Patent Publication Number: US-2023153612-A1

Title: Pruning complex deep learning models based on parent pruning information

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 63/281,045, filed on Nov. 18, 2021, which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     When using a deep learning model in deployment, it&#39;s important that the model is both accurate and efficient. Neural network pruning techniques can reduce the number of parameters in trained networks substantially and improve the computational performance of inferencing operations without compromising accuracy. For example, entire neurons may be pruned from fully connected layers or entire filters from convolutional layers. Various information may be needed in order to determine what portions of a deep learning model can be pruned. The information may need to account for not only constraints and characteristics of all direct parent nodes of the layer—of which there may be multiple—but also corresponding information for any parent layers of those parents, extending back to the input layers of the model. For example, when layers of the model are pruned, the remaining portions of the layers may need to form channels that connect to compatible inputs and outputs extending to the input layers of the model, such that each parent layer may need to be considered for each layer. 
     Conventional approaches to pruning a deep learning model may determine, for each particular layer of the model, all of the information needed to prune connections to the layer. To determine the information for a given layer, a process may be used where the information is recursively determined for each connected upstream layer. Thus, the computing requirements and time required to determine the information may have an exponential relationship with the number of layers and connections in the model. While these conventional approaches may be sufficient to prune simple models with a limited number of dependencies and layers, the computing resources required to prune a more complex model in a reasonable amount of time may be prohibitive. Additionally, conventional approaches to pruning a deep learning model may not be able to handle certain layers that include multiple dependent convolutions, such as separable convolutional layers, and may not be able to handle convolutional layers which have inputs form multiple layers. 
     SUMMARY 
     Embodiments of the present disclosure relate to pruning complex deep learning models based on reusing pruning information from earlier (parent) layers in the neural network. In particular, the disclosure relates to approaches for analyzing one or more connections to a layer for pruning based at least on reusing at least some pruning information determined for one or more parent nodes of the layer. 
     In contrast to conventional approaches, such as those described above, when visiting a child node in a graph corresponding to a deep learning model to determine pruning information for the child node (e.g., a list of prunable parent nodes of the child node), data identifying pruning information corresponding to one or more parent nodes may be incorporated into the pruning information for the child node. Thus, the pruning information need not be re-identified and/or re-generated by iteratively revisiting each parent node for each node being evaluated for pruning. In at least one embodiment, the data identifying the pruning information may represent, at least in part, a list of one or more parent nodes of the node being evaluated (e.g., a list of prunable parent nodes of the node). The list of one or more parent nodes of the parent node may be used to access the pruning information for the child node. In at least one embodiment, the graph may be explored recursively to iteratively visit nodes to determine portions of pruning information for pruning a node. One or more iterations of the recursion may be skipped or made more efficient by reusing a portion of the pruning information determined for one or more prior visits to one or more of the nodes. In further respects, layers of a deep learning model including multiple dependent convolutions, such as separable convolutional layers, may be pruned by treating each convolution as a separate node and/or layer. Further, a convolutional layer that has inputs from multiple layers, and the inputs themselves, may be pruned by treating the convolutional layer as an element-wise layer (e.g., by ensuring input channels from multiple layers, if pruned, have the same number of remaining channels per layer). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present systems and methods for pruning complex deep learning models based on reusing parent pruning information are described in detail below with reference to the attached drawing figures, wherein: 
         FIG.  1    is a data flow diagram illustrating an example process for pruning a deep learning model, in accordance with at least one embodiment of the present disclosure; 
         FIG.  2    illustrates an example of a deep learning model that may be pruned, in accordance with some embodiments of the present disclosure; 
         FIG.  3    illustrates an example of a layer of a deep learning model that may be pruned, in accordance with some embodiments of the present disclosure; 
         FIG.  4    is a flow diagram showing a method for exploring a graph corresponding to a deep learning model based on reusing data, determined for a visit to a node, that identifies pruning information for the node, to access the pruning information for analyzing another node for pruning, in accordance with some embodiments of the present disclosure; 
         FIG.  5    is a flow diagram showing a method for exploring a graph corresponding to a deep learning model based on using a list of one or more parents, generated for a visit to a node, that indicates pruning information for the node, to access the pruning information for analyzing another node for pruning, in accordance with some embodiments of the present disclosure; 
         FIG.  6    is a block diagram of an example computing device suitable for use in implementing some embodiments of the present disclosure; and 
         FIG.  7    is a block diagram of an example data center suitable for use in implementing some embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to pruning complex deep learning models based on parent pruning information. In particular, the disclosure relates to approaches for analyzing one or more connections to a layer for pruning based at least on reusing at least some pruning information determined for one or more parent nodes of the layer. 
     In accordance with one or more embodiments, when visiting a child node in a graph corresponding to a deep learning model to determine pruning information for the child node (e.g., a list of prunable parent nodes of the child node), data identifying pruning information corresponding to one or more parent nodes may be incorporated into the pruning information for the child node. Thus, the pruning information need not be re-identified and/or re-generated by revisiting each parent node for each node that is to be analyzed for pruning. 
     In at least one embodiment, the data identifying the pruning information may represent, at least in part, a list of one or more parent nodes of the parent node (e.g., a list of prunable parent nodes of the node). The list of one or more parent nodes of the parent node may be included in a list of one or more parent nodes of the child node used to access the pruning information for analyzing one or more connections to the child node for pruning. 
     In at least one embodiment, the graph may be explored using recursion to iteratively visit nodes to determine portions of pruning information for pruning a node. One or more iterations of the recursion may be skipped or made more efficient by reusing a portion of the pruning information determined for one or more prior visits to one or more of the nodes. For example, a node of the graph may be explored using a recursive graph traversal algorithm to determine pruning information for pruning the node. The recursive graph traversal algorithm may begin with a visit to the node, and recursively call itself to visit a parent node of determine a portion of the pruning information that corresponds to the parent node. In at least one embodiment, one or more recursive calls may be skipped based at least on determining the parent node has already been visited (e.g., has been fully explored in one or more passes through one or more nodes of the deep learning model) when exploring the node or a different node, and based on the determining, the pruning information for the parent may be used to evaluate the node for pruning. 
     In further respects, layers of deep learning model including multiple dependent convolutions, such as separable convolutional layers, may be pruned by treating each convolution as a separate node and/or layer. Further, a convolutional layer that has multiple inputs may be pruned (as well as the inputs) by treating the convolutional layer as an element-wise layer. For example, disclosed approaches may ensure input channels from multiple layers, if pruned, have the same number of remaining channels per layer, allowing for those inputs to be pruned as well as the convolutional layer. 
