Getting Started

How to Run

In the Azure CLI, prepare the environment and submit the job.

Help link

$ az ml experiment prepare -c (docker | remotevm)
$ az ml experiment submit -c (docker | remotevm) 01_train.py 1e-4 150 10 1 train-jpg/ true

• 1e-4 -> learning rate
• 150 -> image size
• 10 -> batch size
• 1 -> epoch
• train-jpg/ -> training data path
• true -> use prior weights

How to Use Jupyter Notebooks

Go to Notebooks tab and select the sample Notebook to view it. To edit and run cells, click Launch Notebook Server. Select a kernel, and wait for it to start to run cells.

Note: Do not select Python 3 kernel. Instead, select one of the kernels named after your project.

You can also launch Notebook server by opening File, Open Command Prompt, and entering az ml notebook start.

Some Leftover Documentation Covering TDSP

Team Data Science Process From Microsoft (TDSP)

This repository contains an instantiation of the Team Data Science Process (TDSP) from Microsoft for project Azure Machine Learning. The TDSP is an agile, iterative, data science methodology designed to improve team collaboration and learning. It facilitates better coordinated and more productive data science enterprises by providing:

• a lifecycle that defines the steps in project development
• a standard project structure
• artifact templates for reporting
• tools to assist with data science tasks and project execution

Information About TDSP In Azure Machine Learning

When you instantiate the TDSP from Azure Machine Learning, you get the TDSP-recommended standardized directory structure and document templates for project execution and delivery. The workflow then consists of the following steps:

• modify the documentation templates provided here for your project
• execute your project (fill in with your project's code, documents, and artifact outputs)
• prepare the Data Science deliverables for your client or customer, including the ProjectReport.md report.

We provide instructions on how to instantiate and use TDSP in Azure Machine Learning.

Project Folder Structure

The TDSP project template contains following top-level folders:

1. code: Contains code
2. docs: Contains necessary documentation about the project
3. sample_data: Contains SAMPLE (small) data that can be used for early development or testing. Typically, not more than several (5) Mbs. Not for full or large data-sets.