str = "train") -> Optional["Dataset"]:
if not self.input_uri:
LOGGER.debug("S3 input URI not configured, cannot load dataset")
return None
try:
from .sm_io import load_dataset_from_s3
return load_dataset_from_s3(self.input_uri, split=split)
except ImportError as exc:
LOGGER.warning("S3 load failed (missing dependency): %s", exc)
return None
except Exception as exc:
LOGGER.exception("S3 dataset load failed: %s", exc)
return None
class GCSStorage(DatasetStorage):
"""Google Cloud Storage backend."""
def __init__(
self,
output_uri: Optional[str] = None,
input_uri: Optional[str] = None,
):
self.output_uri = output_uri or env("GCS_OUTPUT_URI")
self.input_uri = input_uri or env("GCS_INPUT_URI")
@property
def is_configured(self) -> bool:
return bool(self.output_uri or self.input_uri)
def save_dataset(self, dataset: "Dataset", name: str) -> bool:
if not self.output_uri:
LOGGER.debug("GCS output URI not configured, skipping dataset save")
return False
try:
from .gcr_io import save_dataset_to_gcs
save_dataset_to_gcs(dataset, self.output_uri, name)
return True
except ImportError as exc:
LOGGER.warning("GCS save failed (missing dependency): %s", exc)
return False
except Exception as exc:
LOGGER.exception("GCS dataset save failed: %s", exc)
return False
def load_dataset(self, split: str = "train") -> Optional["Dataset"]:
if not self.input_uri:
LOGGER.debug("GCS input URI not configured, cannot load dataset")
return None
try:
from .gcr_io import load_dataset_from_gcs
return load_dataset_from_gcs(self.input_uri, split=split)
except ImportError as exc:
LOGGER.warning("GCS load failed (missing dependency): %s", exc)
return None
except Exception as exc:
LOGGER.exception("GCS dataset load failed: %s", exc)
return None
def get_storage(
*,
repo_id: Optional[str] = None,
s3_output_uri: Optional[str] = None,
s3_input_uri: Optional[str] = None,
gcs_output_uri: Optional[str] = None,
gcs_input_uri: Optional[str] = None,
) -> DatasetStorage:
"""Get the appropriate storage backend based on configuration.
Priority: GCS > S3 > HF Hub.
Args:
repo_id: Override HF repo ID
s3_output_uri: Override S3 output URI
s3_input_uri: Override S3 input URI
gcs_output_uri: Override GCS output URI
gcs_input_uri: Override GCS input URI
Returns:
Configured DatasetStorage instance
"""
gcs = GCSStorage(output_uri=gcs_output_uri, input_uri=gcs_input_uri)
s3 = S3Storage(output_uri=s3_output_uri, input_uri=s3_input_uri)
hf = HFHubStorage(repo_id=repo_id)
# Return first configured backend
if gcs.is_configured:
return gcs
if s3.is_configured:
return s3
return hf
def get_source_storage(
*,
source_repo_id: Optional[str] = None,
) -> DatasetStorage:
"""Get storage backend for loading source data.
Checks GCS_INPUT_URI first, then S3_INPUT_URI, then falls back to HF Hub.
Args:
source_repo_id: HF repo ID to load from (falls back to SOURCE_REPO_ID env var)
Returns:
Configured DatasetStorage instance for loading
"""
gcs_input = env("GCS_INPUT_URI")
if gcs_input:
return GCSStorage(input_uri=gcs_input)
s3_input = env("S3_INPUT_URI")
if s3_input:
return S3Storage(input_uri=s3_input)
repo_id = source_repo_id or env("SOURCE_REPO_ID") or env("HF_REPO_ID")
return HFHubStorage(repo_id=repo_id)
__all__ = [
"DatasetStorage",
"HFHubStorage",
"S3Storage",
"GCSStorage",
"get_storage",
"get_source_storage",
]
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