     As used herein, a prunable node and/or layer may refer to a node and/or layer that may be pruned (e.g., has a prunable kernel). Examples of a non-prunable node and/or layer may include a layer such as an activation layer, an input layer, a layer designated as non-prunable, a layer that is not supported by the software for pruning, a layer that does not include weights, and/or a layer whose pruning would violate one or more system or user defined pruning criteria. When a node is prunable, kernel of the node may be pruned to produce a set of one or more outputs. In one or more embodiments, a prunable node and/or layer may receive one or more inputs from one or more layers. To prune a convolutional layer, for example, a list of one or more inputs (e.g., input indices) from one or more layers may be used to prune the layer, and a list of one or more outputs (e.g., one or more kernel output indices) may be recoded. 
     However, when a node and/or layer has inputs from multiple nodes and/or layers, pruning the inputs to the node may need to account for combining input connections to the node, such that the resultant deep learning model functions properly. For example, if an element-wise layer is to perform an addition using inputs from two convolutional layers, after pruning those two convolutional layers, inputs to the element-wise layer may need to match across the two convolutional layers (e.g., the same number of inputs and matching input indices) for the addition operation. The process of matching inputs from multiple layers may be referred to as equalization and may include, for example, using an intersection and/or union between the parent layers to match the inputs across the layers. A similar situation may arise for other types of nodes and/or layers that have inputs from multiple nodes and/or layers, such as convolutional layers. 
     Thus, for a child node that has inputs from multiple parent nodes and/or layers, pruning information for each of the parent nodes may be needed (e.g., in order to match channel indices) when analyzing inputs to the child node for pruning. For example, pruning information from at least the nearest prunable parent nodes may be needed to determine inputs that should be analyzed for equalization. Thus, the graph of the deep learning model may be explored for the child node to determine the relevant parent nodes and/or pruning information for those parents, for use in analyzing the inputs to the child node for pruning. For example, a list of the nearest prunable parent nodes may be determined for the child node for use in pruning. In one or more embodiments, where the child node is a parent of another node, rather than fully exploring the graph for the other node, the list of nearest prunable parent nodes and/or other pruning information may be reused (e.g., the list of nearest one or more prunable parent nodes may be incorporated into a list of nearest one or more prunable parent nodes for the other node). While a list is described as a list of nearest prunable parent nodes (e.g., per branch), in one or more embodiments, the list may or may not include the nearest prunable parent node for one or more branches. In at least one embodiment, the list may exclude convolutional layers (and/or other layer types) that have inputs from multiple layers, despite those layers being prunable. However, in one or more embodiments, the list may include convolutional layers (and/or other layer types) that have inputs from multiple layers. 
     The systems and methods described herein may be used for a variety of purposes, by way of example and without limitation, these purposes may include systems or applications for online multiplayer gaming, machine control, machine locomotion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, deep learning, environment simulation, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, digital twin systems, cloud computing and/or any other suitable applications. 
     Disclosed embodiments may be comprised in a variety of different systems such as systems for participating on online gaming, automotive systems (e.g., a control system for an autonomous or semi-autonomous machine, a perception system for an autonomous or semi-autonomous machine), systems implemented using a robot, aerial systems, medial systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems implemented using an edge device, systems incorporating one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, systems for performing conversational AI operations, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems for generating or maintaining digital twin representations of physical objects, systems implemented at least partially using cloud computing resources, and/or other types of systems. 
       FIG.  1    is a data flow diagram illustrating an example process  100  for pruning a deep learning model  106 A (also referred to as “model  106 A”), in accordance with at least one embodiment of the present disclosure. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) may be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. In at least one embodiment, the systems, methods, and processes described herein may be executed using similar components, features, and/or functionality to those of example computing device  600  of  FIG.  6    and/or example data center  700  of  FIG.  7   . 
     The process  100  may be implemented using, among additional or alternative components, one or more model explorers  102  and one or more pruned model generators  104 . 
     At a high level, the process  100  may include the model explorer  102  receiving one or more inputs, such as data representing one or more deep learning models  106 A, and generating one or more outputs, such as pruning information for the one or more deep learning models  106 A, which may be stored in, for example, one or more data objects  140  and/or one or more lists  142 . The process  100  may also include the pruned model generator  104  receiving one or more inputs, such as the one or more deep learning models  106 A, the data objects  140 , and the lists  142 , and generating one or more outputs—such as a pruned version(s)  106 B of the one or more deep learning models  106 A (also referred to as “pruned model  106 B”)—from the one or more inputs. 
     In at least one embodiment, the deep learning model  106 A may be provided to the model explorer  102  using a representation including a graph having nodes corresponding to one or more layers of the deep learning model  106 A and one or more edges corresponding to one or more connections between the one or more layers of the deep learning model  106 A. For example, the deep learning model  106 A is shown as including nodes  110 ,  112 ,  114 ,  116 ,  118 ,  120 ,  122 ,  124 ,  126 ,  128 ,  130 , and  132 . In at least one embodiment, each node may correspond to a respective layer of the deep learning model  106 A. In at least one embodiment, each edge may correspond to one or more connections between the layers. For example, an edge  154  may correspond to one or more connections between a layer corresponding to the node  130  and a layer corresponding to the node  132 . As indicated in  FIG.  1   , the edge  154  corresponds to one or more inputs to a layer corresponding to the node  132  from a layer corresponding to the node  130 . In at least one embodiment, the edge  154  may form a portion of one or more channels between an input layer(s) of the deep learning model  106 A (e.g., corresponding to the node  110 ) and the node  132 . 
     A layer for a node may include any suitable type of layer of a deep learning model. In at least one embodiment, a layer takes as input one or more tensors and outputs one or more tensors. A layer may correspond to a computation performed using the input and one or more parameters to effectuate the computation, such as one or more weights. Non-limiting examples of a layer include a convolutional layer, a separable convolutional layer, a depthwise convolutional layer, a transposed convolutional layer, a pooling layer, a max pooling layer, an average pooling layer, a global max pooling layer, a global average pooling layer, a recurrent layer, a Long Short-Term Memory (LSTM) layer, a Gate Recurrent Unit (GPU) layer, a Recurrent Neural Network (RNN) layer, a time distributed layer, a bidirectional layer, a convolutional LSTM layer, a preprocessing layer, a normalization layer, a regularization layer, an attentional layer, a reshaping layer, a merging layer, a locally-connected layer, an activation layer, an input layer, or an output layer. A layer may comprise a 1-dimensional (1D), 2-dimensional (2D), or 3-dimensional (3D) layer, as examples. 
