Abstract: Provided is a method for training distributed machine learning models. The method may include initializing a distributed machine learning model on a plurality of computing devices. Training data associated with a plurality of samples may be received. Each sample may be forward propagated through the distributed machine learning model to generate an output. A loss for each sample of the plurality of samples may be determined based on the output. The loss for each sample may be backward propagated to each computing device. The parameter(s) of each computational node may be asynchronously updated based on the loss as it is backward propagated and/or while at least one of the samples is forward propagating. The parameter(s) may be stored and/or communicated to the other computing devices. Each of the other computing devices of the plurality of computing devices may store the parameter(s). A system and computer program product are also disclosed.

Abstract: A method, system, and computer program product for maintaining data centers obtain input data; communicate an update request associated with the input data to a node of a plurality of nodes; receive an indication that the update request failed; communicate a result request for result data associated with processing of the input data to the node of the plurality of nodes until the result data associated with processing of the input data is received; and in response to receiving the result data associated with processing of the input data from the node, process the result data.

Abstract: A system includes a processor and a non-transitory computer readable medium coupled to the processor. The non-transitory computer readable medium includes code that requests, via a process, access to the processor using an access request, determines, at the process, a resource-grant status of the access request, and positions the process into an optimum execution schedule based upon a grant-status assessment of the resource-grant status. When a result of the grant-status assessment is that the access request is granted, the process performs an entropic assessment of entropic information associated with the resource-grant status of the process. When the access request entropy of the entropic information is below an optimum entropy access request threshold, an access request time associated with the access request entropy is designated as an optimum request time.

Abstract: A method, system, and computer program product for maintaining data centers obtain input data; communicate an update request associated with the input data to a node of a plurality of nodes; receive an indication that the update request failed; communicate a result request for result data associated with processing of the input data to the node of the plurality of nodes until the result data associated with processing of the input data is received; and in response to receiving the result data associated with processing of the input data from the node, process the result data.

Abstract: Provided is a method for training distributed machine learning models. The method may include initializing a distributed machine learning model on a plurality of computing devices. Training data associated with a plurality of samples may be received. Each sample may be forward propagated through the distributed machine learning model to generate an output. A loss for each sample of the plurality of samples may be determined based on the output. The loss for each sample may be backward propagated to each computing device. The parameter(s) of each computational node may be asynchronously updated based on the loss as it is backward propagated and/or while at least one of the samples is forward propagating. The parameter(s) may be stored and/or communicated to the other computing devices. Each of the other computing devices of the plurality of computing devices may store the parameter(s). A system and computer program product are also disclosed.

Abstract: A method, system, and computer program product for maintaining data centers obtain input data; communicate an update request associated with the input data to a node of a plurality of nodes; receive an indication that the update request failed; communicate a result request for result data associated with processing of the input data to the node of the plurality of nodes until the result data associated with processing of the input data is received; and in response to receiving the result data associated with processing of the input data from the node, process the result data.

Abstract: A method, system, and computer program product for maintaining data centers obtain input data; communicate an update request associated with the input data to a node of a plurality of nodes; receive an indication that the update request failed; communicate a result request for result data associated with processing of the input data to the node of the plurality of nodes until the result data associated with processing of the input data is received; and in response to receiving the result data associated with processing of the input data from the node, process the result data.