Does the Google Cloud Machine Learning Engine (CMLE) offer automatic resource acquisition and configuration and handle resource shutdown after the training of the model is finished?
Cloud Machine Learning Engine (CMLE) is a powerful tool provided by Google Cloud Platform (GCP) for training machine learning models in a distributed and parallel manner. However, it does not offer automatic resource acquisition and configuration, nor does it handle resource shutdown after the training of the model is finished. In this answer, we will consider the details of CMLE, its capabilities, and the need for manual resource management.
CMLE is designed to simplify the process of training and deploying machine learning models at scale. It provides a managed environment that allows users to focus on model development rather than infrastructure management. CMLE leverages the power of GCP's infrastructure to distribute the training workload across multiple machines, enabling faster training times and handling large datasets.
When using CMLE, users have the flexibility to choose the type and number of resources required for their training job. They can select the machine type, number of workers, and other parameters based on their specific requirements. However, CMLE does not automatically acquire and configure these resources. It is the responsibility of the user to provision the necessary resources before starting the training job.
To acquire the resources, users can utilize GCP services such as Compute Engine or Kubernetes Engine. These services provide a scalable and flexible infrastructure to accommodate the training workload. Users can create virtual machine instances or containers, configure them with the required software dependencies, and then use them as workers in CMLE.
Once the training job is completed, CMLE does not automatically shut down the resources used for training. This is because the trained model might need to be deployed and served for inference purposes. It is up to the user to decide when and how to terminate the resources to avoid unnecessary costs.
To summarize, CMLE offers a powerful platform for parallel machine learning model training. However, it requires manual acquisition and configuration of resources and does not handle resource shutdown after the training is finished. Users need to provision the necessary resources using GCP services like Compute Engine or Kubernetes Engine and manage their lifecycle based on their specific requirements.
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