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PicklingError)AnyDictListOptionalUnion)version) constantssnapshot_download)HfApi)SoftTemporaryDirectoryget_fastai_versionget_fastcore_versionget_python_version )loggingvalidate_hf_hub_args) _PY_VERSIONfastai_min_versionfastcore_min_versionc t xs
t dk( r(t d| d| dt dt d t j t }t j t }|t j | k rt d| dt d |t j | k rt d
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Checks that the installed fastai and fastcore versions are compatible for pickle serialization.
Args:
fastai_min_version (`str`, *optional*):
The minimum fastai version supported.
fastcore_min_version (`str`, *optional*):
The minimum fastcore version supported.
Raises the following error:
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the fastai or fastcore libraries are not available or are of an invalid version.
zN/Azfastai>=z and fastcore>=z' are required. Currently using fastai==z and fastcore==.zD`push_to_hub_fastai` and `from_pretrained_fastai` require a fastai>=z+ version, but you are using fastai version zA which is incompatible. Upgrade with `pip install fastai==2.5.6`.zF`push_to_hub_fastai` and `from_pretrained_fastai` require a fastcore>=- version, but you are using fastcore version zD which is incompatible. Upgrade with `pip install fastcore==1.3.27`.N)r r ImportErrorr Version)r r current_fastai_versioncurrent_fastcore_versions Z/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/huggingface_hub/fastai_utils.py _check_fastai_fastcore_versionsr! s , 6"4"65@)*/:N9O P22D2F1G H.01
4
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H storage_folderc < ddl }t j j | d st j d y|j | d }d|j vrt j d y|d }d|j vrt j d y|d }|D cg c] }|j d
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} | dk7 r>t j | t j | k rt d| d| d yy# t $ r t d w xY wc c}w c c}w )a
Checks that the `pyproject.toml` file in the directory `storage_folder` has fastai and fastcore versions
that are compatible with `from_pretrained_fastai` and `push_to_hub_fastai`. If `pyproject.toml` does not exist
or does not contain versions for fastai and fastcore, then it logs a warning.
Args:
storage_folder (`str`):
Folder to look for the `pyproject.toml` file.
fastai_min_version (`str`, *optional*):
The minimum fastai version supported.
fastcore_min_version (`str`, *optional*):
The minimum fastcore version supported.
Raises the following errors:
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the `toml` module is not installed.
- [`ImportError`](https://docs.python.org/3/library/exceptions.html#ImportError)
if the `pyproject.toml` indicates a lower than minimum supported version of fastai or fastcore.
r Nzn`push_to_hub_fastai` and `from_pretrained_fastai` require the toml module. Install it with `pip install toml`.z/pyproject.tomlzThere is no `pyproject.toml` in the repository that contains the fastai `Learner`. The `pyproject.toml` would allow us to verify that your fastai and fastcore versions are compatible with those of the model you want to load.z build-systemzThere is no `build-system` section in the pyproject.toml of the repository that contains the fastai `Learner`. The `build-system` would allow us to verify that your fastai and fastcore versions are compatible with those of the model you want to load.requireszThere is no `requires` section in the pyproject.toml of the repository that contains the fastai `Learner`. The `requires` would allow us to verify that your fastai and fastcore versions are compatible with those of the model you want to load.fastaizPThe repository does not have a fastai version specified in the `pyproject.toml`.= z*`from_pretrained_fastai` requires fastai>=z$ version but the model to load uses z which is incompatible.fastcorezRThe repository does not have a fastcore version specified in the `pyproject.toml`.z,`from_pretrained_fastai` requires fastcore>=r )tomlModuleNotFoundErrorr ospathisfileloggerwarningloadkeys
startswithlenstr partitionr r ) r# r r r+ pyproject_tomlbuild_system_tomlpackage_versionspckfastai_packagesfastai_versionfastcore_packagesfastcore_versions r )_check_fastai_fastcore_pyproject_versionsr@ J sD :
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tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
zB[build-system]
requires = ["setuptools>=40.8.0", "wheel", "python=z ", "fastai=z
", "fastcore=z6"]
build-backend = "setuptools.build_meta:__legacy__"
repo_dirc | dz }|j s2|j dd 5 }|j t ddd yy# 1 sw Y yxY w)z
Creates a model card for the repository.
