A compute instance is a cloud-based virtual workstation which is fully managed by Azure Machine Learning. Unlike other Azure resources, it is not a shared resource and is designed for your individual workflows including data exploration, model development and model training. The main purpose of the compute instance is to provide you with a workspace where you can perform your data science tasks.
Importantly, the Microsoft Azure compute instance comes preinstalled with popular data science tools. This can make Data Science tasks more straightforward, as there is less need for setup and configuration.
Setting up a Compute Instance
The process of setting up a compute instance on Azure is pretty straightforward.
- Go to Azure portal and in the machine learning workspace, click on the “Compute” button.
- In the compute section, select “Compute Instances”, and click on “+ New”.
- Provide necessary details such as the virtual machine size, compute name, etc.
- Click on the “Create” button to create the instance.
Developing Code Using a Compute Instance
Once your compute instance is up and running, you can start developing code on it using Jupyter notebooks, JupyterLab or RStudio. Importantly, you can install other packages, libraries, or tools that you require for your project.
For instance, developing a simple classification model could look something like:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
# load dataset
data = load_iris()
# split data
X = data.data
Y = data.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=42)
# define model
model = RandomForestClassifier(n_estimators=100)
# train model
model.fit(X_train, Y_train)
# make predictions
Y_pred = model.predict(X_test)
# check accuracy
print(“Accuracy:”, metrics.accuracy_score(Y_test, Y_pred))
The Azure compute instance makes it easy to develop, test, and troubleshoot your code before deploying it to production.
Advantages of Using a Compute Instance
- Pre-installed tools: As mentioned earlier, Azure’s compute instances come preloaded with popular data science tools, making it easier to start working on your projects.
- Scalability: You can easily ramp up or down the virtual machine size based on your computation needs. This is especially handy when running complex machine learning algorithms that require high computational power.
- Flexibility: One of the key benefits of a cloud-based compute instance is that it provides you with the ability to work from anywhere. Also, you can easily share your workspace with teammates.
- Cost-efficiency: Azure’s pricing model ensures that you only pay for the computation power you use.
In conclusion, prepare for the DP-100 exam demands a deep understanding of how to use Azure’s Compute Instances. It plays a pivotal role in creating and managing your data science tasks effectively on Azure.
Practice Test
True or False: Compute instances in Azure can have more than one vCPU.
- True
- False
Answer: True
Explanation: The number of virtual CPUs that a compute instance can have depends on the selected size. Azure compute instances can have up to 416 vCPUs.
Which of the following is not a feature offered by Azure compute instances?
- a) Remote desktop
- b) Customization of hardware
- c) Pay as you go pricing
- d) Pre-installed data science tools
Answer: b) Customization of hardware
Explanation: While Azure compute instances do allow for selection of different sizes and configurations, direct hardware customization is not a feature.
True or False: Deleting the compute instance also deletes the storage attached to it.
- True
- False
Answer: False
Explanation: The storage is persisted even after the compute instance is deleted.
Single select: Which Azure service is primarily used to develop code using a compute instance?
- a) Azure Machine Learning
- b) Azure SQL Database
- c) Azure Cosmos DB
- d) Azure Functions
Answer: a) Azure Machine Learning
Explanation: Azure Machine Learning provides Jupyter notebooks to develop code directly within a compute instance.
Multiple Select: What are the benefits of using compute instances in Azure?
- a) You can choose the hardware specifics of your instances
- b) You can have pre-installed data science tools
- c) You only pay for what you use
- d) Auto scaling is not supported
Answer: a) You can choose the hardware specifics of your instances, b) You can have pre-installed data science tools, c) You only pay for what you use
Explanation: Compute instances in Azure allows you to choose the specifics of your instance and comes with pre-installed data science tools. You pay only for what you use but auto scaling is not a feature of compute instances.
True or False: You can increase the storage attached to a compute instance after it is created.
- True
- False
Answer: True
Explanation: The storage attached to a compute instance can be increased after the instance is created.
