캐시 시스템 참조
버전 0.8.0에서의 업데이트로, 캐시 시스템은 Hub에 의존하는 라이브러리 전체에서 공유되는 중앙 캐시 시스템으로 발전하였습니다. Hugging Face 캐싱에 대한 자세한 설명은 캐시 시스템 가이드를 참조하세요.
도우미 함수
try_to_load_from_cache
huggingface_hub.try_to_load_from_cache
< source >( repo_id: str filename: str cache_dir: Union = None revision: Optional = None repo_type: Optional = None ) → Optional[str] or _CACHED_NO_EXIST
Parameters
- cache_dir (
stroros.PathLike) — The folder where the cached files lie. - repo_id (
str) — The ID of the repo on huggingface.co. - filename (
str) — The filename to look for insiderepo_id. - revision (
str, optional) — The specific model version to use. Will default to"main"if it’s not provided and nocommit_hashis provided either. - repo_type (
str, optional) — The type of the repository. Will default to"model".
Returns
Optional[str] or _CACHED_NO_EXIST
Will return None if the file was not cached. Otherwise:
- The exact path to the cached file if it’s found in the cache
- A special value
_CACHED_NO_EXISTif the file does not exist at the given commit hash and this fact was cached.
Explores the cache to return the latest cached file for a given revision if found.
This function will not raise any exception if the file in not cached.
cached_assets_path
huggingface_hub.cached_assets_path
< source >( library_name: str namespace: str = 'default' subfolder: str = 'default' assets_dir: Union = None )
Parameters
- library_name (
str) — Name of the library that will manage the cache folder. Example:"dataset". - namespace (
str, optional, defaults to “default”) — Namespace to which the data belongs. Example:"SQuAD". - subfolder (
str, optional, defaults to “default”) — Subfolder in which the data will be stored. Example:extracted. - assets_dir (
str,Path, optional) — Path to the folder where assets are cached. This must not be the same folder where Hub files are cached. Defaults toHF_HOME / "assets"if not provided. Can also be set withHF_ASSETS_CACHEenvironment variable.
Return a folder path to cache arbitrary files.
huggingface_hub provides a canonical folder path to store assets. This is the
recommended way to integrate cache in a downstream library as it will benefit from
the builtins tools to scan and delete the cache properly.
The distinction is made between files cached from the Hub and assets. Files from the
Hub are cached in a git-aware manner and entirely managed by huggingface_hub. See
related documentation.
All other files that a downstream library caches are considered to be “assets”
(files downloaded from external sources, extracted from a .tar archive, preprocessed
for training,…).
Once the folder path is generated, it is guaranteed to exist and to be a directory.
The path is based on 3 levels of depth: the library name, a namespace and a
subfolder. Those 3 levels grants flexibility while allowing huggingface_hub to
expect folders when scanning/deleting parts of the assets cache. Within a library,
it is expected that all namespaces share the same subset of subfolder names but this
is not a mandatory rule. The downstream library has then full control on which file
structure to adopt within its cache. Namespace and subfolder are optional (would
default to a "default/" subfolder) but library name is mandatory as we want every
downstream library to manage its own cache.
Expected tree:
assets/
└── datasets/
│ ├── SQuAD/
│ │ ├── downloaded/
│ │ ├── extracted/
│ │ └── processed/
│ ├── Helsinki-NLP--tatoeba_mt/
│ ├── downloaded/
│ ├── extracted/
│ └── processed/
└── transformers/
├── default/
│ ├── something/
├── bert-base-cased/
│ ├── default/
│ └── training/
hub/
└── models--julien-c--EsperBERTo-small/
├── blobs/
│ ├── (...)
│ ├── (...)
├── refs/
│ └── (...)
└── [ 128] snapshots/
├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/
│ ├── (...)
└── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/
└── (...)Example:
>>> from huggingface_hub import cached_assets_path
>>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download")
PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download')
>>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted")
PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted')
>>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt")
PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default')
>>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456")
PosixPath('/tmp/tmp123456/datasets/default/default')scan_cache_dir
huggingface_hub.scan_cache_dir
< source >( cache_dir: Union = None )
Parameters
- cache_dir (
strorPath,optional) — Cache directory to cache. Defaults to the default HF cache directory.
