How To Run Code In Jupyter Notebook Inside Azure Machine Learning Studio

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.

  1. You can easily run code using Jupyter Notebooks in Azure Laching Learning Studio.
  2. To do this, first, log in to your Azure account and select the “Data Science Virtual Machines” (DSVM) option.
  3. Once you are on the DSVM page, select the “Jupyter Notebook” option and you will be taken to the Jupyter Notebook page.
  4. Once you are on the Jupyter Notebook page, select the “New” button and this will open up a new window.
  5. Select the language for which you want to run the code (for example, Python, R, etc.) and then click on the “Create” button.
  6. You will then be taken to your newly created Jupyter Notebook, which is where you can start running your code.
  7. To run your code, simply type it in the cell and press the “Run” button or use the shortcut: CTRL + ENTER.
  8. You can also use the “Run All” button to execute all the code in the notebook.
  9. 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.

  1. Setting up a containerized Jupyter Notebook to run code within Azure Machine Learning studio can be done in a few simple steps.
  2. 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.
  3. Enter the details for the container, such as the size and type, and hit “Create”.
  4. 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.
  5. 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
  6. Once the container is running, you can access the Jupyter Notebook by using the URL given when the container was launched.
  7. 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.
  8. By following these steps, you can get a containerized Jupyter Notebook to run code within Azure ML instance.

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