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Answer: Execute `%pip install gpu_ml` in any notebook connected to the cluster.
The most straightforward and efficient method to install the `gpu_ml` library on a Databricks GPU-enabled cluster is by executing `%pip install gpu_ml` within a notebook attached to the cluster. This approach is preferred because: - **A.** Modifying the cluster to use Databricks Runtime for GPU is unnecessary as it already includes the required GPU drivers and libraries. - **B.** Adding `gpu_ml` to the cluster's GPU library dependencies is not required since the library can be directly installed via the notebook. - **D.** Setting the `PYTHON_GPU_LIB` variable is not advised as it may cause conflicts with other GPU libraries. - **E.** The statement that GPU-accelerated libraries cannot be installed is incorrect; they can indeed be installed using the `%pip` command. Thus, using the `%pip install gpu_ml` command within the notebook is the recommended approach for enabling GPU-accelerated computations.
Author: LeetQuiz Editorial Team
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A data scientist is looking to utilize a GPU-accelerated version of a machine learning library, gpu_ml, on a Databricks GPU-enabled cluster. What is the most effective method to install this library?
A
Modify the cluster to utilize the Databricks Runtime for GPU.
B
Include gpu_ml in the cluster's GPU library dependencies.
C
Execute %pip install gpu_ml in any notebook connected to the cluster.
D
Configure the PYTHON_GPU_LIB variable in the cluster settings to include gpu_ml.
E
GPU-accelerated libraries cannot be installed on a Databricks cluster.
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