Bug description
When training the all recommender systems models with NVIDIA Merlin at:
...
history = model.fit( train_data, epochs=3, batch_size=512, pre=predict_last )
...
runtime error: "ValueError: high is out of bounds for int32"
Steps/Code to reproduce bug
- I solved it like this:
at file C:\Users\Xavier\AppData\Roaming\Python\Python310\site-packages\merlin\core\dispatch.py line 778
from:
seeds = random_state.randint(0, 2 ** 32, size=global_size)
to:
seeds = random_state.randint(0, 2 ** 32, size=global_size, dtype='int64') # include ", dtype='int64'"
Expected behavior
After the change in line 778 setting the type, all models trained normally.
Environment details
- Merlin version: (23.5.0)
- Platform: Windows, Lenovo GPU NVIDIA GeForce GTX 1650
- Python version: 3.10.0
- PyTorch version (GPU?):
- Tensorflow version (GPU?): 2.10.0
Additional context
with pip install protobuf==3.20.0
Bug description
When training the all recommender systems models with NVIDIA Merlin at:
...
history = model.fit( train_data, epochs=3, batch_size=512, pre=predict_last )
...
runtime error: "ValueError: high is out of bounds for int32"
Steps/Code to reproduce bug
at file C:\Users\Xavier\AppData\Roaming\Python\Python310\site-packages\merlin\core\dispatch.py line 778
from:
seeds = random_state.randint(0, 2 ** 32, size=global_size)
to:
seeds = random_state.randint(0, 2 ** 32, size=global_size, dtype='int64') # include ", dtype='int64'"
Expected behavior
After the change in line 778 setting the type, all models trained normally.
Environment details
Additional context
with pip install protobuf==3.20.0