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Interact with the Hub through the Filesystem API

In addition to the HfApi, the huggingface_hub library provides HfFileSystem, a pythonic fsspec-compatible file interface to the Hugging Face Hub. The HfFileSystem builds of top of the HfApi and offers typical filesystem style operations like cp, mv, ls, du, glob, get_file, and put_file.


>>> from huggingface_hub import HfFileSystem
>>> fs = HfFileSystem()

>>> # List all files in a directory
>>>"datasets/my-username/my-dataset-repo/data", detail=False)
['datasets/my-username/my-dataset-repo/data/train.csv', 'datasets/my-username/my-dataset-repo/data/test.csv']

>>> # List all ".csv" files in a repo
>>> fs.glob("datasets/my-username/my-dataset-repo/**.csv")
['datasets/my-username/my-dataset-repo/data/train.csv', 'datasets/my-username/my-dataset-repo/data/test.csv']

>>> # Read a remote file 
>>> with"datasets/my-username/my-dataset-repo/data/train.csv", "r") as f:
...     train_data = f.readlines()

>>> # Read the content of a remote file as a string
>>> train_data = fs.read_text("datasets/my-username/my-dataset-repo/data/train.csv", revision="dev")

>>> # Write a remote file
>>> with"datasets/my-username/my-dataset-repo/data/validation.csv", "w") as f:
...     f.write("text,label")
...     f.write("Fantastic movie!,good")

The optional revision argument can be passed to run an operation from a specific commit such as a branch, tag name, or a commit hash.

Unlike Python’s built-in open, fsspec’s open defaults to binary mode, "rb". This means you must explicitly set mode as "r" for reading and "w" for writing in text mode. Appending to a file (modes "a" and "ab") is not supported yet.


The HfFileSystem can be used with any library that integrates fsspec, provided the URL follows the scheme:


The repo_type_prefix is datasets/ for datasets, spaces/ for spaces, and models don’t need a prefix in the URL.

Some interesting integrations where HfFileSystem simplifies interacting with the Hub are listed below:

  • Reading/writing a Pandas DataFrame from/to a Hub repository:

    >>> import pandas as pd
    >>> # Read a remote CSV file into a dataframe
    >>> df = pd.read_csv("hf://datasets/my-username/my-dataset-repo/train.csv")
    >>> # Write a dataframe to a remote CSV file
    >>> df.to_csv("hf://datasets/my-username/my-dataset-repo/test.csv")

The same workflow can also be used for Dask and Polars DataFrames.

  • Querying (remote) Hub files with DuckDB:

    >>> from huggingface_hub import HfFileSystem
    >>> import duckdb
    >>> fs = HfFileSystem()
    >>> duckdb.register_filesystem(fs)
    >>> # Query a remote file and get the result back as a dataframe
    >>> fs_query_file = "hf://datasets/my-username/my-dataset-repo/data_dir/data.parquet"
    >>> df = duckdb.query(f"SELECT * FROM '{fs_query_file}' LIMIT 10").df()
  • Using the Hub as an array store with Zarr:

    >>> import numpy as np
    >>> import zarr
    >>> embeddings = np.random.randn(50000, 1000).astype("float32")
    >>> # Write an array to a repo
    >>> with zarr.open_group("hf://my-username/my-model-repo/array-store", mode="w") as root:
    ...    foo = root.create_group("embeddings")
    ...    foobar = foo.zeros('experiment_0', shape=(50000, 1000), chunks=(10000, 1000), dtype='f4')
    ...    foobar[:] = embeddings
    >>> # Read an array from a repo
    >>> with zarr.open_group("hf://my-username/my-model-repo/array-store", mode="r") as root:
    ...    first_row = root["embeddings/experiment_0"][0]


In many cases, you must be logged in with a Hugging Face account to interact with the Hub. Refer to the Login section of the documentation to learn more about authentication methods on the Hub.

It is also possible to login programmatically by passing your token as an argument to HfFileSystem:

>>> from huggingface_hub import HfFileSystem
>>> fs = HfFileSystem(token=token)

If you login this way, be careful not to accidentally leak the token when sharing your source code!