Raises
CacheNotFound or ValueError
-
CacheNotFound— If the cache directory does not exist. -
ValueError— If the cache directory is a file, instead of a directory.
Scan the entire HF cache-system and return a ~HFCacheInfo structure.
Use scan_cache_dir in order to programmatically scan your cache-system. The cache
will be scanned repo by repo. If a repo is corrupted, a ~CorruptedCacheException
will be thrown internally but captured and returned in the ~HFCacheInfo
structure. Only valid repos get a proper report.
>>> from huggingface_hub import scan_cache_dir
>>> hf_cache_info = scan_cache_dir()
HFCacheInfo(
size_on_disk=3398085269,
repos=frozenset({
CachedRepoInfo(
repo_id='t5-small',
repo_type='model',
repo_path=PosixPath(...),
size_on_disk=970726914,
nb_files=11,
revisions=frozenset({
CachedRevisionInfo(
commit_hash='d78aea13fa7ecd06c29e3e46195d6341255065d5',
size_on_disk=970726339,
snapshot_path=PosixPath(...),
files=frozenset({
CachedFileInfo(
file_name='config.json',
size_on_disk=1197
file_path=PosixPath(...),
blob_path=PosixPath(...),
),
CachedFileInfo(...),
...
}),
),
CachedRevisionInfo(...),
...
}),
),
CachedRepoInfo(...),
...
}),
warnings=[
CorruptedCacheException("Snapshots dir doesn't exist in cached repo: ..."),
CorruptedCacheException(...),
...
],
)You can also print a detailed report directly from the huggingface-cli using:
> huggingface-cli scan-cache REPO ID REPO TYPE SIZE ON DISK NB FILES REFS LOCAL PATH --------------------------- --------- ------------ -------- ------------------- ------------------------------------------------------------------------- glue dataset 116.3K 15 1.17.0, main, 2.4.0 /Users/lucain/.cache/huggingface/hub/datasets--glue google/fleurs dataset 64.9M 6 main, refs/pr/1 /Users/lucain/.cache/huggingface/hub/datasets--google--fleurs Jean-Baptiste/camembert-ner model 441.0M 7 main /Users/lucain/.cache/huggingface/hub/models--Jean-Baptiste--camembert-ner bert-base-cased model 1.9G 13 main /Users/lucain/.cache/huggingface/hub/models--bert-base-cased t5-base model 10.1K 3 main /Users/lucain/.cache/huggingface/hub/models--t5-base t5-small model 970.7M 11 refs/pr/1, main /Users/lucain/.cache/huggingface/hub/models--t5-small Done in 0.0s. Scanned 6 repo(s) for a total of 3.4G. Got 1 warning(s) while scanning. Use -vvv to print details.
Returns: a ~HFCacheInfo object.
데이터 구조
모든 구조체는 scan_cache_dir()에 의해 생성되고 반환되며, 불변(immutable)입니다.
HFCacheInfo
class huggingface_hub.HFCacheInfo
< source >( size_on_disk: int repos: FrozenSet warnings: List )
Parameters
- size_on_disk (
int) — Sum of all valid repo sizes in the cache-system. - repos (
FrozenSet[CachedRepoInfo]) — Set of ~CachedRepoInfo describing all valid cached repos found on the cache-system while scanning. - warnings (
List[CorruptedCacheException]) — List of ~CorruptedCacheException that occurred while scanning the cache. Those exceptions are captured so that the scan can continue. Corrupted repos are skipped from the scan.
Frozen data structure holding information about the entire cache-system.
This data structure is returned by scan_cache_dir() and is immutable.
Here size_on_disk is equal to the sum of all repo sizes (only blobs). However if
some cached repos are corrupted, their sizes are not taken into account.
Prepare the strategy to delete one or more revisions cached locally.
Input revisions can be any revision hash. If a revision hash is not found in the local cache, a warning is thrown but no error is raised. Revisions can be from different cached repos since hashes are unique across repos,
Examples:
>>> from huggingface_hub import scan_cache_dir
>>> cache_info = scan_cache_dir()
>>> delete_strategy = cache_info.delete_revisions(
... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa"
... )
>>> print(f"Will free {delete_strategy.expected_freed_size_str}.")
