CODEWITHSUNDEEP
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Clem Wang
Clem Wang

Reputation: 739

Is it possible to train a H2O model with GPU and predict with a CPU?

For trainining speed, it would be nice to be able to train a H2O model with GPUs, take the model file, and then predict on a machine without GPUs.

It seems like that should be possible in theory, but with the H2O release 3.13.0.341, that doesn't seem to happen, except for XGBoost model.

When I run gpustat -cup I can see the GPUs kick in when I train H2O's XGBoost model. This doesn't happen with DL, DRF, GLM, or GBM.

I wouldn't be surprised if a difference in float point size (16, 32, 64) could cause some inconsistency, not to mention the vagaries due to multiprocessor modeling, but I think I could live with that.

(This is related to my question here, but now that I understand the environment better I can see that the GPUs aren't used all the time.)

How can I tell if H2O 3.11.0.266 is running with GPUs?

Upvotes: 2

Views: 1101

Answers (2)

Jeyanth Krishna - Intel
Jeyanth Krishna - Intel

Reputation: 116

Yes, you do the heavy job of training on a GPU, save weights and then, your CPU will only do the matrix multiplication for predictions.

In Keras you can train your model and save Neural Network weights:

model.save_weights('your_model_weights.h5')
model.load_weights('your_model_weights.h5')

Upvotes: 0

Erin LeDell
Erin LeDell

Reputation: 8819

The new XGBoost integration in H2O is the only GPU-capable algorithm in H2O (proper) at this time. So you can train an XGBoost model on GPUs and score on CPUs, but that's not true for the other H2O algorithms.

There is also the H2O Deep Water project, which provides integration between H2O and three third-party deep learning backends (MXNet, Caffe and TensorFlow), all of which are GPU-capable. So you can train those models using a GPU and score on a CPU as well. You can download the H2O Deep Water jar file (or R package, or Python module) at the Deep Water link above, and you can find out more info in the Deep Water GitHub repo README.

Upvotes: 1

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