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Answer: Create a cluster with the Databricks Runtime for Machine Learning, then use the cluster's library management feature to install TensorFlow and PyTorch globally.
Option B is correct because it leverages the Databricks Runtime for Machine Learning, which is optimized for machine learning workloads. By using the cluster's library management feature, TensorFlow and PyTorch can be installed globally, ensuring that all notebooks running on the cluster have access to these libraries without the need for manual installation in each session.
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You are tasked with creating a Databricks cluster using the Databricks Runtime for Machine Learning. The cluster needs to support a deep learning project that requires TensorFlow and PyTorch libraries. Describe the steps you would take to create this cluster, including how you would ensure that both TensorFlow and PyTorch are available to all notebooks running on this cluster.
A
Create a standard Databricks cluster and manually install TensorFlow and PyTorch in each notebook session.
B
Create a cluster with the Databricks Runtime for Machine Learning, then use the cluster's library management feature to install TensorFlow and PyTorch globally.
C
Create a cluster with the Databricks Runtime for Machine Learning, but only install TensorFlow globally and expect users to install PyTorch individually.
D
Create a cluster with the Databricks Runtime for Machine Learning, but do not install any additional libraries; users should install their own libraries as needed.
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