The Machine Learning version of XNAT relies on the Docker system for both the short-term transfer learning and inference tasks as well as persistent containers for XNAT itself and other required components such as the PostgreSQL database. Production work will require a computing environment with sufficient memory, CPU and GPU resources. A specific example is the amount of memory reserved for the Docker server. You can run the software stack on a desktop system, but the default memory allocation for the Docker server will be too low to support all of the components. In our development environments, we use:
|
Resource |
Docker Desktop |
Docker Server |
|---|---|---|
|
CPUs |
4 |
16 |
|
Memory |
16 GB |
64GB |
|
Swap |
1 GB |
6GB |
|
GPU |
N/A |
12GB |