It’s crucial to understand the concept of ‘early termination options’. This concept is mainly associated with Azure’s automated machine learning; more specifically, it comes into play when discussing the model selection and hyper-parameter tuning phase.
Understanding Early Termination Options
Early termination is a unique feature provided by Automated Machine Learning in Azure. This feature is useful to optimize computational efficiency during model training, especially when dealing with large datasets and complex models. Essentially, it provides a way to stop the training process of the machine learning model if it’s not likely to produce a better result than the previous models. It avoids wasting computational resources on models that are less likely to offer improved performance.
There are different types of early termination policies that one can choose from when configuring automated ML experiments. The three primary early termination policies are:
- BanditPolicy: This policy terminates any run that doesn’t fall within the slack factor or slack amount of the evaluation metric with the best performing run.
- MedianStoppingPolicy: This policy computes running averages across all runs and terminates runs with performance worse than the median of the running averages.
- TruncationSelectionPolicy: This policy cancels a certain percentage of low-performance runs at each evaluation interval.
Application of Early Termination in Azure
To utilize an early termination option, you need to provide a termination policy when creating an AutoMLConfig object. Here is a simple Python example of how to invoke the BanditPolicy:
from azureml.train.automl import AutoMLConfig
from azureml.train.hyperdrive import BanditPolicy
early_termination_policy = BanditPolicy(slack_amount = 0.2)
automl_config = AutoMLConfig(task = “classification”,
primary_metric = “accuracy”,
training_data = train_data,
label_column_name = “Label”,
experiment_timeout_minutes = 20,
enable_early_stopping = True,
early_termination_policy = early_termination_policy
)
In the case above, the BanditPolicy will terminate any model training that doesn’t achieve at least 80% (1-0.2) of the best accuracy at the same point in the process. The AutoMLConfig object then includes the policy as a parameter.
Using early termination policies is not required but is beneficial. They provide a method to sidestep spending excessive time and resources training models destined to perform poorly. This functionality is important in the Azure environment, where machine learning often operates on large amounts of data. With the feature of early termination, Azure enables us to utilize our resources effectively and efficiently, thus making Azure a reliable platform for machine learning and data science tasks.
That said, it is recommended that when going for the DP-100 certification exam, one must have a clear understanding about these policies and should know how to apply them, as they form a crucial part of Azure’s Automated Machine Learning functionality.
Practice Test
True or False: Early termination options are features in Azure Machine Learning that enable the cancellation of a run if the score is not improving over the course of a certain number of iterations.
- True
- False
Answer: True
Explanation: Early Termination is a feature introduced by Azure Machine learning to stop the process if no positive improvement is seen over the course of certain pre-determined iterations.
What is the main goal of early termination options in Azure Machine Learning?
- a) To reduce computation time
- b) To speed up model training
- c) To decide which features to keep in the model
- d) All of the above
Answer: d) All of the above
Explanation: Early termination not only speeds up model training and reduces computation time, but also assists in determining whether a feature should be included in the model.
True or False: An early termination policy is beneficial only for smaller datasets.
- True
- False
Answer: False
Explanation: Early termination policies can be useful for both small and large datasets by saving time and resources during iterations.
Which Azure Machine Learning feature helps to stop training if the specified performance metric is not improving?
- a) Automated ML
- b) Early Termination
- c) Bandit Policy
- d) None of the above
Answer: b) Early Termination
Explanation: Early termination helps in stopping model training if no improvement is noticed in the performance metric over iterations.
Which early termination policy in Azure Machine Learning provides a balance between exploration and exploitation?
- a) Median stopping policy
- b) Bandit policy
- c) Truncation selection policy
- d) None of the above
Answer: b) Bandit policy
Explanation: The Bandit policy balances between exploration and exploitation by defining a slack factor and frequency.
True or False: Early termination in Azure Machine Learning requires manual intervention to stop a process.
- True
- False
Answer: False
Explanation: Early termination automatically stops the model training process, without requiring any manual intervention.
