This article briefly describes the steps for running Visiopharm on a virtual machine (VM) that's deployed on Azure. It also presents the performance results of running Visiopharm on Azure.
Visiopharm is an AI-based image analysis and tissue mining tool that supports drug development research and other research.
- Enables researchers to align and subsequently analyze digitized serial sections.
- Enables tissue researchers to analyze both simple and complex datasets to generate reliable quantitative results.
- Uses pre-trained nuclei segmentation APPs, suitable for bright-field and fluorescence applications.
Visiopharm is used in academic institutions, the biopharmaceutical industry, and diagnostic centers. It's ideal for the education, healthcare, and manufacturing industries.
Why deploy Visiopharm on Azure?
- Modern and diverse compute options to meet your workload's needs
- The flexibility of virtualization without the need to buy and maintain physical hardware
- Rapid provisioning
Download a Visio file of this architecture.
- Azure Virtual Machines is used to create a Windows VM. For information about deploying the VM and installing the drivers, see Windows VMs on Azure.
- Azure Virtual Network is
used to create a private network infrastructure in the cloud.
- Network security groups are used to restrict access to the VM.
- A public IP address connects the internet to the VM.
- A physical solid-state drive (SSD) is used for storage.
Compute sizing and drivers
|GPU memory (GiB)
|Maximum data disks
To use AMD processors on Standard_NC16as_T4_v3 VMs, you need to install AMD drivers.
Before you install Visiopharm, you need to deploy and connect a VM, install an eligible Windows 10 or Windows 11 image, and install NVIDIA and AMD drivers, as needed.
For information about eligible Windows images, see How to deploy Windows 10 on Azure and Use Windows client in Azure for dev/test scenarios.
For information about deploying the VM and installing the drivers, see Run a Windows VM on Azure.
For information about installing the software, contact Visiopharm.
Visiopharm performance results
To test the performance of Visiopharm, image analysis was performed on Standard_NC24s_v3 and Standard_NC16as_T4_v3 VMs, and the performance was compared. Visiopharm version 2022.03 was used for testing.
Three Visiopharm solutions (APPs) were run:
- APP1: Tissue detection
- APP2: Segmentation
- APP3: Cell detection AI
The image is called LuCa 6Plex. It's a set of pathology data that's provided by Visiopharm. The three APPs predominantly use the GPU capabilities of the VMs to run analyses.
The following table shows the results.
|APP1 elapsed time
|APP2 elapsed time
|APP3 elapsed time
* This test was performed with an artificially limited one-GPU configuration. This VM has four GPUs.
The following graph shows the relative performance of the two VMs. Note that this comparison uses a one-GPU configuration for both VMs. The Standard_NC24s_v3 has four GPUs, although the NCv3 series provides options with one, two, and four GPUs.
The following table presents the wall-clock times for running the analyses. You can use these times and the Azure VM hourly costs for NCas_T4_v3 series VMs to compute costs. For the current hourly costs, see Windows Virtual Machines Pricing.
Only analysis time is considered for these calculations. Application installation time isn't included.
You can use the Azure pricing calculator to estimate the costs for your configuration.
|APP1: Tissue detection
|APP3: Cell detection AI
|3 hours, 33 minutes
|42 minutes, 43 seconds
- Visiopharm was successfully tested on Standard_NC24s_v3 and Standard_NC16as_T4_v3 VMs.
- Based on a one-GPU configuration for both VMs, Standard_NC16as_T4_v3 performs better.
This article is maintained by Microsoft. It was originally written by the following contributors.
- Hari Bagudu | Senior Manager
- Gauhar Junnarkar | Principal Program Manager
- Vinod Pamulapati | HPC Performance Engineer
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- GPU-optimized virtual machine sizes
- Virtual machines on Azure
- Virtual networks and virtual machines on Azure
- Learning path: Run high-performance computing (HPC) applications on Azure