     In at least one embodiment, the representation of the deep learning model  106 A may be provided using a deep learning framework. For example, the deep learning model  106 A may be defined using an application programming interface (API) of a deep learning framework. 
     The deep learning model  106 A may be trained to perform any of a variety of tasks. Non-limiting examples of the tasks include one or more tasks for online multiplayer gaming, machine control, machine locomotion, machine driving, synthetic data generation, model training, perception (e.g., visual perception), augmented reality, virtual reality, mixed reality, robotics, security and surveillance, autonomous or semi-autonomous machine applications, deep learning, environment simulation, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, smart area monitoring, simulation, generating or maintaining digital twin representations of physical objects, and/or any other suitable applications. 
     The model explorer  102  and the pruned model generator  104  may be used to remove one or more parameters from the deep learning model  106 A to produce the pruned model  106 B having a reduced size while maintaining the functionality and accuracy of the deep learning model  106 A. In at least one embodiment, additional training may be performed on the pruned model  106 B (e.g., using the same dataset used to train the deep learning model  106 A). 
     In at least one embodiment, the model explorer  102  may be configured to determine and/or identify at least some pruning information for the nodes of the deep learning model  106 A. The pruned model generator  104  may use at least some of the determined and/or identified pruning information to generate the pruned model  106 B. For example, the pruning information determined by the model explorer  102  and used by the pruned model generator  104  may represent and/or indicate one or more of the connections and/or parameters selected by the model explorer  102  for pruning and/or retaining, one or more one or more prunable layers and/or nodes of the deep learning model  106 A, one or more prunable parent nodes for a node and/or layer, and/or a list(s) (e.g., an ordered list) of parent nodes (e.g., prunable parent nodes) for a node and/or layer. 
     By way of example, and not limitation, the pruning information may include lists  142 . For example, a list  142  may include a list of parent nodes (e.g., one or more prunable parent nodes) of one or more layers and/or nodes in the deep learning model  106 A. In at least one embodiment, a list  142  may include a list of parent nodes for a particular node. For example, the lists  142  include a list  142 A for the node  118 , a list  142 B for the node  122 , a list  142 C for the node  130 , and a list  142 D for the node  132 . As shown, each list of lists  142  may include parent nodes of a corresponding node. The lists  142  may include more or fewer nodes than what is shown. In at least one embodiment, the lists  142  include lists of prunable parent nodes (a node and/or layer) to the nodes and/or layers. For example, each list  142  may include a list of the nearest prunable parent nodes of a corresponding node. 
     The lists  142  may be stored in various ways, such as using one or more dictionaries, arrays, linked lists, queues, hash maps, stacks, pointers, and/or variables. In at least one embodiment, the lists  142  may be used by the model explorer  102  and/or the pruned model generator  104  to store and/or access pruning information for nodes and/or layers corresponding to the lists  142 . For example, the lists  142  may include pointers and/or information used to determine pointers to sets of one or more data objects  140  storing one or more portions of pruning information for one or more nodes and/or layers. Thus, data representing a list  142  may be an example of data identifying pruning information stored in a set of one or more data objects, as described herein. In one or more embodiments, a list may include pointers and/or information used to determine pointers to one or more other lists. For example, a list for a child node may point to a list(s) for a parent node(s). 
     In at least one embodiment, a list  142  may include and/or be used to determine a pointer for each node in the list  142 , such that only one data object  140  need be stored for each node and/or layer. In one or more embodiments, the lists  142  may be stored using ordered key-value pairs, where the keys may include or correspond to node and/or layer names or identifiers and the values may store at least some of the pruning information. For example, the lists  142  may be stored using dictionaries where node names are used as keys. 
     As described herein, at least some of the pruning information may be stored using the data objects  140 . Each data object  140  may store one or more portions of the pruning information for one or more layers and/or nodes (e.g., determined for a visit to that node(s)). In at least one embodiment, each data object  140  stores the pruning information for a corresponding node. For example, a data object  144  may store pruning information for the node  112 , a data object  146  may store pruning information for the node  114 , a data object  148  may store pruning information for the node  116 , a data object  150  stores pruning information for the node  126 , and a data object  152  may store pruning information for the node  128 . 
     In at least one embodiment, a portion of pruning information stored for a node may indicate and/or represent any of the various information used to analyze the node for pruning and/or one or more results of the analysis. Examples of the information include a set of one or more connections to the node to retain and/or prune, one or more pruning thresholds for the node (e.g., one or more threshold values representing a limit on a quantity of kernels to prune from the node), and/or other pruning information described herein. In at least one embodiment, at least some of the pruning information stored for the node may be specific to the node. Using the list  142  and/or other data identifying the portions or pruning information for one or more prunable parent nodes of a node, the information may be readily reused when analyzing the node for pruning. 
     In at least one embodiment, the pruned model generator  104  uses the pruning information to generate at least a portion of the pruned model  106 B in accordance with the pruning information. For example, generating the pruned model  106 B may include one or more of the pruned model generator  104  removing one or more selected connections and/or parameters from the deep learning model  106 A, reforming and/or determining one or more weights and/or weight matrices and/or portions thereof for one or more layers, and reloading one or more of the weights and/or weight matrices to one or more layers based on selections made using the model explorer  102 . The pruned model generator  104  may generate the pruned model generator  106 B in the same and/or a different format than the deep learning model  106   a . In at least one embodiment, the pruned model generator  104  operates, at least partially, in parallel with the model explorer  102  determining and/or generating pruning information. In at least one embodiment, the pruned model generator  104  operates, at least partially, in serial with the model explorer  102  determining and/or generating pruning information. As non-limiting examples, the pruned model  106 B may generate one or more portions of the pruned model  106 B as corresponding pruning information becomes available or may wait for all of the pruning information to become available before generating the pruned model  106 B . 
     In at least one embodiment, the model explorer  102  may determine and/or identify at least a portion of the pruning information based at least on exploring the graph corresponding to the deep learning model  106 A. Various graph exploration algorithms may be used. In at least one embodiment, the model explorer  102  may explore each node of the deep learning model  106 A using a recursive graph traversal algorithm (e.g., initiated on any number of the nodes individually in parallel and/or serially to explore that node). 
