fastaivm

The goal of this repository is to supply a simple mechanism for deploying the requirements for the fastai course: Practical Deep Learning for Coders: Part 1. The button below deploys to an Ubuntu-based Data Science VM (NC-series) to azure and installs the relevant dependencies.

NOTE: An Azure subscription is required - see below.

Deployment

Azure subscription required.

Hardware compute fees apply. Free Trial available for new customers.

IMPORTANT NOTE: Before you proceed to use the Deploy to Azure button you must perform a one-time task to accept the terms of the data science virtual machine on your Azure subscription. You can do this by visiting Configure Programmatic Deployment

Just click this button (see below for details).

VM size options

The default option (NC6) is the cheapest GPU option, but it's also the oldest and slowest. Other GPU-enabled vmSizes are available from the template, but please see the notes in the vm sizes documentation.

Specifically note that the vCPU core quota for _v2 and _v3 series VMs is initially set to 0 in each region for every subscription. You must request a vCPU quota increase for these families in an available region. If you choose one of the NCXX_v2 or NCXX_v3 VM sizes in the template without increasing your quota, the deployment WILL FAIL.

What is deployed?

You define the name of a resource group, and the following services get deployed to that resource group:

An Ubuntu-based Data Science VM with your defined user name and password.
Storage: A storage account and 2 disks for storing data
A network interface, virtual network, and public IP address

What do I as a user control?

When you click on the Deploy to Azure button above, a custom template will launch in the Azure portal that asks you for some information:

Subscription you are creating the resources in
Resource group you want to create all the resources in
Location (i.e. Data Center where the resource group and resources physically live)
DNS label prefix
Admin Username
Admin Password
VmSize (NC6 as default; other NC options avail, see note above)
Vm Name

All of this information is controlled with the azuredeploy.json file in the root directory of this repository.

What happens when I deploy?

The resources are created, and then a custom script extension is used to download the dsvm-setup.sh script from this github repository and then run it. This script is run as root and takes a single argument: the name of the admin you specify in the portal.

What does the script do?

It does the following:

Clones the fastai repository so all the notebooks are available
Downloads and unzips the data for the first lab.
Cleans up anaconda and installs a new conda env based on the environment file in the fastai repository (the env name is fastai and it is installed in /home/<adminUser>/.conda/envs).
Installs the environment as a kernel for jupyter notebooks so that the notebooks can be run.
Makes sure that permissions are appropriate to the admin user.

See the dsvm-setup.sh file for details.

How do I access the course materials?

Identify the DNS name prefix and geo used in deployment.
Proceed to https://<dnsprefix>.<location>.cloudapp.azure.com:8000 to access the jupyter server
- NOTE: You will encounter a certificate error - proceed through it
Log in with the username and password you specified when you deployed.
Within the jupyter file navigator, you should see a large list of directories and files.
- Navigate to fastai -> courses -> dl1
- Within, dl1, click on lesson1.ipynb to launch the notebook.
In the tab that has launched notebook, click on the kernel menu item, Select Change kernel, and select fastai in order to change the kernel to the right environment.
- Note - you will likely need to do this step for each notebook!
Watch videos at fastai, execute cells, experiment, and learn!

wmpauli / fastaivm