Args:
repo_dir (`Path`):
Directory where model card is created.
z README.mdwutf-8encodingN)existsopenwriteREADME_TEMPLATE)rA readme_pathfs r _create_model_cardrM sZ [(K
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4 %
GGO $ % % % % A Ac | dz }|j s2|j dd 5 }|j t ddd yy# 1 sw Y yxY w)z
Creates a `pyproject.toml` for the repository.
Args:
repo_dir (`Path`):
Directory where `pyproject.toml` is created.
zpyproject.tomlrC rD rE N)rG rH rI PYPROJECT_TEMPLATE)rA pyproject_pathrL s r _create_model_pyprojectrR s\ 00N "
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| d t j j |t j }t |d 5 }t j || ddd t t | t! t | t | | _ t j |d | j# dt$ y# 1 sw Y qxY w# t& $ r t' d w xY w)
a
Saves a fastai learner to `save_directory` in pickle format using the default pickle protocol for the version of python used.
Args:
learner (`Learner`):
The `fastai.Learner` you'd like to save.
save_directory (`str` or `Path`):
Specific directory in which you want to save the fastai learner.
config (`dict`, *optional*):
Configuration object. Will be uploaded as a .json file. Example: 'https://huggingface.co/espejelomar/fastai-pet-breeds-classification/blob/main/config.json'.
Raises the following error:
- [`RuntimeError`](https://docs.python.org/3/library/exceptions.html#RuntimeError)
if the config file provided is not a dictionary.
T)exist_okNz(Provided config should be a dict. Got: ''rC model.pkl)fnamepickle_protocolzYou are using a lambda function, i.e., an anonymous function. `pickle` cannot pickle function objects and requires that all functions have names. One possible solution is to name the function.)r! r- makedirs
isinstancedict RuntimeErrortyper. joinr CONFIG_NAMErH jsondumprM r rR exportr r )learnerrS rT r. rL s r _save_pretrained_fastairf s 2 $%KK. &$' !I$v, WX YZ Zww||NI,A,AB
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Load pretrained fastai model from the Hub or from a local directory.
Args:
repo_id (`str`):
The location where the pickled fastai.Learner is. It can be either of the two:
- Hosted on the Hugging Face Hub. E.g.: 'espejelomar/fatai-pet-breeds-classification' or 'distilgpt2'.
You can add a `revision` by appending `@` at the end of `repo_id`. E.g.: `dbmdz/bert-base-german-cased@main`.
Revision is the specific model version to use. Since we use a git-based system for storing models and other
artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.
- Hosted locally. `repo_id` would be a directory containing the pickle and a pyproject.toml
indicating the fastai and fastcore versions used to build the `fastai.Learner`. E.g.: `./my_model_directory/`.
revision (`str`, *optional*):
Revision at which the repo's files are downloaded. See documentation of `snapshot_download`.
Returns:
The `fastai.Learner` model in the `repo_id` repo.
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!-n=+ ^[A BBr" z(Push FastAI model using huggingface_hub.)
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Upload learner checkpoint files to the Hub.
Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
`delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
details.
Args:
learner (`Learner`):
The `fastai.Learner' you'd like to push to the Hub.
repo_id (`str`):
The repository id for your model in Hub in the format of "namespace/repo_name". The namespace can be your individual account or an organization to which you have write access (for example, 'stanfordnlp/stanza-de').
commit_message (`str`, *optional*):
Message to commit while pushing. Will default to :obj:`"add model"`.
private (`bool`, *optional*):
Whether or not the repository created should be private.
If `None` (default), will default to been public except if the organization's default is private.
token (`str`, *optional*):
The Hugging Face account token to use as HTTP bearer authorization for remote files. If :obj:`None`, the token will be asked by a prompt.
config (`dict`, *optional*):
Configuration object to be saved alongside the model weights.
branch (`str`, *optional*):
The git branch on which to push the model. This defaults to
the default branch as specified in your repository, which
defaults to `"main"`.
create_pr (`boolean`, *optional*):
Whether or not to create a Pull Request from `branch` with that commit.
Defaults to `False`.
api_endpoint (`str`, *optional*):
The API endpoint to use when pushing the model to the hub.
allow_patterns (`List[str]` or `str`, *optional*):
If provided, only files matching at least one pattern are pushed.
ignore_patterns (`List[str]` or `str`, *optional*):
If provided, files matching any of the patterns are not pushed.
delete_patterns (`List[str]` or `str`, *optional*):
If provided, remote files matching any of the patterns will be deleted from the repo.
Returns:
The url of the commit of your model in the given repository.
Raises the following error:
- [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError)
if the user is not log on to the Hugging Face Hub.
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