Adds a new service for trained model allocation to nodes. Initially, this only supports PyTorch models and simply allocates to nodes with the ML roles. Design is fairly simple: - A master node service runs allowing for new allocations to be created/updated/deleted from cluster state - A node service runs listening to updates referencing the local node + any models it may have allocated and updates accordingly. This type of service sort of splits the difference between the logic of shard allocation and persistent tasks. Neither really fully addressed the need here. |
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inspectionProfiles | ||
runConfigurations | ||
scopes | ||
checkstyle-idea.xml | ||
eclipseCodeFormatter.xml |