This article provides guidance on Azure Virtual Machine (VM)'s size, instance type, and step-by-step instructions on how to use PIX4Dmapper on Azure (VM).
Pix4Dmapper can be run in the Azure cloud. Users can take advantage of large servers for image processing. Image processing can be performed by accessing Pix4Dmapper via Microsoft Remote Desktop from any Windows or Apple computer. However, due to OpenGL limitations, the rayCloud tool is not accessible from Azure.
If rayCloud access is required, the dataset can be moved after initial processing to a local machine and further processed locally.
Guidance on Virtual Machine size
The table below recommends four Azure VM instance types which are quality-tested options for Pix4D processing. These are ranked from least to most expensive. Note that not all cores will be leveraged during all parts of processing. The 32 core instances will not be twice as fast as 16 core instances, but overall will reduce processing time significantly. A10 and A11 are recommended as a good price/performance balance for typical workloads.
|D3||4||14 GB||Performance similar to a modern quad-core desktop|
|A10||8||56 GB||Significantly faster than a quad-core desktop|
|G5||32||448 GB||Fastest processing available at high costs|
When considering the price for a virtual machine instance, please be aware that the user will only be charged when the machine is running. After processing, the user can stop the virtual machine via the Azure Portal and will not be billed for compute usage until it is started again. Please keep in mind that when shutting down the computer via Remote Desktop does not stop billing. It is necessary to use the stop button in the Azure Portal.
Everything is available on the desktop computer (processing, Map View, Mosaic Editor, Index Calculator), except the rayCloud 3D interface and CUDA computing which are not currently supported in Azure.
Instructions to run PIX4Dmapper on Azure:
1. Create a virtual machine instance in Azure and connect via RDP.
2. Copy PIX4Dmapper's installer and the user's source data to the virtual machine using one of the following methods.
2a. A good option for large datasets is to modify the RDP connection file you downloaded from Azure to enable local drive sharing. Then you will see your local drives as network drives when logged into the remote machine.
2b. For smaller datasets, simply performing a Windows copy/paste from your local machine into the remote desktop session works well.
3. Install and launch PIX4Dmapper on the remote machine
4. Perform processing
5. Copy processed data back to your local machine using one of the options described at step 2.
6. Stop the VM in the Azure Portal when complete to halt hourly billing.