Getting started with Jupyter Notebooks in Azure
Setup a NotebookVM in Azure Machine Learning Workspace.
If you have an Azure Machine Learning service workspace, skip to step #2. Otherwise, create one now.
- Sign in to the Azure portal by using the credentials for the Azure subscription you use.
- In the upper-left corner of the portal, select Create a resource.
- In the search bar, enter Machine Learning. Select the Machine Learning service workspace search result.
- In the ML service workspace pane, scroll to the bottom and select Create to begin.
- In the ML service workspace pane, configure your workspace and select Create. It can take a few minutes to create the workspace. When the process is finished, a deployment success message appears. It's also present in the notifications section. To view the new workspace, select Go to resource.
Create a cloud-based notebook server.
- Open your Machine Learning workspace in the Azure portal.
- On your workspace page in the Azure portal, select Notebook VMs on the left.
- Select +New to create a notebook VM.
- Provide a name for your VM and select Create.
- Wait approximately 4-5 minutes until the status changes to Running
Launch the Jupyter wed interface in your Notebook VM
- Select Jupyter in the URI column for your VM.
- On the Jupyter notebook webpage, the top foldername is your username.
More details about quickstart setup instructions are located here.
Clone this repo to your Notebook VM
From the Notebook VM launch the Jupyter web interface as descriped in step #3 above. Click New -> Terminal on the upper right corner of the web interface. You will get a new browser tab with the bash prompt.
You can use regular
git clone --recursive https://github.com/microsoft/vision-ai-developer-kit command line commands to clone this repository into a desired folder.
Important update jupyter in the Notebook VM: (this is a temporary step)
pip install –upgrade notebook
sudo -i systemctl restart jupyter
Select a notebook in
machine-learning-reference\notebooks to run it. Set the kernel to Python 3.6 - AzureML.
Notebooks in this repo
00-aml-configuration.ipynb contains the basic steps to setup the environment for Azure ML. This step is important to complete your
There are 4 notebooks in this folder as sample tutorials for the Vision AI Developer Kit.
01-convert-model-containerize.ipynb uses a pretained Mobilenet/Tensorflow model to convert and deploy on the Vision AI Dev Kit.
02-mobilenet-transfer-learning shows a transfer learning example to deploy the Mobilenet model on the Vision AI Dev Kit device.
03-squeezenet-custom-vision.ipynb converts a model that is imported from CustomVision.ai.
04-Deploy-Trained-Model.ipynb is an example to deploy a trained model to an existing IoT Edge device.
Also find other quickstarts and how-tos on the official documentation site for Azure Machine Learning service.