keras-team/keras · error · ImportError

`load_model()` using h5 format requires h5py. Could not impo

Error message

`load_model()` using h5 format requires h5py. Could not import h5py.

What it means

Mirror of the save-side check: loading a legacy .h5 model requires h5py to read the file, and Keras raises ImportError at the top of load_model_from_hdf5 if the h5py import failed.

Source

Thrown at keras/src/legacy/saving/legacy_h5_format.py:106

            (strings) to custom classes or functions to be
            considered during deserialization.
        compile: Boolean, whether to compile the model
            after loading.

    Returns:
        A Keras model instance. If an optimizer was found
        as part of the saved model, the model is already
        compiled. Otherwise, the model is uncompiled and
        a warning will be displayed. When `compile` is set
        to `False`, the compilation is omitted without any
        warning.

    Raises:
        ImportError: if h5py is not available.
        ValueError: In case of an invalid savefile.
    """
    if h5py is None:
        raise ImportError(
            "`load_model()` using h5 format requires h5py. Could not "
            "import h5py."
        )

    if not custom_objects:
        custom_objects = {}

    gco = object_registration.GLOBAL_CUSTOM_OBJECTS
    tlco = global_state.get_global_attribute("custom_objects_scope_dict", {})
    custom_objects = {**custom_objects, **gco, **tlco}

    opened_new_file = not isinstance(filepath, h5py.File)
    if opened_new_file:
        f = h5py.File(filepath, mode="r")
    else:
        f = filepath

    model = None

View on GitHub (pinned to 7a34a03db6)

Solutions

1. pip install h5py in the loading environment
2. Add h5py to deployment requirements if you ship .h5 checkpoints
3. Convert .h5 files to .keras once, in an environment that has h5py, to drop the dependency

Example fix

# before
model = keras.saving.load_model('model.h5')  # ImportError
# after
# pip install h5py
model = keras.saving.load_model('model.h5')

try:
    import h5py
except ImportError:
    raise SystemExit('pip install h5py or convert the file to .keras')

try:
    keras.saving.load_model('m.h5')
except ImportError:
    raise SystemExit('h5py missing: pip install h5py')

When it happens

Trigger: keras.saving.load_model('model.h5') or load_model_from_hdf5 on a machine without h5py, e.g. a minimal inference container.

Common situations: Training image had h5py but the deployment image does not; CI runners installing only runtime deps.

Related errors

• `save_model()` using h5 format requires h5py. Could not impo
• No model config found in the file at {filepath}.
• Layer count mismatch when loading weights from file. Model e
• Weight count mismatch for layer #{k} (named {layer.name} in
• You called `set_weights(weights)` on layer '{self.name}' wit

AI-assisted analysis of keras-team/keras@7a34a03db6 (2026-08-25). Data as JSON: /api/errors/1bac2f3444d7129b. Report an issue: GitHub.