Will free 7.9K.
>>> delete_strategy.execute()
Cache deletion done. Saved 7.9K.>>> from huggingface_hub import scan_cache_dir
>>> scan_cache_dir().delete_revisions(
... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa",
... "e2983b237dccf3ab4937c97fa717319a9ca1a96d",
... "6c0e6080953db56375760c0471a8c5f2929baf11",
... ).execute()
Cache deletion done. Saved 8.6G.delete_revisions returns a DeleteCacheStrategy object that needs to
be executed. The DeleteCacheStrategy is not meant to be modified but
allows having a dry run before actually executing the deletion.
export_as_table
< source >( verbosity: int = 0 ) → str
Generate a table from the HFCacheInfo object.
Pass verbosity=0 to get a table with a single row per repo, with columns
“repo_id”, “repo_type”, “size_on_disk”, “nb_files”, “last_accessed”, “last_modified”, “refs”, “local_path”.
Pass verbosity=1 to get a table with a row per repo and revision (thus multiple rows can appear for a single repo), with columns
“repo_id”, “repo_type”, “revision”, “size_on_disk”, “nb_files”, “last_modified”, “refs”, “local_path”.
Example:
>>> from huggingface_hub.utils import scan_cache_dir
>>> hf_cache_info = scan_cache_dir()
HFCacheInfo(...)
>>> print(hf_cache_info.export_as_table())
REPO ID REPO TYPE SIZE ON DISK NB FILES LAST_ACCESSED LAST_MODIFIED REFS LOCAL PATH
--------------------------------------------------- --------- ------------ -------- ------------- ------------- ---- --------------------------------------------------------------------------------------------------
roberta-base model 2.7M 5 1 day ago 1 week ago main ~/.cache/huggingface/hub/models--roberta-base
suno/bark model 8.8K 1 1 week ago 1 week ago main ~/.cache/huggingface/hub/models--suno--bark
t5-base model 893.8M 4 4 days ago 7 months ago main ~/.cache/huggingface/hub/models--t5-base
t5-large model 3.0G 4 5 weeks ago 5 months ago main ~/.cache/huggingface/hub/models--t5-large
>>> print(hf_cache_info.export_as_table(verbosity=1))
REPO ID REPO TYPE REVISION SIZE ON DISK NB FILES LAST_MODIFIED REFS LOCAL PATH
--------------------------------------------------- --------- ---------------------------------------- ------------ -------- ------------- ---- -----------------------------------------------------------------------------------------------------------------------------------------------------
roberta-base model e2da8e2f811d1448a5b465c236feacd80ffbac7b 2.7M 5 1 week ago main ~/.cache/huggingface/hub/models--roberta-base/snapshots/e2da8e2f811d1448a5b465c236feacd80ffbac7b
suno/bark model 70a8a7d34168586dc5d028fa9666aceade177992 8.8K 1 1 week ago main ~/.cache/huggingface/hub/models--suno--bark/snapshots/70a8a7d34168586dc5d028fa9666aceade177992
t5-base model a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 893.8M 4 7 months ago main ~/.cache/huggingface/hub/models--t5-base/snapshots/a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1
t5-large model 150ebc2c4b72291e770f58e6057481c8d2ed331a 3.0G 4 5 months ago main ~/.cache/huggingface/hub/models--t5-large/snapshots/150ebc2c4b72291e770f58e6057481c8d2ed331aCachedRepoInfo
class huggingface_hub.CachedRepoInfo
< source >( repo_id: str repo_type: Literal repo_path: Path size_on_disk: int nb_files: int revisions: FrozenSet last_accessed: float last_modified: float )
Parameters
- repo_id (
str) — Repo id of the repo on the Hub. Example:"google/fleurs". - repo_type (
Literal["dataset", "model", "space"]) — Type of the cached repo. - repo_path (
Path) — Local path to the cached repo. - size_on_disk (
int) — Sum of the blob file sizes in the cached repo. - nb_files (
int) — Total number of blob files in the cached repo. - revisions (
FrozenSet[CachedRevisionInfo]) — Set of ~CachedRevisionInfo describing all revisions cached in the repo. - last_accessed (
float) — Timestamp of the last time a blob file of the repo has been accessed. - last_modified (
float) — Timestamp of the last time a blob file of the repo has been modified/created.
