To give a trained model a meaningful name, you can register the output of Train Model component as a file dataset. Naming convention follows "MD - pipeline draft name - component name - Trained model ID" pattern. In designer, the trained models are automatically registered as file dataset with a system generated name. In Studio (classic), all trained models are saved in "Trained Models" category in the module list. Sometimes you may want to save the model trained in a pipeline and use the model in another pipeline later. Save trained model to use in another pipeline Therefore, prediction result may vary between the designer and Studio (classic). However Studio (classic) uses a Microsoft internal C# library. Use the 70_driver_log to see information related to your user-submitted script such as errors and exceptions.ĭesigner components use open source Python packages to implement machine learning algorithms. View Log: View driver and system logs.Use this to explore or download the output. View Output: Open a link to the output storage location.Visualize: Preview the results dataset.Select either Visualize, View Output, or View Log. Right-click the module whose output you want to see. To speed up the running time, you can create a compute resource with a minimum node size of 1 or greater.Īfter the job finishes, you can check the results of each module: Successive jobs take less time, since the nodes are already allocated. Since the default compute settings have a minimum node size of 0, the designer must allocate resources after being idle. This is useful for logging and tracking.Įnter an experiment name. If you run a pipeline multiple times, you can select the same experiment for successive jobs. Select Create new to create a new experiment.Įxperiments organize similar pipeline jobs together. Now that your compute target is set, you can submit a pipeline job:
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