tf.keras.datasets.cifar10.load_data - FileNotFoundError: [Errno 2] No such file or directory

[ashep29]

Issue with tar.gz file extraction - inconsistent behaviour across systems/platforms:

(train_images, train_labels), (test_images, test_labels) = keras.datasets.cifar10.load_data()

• works on Google colab
• works on Windows > VS Code > Jupyter
• fails on linux RHEL with details below:

NAME="Rocky Linux"
VERSION="8.10 (Green Obsidian)"
ID="rocky"
ID_LIKE="rhel centos fedora"
VERSION_ID="8.10"
PLATFORM_ID="platform:el8"

Python 3.11.0rcl

absl-py==2.1.0
anyio==4.4.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
array_record==0.5.1
arrow==1.3.0
asttokens==2.4.1
astunparse==1.6.3
async-lru==2.0.4
attrs==24.2.0
babel==2.16.0
beautifulsoup4==4.12.3
bleach==6.1.0
blinker==1.4
certifi==2024.7.4
cffi==1.17.0
charset-normalizer==3.3.2
click==8.1.7
comm==0.2.2
contourpy==1.3.0
cryptography==3.4.8
cycler==0.12.1
dbus-python==1.2.18
debugpy==1.8.5
decorator==5.1.1
defusedxml==0.7.1
distro==1.7.0
dm-tree==0.1.8
docstring_parser==0.16
etils==1.9.2
executing==2.0.1
fastjsonschema==2.20.0
flatbuffers==24.3.25
fonttools==4.53.1
fqdn==1.5.1
fsspec==2024.6.1
gast==0.6.0
google-pasta==0.2.0
googleapis-common-protos==1.65.0
grpcio==1.64.1
h11==0.14.0
h5py==3.11.0
httpcore==1.0.5
httplib2==0.20.2
httpx==0.27.2
idna==3.7
immutabledict==4.2.0
importlib-metadata==4.6.4
importlib_resources==6.4.4
ipykernel==6.29.5
ipython==8.26.0
ipywidgets==8.1.5
isoduration==20.11.0
jedi==0.19.1
jeepney==0.7.1
Jinja2==3.1.4
joblib==1.4.2
json5==0.9.25
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
jupyter==1.0.0
jupyter-console==6.6.3
jupyter-events==0.10.0
jupyter-lsp==2.2.5
jupyter_client==8.6.2
jupyter_core==5.7.2
jupyter_server==2.14.2
jupyter_server_terminals==0.5.3
jupyterlab==4.2.5
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
kagglehub==0.2.9
keras==3.4.1
keras-core==0.1.7
keras-cv==0.9.0
keras-nlp==0.14.4
keyring==23.5.0
kiwisolver==1.4.5
launchpadlib==1.10.16
lazr.restfulclient==0.14.4
lazr.uri==1.0.6
libclang==18.1.1
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.9.2
matplotlib-inline==0.1.7
mdurl==0.1.2
mistune==3.0.2
ml-dtypes==0.4.0
more-itertools==8.10.0
mplfinance==0.12.10b0
namex==0.0.8
nbclient==0.10.0
nbconvert==7.16.4
nbformat==5.10.4
nest-asyncio==1.6.0
notebook==7.2.2
notebook_shim==0.2.4
numpy==1.26.4
oauthlib==3.2.0
opt-einsum==3.3.0
optree==0.12.1
overrides==7.7.0
packaging==24.1
pandas==2.2.2
pandocfilters==1.5.1
parso==0.8.4
pexpect==4.9.0
pillow==10.4.0
platformdirs==4.2.2
prometheus_client==0.20.0
promise==2.3
prompt_toolkit==3.0.47
protobuf==4.25.3
psutil==6.0.0
ptyprocess==0.7.0
pure_eval==0.2.3
pyarrow==17.0.0
pycparser==2.22
pydot==3.0.1
Pygments==2.18.0
PyGObject==3.42.1
PyJWT==2.3.0
pyparsing==3.1.4
python-apt==2.4.0+ubuntu3
python-dateutil==2.9.0.post0
python-json-logger==2.0.7
pytz==2024.1
PyYAML==6.0.2
pyzmq==26.2.0
qtconsole==5.6.0
QtPy==2.4.1
referencing==0.35.1
regex==2024.7.24
requests==2.32.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==13.7.1
rpds-py==0.20.0
scikit-learn==1.5.1
scipy==1.14.1
SecretStorage==3.3.1
Send2Trash==1.8.3
simple_parsing==0.1.5
six==1.16.0
sniffio==1.3.1
soupsieve==2.6
stack-data==0.6.3
tensorboard==2.17.0
tensorboard-data-server==0.7.2
tensorflow==2.17.0
tensorflow-datasets==4.9.6
tensorflow-io-gcs-filesystem==0.37.1
tensorflow-metadata==1.15.0
tensorflow-text==2.17.0
termcolor==2.4.0
terminado==0.18.1
threadpoolctl==3.5.0
tinycss2==1.3.0
toml==0.10.2
tornado==6.4.1
tqdm==4.66.5
traitlets==5.14.3
types-python-dateutil==2.9.0.20240821
typing_extensions==4.12.2
tzdata==2024.1
uri-template==1.3.0
urllib3==2.2.2
wadllib==1.3.6
wcwidth==0.2.13
webcolors==24.8.0
webencodings==0.5.1
websocket-client==1.8.0
Werkzeug==3.0.3
widgetsnbextension==4.0.13
wrapt==1.16.0
zipp==1.0.0

error:

FileNotFoundError Traceback (most recent call last)
Cell In[25], line 1
----> 1 (train_images, train_labels), (test_images, test_labels) = keras.datasets.cifar10.load_data()
File ~/.conda/envs/env_name/lib/python3.12/site-packages/keras/src/datasets/cifar10.py:84, in load_data()
79 for i in range(1, 6):
80 fpath = os.path.join(path, "data_batch_" + str(i))
81 (
82 x_train[(i - 1) * 10000 : i * 10000, :, :, :],
83 y_train[(i - 1) * 10000 : i * 10000],
---> 84 ) = load_batch(fpath)
86 fpath = os.path.join(path, "test_batch")
87 x_test, y_test = load_batch(fpath)

File ~/.conda/envs/env_name/lib/python3.12/site-packages/keras/src/datasets/cifar.py:17, in load_batch(fpath, label_key)
6 def load_batch(fpath, label_key="labels"):
7 """Internal utility for parsing CIFAR data.
8
9 Args:
(...)
15 A tuple (data, labels).
16 """
---> 17 with open(fpath, "rb") as f:
18 d = cPickle.load(f, encoding="bytes")
19 # decode utf8

FileNotFoundError: [Errno 2] No such file or directory: '/home/username/.keras/datasets/cifar-10-batches-py/data_batch_1'

Data is being saved to '/home/username/.keras/datasets/cifar-10-batches-py/cifar-10-batches-py/data_batch_1'

[mehtamansi29]

Hi @ashep29 -

Thanks for reporting the issue. I am not able to reproduce this issue on RHEL CentOS Stream release 9 machine with Python env: Python 3.11.9 and keras version: 3.5.0. Can you try to upgrade keras version(pip install --upgrade keras) and try to rerun the code ?

[ashep29]

Thanks for taking a look.

I'm running a jupyter notebook (screenshot below) with tensorflow install using docker://tensorflow/tensorflow:latest-gpu (which installs keras). I've separately installed keras-cv and keras-nlp.

Even after the keras upgrade, the error persists:

[newresu]

Actually, I realised you were right while testing smth else, it does reproduce in Colab.

Here is a simple repro (unrelated code, but shows up in first cells.)

Tagging @mehtamansi29 since he may realise what's wrong (this uses 3.5.0)

https://colab.research.google.com/gist/ghsanti/f5111b30cd9dbe778d0f127d6b7b7647/print.ipynb

[ashep29]

Thanks @ghsanti, definitely the same error you have reproduced. Hopefully this can be resolved soon!

[newresu]

It happens with cifar100 as well, but not with MNIST.

Datasets' loading code are here: https://github.com/keras-team/keras/tree/master/keras/src/datasets

[ashep29]

Perhaps it has something to do with how the tar.gz is being extracted? Seems to be a difference between mnist and the cifar datasets.

[newresu]

The files are downloaded to:

/root/.keras/datasets/cifar-10-batches-py/cifar-10-batches-py

Here is where it looks for:

/root/.keras/datasets/cifar-10-batches-py/data_batch_1

So there is a repetition of cifar-10-batches-py before the batches' files.
Does commenting out dirname work?
(It may be a duplication by some un-tarring artifact, just like you suggested.)

[ashep29]

Thanks @ghsanti, I've commented out the dirname argument, but the value returned by get_file is still '/home/username/.keras/datasets/cifar-10-batches-py/data_batch_1', so the error persists. Perhaps the error actually lies in keras.src.utils.file_utils (get_file)?

[dwgily]

If you get it please, let us know!!!

[newresu]

This works in colab @ashep29 @himanshurana-27

    origin = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
    path = get_file(
        fname="cifar-10-batches-py",
        cache_dir="cifar-10-batches-py",
        origin=origin,
        untar=True,
        file_hash=(  # noqa: E501
            "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"
        ),
    

Added cache_dir since fname can not use /.

The function recommends replacing untar with extract, but those do not do the same thing, so I left untar.

[ashep29]

OK so the only change that needed to be done was to add extract=True in the get_file method. It is now working. Didn't need the cache_dir and left fname as it was originally, so final working code:

dirname = "cifar-10-batches-py"
    origin = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
    path = get_file(
        fname=dirname,
        origin=origin,
        extract=True,
        untar=True,
        file_hash=(  # noqa: E501
            "6d958be074577803d12ecdefd02955f39262c83c16fe9348329d7fe0b5c001ce"
        ),
    )

Thank you for your help!