Frozen data structure holding information about a cached repository.
size_on_disk is not necessarily the sum of all revisions sizes because of
duplicated files. Besides, only blobs are taken into account, not the (negligible)
size of folders and symlinks.
last_accessed and last_modified reliability can depend on the OS you are using.
See python documentation
for more details.
(property) Sum of the blob file sizes as a human-readable string.
Example: “42.2K”.
(property) Mapping between refs and revision data structures.
CachedRevisionInfo
class huggingface_hub.CachedRevisionInfo
< source >( commit_hash: str snapshot_path: Path size_on_disk: int files: FrozenSet refs: FrozenSet last_modified: float )
Parameters
- commit_hash (
str) — Hash of the revision (unique). Example:"9338f7b671827df886678df2bdd7cc7b4f36dffd". - snapshot_path (
Path) — Path to the revision directory in thesnapshotsfolder. It contains the exact tree structure as the repo on the Hub. files — (FrozenSet[CachedFileInfo]): Set of ~CachedFileInfo describing all files contained in the snapshot. - refs (
FrozenSet[str]) — Set ofrefspointing to this revision. If the revision has norefs, it is considered detached. Example:{"main", "2.4.0"}or{"refs/pr/1"}. - size_on_disk (
int) — Sum of the blob file sizes that are symlink-ed by the revision. - last_modified (
float) — Timestamp of the last time the revision has been created/modified.
Frozen data structure holding information about a revision.
A revision correspond to a folder in the snapshots folder and is populated with
the exact tree structure as the repo on the Hub but contains only symlinks. A
revision can be either referenced by 1 or more refs or be “detached” (no refs).
last_accessed cannot be determined correctly on a single revision as blob files
are shared across revisions.
size_on_disk is not necessarily the sum of all file sizes because of possible
duplicated files. Besides, only blobs are taken into account, not the (negligible)
size of folders and symlinks.
(property) Sum of the blob file sizes as a human-readable string.
Example: “42.2K”.
(property) Total number of files in the revision.
CachedFileInfo
class huggingface_hub.CachedFileInfo
< source >( file_name: str file_path: Path blob_path: Path size_on_disk: int blob_last_accessed: float blob_last_modified: float )
Parameters
- file_name (
str) — Name of the file. Example:config.json. - file_path (
Path) — Path of the file in thesnapshotsdirectory. The file path is a symlink referring to a blob in theblobsfolder. - blob_path (
Path) — Path of the blob file. This is equivalent tofile_path.resolve(). - size_on_disk (
int) — Size of the blob file in bytes. - blob_last_accessed (
float) — Timestamp of the last time the blob file has been accessed (from any revision). - blob_last_modified (
float) — Timestamp of the last time the blob file has been modified/created.
Frozen data structure holding information about a single cached file.
blob_last_accessed and blob_last_modified reliability can depend on the OS you
are using. See python documentation
for more details.
(property) Size of the blob file as a human-readable string.
Example: “42.2K”.
DeleteCacheStrategy
class huggingface_hub.DeleteCacheStrategy
< source >( expected_freed_size: int blobs: FrozenSet refs: FrozenSet repos: FrozenSet snapshots: FrozenSet )
Parameters
- expected_freed_size (
float) — Expected freed size once strategy is executed. - blobs (
FrozenSet[Path]) — Set of blob file paths to be deleted. - refs (
FrozenSet[Path]) — Set of reference file paths to be deleted. - repos (
FrozenSet[Path]) — Set of entire repo paths to be deleted. - snapshots (
FrozenSet[Path]) — Set of snapshots to be deleted (directory of symlinks).
Frozen data structure holding the strategy to delete cached revisions.
This object is not meant to be instantiated programmatically but to be returned by delete_revisions(). See documentation for usage example.
(property) Expected size that will be freed as a human-readable string.
Example: “42.2K”.
예외
CorruptedCacheException
Exception for any unexpected structure in the Huggingface cache-system.