Microsoft
DP-100 · Question #410
You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20…
The correct answer is B. Set the value of max_concurrent_runs of HyperDriveConfig to 4. The number of concurrent jobs has an impact on the effectiveness of the tuning process. A "smaller number of concurrent jobs" may lead to "better sampling convergence", since the smaller degree of parallelism increases the number of jobs that benefit from previously completed…
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Question
You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20 nodes. The code implements Bandit termination policy with slack factor set to 0.2 and the HyperDriveConfig class instance with max_concurrent_runs set to 10. You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?
Options
- ASet the value of slack factor of early_termination_policy to 09.
- BSet the value of max_concurrent_runs of HyperDriveConfig to 4.
- CSet the value of slack factor of early_termination_policy to 0.1.
- DSet the value of max_concurrent_runs of HyperDriveConfig to 20.
How the community answered
(29 responses)- A14% (4)
- B72% (21)
- C3% (1)
- D10% (3)
Explanation
The number of concurrent jobs has an impact on the effectiveness of the tuning process. A "smaller number of concurrent jobs" may lead to "better sampling convergence", since the smaller degree of parallelism increases the number of jobs that benefit from previously completed jobs.
Topics
#Hyperparameter Tuning#Bayesian Sampling#Azure Machine Learning#HyperDrive
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