Server Requirements

• Windows Server 2012/2016/2019
• Fast access to image storage (gigabit ethernet) or local hard drives for images
• VisionHub 2.5 GB; SQL Express 6 GB; NodeJs 10GB
• 32 GB main memory
• 8 Core CPU
• VisionHub components: database, api-server, webapp, tile-server, import-worker

Each CPU core pack allows the analysis workers to use an additional 16 Cores.  For parallelized processing, workers can be deployed on multiple servers. As stated above, each worker should ideally get its own dedicated server so that it can use all CPU/GPU power that is available. It is possible to configure a server to use multiple analysis workers, but note however, they will compete for memory and CPU/GPU power.

• Windows Server 2012/2016/2019 
• Fast access to image storage (gigabit ethernet) or local hard drives for images
• VisionHub 1.5 GB; NodeJS 10 GB
• 32 GB main memory
• Optional: local temporary storage space: X TB
• If image modifying operators such as pre-processing operations are required in the analysis pipeline, then each worker will create temporary data for each image modifying operator. Therefore, each worker requires space for this temporary data. For example, if you plan to analyze a 100 GB file with a complex pipeline that has 3 pre-processing operations (2 denoising and one correction filter) this would require 300 GB temp space for each worker or workflow run. Note, this will not always be required and depends on the pipeline being used.
• Another option for handling temporary data storage is to use a high-speed central storage for the cache folder.
• CPU Cores: The number of cores required depends on the number of required workers. Each worker requires one core pack utilizing 16 cores.
• Optional: GPU
• Analysis workers can utilize available GPU for certain operators. GPU is only required when a machine learning operator is being used in the pipeline. If no GPU is available, the worker will use CPU instead. The GPU must be on the analysis worker server; it cannot use the GPU on the render server.

The 3D SSR render server gets the volume data for the images to render via the TileServer interface and does not require direct access to the central image storage. This allows deployment of the render server outside the intranet and allows for the possibility to use mixed cloud/on-premises installations for this component.

Each 3D SSR render server can provide multiple render streams. Graphics Memory and power will be split. E.g. with an 8 GB graphics card you could either provide one stream with 8 GB possible volume load, or two streams with 4 GB possible volume load. This decision should be discussed with your account manager in preparation for deployment, and advisement can be provided based on expected use.

• Windows Server 2012/2016/2019
• VisionHub 1 GB; NodeJS 10 GB
• Local cache: X TB (depending on the number of images)
• Required for caching tiles before rendering. If the system is configured to provide a single stream with 8 GB of volume load, then each dataset might load up to 8 GB of image tiles from the VisionHub tile server. To avoid re-loading, we suggest choosing a cache size based on the number of images on the system. E.g. if there are 4 images and you have a 8 GB graphics card you would need 32 GB of cache; 500 images X 8 GB = 4 TB etc. The more images, the greater the cache volume needed.
• Another option is to use a high-speed central storage for cache, but this is only possible if installed in same intranet.