Simplifying machine learning workload composition

An apparatus, comprising:

• one or more network interfaces;
• a processor coupled to the one or more network interfaces and configured to execute one or more processes;
• and
• a memory configured to store a process that is executable by the processor, the process when executed configured to:
• receive, via a user interface, definition data for a machine learning workload, wherein the user interface represents the machine learning workload as a set of roles and channels, each role representing a task to be performed by a node in a network and each channel representing a communication channel between roles;
• identify, based on the definition data, groups of training nodes in the network that store training datasets, to perform training roles for the machine learning workload by training machine learning models on their respective training datasets;
• select a set of intermediate aggregator nodes for the groups of training nodes, each intermediate aggregator node performing an aggregation role to aggregate those machine learning models trained by an assigned group of training nodes into intermediate models;
• and
• provision the machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes and by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node.

A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

• receiving, at the device and via a user interface, definition data for a machine learning workload, wherein the user interface represents the machine learning workload as a set of roles and channels, each role representing a task to be performed by a node in a network and each channel representing a communication channel between roles;
• identifying, by the device and based on the definition data, groups of training nodes in the network that store training datasets, to perform training roles for the machine learning workload by training machine learning models on their respective training datasets;
• selecting, by the device, a set of intermediate aggregator nodes for the groups of training nodes, each intermediate aggregator node performing an aggregation role to aggregate those machine learning models trained by an assigned group of training nodes into intermediate models;
• and
• provisioning, by the device, the machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes and by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node.