     To explore a node, the model explorer  102  may visit the node to determine and/or identify pruning information and use the pruning information to analyze the node for pruning. In at least one embodiment, analyzing the node for pruning may include selecting one or more connections and/or parameters to prune from and/or retain in the deep learning model  106 A. In at least one embodiment, data indicating results of the analysis may be stored, at least in part, in a data object  140  for the node (e.g., data representing one or more remaining output channel indices). 
     In at least one embodiment, analyzing a node for pruning may include determining not to prune the node. In at least one embodiment, analyzing a node for pruning may include evaluating one or more pruning thresholds to the node. In at least one embodiment, analyzing the node for pruning may include performing equalization, where input connections to the node from at least two parent nodes may be combined, such as using an intersection or union. 
     The determined and/or identified pruning information for a visit to a node may include pruning information for the current node being explored and/or one or more parent nodes for which pruning information is to be used to select the connection(s) and/or parameter(s) to prune from and/or retain in the deep learning model  106 A for the node. For example, for a visit to the node  132 , the model explorer  102  may use the list  142 D to identify and access the data objects  140  to analyze one or more connections corresponding to the edge  154  for pruning. By way of example, and not limitation, the model explorer  102  may select for the edge  154  one or more connections to retain based at least on selections made for edges corresponding to one or more parent nodes, such as the nearest prunable parent nodes of the node  132 . 
     In at least one embodiment, the visit to a node may include determining data identifying at least some of the pruning information for the node, such as pruning information used to analyze the node for pruning (e.g., for the visit). For example, the visit for a node may include generating and/or identifying at least a portion of a list  142  for the node. As a more specific example, for a visit to the node  132 , the list  142 D may be generated and may be used to identify pruning information to analyze the node  132 . 
     In  FIG.  1   , the nodes  132 ,  130 ,  124 , and  118  that are indicated using dashed lines are examples of nodes that include element-wise layers. The nodes  110 ,  112 ,  114 ,  116 ,  120 ,  126 ,  128 , and  130  that are indicated using solid lines and no shading are examples of noes that have one or more inputs from a single layer and include a convolutional layer. The node  122  that is indicated using a sold line and shading is an example of a node that has inputs from multiple nodes and includes a convolutional layer. As the node  132  is an element-wise layer, analyzing the node  132  may require pruning information corresponding to the nodes  126 ,  128 , and  130  providing inputs to the node  132  (e.g., retained output indices from pruning those layers for equalization). The nodes  126  and  128  are prunable, and therefore the pruning information from those nodes may be used for analyzing the node  132 . However, the node  130  is also an equalization layer such that pruning information for the node  130  may depend on prunable parent nodes of that node. For example, the pruning information for the node  130  may depend on pruning information for the nodes  122  and  116 . The node  116  is prunable, and therefore the pruning information for the node  116  may be used for analyzing the node  130 . However, the node  122  is has inputs from multiple nodes such that pruning information for the node  122  may depend on prunable parent nodes of that node. For example, the pruning information for the node  122  may depend on pruning information for the nodes  118 ,  114 , and  116 . The nodes  114  and  116  are prunable, and therefore the pruning information for the nodes  114  and  116  may be used for analyzing the node  122 . However, the node  118  is an element-wise layer having inputs from multiple nodes such that pruning information for the node  118  may depend on prunable parent nodes of that node (the nodes  112  and  114 ). Thus, analyzing the node  132  for pruning may require pruning information corresponding to the nodes  126 ,  128 ,  112 ,  114 , and  116 , as indicated in the list  142 D for the node  132 . 
     Using conventional approaches, when exploring the node  132  to determine the pruning information for analyzing the node  132  (e.g., to determine the elements of the list  142 D), a recursive call to a function may be performed on each parent node of determine the pruning information for the parent node. Thus, for example, when exploring the node  132 , a call may be made to explore the nodes  126 ,  128 , and  130 . While the calls for nodes  126  and  128  may terminate as they represent prunable layers, when a node that represents layers having inputs from multiple parent layers is encountered, a recursive call to the function may again be performed for each parent layer, such as for the nodes  122  and  116  for the node  130 . This process may continue for parent nodes that correspond to layers having inputs from multiple parent nodes (e.g., for the nodes  122  and  118 ). 
     In at least one embodiment, the data identifying pruning information for a visit to one node may be used by another node. For example, for a visit to the node  132 , the list  142 C may be used to identify pruning information to analyze the node  132 . For example, in embodiments where the node  130  is visited prior to the node  132  (e.g., where the node  130  is fully explored), a similar process as described above for determining the list  142 D may have already been performed to determine the list  142 C for the node  130 . As the list  142 C may correspond to pruning information for the node  130 , the pruning information for the node  130  may be reused for the node  132 . Thus, work performed for a visit to the parent (the node  130 ) may be reused for the visit to the child (the node  132 ). In at least one embodiment, determining data identifying at least some of the pruning information for the node  132  may include incorporating one or more portions of the list  142 C into the list  142 D. Similarly, as indicated in  FIG.  1   , if the nodes  118  and  122  are visited prior to the node  130 , work from those visits may be reused for the node  130 . As an example, the list  142 C for the node  130  may be determined from the list  142 A and/or  142 B. 
     Thus, in at least one embodiment, when exploring a node, rather than always visiting each parent of the node in the deep learning model  106 A to determine and/or identify pruning information for the parent node, the model explorer  102  may reuse at least some of the work performed for visiting the parent node in the same and/or a different traversal of the deep learning model  106 A. For example, the model explorer  102  may use—for one or more child nodes—at least a portion of a list  142  generated for the parent node(s), selections of one or more connections and/or parameters to prune from and/or retain in the parent node(s), and/or a data object(s)  140  generated for the parent node(s). 
     In at least one embodiment, the model explorer  102  may store, for a first visit to a first node of the nodes, at least a portion of pruning information in one or more of the data objects  140 . For a second visit to a second node of the nodes, the pruning information from the data object(s)  140  may be accessed based at least on the first node being a parent of the second node. Additionally, or alternatively, the model explorer  102  may generate, for the first visit to the first node, a list of parent nodes of the first node. The model explorer  102  may incorporate, for the second visit to the second node, the list of parent nodes of the first node in a list of parent nodes of the second node and use the list of parent nodes of the second node to access pruning information for the second node. Thus, the first node need not be revisited for the visit to the second node and/or at least some of the work performed for the first visit can be re-used. 
