Topic 1 Question 151
While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?
Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
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- 正解だと思う選択肢: D
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
Limiting the number of metrics in the evaluation step using tfma.MetricsSpec() can reduce the memory usage during evaluation and address the out-of-memory error. This can help stabilize the pipeline without downgrading the evaluation quality or incurring additional infrastructure overhead. Running the evaluation step on Dataflow or custom Compute Engine VMs can be resource-intensive and expensive, while migrating the pipeline to Kubeflow would require additional setup and configuration.
ANSWER D
👍 4TNT872023/03/07 - 正解だと思う選択肢: A👍 3RaghavAI2023/02/05
- 正解だと思う選択肢: A
Answer A
👍 2TNT872023/02/16
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