if i have saved a pytorch_based model, i want to run inference on cpu, how to change the codes?

[Bobby-youngking]

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from hummingbird.ml import convert, load
iris = datasets.load_iris()
X, y = iris.data, iris.target
rfmodel = load('hb_model')
rfmodel.to('cpu')
print(rfmodel.predict(X))

i tried this way, but the result showed that i was running on gpu

[interesaaat]

Hi! Just change rfmodel.to('cpu') to rfmodel = rfmodel.to('cpu'). This should work.

[mshr-h]

I think we have to update the readme.

https://github.com/microsoft/hummingbird#examples

# Use Hummingbird to convert the model to PyTorch
model = convert(skl_model, 'pytorch')

# Run predictions on CPU
model.predict(X)

# Run predictions on GPU
model.to('cuda')
model.predict(X)

[ksaur]

Was this an API change in a newer version of torch? Because the README example definitely works, or used to!

[ksaur]

I was not able to reproduce this issue with the latest (torch==2.1.0), and the example in the documentation still worked as written for me.

@Bobby-youngking can you please post which versions you are using and a bit more info? you shouldn't have to reassign the model

[ksaur]

Closing due to inactivity and we cannot repro, please reopen if you still have problems with this.