Single select: What is the minimal hardware requirement for a compute instance to run in Azure?
- a) 1 vCPU and 1 GB RAM
- b) 1 vCPU and 2 GB RAM
- c) 2 vCPU and 4 GB RAM
- d) 2 vCPU and 8 GB RAM
Answer: a) 1 vCPU and 1 GB RAM
Explanation: Azure compute instances can be provisioned with as little as 1 vCPU and 1 GB RAM.
True or False: You can run Jupyter notebooks directly in an Azure compute instance.
- True
- False
Answer: True
Explanation: Azure Machine Learning’s compute instance provides the capability to run Jupyter notebooks directly in the instances.
Single select: Which one of the following services can be used to monitor the performance of compute instances in Azure?
- a) Azure Advisor
- b) Azure Monitor
- c) Azure Logic Apps
- d) Azure App Service
Answer: b) Azure Monitor
Explanation: Azure Monitor can be used to monitor and analyze the performance and usage metrics of Azure compute instances.
True or False: Developing code on a compute instance is a one-time task; you cannot save and reopen the development environment.
- True
- False
Answer: False
Explanation: The state of your development environment is saved, and you can reopen it at any time.
Multiple Select: Which of the following are possible reasons to stop a compute instance in Azure?
- a) To reduce cost
- b) To maintain the state
- c) To delete the instance
- d) To increase the storage
Answer: a) To reduce cost, b) To maintain the state, c) To delete the instance
Explanation: Stopping a compute instance helps to reduce costs when not in use, the current state is maintained, and it must be stopped before it can be deleted. However, to increase storage, stopping the instance is not necessarily required.
Interview Questions
What is a compute instance in Azure?
A compute instance in Azure is a managed service that provides hassle-free compute for running notebook workloads and automating tasks.
What can you do with an Azure compute instance?
With an Azure compute instance, you can develop, run, and test code in an isolated and configurable virtual machine environment.
How can Azure compute instances be used for data science solutions?
Data scientists can use compute instances to work with data exploration, data cleaning, and feature engineering, as well as to develop, test, and run machine learning models.
What is the primary benefit of using a compute instance in Azure instead of a regular VM?
The primary benefit is that a compute instance is directly integrated with Azure Machine Learning Studio, allowing for easier access and utilization of machine learning tools and resources.
How can you secure a compute instance in Azure?
You can secure a compute instance by assigning it to a virtual network for isolation, restricting access through network security groups, implementing private link, and enabling Azure role-based access control.
Can you use a compute instance to access Azure Machine Learning studio?
Yes, you can directly use a compute instance to connect to Azure Machine Learning studio for creating and managing machine learning models.
How do you choose the size of a compute instance?
The size of the compute instance should be selected based on the workload requirements. This includes considerations like memory, CPU, and storage needs.
What programming languages can be used in a compute instance on Azure?
You can use any language supported by the Jupyter notebook including Python, R, and Scala.
How do you configure networking for a newly created compute instance?
Azure automatically configures standard internet routes in a virtual network when you create a compute instance. You can customize these settings later as per your requirements.
What happens when you stop a compute instance?
When you stop a compute instance, the state of the instance is de-allocated. The instance is not billed, and its dynamic public IP address, if assigned, is released.
Is it possible to change the size of a compute instance in Azure after it’s been created?
Yes, you are allowed to change the size of the compute instance after it’s been created. This is often necessary when the workload requirements change.
Is there any limitation to the number of compute instances that can be created in Azure?
Azure does have certain limits for the maximum number of compute instances that can be created, but these depend on several factors such as the type of subscription and the region.
Can a compute instance be linked with on-premises datacenter in Azure?
Yes, you can connect a compute instance with your on-premises datacenter using Azure ExpressRoute or VPN Gateway.
How to track the cost associated with a compute instance in Azure?
Azure Cost Management can be used to track and manage the costs associated with a compute instance.
Can you use the Azure command-line interface (CLI) to manage compute instances?
Yes, you can use the Azure command-line interface to create, manage, and delete compute instances.
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