Abstract: A network verification system uses general-purpose programming language to create network verification tests. A test orchestrator builds a model of the network only using data from the network verification test. An optimization testing manager creates symbolic packets for verification tests using assertions based on a packet library embedded into the testing manager and the general-purpose programming language.

Abstract: Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Abstract: Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Abstract: Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Abstract: A computation graph of a machine learning model is accessed from memory and a constraint solver is used to compute a partition of the computation graph into ordered stages of an execution pipeline. In use, when inference or training of the machine learning model takes place by executing the pipeline, execution cost of the stages are balanced according to the computed partition.

Abstract: A network verification system uses general-purpose programming language to create network verification tests. A test orchestrator builds a model of the network only using data from the network verification test. An optimization testing manager creates symbolic packets for verification tests using assertions based on a packet library embedded into the testing manager and the general-purpose programming language.

Abstract: Embodiments of the present disclosure include an error recovery method comprising detecting a computing error, restarting a first artificial intelligence processor of a plurality of artificial intelligence processors processing a data set, and loading a model in the artificial intelligence processor, wherein the model corresponds to a same model processed by the plurality of artificial intelligence processors during a previous processing iteration by the plurality of artificial intelligence processors on data from the data set.

Abstract: A communications network node has a memory storing information about the topology of a network, configuration data comprising attributes of the network, and information about a routing protocol of the network. The memory stores a comparison function derived from a best path definition of the routing protocol. A path finding component of the node computes, for an individual one of the nodes, a routing table specifying which neighbors of the individual node incoming data received at the individual node is to be routed to, where computing the routing table comprises searching for optimal paths between the nodes, using the information about the topology of the network and the configuration data, and using the comparison function to guide the search; and wherein optimal paths between the nodes are optimal with respect to one or more of the attributes.

Abstract: A communications network node has a memory storing information about the topology of a network, configuration data comprising attributes of the network, and information about a routing protocol of the network. The memory stores a comparison function derived from a best path definition of the routing protocol. A path finding component of the node computes, for an individual one of the nodes, a routing table specifying which neighbors of the individual node incoming data received at the individual node is to be routed to, where computing the routing table comprises searching for optimal paths between the nodes, using the information about the topology of the network and the configuration data, and using the comparison function to guide the search; and wherein optimal paths between the nodes are optimal with respect to one or more of the attributes.