     In at least one embodiment, a visit to a node may include the model explorer  102  marking the node as visited or otherwise storing data indicating a visit to the node. In at least one embodiment, the visit to the node may include for at least one parent node of the node (e.g., each parent of the node), determining whether the parent node has been visited (e.g., is marked or otherwise indicated as visited or explored using one or more passes through one or more nodes of the deep learning model). In at least one embodiment, based at least on the parent node having been visited (e.g., explored using one or more passes through one or more nodes of the deep learning model), the model explorer  102  may reuse at least some of the pruning information determined and/or identified for the parent node. For example, the model explorer  102  may incorporate at least a portion of a list  142  of parent nodes of the parent node in a list of parent nodes of the node being visited or otherwise use data indicating pruning information for the parent node. In at least one embodiment, determining a visit to a node has already occurred may indicate that the node has been fully explored (e.g., all prunable parent nodes of the node, if any, have been visited using one or more passes through one or more nodes of the deep learning model), the list  142  for the node is complete, the node has been analyzed for pruning, and/or the pruning information includes all information from the parent node(s) needed by the child node for pruning the child node. 
     As described herein, the reused pruning information may be used to analyze the node for pruning. In at least one embodiment, the reused pruning information may be used to select connection(s) and/or parameter(s) to prune based on the node being visited. For example, the model explorer  102  may use a list  142  for the node to identify and access pruning information stored in the data object(s)  140  for each prunable parent node of the node. In at least one embodiment, the visit may include storing the selected connection(s), parameter(s), and/or other results of analyzing the node for pruning (e.g., a determination to not prune a connection(s) and/or parameter(s)) in a data object  140  corresponding to the node being visited (e.g., data indicating one or more retained output indices). In at least one embodiment, the visit may include based at least on determining a parent has not been visited, visiting the parent. In at least one embodiment, the visit to the parent may be similar to the visit to child. For example, visiting the parent may be part of a recursive call to a visit or exploration function for a node (e.g., the recursive graph traversal algorithm). 
     In at least one embodiment, when a recursive algorithm is used to explore the nodes, by determining the parent node has already been visited and reusing work from the visit, one or more branches of the recursive algorithm can be bypassed and/or executed using a reduced workload (e.g., the parent node need not be analyzed again for pruning and/or a list of prunable parent nodes need not be generated again and/or in full for the parent node, etc.), thereby saving computational resources. One or more embodiments of the present disclosure may use dynamic programming to recursively explore the deep learning model  106 A while reusing work performed during one or more iterations of the recursion in one or more other iterations of the recursion. For example, in accordance with one or more embodiments of the disclosure, each time a node is visited, the model explorer  102  need not visit and determine and/or identify pruning information for each parent of that node. Further, as described herein, in at least one embodiment, the pruned model generator  104  may use at least some of the determined and/or identified pruning information to generate the pruned model  106 B. Thus, the pruned model generator  104  may generate the pruned model  106 B more efficiently than otherwise possible. 
     In at least one embodiment, the model explorer  102  may start at each input node, such as the input node  110 , and create a queue with the input node. The model explorer  102  may pop the node from the queue and determine the node type of the node (e.g., determine whether the node is prunable and/or whether the node is a convolutional layer or an element-wise layer). If the model explorer  102  determines the node is prunable, the model explorer  102  may analyze the node for pruning, which may use a pruning threshold and/or other criteria, to determine data indicating one or more retained indices for the kernel. Otherwise, the model explorer  102  may determine the list  142  for the node. In one or more embodiments, the list  142  for the node may be determined based at least on backtracking the nodes that provide inputs to the current node until the first prunable nodes are found. Once found, equalization may be performed and used to determine the retained indices providing input to the current node. As described here, at least some of the backtracking may be avoided based on determining a parent have already been visited. The output layers of the popped node may be added to the queue and the process may be repeated until the queue is empty. 
     In at least one embodiment, the pruned model generator  104  may perform pruning operations on each layer and/or node of the deep learning model  106 A using a tree traversal algorithm, such as a breadth-first search algorithm. Other tree traversal algorithms may be used, such as a depth-first search algorithm. When visiting a node, the pruned model generator  104  may use a list  142  for the node to access the data object(s)  140  for one or more parent nodes of the node. In at least one embodiment, the order of the parent nodes in the list  142  (e.g., defined by the traversal order used by the model explorer  102  when generating the list  142 ) may be used to determine the order in which the pruned model generator  104  analyzes the parent nodes. For example, the pruned model generator  104  may iteratively pop the list  142  starting from the input node to gradually reconstruct the node and/or layer based at least on the connection(s) (e.g., outputs) and/or parameter(s) that is to be retained according to the pruning information (e.g., using indices of retained and/or cut connections that are stored in the pruning information) for the popped node and/or layer. 
     In at least one embodiment, the pruned model generator  104  may traverse the graph using a similar approach as the model explorer  102 . For example, a queue may be populated with an input node(s) which is popped and analyzed, then any output nodes may be added to the queue and the process may repeat. In at least one embodiment, if a layer is pruned or prunable, the pruned model generator  104  may update the kernel if the layer based on the retained indices for the inputs to the layer and the retained indices of the current layer. The graph may then be reconstructed with the pruned layers. 
     Referring now to  FIG.  2   ,  FIG.  2    illustrates an example of the deep learning model  106 A that may be pruned, in accordance with some embodiments of the present disclosure. 
     The deep learning model  106 A of  FIG.  2    may be comprise a single-stage object detector including a weighted Bi-directional Feature Pyramid Network (BiFPN) and feature fusion. As the deep learning model  106 A of  FIG.  2    integrates bidirectional cross-scale connections and is jointly scaled up in width and depth, when evaluating a node for pruning, pruning information for a parent node may be frequently needed. Without reusing work performed when visiting a parent node—in accordance with aspects of the present disclosure—the deep learning model  106 A of  FIG.  2    may take approximately 12 hours to prune. To provide a non-limiting example of a performance improvement that may be achieved when reusing work, the deep learning model  106 A of  FIG.  2    may take the same processing components approximately 90 minutes to prune. 
     The deep learning model  106 A of  FIG.  2    also includes subnets  204 A and  204 B. The subnet  204 A may include a class prediction network for classification of one or more detected objects (e.g., to predict the probability of object presence). The subnet  204 B may include a box prediction network for localization of one or more detected objects (e.g., to predict an offset of the object at each spatial position for each anchor). The subnets  204 A and  204 B may include a convolutional network (e.g., a fully convolutional network) attached to a corresponding BiFPN level. For example, the convolutional network for the subnet  204 A may include a convolutional layer  210 A and a convolutional layer  210 B, and the convolutional network for the subnet  204 B may include a convolutional layer  210 C and a convolutional layer  210 D. 
