In this article, I will tell you how to run code in Jupyter Notebook inside azure machine learning studio. It will also tell you how to start Jupyter notebook as a container in Azure machine learning instance and then run code in it.
1. How To Run Code In Jupyter Notebook Inside Azure Machine Learning Studio.
- You can easily run code using Jupyter Notebooks in Azure Laching Learning Studio.
- To do this, first, log in to your Azure account and select the “Data Science Virtual Machines” (DSVM) option.
- Once you are on the DSVM page, select the “Jupyter Notebook” option and you will be taken to the Jupyter Notebook page.
- Once you are on the Jupyter Notebook page, select the “New” button and this will open up a new window.
- Select the language for which you want to run the code (for example, Python, R, etc.) and then click on the “Create” button.
- You will then be taken to your newly created Jupyter Notebook, which is where you can start running your code.
- To run your code, simply type it in the cell and press the “Run” button or use the shortcut: CTRL + ENTER.
- You can also use the “Run All” button to execute all the code in the notebook.
- After the code has been executed, the output will be shown in the output cell.
2. How To Start Jupyter Notebook As A Container In Azure Machine Learning Instance.
- Setting up a containerized Jupyter Notebook to run code within Azure Machine Learning studio can be done in a few simple steps.
- First, you need to launch a container instance in your Azure Portal. You can do this by clicking on the “Create a resource” button in the left-hand navigation and then selecting “Container Instances” from the list of services.
- Enter the details for the container, such as the size and type, and hit “Create”.
- Once the container is created, you can use SSH to connect to the instance and then pull the image for the Jupyter Notebook. For example, you can use a command such as docker pull jupyter/base-notebook.
- Once the image is pulled, you can run the containerized version of Jupyter Notebook on your Azure ML instance. You can do this by running the following command docker run -it -p 8888:8888 jupyter/base-notebook
- Once the container is running, you can access the Jupyter Notebook by using the URL given when the container was launched.
- At this point, you can start writing code and running it within your Azure ML instance. You can either use the web-based editor provided by Jupyter Notebook or upload your own notebook from your local machine.
- By following these steps, you can get a containerized Jupyter Notebook to run code within Azure ML instance.
