Inference with PyTorch

Here are the steps to use Galileo for Inference with a text classification model.

1.Initialize the Galileo project - Use the dq.init function to initialize the Galileo project. The function takes three arguments: the project type, the project name, and the run name.
2.Log the inference data - Use the dq.log_dataset function to log the inference data. The function takes two arguments: the inference data and the split name.
3.Set the class labels - Use the dq.set_labels_for_run function to set the class labels for the run. The function takes one argument: the names of the classes.
4.Set the split to inference with dq.set_split
5.Monitor the model - Use the watch function from the Galileo's PyTorch integration to monitor the model. The function takes two arguments: the model and the dataloader.
6.Run the model on the data using the data loader
7.Finish the run - Use the dq.finish function to finish the run.
In the example notebook you can see how it is called:
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Once the training is complete in the console you select the inference dataset as your subset in the top right corner.
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Example Notebook: PyTorch Inference​

Natural Language Inference

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Last modified 1mo ago