     Each of the output features from a corresponding level of the BiFPN may be connected to at least the first convolutional layer in the subnet. This approach may allow, the subnets  204 A and  204 B to effectively learn features from multiple (e.g., all) resolutions at the same time. However, conventional pruning algorithms may be unable to prune the deep learning model  106 A, as the number of channels of P3 to P7 after pruning can be quite different while the next convolutional layer always remains the same. In particular, conventional pruning algorithms may be unable to prune inputs to convolutional layers, such as the convolutional layer  210 A or the convolutional layer  210 C, that have inputs from multiple nodes and/or correspond to multiple dependent convolutions performed using the inputs. 
     In one or more embodiments, a convolutional layer that has inputs from multiple nodes may be explored similar to an element-wise layer, allowing for pruning inputs to the convolutional layer. Further, a layer that include multiple dependent convolutions, such as a separable convolutional layer, may be pruned by treating each convolution as a separate node and/or layer. 
     Referring now to  FIG.  3   ,  FIG.  3    illustrates an example of the convolutional layer  210 A of the deep learning model  106 A that may be pruned, in accordance with some embodiments of the present disclosure. The convolutional layer  210 A corresponds to a separable convolutional layer including multiple convolutions. For example, the convolutional layer  210 A of  FIG.  3    includes a depthwise convolution and a pointwise convolution. Pruning the inputs to the convolutional layer  210 A may result in incompatibilities between the dependent convolutions. For example, the pointwise convolution may use information produced by the depthwise convolution, which may no longer be available after pruning. Thus, conventional pruning algorithms are unable to prune layers that include dependent convolutions. 
     In accordance with aspects of the disclosure, a layer having dependent convolutions, such as a separable convolutional layer, may be pruned based at least on evaluating each convolution as a respective node and/or layer of the deep learning model  106 A. For example, model explorer  102  may treat the convolutional layer  210 A of  FIG.  3    as a combination of a depthwise convolutional layer and a 1×1 regular convolutional layer and explore the convolutional layer  210 A as a potential element-wise operation. This approach may be used for any layer containing a shared computational kernel, such as, a transposed convolutional 2D layer. 
     Now referring to  FIGS.  4 - 5   , each block of method  400 , and  500 , and other methods described herein, comprises a computing process that may be performed using any combination of hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few. In addition, methods are described, by way of example, with respect to particular figures. However, the methods may additionally or alternatively be executed by any one system, or any combination of systems, including, but not limited to, those described herein. 
       FIG.  4    is a flow diagram showing a method  400  for exploring a graph corresponding to a deep learning model based on reusing data, determined for a visit to a node, that identifies pruning information for the node, to access the pruning information for analyzing another node for pruning, in accordance with some embodiments of the present disclosure. The method  400 , at block B 402 , includes determining, for a first visit to a first node in a graph corresponding to a deep learning model, a first list of one or more parent nodes of the first node. For example, the model explorer  102  may explore a graph comprising nodes corresponding to layers of the deep learning model  106 A and edges corresponding to connections between the layers of the deep learning model  106 A. The exploring may include the model explorer  102  determining, for a visit to the node  130  of the nodes, the list  142 C. 
     At block B 404 , the method  400  includes incorporating, for a second visit to a second node of the nodes, the first list into a second list for the second node. For example, the model manager  102  may incorporate the list  142 C into the list  142 C. 
     At block B 406 , the method  400  includes accessing pruning information using the second list. For example, the model manager  102  may use the list  142 D to access pruning information for the nodes  126 ,  128 ,  112 ,  114 , and  116 . 
     At block B 408 , the method  400  includes analyzing the second node for pruning using the pruning information. For example, the model explorer  102  may analyze, using the pruning information accessed using the list  142 D, the node  132  for pruning. 
     At block B 410 , the method  400  includes generating a pruned version of the deep learning model. For example, the pruned model generator  104  may generate the pruned model  106 B based at least on one or more results of the analyzing. 
     Referring now to  FIG.  5   ,  FIG.  5    is a flow diagram showing a method  500  for exploring a graph corresponding to a deep learning model based on using a list of one or more parents, generated for a visit to a node, that indicates pruning information for the node, to access the pruning information for analyzing another node for pruning, in accordance with some embodiments of the present disclosure. The method  500 , at block B 502 , includes determining, for a first node in a graph corresponding to a deep learning mode, data identifying one or more parent nodes of the first node. For example, the model explorer  102  may determine, for the node  130  of the nodes, the list  142 C. 
     At block B 504 , the method  500  includes determining the first node is a parent of the second node. For example, the model manager  102  may determine the node  130  is a parent of the node  132 . 
     At block B 506 , the method  500  includes accessing pruning information for the second node using the data. For example, the model manager  102  may access pruning information for the node  132  using the list  142 C. 
     At block B 508 , the method  500  includes analyzing the second node for pruning using the pruning information. For example, the model explorer  102  may analyze the node  128  for pruning using the pruning information accessed using the list  142 C. 
     At block B 510 , the method  500  includes generating a pruned version of the deep learning model. For example, the pruned model generator  104  may generate the pruned model  106 B based at least on results of the analyzing the node  132  for pruning. 
     Example Computing Device 
       FIG.  6    is a block diagram of an example computing device(s)  600  suitable for use in implementing some embodiments of the present disclosure. Computing device  600  may include an interconnect system  602  that directly or indirectly couples the following devices: memory  604 , one or more central processing units (CPUs)  606 , one or more graphics processing units (GPUs)  608 , a communication interface  610 , input/output (I/O) ports  612 , input/output components  614 , a power supply  616 , one or more presentation components  618  (e.g., display(s)), and one or more logic units  620 . In at least one embodiment, the computing device(s)  600  may comprise one or more virtual machines (VMs), and/or any of the components thereof may comprise virtual components (e.g., virtual hardware components). For non-limiting examples, one or more of the GPUs  608  may comprise one or more vGPUs, one or more of the CPUs  606  may comprise one or more vCPUs, and/or one or more of the logic units  620  may comprise one or more virtual logic units. As such, a computing device(s)  600  may include discrete components (e.g., a full GPU dedicated to the computing device  600 ), virtual components (e.g., a portion of a GPU dedicated to the computing device  600 ), or a combination thereof. 