True or False: Early termination options cannot be used with HyperDrive in Azure Machine Learning.
- True
- False
Answer: False
Explanation: Early termination options can be used with HyperDrive, a service which allows you to automate hyperparameter tuning to achieve optimal model performance.
In Azure Machine Learning, what does the early termination option do?
- a) It cancels a run if the score is not improving
- b) It continually tries to improve the score even if it is not improving
- c) It works only for the large dataset
- d) None of the above
Answer: a) It cancels a run if the score is not improving
Explanation: Early termination is used to stop the model training process if no improvement is seen in the score after a specified number of iterations.
Which policy in Azure Machine Learning terminates any run whose best metric is worse than the median of the running averages?
- a) Median stopping policy
- b) Bandit policy
- c) Truncation selection policy
- d) None of the above
Answer: a) Median stopping policy
Explanation: The Median stopping policy in Azure Machine Learning terminates runs whose best metric is worse than the median of the running averages.
True or False: Early termination option in Azure Machine Learning is a cost-effective solution.
- True
- False
Answer: True
Explanation: Early termination options do provide a cost-effective solution by reducing the computational time and resources used in running processes that show no improvement.
Interview Questions
What is early termination in Azure Machine Learning?
Early termination is a useful feature in Azure Machine Learning which is used when training machine learning models. It is a way to automatically stop an experiment run when the model’s performance has stopped improving or has started to decrease.
Which Azure Machine Learning feature is most commonly used to implement early termination?
HyperDrive is Azure’s hyperparameter tuning capability that relies on early termination to optimize the model.
How does the HyperDrive in Azure Machine Learning implement early termination?
HyperDrive implements early termination by using various policies that monitor the performance of a machine learning model over time during training. If a model’s performance does not improve or worsens, HyperDrive implements the early termination policies.
Can you name a few early termination policies in Azure Machine Learning?
Some of the early termination policies in Azure Machine Learning include Bandit policy, Median stopping policy, and Truncation selection policy.
How does the Bandit policy work for early termination in Azure Machine Learning?
Bandit policy works based on a slack factor. It will terminate any run that doesn’t fall within the slack factor when compared with the best performing run.
What is the use of the Median stopping policy in Azure Machine Learning?
The Median stopping policy is an early termination policy which stops training runs where performance is worse than the median of the running averages up to the current iteration.
Can you explain the truncation selection policy?
Truncation selection policy cancels a specified percentage of runs of the lowest performing models at each evaluation interval. Users need to specify truncation_percentage which indicates the percentage of lowest performing runs to be cancelled.
How are early termination policies beneficial in Azure Machine Learning?
Early termination policies help in saving resources by stopping non-productive runs. They also help improve efficiency by focusing on most promising configurations.
How is early termination used in HyperDriveConfig in Azure Machine Learning?
Early termination is used in HyperDriveConfig by specifying the policy parameter with an instantiated termination policy.
Can we use early termination policies for Bayesian sampling in Azure Machine Learning?
Yes, early termination can be utilized with Bayesian sampling in Azure Machine Learning, but it is less effective in comparison to other sampling methods, since Bayesian sampling relies on exploring all configuration options.
Can you experiment without applying any early termination policy in Azure Machine Learning?
Yes, users can conduct an experiment without early termination by setting the policy parameter to None.
What parameter should be provided to use Median stopping policy during HyperDrive run?
The Median stopping policy requires a delay evaluation parameter which specifies the number of intervals to delay the policy enforcement.
Which policy should be used in HyperDrive to save the most amount of resources?
The Bandit policy helps in saving the most resources as it stops the run earlier than other policies by maintaining a slack factor.
Is it possible to manually terminate an experiment run in Azure Machine Learning?
Yes, users can manually terminate a run through the Azure portal, the Python SDK, or the REST API.
Can the early termination saving be directly translated into cost saving in Azure Machine Learning?
Yes, early termination can often lead to cost savings by freeing up Azure resources that can be used elsewhere. This is particularly significant in larger studies with many hyperparameters or large training datasets.
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