"Could not deserialize ATN" exception

What happened + What you expected to happen

Exception Traceback (most recent call last) Cell In[9], line 10 3 from neuralforecast.utils import AirPassengersDF 5 nf = NeuralForecast( 6 models = [NBEATS(input_size=24, h=12, max_steps=100)], 7 freq = 'M' 8 ) ---> 10 nf.fit(df=AirPassengersDF) 11 nf.predict()

File ~/miniforge3/envs/statsforecast/lib/python3.10/site-packages/neuralforecast/core.py:462, in NeuralForecast.fit(self, df, static_df, val_size, sort_df, use_init_models, verbose, id_col, time_col, target_col, distributed_config) 459 self._reset_models() 461 for i, model in enumerate(self.models): --> 462 self.models[i] = model.fit( 463 self.dataset, val_size=val_size, distributed_config=distributed_config 464 ) 466 self._fitted = True

File ~/miniforge3/envs/statsforecast/lib/python3.10/site-packages/neuralforecast/common/_base_windows.py:651, in BaseWindows.fit(self, dataset, val_size, test_size, random_seed, distributed_config) 622 def fit( 623 self, 624 dataset, (...) 628 distributed_config=None, ... 48 version = self.readInt() 49 if version != SERIALIZED_VERSION: ---> 50 raise Exception("Could not deserialize ATN with version " + str(version) + " (expected " + str(SERIALIZED_VERSION) + ").")

Exception: Could not deserialize ATN with version (expected 4).

Versions / Dependencies

name: statsforecast channels:

conda-forge dependencies:
_libgcc_mutex=0.1=conda_forge
_openmp_mutex=4.5=2_gnu
asttokens=2.4.1=pyhd8ed1ab_0
bzip2=1.0.8=hd590300_5
ca-certificates=2024.6.2=hbcca054_0
comm=0.2.2=pyhd8ed1ab_0
debugpy=1.8.1=py310hc6cd4ac_0
decorator=5.1.1=pyhd8ed1ab_0
exceptiongroup=1.2.0=pyhd8ed1ab_2
executing=2.0.1=pyhd8ed1ab_0
importlib-metadata=7.2.1=pyha770c72_0
importlib_metadata=7.2.1=hd8ed1ab_0
ipykernel=6.29.4=pyh3099207_0
ipython=8.25.0=pyh707e725_0
jedi=0.19.1=pyhd8ed1ab_0
jupyter_client=8.6.2=pyhd8ed1ab_0
jupyter_core=5.7.2=py310hff52083_0
keyutils=1.6.1=h166bdaf_0
krb5=1.21.2=h659d440_0
ld_impl_linux-64=2.40=hf3520f5_7
libedit=3.1.20191231=he28a2e2_2
libffi=3.4.2=h7f98852_5
libgcc-ng=13.2.0=h77fa898_13
libgomp=13.2.0=h77fa898_13
libnsl=2.0.1=hd590300_0
libsodium=1.0.18=h36c2ea0_1
libsqlite=3.46.0=hde9e2c9_0
libstdcxx-ng=13.2.0=hc0a3c3a_13
libuuid=2.38.1=h0b41bf4_0
libxcrypt=4.4.36=hd590300_1
libzlib=1.3.1=h4ab18f5_1
matplotlib-inline=0.1.7=pyhd8ed1ab_0
ncurses=6.5=h59595ed_0
nest-asyncio=1.6.0=pyhd8ed1ab_0
openssl=3.3.1=h4ab18f5_0
packaging=24.1=pyhd8ed1ab_0
parso=0.8.4=pyhd8ed1ab_0
pexpect=4.9.0=pyhd8ed1ab_0
pickleshare=0.7.5=py_1003
pip=24.0=pyhd8ed1ab_0
platformdirs=4.2.2=pyhd8ed1ab_0
prompt-toolkit=3.0.47=pyha770c72_0
psutil=6.0.0=py310hc51659f_0
ptyprocess=0.7.0=pyhd3deb0d_0
pure_eval=0.2.2=pyhd8ed1ab_0
pygments=2.18.0=pyhd8ed1ab_0
python=3.10.14=hd12c33a_0_cpython
python-dateutil=2.9.0=pyhd8ed1ab_0
python_abi=3.10=4_cp310
pyzmq=26.0.3=py310h6883aea_0
readline=8.2=h8228510_1
setuptools=70.1.0=pyhd8ed1ab_0
six=1.16.0=pyh6c4a22f_0
stack_data=0.6.2=pyhd8ed1ab_0
tk=8.6.13=noxft_h4845f30_101
tornado=6.4.1=py310hc51659f_0
traitlets=5.14.3=pyhd8ed1ab_0
typing_extensions=4.12.2=pyha770c72_0
tzdata=2024a=h0c530f3_0
wcwidth=0.2.13=pyhd8ed1ab_0
wheel=0.43.0=pyhd8ed1ab_1
xz=5.2.6=h166bdaf_0
zeromq=4.3.5=h75354e8_4
zipp=3.19.2=pyhd8ed1ab_0
pip:
- adagio==0.2.4
- alembic==1.13.1
- antlr4-python3-runtime==4.11.1
- certifi==2024.6.2
- charset-normalizer==3.3.2
- cloudpickle==3.0.0
- cmake==3.29.6
- colorlog==6.8.2
- contourpy==1.2.1
- coreforecast==0.0.10
- cycler==0.12.1
- filelock==3.15.4
- fonttools==4.53.0
- fs==2.4.16
- fsspec==2024.6.0
- fugue==0.9.1
- greenlet==3.0.3
- idna==3.7
- ipywidgets==8.1.3
- jinja2==3.1.4
- joblib==1.4.2
- jsonschema==4.22.0
- jsonschema-specifications==2023.12.1
- jupyterlab-widgets==3.0.11
- kiwisolver==1.4.5
- lightning-utilities==0.11.2
- lit==18.1.7
- llvmlite==0.43.0
- mako==1.3.5
- markupsafe==2.1.5
- matplotlib==3.9.0
- mlforecast==0.13.0
- mpmath==1.3.0
- msgpack==1.0.8
- neuralforecast==1.7.2
- numba==0.60.0
- numpy==1.24.0
- nvidia-cublas-cu11==11.10.3.66
- nvidia-cuda-cupti-cu11==11.7.101
- nvidia-cuda-nvrtc-cu11==11.7.99
- nvidia-cuda-runtime-cu11==11.7.99
- nvidia-cudnn-cu11==8.5.0.96
- nvidia-cufft-cu11==10.9.0.58
- nvidia-curand-cu11==10.2.10.91
- nvidia-cusolver-cu11==11.4.0.1
- nvidia-cusparse-cu11==11.7.4.91
- nvidia-nccl-cu11==2.14.3
- nvidia-nvtx-cu11==11.7.91
- optuna==3.6.1
- pandas==2.2.2
- patsy==0.5.6
- pillow==10.3.0
- protobuf==5.27.1
- pyarrow==16.1.0
- pytorch-lightning==2.3.0
- pytz==2024.1
- pyyaml==6.0.1
- ray==2.30.0
- referencing==0.35.1
- requests==2.32.3
- rpds-py==0.18.1
- scikit-learn==1.5.0
- scipy==1.14.0
- sqlalchemy==2.0.31
- statsforecast==1.7.5
- statsmodels==0.14.2
- sympy==1.12.1
- tensorboardx==2.6.2.2
- threadpoolctl==3.5.0
- torch==2.0.1
- torchmetrics==1.4.0.post0
- tqdm==4.66.4
- triad==0.9.7
- triton==2.0.0
- urllib3==2.2.2
- utilsforecast==0.1.11
- widgetsnbextension==4.0.11
- window-ops==0.0.15 prefix: /home/den/miniforge3/envs/statsforecast

Reproduction script

from neuralforecast import NeuralForecast from neuralforecast.models import NBEATS from neuralforecast.utils import AirPassengersDF

nf = NeuralForecast( models = [NBEATS(input_size=24, h=12, max_steps=100)], freq = 'M' )

nf.fit(df=AirPassengersDF) nf.predict()

Issue Severity

None

Nixtla / neuralforecast