     Although the various blocks of  FIG.  6    are shown as connected via the interconnect system  602  with lines, this is not intended to be limiting and is for clarity only. For example, in some embodiments, a presentation component  618 , such as a display device, may be considered an I/O component  614  (e.g., if the display is a touch screen). As another example, the CPUs  606  and/or GPUs  608  may include memory (e.g., the memory  604  may be representative of a storage device in addition to the memory of the GPUs  608 , the CPUs  606 , and/or other components). In other words, the computing device of  FIG.  6    is merely illustrative. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,” “mobile device,” “hand-held device,” “game console,” “electronic control unit (ECU),” “virtual reality system,” and/or other device or system types, as all are contemplated within the scope of the computing device of  FIG.  6   . 
     The interconnect system  602  may represent one or more links or busses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnect system  602  may include one or more bus or link types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standards association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and/or another type of bus or link. In some embodiments, there are direct connections between components. As an example, the CPU  606  may be directly connected to the memory  604 . Further, the CPU  606  may be directly connected to the GPU  608 . Where there is direct, or point-to-point connection between components, the interconnect system  602  may include a PCIe link to carry out the connection. In these examples, a PCI bus need not be included in the computing device  600 . 
     The memory  604  may include any of a variety of computer-readable media. The computer-readable media may be any available media that may be accessed by the computing device  600 . The computer-readable media may include both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer-storage media and communication media. 
     The computer-storage media may include both volatile and nonvolatile media and/or removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, and/or other data types. For example, the memory  604  may store computer-readable instructions (e.g., that represent a program(s) and/or a program element(s), such as an operating system. Computer-storage media may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device  600 . As used herein, computer storage media does not comprise signals per se. 
     The computer storage media may embody computer-readable instructions, data structures, program modules, and/or other data types in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may refer to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, the computer storage media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. 
     The CPU(s)  606  may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device  600  to perform one or more of the methods and/or processes described herein. The CPU(s)  606  may each include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) that are capable of handling a multitude of software threads simultaneously. The CPU(s)  606  may include any type of processor, and may include different types of processors depending on the type of computing device  600  implemented (e.g., processors with fewer cores for mobile devices and processors with more cores for servers). For example, depending on the type of computing device  600 , the processor may be an Advanced RISC Machines (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device  600  may include one or more CPUs  606  in addition to one or more microprocessors or supplementary co-processors, such as math co-processors. 
     In addition to or alternatively from the CPU(s)  606 , the GPU(s)  608  may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device  600  to perform one or more of the methods and/or processes described herein. One or more of the GPU(s)  608  may be an integrated GPU (e.g., with one or more of the CPU(s)  606  and/or one or more of the GPU(s)  608  may be a discrete GPU. In embodiments, one or more of the GPU(s)  608  may be a coprocessor of one or more of the CPU(s)  606 . The GPU(s)  608  may be used by the computing device  600  to render graphics (e.g., 3D graphics) or perform general purpose computations. For example, the GPU(s)  608  may be used for General-Purpose computing on GPUs (GPGPU). The GPU(s)  608  may include hundreds or thousands of cores that are capable of handling hundreds or thousands of software threads simultaneously. The GPU(s)  608  may generate pixel data for output images in response to rendering commands (e.g., rendering commands from the CPU(s)  606  received via a host interface). The GPU(s)  608  may include graphics memory, such as display memory, for storing pixel data or any other suitable data, such as GPGPU data. The display memory may be included as part of the memory  604 . The GPU(s)  608  may include two or more GPUs operating in parallel (e.g., via a link). The link may directly connect the GPUs (e.g., using NVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch). When combined together, each GPU  608  may generate pixel data or GPGPU data for different portions of an output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory, or may share memory with other GPUs. 
     In addition to or alternatively from the CPU(s)  606  and/or the GPU(s)  608 , the logic unit(s)  620  may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device  600  to perform one or more of the methods and/or processes described herein. In embodiments, the CPU(s)  606 , the GPU(s)  608 , and/or the logic unit(s)  620  may discretely or jointly perform any combination of the methods, processes and/or portions thereof. One or more of the logic units  620  may be part of and/or integrated in one or more of the CPU(s)  606  and/or the GPU(s)  608  and/or one or more of the logic units  620  may be discrete components or otherwise external to the CPU(s)  606  and/or the GPU(s)  608 . In embodiments, one or more of the logic units  620  may be a coprocessor of one or more of the CPU(s)  606  and/or one or more of the GPU(s)  608 . 
     Examples of the logic unit(s)  620  include one or more processing cores and/or components thereof, such as Data Processing Units (DPUs), Tensor Cores (TCs), Tensor Processing Units(TPUs), Pixel Visual Cores (PVCs), Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), Artificial Intelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits (ASICs), Floating Point Units (FPUs), input/output (I/O) elements, peripheral component interconnect (PCI) or peripheral component interconnect express (PCIe) elements, and/or the like. 
     The communication interface  610  may include one or more receivers, transmitters, and/or transceivers that enable the computing device  600  to communicate with other computing devices via an electronic communication network, included wired and/or wireless communications. The communication interface  610  may include components and functionality to enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet. In one or more embodiments, logic unit(s)  620  and/or communication interface  610  may include one or more data processing units (DPUs) to transmit data received over a network and/or through interconnect system  602  directly to (e.g., a memory of) one or more GPU(s)  608 . 
     The I/O ports  612  may enable the computing device  600  to be logically coupled to other devices including the I/O components  614 , the presentation component(s)  618 , and/or other components, some of which may be built in to (e.g., integrated in) the computing device  600 . Illustrative I/O components  614  include a microphone, mouse, keyboard, joystick, game pad, game controller, satellite dish, scanner, printer, wireless device, etc. The I/O components  614  may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of the computing device  600 . The computing device  600  may be include depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device  600  may include accelerometers or gyroscopes (e.g., as part of an inertia measurement unit (IMU)) that enable detection of motion. In some examples, the output of the accelerometers or gyroscopes may be used by the computing device  600  to render immersive augmented reality or virtual reality. 
     The power supply  616  may include a hard-wired power supply, a battery power supply, or a combination thereof. The power supply  616  may provide power to the computing device  600  to enable the components of the computing device  600  to operate. 
     The presentation component(s)  618  may include a display (e.g., a monitor, a touch screen, a television screen, a heads-up-display (HUD), other display types, or a combination thereof), speakers, and/or other presentation components. The presentation component(s)  618  may receive data from other components (e.g., the GPU(s)  608 , the CPU(s)  606 , DPUs, etc.), and output the data (e.g., as an image, video, sound, etc.). 
