pypi huggingface-hub 0.25.0
v0.25.0: Large uploads made simple + quality of life improvements
on Python PyPI

latest releases: 0.26.2, 0.26.1, 0.26.0...
one month ago

📂 Upload large folders

Uploading large models or datasets is challenging. We've already written some tips and tricks to facilitate the process but something was still missing. We are now glad to release the huggingface-cli upload-large-folder command. Consider it as a "please upload this no matter what, and be quick" command. Contrarily to huggingface-cli download, this new command is more opinionated and will split the upload into several commits. Multiple workers are started locally to hash, pre-upload and commit the files in a way that is resumable, resilient to connection errors, and optimized against rate limits. This feature has already been stress tested by the community over the last months to make it as easy and convenient to use as possible.

Here is how to use it: 

huggingface-cli upload-large-folder <repo-id> <local-path> --repo-type=dataset

Every minute, a report is logged with the current status of the files and workers: 

---------- 2024-04-26 16:24:25 (0:00:00) ----------
Files:   hashed 104/104 (22.5G/22.5G) | pre-uploaded: 0/42 (0.0/22.5G) | committed: 58/104 (24.9M/22.5G) | ignored: 0
Workers: hashing: 0 | get upload mode: 0 | pre-uploading: 6 | committing: 0 | waiting: 0
---------------------------------------------------

You can also run it from a script: 

>>> from huggingface_hub import HfApi
>>> api = HfApi()
>>> api.upload_large_folder(
...     repo_id="HuggingFaceM4/Docmatix",
...     repo_type="dataset",
...     folder_path="/path/to/local/docmatix",
... )

For more details about the command options, run: 

huggingface-cli upload-large-folder --help

or visit the upload guide.

  • CLI to upload arbitrary huge folder by @Wauplin in #2254
  • Reduce number of commits in upload large folder by @Wauplin in #2546
  • Suggest using upload_large_folder when appropriate by @Wauplin in #2547

✨ HfApi & CLI improvements

🔍 Search API

The search API have been updated. You can now list gated models and datasets, and filter models by their inference status (warm, cold, frozen).

More complete support for the expand[] parameter:

  • Document baseModels and childrenModelCount as expand parameters by @Wauplin in #2475
  • Better support for trending score by @Wauplin in #2513
  • Add GGUF as supported expand[] parameter by @Wauplin in #2545

👤 User API

Organizations are now included when retrieving the user overview:

get_user_followers and get_user_following are now paginated. This was not the case before, leading to issues for users with more than 1000 followers.

  • Paginate followers and following endpoints by @Wauplin in #2506

📦 Repo API

Added auth_check to easily verify if a user has access to a repo. It raises GatedRepoError if the repo is gated and the user don't have the permission or RepositoryNotFoundError if the repo does not exist or is private. If the method does not raise an error, you can assume the user has the permission to access the repo. 

>>> from huggingface_hub import auth_check
>>> from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
try:
    auth_check("user/my-cool-model")
except GatedRepoError:
    # Handle gated repository error
    print("You do not have permission to access this gated repository.")
except RepositoryNotFoundError:
    # Handle repository not found error
    print("The repository was not found or you do not have access.")

It is now possible to set a repo as gated from a script: 

>>> from huggingface_hub import HfApi

>>> api = HfApi()
>>> api.update_repo_settings(repo_id=repo_id, gated="auto")  # Set to "auto", "manual" or False

⚡️ Inference Endpoint API

A few improvements in the InferenceEndpoint API. It's now possible to set a scale_to_zero_timeout parameter + to configure secrets when creating or updating an Inference Endpoint.

  • Add scale_to_zero_timeout parameter to HFApi.create/update_inference_endpoint by @hommayushi3 in #2463
  • Update endpoint.update signature by @Wauplin in #2477
  • feat: ✨ allow passing secrets to the inference endpoint client by @LuisBlanche in #2486

💾 Serialization

The torch serialization module now supports tensor subclasses.
We also made sure that now the library is tested with both torch 1.x and 2.x to ensure compatibility.

  • Making wrapper tensor subclass to work in serialization by @jerryzh168 in #2440
  • Torch: test on 1.11 and latest versions + explicitly load with weights_only=True by @Wauplin in #2488

💔 Breaking changes

Breaking changes:

  • InferenceClient.conversational task has been removed in favor of InferenceClient.chat_completion. Also removed ConversationalOutput data class.
  • All InferenceClient output values are now dataclasses, not dictionaries.
  • list_repo_likers is now paginated. This means the output is now an iterator instead of a list.

Deprecation:

  • multi_commit: bool parameter in upload_folder is not deprecated, along the create_commits_on_pr. It is now recommended to use upload_large_folder instead. Thought its API and internals are different, the goal is still to be able to upload many files in several commits.

🛠️ Small fixes and maintenance

⚡️ InferenceClient fixes

Thanks to community feedback, we've been able to improve or fix significant things in both the InferenceClient and its async version AsyncInferenceClient. This fixes have been mainly focused on the OpenAI-compatible chat_completion method and the Inference Endpoints services.

  • [Inference] Support stop parameter in text-generation instead of stop_sequences by @Wauplin in #2473
  • [hot-fix] Handle [DONE] signal from TGI + remove logic for "non-TGI servers" by @Wauplin in #2410
  • Fix chat completion url for OpenAI compatibility by @Wauplin in #2418
  • Bug - [InferenceClient] - use proxy set in var env by @morgandiverrez in #2421
  • Document the difference between model and base_url by @Wauplin in #2431
  • Fix broken AsyncInferenceClient on [DONE] signal by @Wauplin in #2458
  • Fix InferenceClient for HF Nvidia NIM API by @Wauplin in #2482
  • Properly close session in AsyncInferenceClient by @Wauplin in #2496
  • Fix unclosed aiohttp.ClientResponse objects by @Wauplin in #2528
  • Fix resolve chat completion URL by @Wauplin in #2540

😌 QoL improvements

When uploading a folder, we validate the README.md file before hashing all the files, not after.
This should save some precious time when uploading large files and a corrupted model card.

Also, it is now possible to pass a --max-workers argument when uploading a folder from the CLI

  • huggingface-cli upload - Validate README.md before file hashing by @hlky in #2452
  • Solved: Need to add the max-workers argument to the huggingface-cli command by @devymex in #2500

All custom exceptions raised by huggingface_hub are now defined in huggingface_hub.errors module. This should make it easier to import them for your try/except statements.

At the same occasion, we've reworked how errors are formatted in hf_raise_for_status to print more relevant information to the users.

All constants in huggingface_hub are now imported as a module. This makes it easier to patch their values, for example in a test pipeline.

Other quality of life improvements:

🐛 fixes

🏗️ internal

Significant community contributions

The following contributors have made significant changes to the library over the last release:

  • @morgandiverrez
    • Bug - [InferenceClient] - use proxy set in var env (#2421)
  • @hlky
    • huggingface-cli upload - Validate README.md before file hashing (#2452)
    • http_backoff retry with SliceFileObj (#2542)
  • @cjfghk5697
    • Define error (#2444)
    • implemented auth_check (#2497)
  • @WizKnight
    • Update constants import to use module-level access #1172 (#2453)
    • Update constants imports with module level access #1172 (#2469)
    • Refactor all constant imports to module-level access (#2489)
    • [Feature] Add update_repo_settings function to HfApi #2447 (#2502)
  • @rsxdalv
    • expose scan_cache table generation to python (#2437)
  • @010kim
    • Define cache errors in errors.py (#2470)
    • Add version cli command (#2498)
  • @jerryzh168
    • Making wrapper tensor subclass to work in serialization (#2440)
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