NeuralNetTorch Hyperparameter Tuning Fails with URI Scheme Error in PyArrow #4186
Description
Bug Report Checklist
[V] I provided code that demonstrates a minimal reproducible example.
[X] I confirmed bug exists on the latest mainline of AutoGluon via source install.
[V] I confirmed bug exists on the latest stable version of AutoGluon.
Describe the bug
When attempting to run hyperparameter tuning with NN_TORCH as the model on a TabularDataset, an exception is thrown related to URI handling by pyarrow library. The error message indicates an "ArrowInvalid: URI has empty scheme".
Expected behavior
The training should proceed without errors, and the model should handle the URI scheme appropriately or provide more specific guidance on expected URI formats.
To Reproduce
Install a fresh environment with Python 3.10 and AutoGluon 1.1.0
Run the following script:
from autogluon.tabular import TabularDataset, TabularPredictor
data_url = 'https://raw.githubusercontent.com/mli/ag-docs/main/knot_theory/'
train_data = TabularDataset(f'{data_url}train.csv')
label = 'signature'
hp_args = {"num_trials": 3, "scheduler": "local", "searcher": "random"}
fit_args = {"hyperparameter_tune_kwargs": hp_args, "included_model_types": ["NN_TORCH"]}
predictor = TabularPredictor(label=label).fit(train_data, **fit_args)
Screenshots / Logs
Logs from the error:
pyarrow.lib.ArrowInvalid: URI has empty scheme: 'AutogluonModels/ag-20240509_084509/models
Installed Versions
Details
INSTALLED VERSIONS ------------------ date : 2024-05-09 time : 08:47:49.707205 python : 3.10.14.final.0 OS : Linux OS-release : 5.15.0-1040-azure Version : #47~20.04.1-Ubuntu SMP Fri Jun 2 21:38:08 UTC 2023 machine : x86_64 processor : x86_64 num_cores : 16 cpu_ram_mb : 128812.6796875 cuda version : None num_gpus : 0 gpu_ram_mb : [] avail_disk_size_mb : 4284286accelerate : 0.21.0
autogluon : 1.1.0
autogluon.common : 1.1.0
autogluon.core : 1.1.0
autogluon.features : 1.1.0
autogluon.multimodal : 1.1.0
autogluon.tabular : 1.1.0
autogluon.timeseries : 1.1.0
boto3 : 1.34.101
catboost : 1.2.5
defusedxml : 0.7.1
evaluate : 0.4.2
fastai : 2.7.15
gluonts : 0.14.3
hyperopt : 0.2.7
imodels : None
jinja2 : 3.1.4
joblib : 1.4.2
jsonschema : 4.21.1
lightgbm : 4.3.0
lightning : 2.1.4
matplotlib : 3.8.4
mlforecast : 0.10.0
networkx : 3.3
nlpaug : 1.1.11
nltk : 3.8.1
nptyping : 2.4.1
numpy : 1.26.4
nvidia-ml-py3 : 7.352.0
omegaconf : 2.2.3
onnxruntime-gpu : None
openmim : 0.3.9
optimum : 1.18.1
optimum-intel : None
orjson : 3.10.3
pandas : 2.2.2
pdf2image : 1.17.0
Pillow : 10.3.0
psutil : 5.9.8
pytesseract : 0.3.10
pytorch-lightning : 2.1.4
pytorch-metric-learning: 2.3.0
ray : 2.10.0
requests : 2.28.2
scikit-image : 0.20.0
scikit-learn : 1.4.0
scikit-learn-intelex : None
scipy : 1.12.0
seqeval : 1.2.2
setuptools : 60.2.0
skl2onnx : None
statsforecast : 1.4.0
tabpfn : None
tensorboard : 2.16.2
text-unidecode : 1.3
timm : 0.9.16
torch : 2.1.2
torchmetrics : 1.2.1
torchvision : 0.16.2
tqdm : 4.65.2
transformers : 4.38.2
utilsforecast : 0.0.10
vowpalwabbit : None
xgboost : 2.0.3