     Example Data Center 
       FIG.  7    illustrates an example data center  700  that may be used in at least one embodiments of the present disclosure. The data center  700  may include a data center infrastructure layer  710 , a framework layer  720 , a software layer  730 , and/or an application layer  740 . 
     As shown in  FIG.  7   , the data center infrastructure layer  710  may include a resource orchestrator  712 , grouped computing resources  714 , and node computing resources (“node C.R.s”)  716 ( 1 )- 716 (N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s  716 ( 1 )- 716 (N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (NW I/O) devices, network switches, virtual machines (VMs), power modules, and/or cooling modules, etc. In some embodiments, one or more node C.R.s from among node C.R.s  716 ( 1 )- 716 (N) may correspond to a server having one or more of the above-mentioned computing resources. In addition, in some embodiments, the node C.R.s  716 ( 1 )- 7161 (N) may include one or more virtual components, such as vGPUs, vCPUs, and/or the like, and/or one or more of the node C.R.s  716 ( 1 )- 716 (N) may correspond to a virtual machine (VM). 
     In at least one embodiment, grouped computing resources  714  may include separate groupings of node C.R.s  716  housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s  716  within grouped computing resources  714  may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s  716  including CPUs, GPUs, DPUs, and/or other processors may be grouped within one or more racks to provide compute resources to support one or more workloads. The one or more racks may also include any number of power modules, cooling modules, and/or network switches, in any combination. 
     The resource orchestrator  712  may configure or otherwise control one or more node C.R.s  716 ( 1 )- 716 (N) and/or grouped computing resources  714 . In at least one embodiment, resource orchestrator  712  may include a software design infrastructure (SDI) management entity for the data center  700 . The resource orchestrator  712  may include hardware, software, or some combination thereof. 
     In at least one embodiment, as shown in  FIG.  7   , framework layer  720  may include a job scheduler  728 , a configuration manager  734 , a resource manager  736 , and/or a distributed file system  738 . The framework layer  720  may include a framework to support software  732  of software layer  730  and/or one or more application(s)  742  of application layer  740 . The software  732  or application(s)  742  may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. The framework layer  720  may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark&#39; (hereinafter “Spark”) that may utilize distributed file system  738  for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler  728  may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center  700 . The configuration manager  734  may be capable of configuring different layers such as software layer  730  and framework layer  720  including Spark and distributed file system  738  for supporting large-scale data processing. The resource manager  736  may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system  738  and job scheduler  728 . In at least one embodiment, clustered or grouped computing resources may include grouped computing resource  714  at data center infrastructure layer  710 . The resource manager  736  may coordinate with resource orchestrator  712  to manage these mapped or allocated computing resources. 
     In at least one embodiment, software  732  included in software layer  730  may include software used by at least portions of node C.R.s  716 ( 1 )- 716 (N), grouped computing resources  714 , and/or distributed file system  738  of framework layer  720 . One or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software. 
     In at least one embodiment, application(s)  742  included in application layer  740  may include one or more types of applications used by at least portions of node C .R.s  716 ( 1 )- 716 (N), grouped computing resources  714 , and/or distributed file system  738  of framework layer  720 . One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and/or other machine learning applications used in conjunction with one or more embodiments. 
     In at least one embodiment, any of configuration manager  734 , resource manager  736 , and resource orchestrator  712  may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. Self-modifying actions may relieve a data center operator of data center  700  from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center. 
     The data center  700  may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described above with respect to the data center  700 . In at least one embodiment, trained or deployed machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to the data center  700  by using weight parameters calculated through one or more training techniques, such as but not limited to those described herein. 
     In at least one embodiment, the data center  700  may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, and/or other hardware (or virtual compute resources corresponding thereto) to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services. 
     Example Network Environments 
     Network environments suitable for use in implementing embodiments of the disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and/or other device types. The client devices, servers, and/or other device types (e.g., each device) may be implemented on one or more instances of the computing device(s)  600  of  FIG.  6   —e.g., each device may include similar components, features, and/or functionality of the computing device(s)  600 . In addition, where backend devices (e.g., servers, NAS, etc.) are implemented, the backend devices may be included as part of a data center  700 , an example of which is described in more detail herein with respect to  FIG.  7   . 
     Components of a network environment may communicate with each other via a network(s), which may be wired, wireless, or both. The network may include multiple networks, or a network of networks. By way of example, the network may include one or more Wide Area Networks (WANs), one or more Local Area Networks (LANs), one or more public networks such as the Internet and/or a public switched telephone network (PSTN), and/or one or more private networks. Where the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) may provide wireless connectivity. 
     Compatible network environments may include one or more peer-to-peer network environments—in which case a server may not be included in a network environment—and one or more client-server network environments—in which case one or more servers may be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to a server(s) may be implemented on any number of client devices. 
     In at least one embodiment, a network environment may include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more of servers, which may include one or more core network servers and/or edge servers. A framework layer may include a framework to support software of a software layer and/or one or more application(s) of an application layer. The software or application(s) may respectively include web-based service software or applications. In embodiments, one or more of the client devices may use the web-based service software or applications (e.g., by accessing the service software and/or applications via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open-source software web application framework such as that may use a distributed file system for large-scale data processing (e.g., “big data”). 
     A cloud-based network environment may provide cloud computing and/or cloud storage that carries out any combination of computing and/or data storage functions described herein (or one or more portions thereof). Any of these various functions may be distributed over multiple locations from central or core servers (e.g., of one or more data centers that may be distributed across a state, a region, a country, the globe, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) may designate at least a portion of the functionality to the edge server(s). A cloud-based network environment may be private (e.g., limited to a single organization), may be public (e.g., available to many organizations), and/or a combination thereof (e.g., a hybrid cloud environment). 
     The client device(s) may include at least some of the components, features, and functionality of the example computing device(s)  600  described herein with respect to  FIG.  6   . By way of example and not limitation, a client device may be embodied as a Personal Computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a Personal Digital Assistant (PDA), an MP3 player, a virtual reality headset, a Global Positioning System (GPS) or device, a video player, a video camera, a surveillance device or system, a vehicle, a boat, a flying vessel, a virtual machine, a drone, a robot, a handheld communications device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these delineated devices, or any other suitable device. 
     The disclosure may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The disclosure may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network. 
     As used herein, a recitation of “and/or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and/or element C” may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, “at least one of element A or element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, “at least one of element A and element B” may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. 
     The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and/or “block” may be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.