[BUG] fastai Exception caused NeuralNetFastAI to fail during training... Skipping this model. 'list' object has no attribute 'starmap'
Bug Report Checklist
- [√] I provided code that demonstrates a minimal reproducible example.
- [√ ] I confirmed bug exists on the latest mainline of AutoGluon via source install.
- I confirmed bug exists on the latest stable version of AutoGluon.
Describe the bug In July 2026, the new FastCore 2.x version removed the L.starmap API. The new Ubuntu installation was set to fastcore==2.x, which caused the NeuralNetFastAI of AutoGluon to fail during the initialization of the FastAI optimizer and be skipped.
Fitting model: NeuralNetFastAI ... Fitting with cpus=1, gpus=0, mem=0.0/5.0 GB Warning: Exception caused NeuralNetFastAI to fail during training... Skipping this model. 'list' object has no attribute 'starmap'
Expected behavior As before, I hope the model can be successfully trained when I only install Autogluon and do not need to control other libraries.
Fitting model: NeuralNetFastAI ... Fitting with cpus=1, gpus=0, mem=0.0/5.2 GB -0.0264 = Validation score (-root_mean_squared_error) 9.1s = Training runtime 0.03s = Validation runtime
To Reproduce ts = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S") model_dir = os.path.join(WORK_DIR, f"model_{dataset_name}{TARGET}{ts}")
predictor = TabularPredictor(label=TARGET, path=model_dir).fit( train_data=train_data, presets="medium_quality", time_limit=None, )
Screenshots / Logs
Installed Versions
# Replace this code with the output of the following:
INSTALLED VERSIONS
------------------
date : 2026-08-26
time : 06:00:55.642351
python : 3.10.20.final.0
OS : Linux
OS-release : 5.15.0-1084-aws
Version : #91~20.04.1-Ubuntu SMP Fri May 2 06:59:36 UTC 2025
machine : x86_64
processor :
num_cores : 4
cpu_ram_mb : 15847.49609375
cuda version : None
num_gpus : 0
gpu_ram_mb : []
avail_disk_size_mb : 45781
accelerate : 1.14.0
autogluon : 1.5.0
autogluon.common : 1.5.0
autogluon.core : 1.5.0
autogluon.features : 1.5.0
autogluon.multimodal : 1.5.0
autogluon.tabular : 1.5.0
autogluon.timeseries : 1.5.0
boto3 : 1.40.61
catboost : 1.2.10
chronos-forecasting : 2.3.1
coreforecast : 0.0.16
defusedxml : 0.7.1
einops : 0.8.2
einx : 0.4.3
evaluate : 0.4.6
fastai : 2.8.7
fsspec : 2024.3.1
fugue : 0.9.7
gluonts : 0.16.3
huggingface_hub : 0.36.2
hyperopt : 0.2.7
imodels : None
interpret-core : None
jinja2 : 3.1.6
joblib : 1.5.3
jsonschema : 4.23.0
lightgbm : 4.6.0
lightning : 2.5.6
loguru : 0.7.3
matplotlib : 3.10.0
mlforecast : 0.14.0
networkx : 3.4.2
nlpaug : 1.1.11
nltk : 3.9.4
numpy : 2.1.3
nvidia-ml-py3 : 7.352.0
omegaconf : 2.3.1
onnx : None
onnxruntime : None
onnxruntime-gpu : None
openmim : 0.3.9
orjson : 3.11.9
pandas : 2.3.3
pdf2image : 1.17.0
peft : 0.17.1
Pillow : 11.3.0
psutil : 7.1.3
pyarrow : 20.0.0
pytabkit : None
pytesseract : 0.3.13
pytorch-metric-learning: 2.8.1
pyyaml : 6.0.3
ray : 2.52.1
requests : 2.34.2
scikit-image : 0.25.2
scikit-learn : 1.7.2
scikit-learn-intelex : None
scipy : 1.15.3
seqeval : 1.2.2
skl2onnx : None
spacy : 3.8.14
statsforecast : 2.0.1
stevedore : 5.4.1
tabdpt : None
tabicl : None
tabpfn : None
tensorboard : 2.21.0
text-unidecode : 1.3
timm : 1.0.3
torch : 2.9.1+cpu
torchmetrics : 1.7.4
torchvision : 0.24.1+cpu
tqdm : 4.70.0
transformers : 4.57.6
utilsforecast : 0.2.11
xgboost : 3.1.3Source: autogluon/autogluon