Bidirectional synchronization between heterogeneous data sources in distributed content creation environments

Abstract

The approaches presented here are systems and methods that include bidirectional connectors for transferring information between different address locations associated with different data sources. One or more features can have associated values ​​corresponding to one or more parameters of the feature. These features can be stored in several different data sources, which may have different properties or functionality. A bidirectional connector can be established to link the respective address locations for common features, enabling changes to one data source to be detected, evaluated, and implemented in the other connected data sources.

Description

STATE OF THE ART Various applications attempt to foster collaboration between different parties by presenting scenes, objects, files, and the like within a shared interaction environment. This environment can receive an initial input file from a primary data source, which may include converted or modified versions of a range of different file types, and present a representation of that file to a number of users within the environment. The scenes or objects represented within the interaction environment can be linked to other file sources and associated files, which may have different file types or capabilities compared to the primary file source.Due to the different capabilities between the files, attempts to merge or otherwise mirror changes between the files can be done manually, which is prone to errors and delays. BRIEF DESCRIPTION OF THE DRAWINGS Various embodiments in accordance with the present disclosure are described with reference to the drawings, wherein: Fig. 1 illustrates an exemplary interactive environment in accordance with at least one embodiment; Fig. 2A illustrates an exemplary interactive environment connected to data sources in accordance with at least one embodiment; Fig. 2B illustrates an exemplary modification process between a first state of an object in an interaction environment and a second state in accordance with at least one embodiment; Fig. 3A illustrates an exemplary system for generating a bidirectional connector for features in an interactive environment in accordance with at least one embodiment; Fig.Figure 3B illustrates an exemplary system for establishing and maintaining bidirectional connectors between data sources in accordance with at least one embodiment; Figure 4A illustrates an exemplary process for updating a feature value using a bidirectional connector in accordance with various embodiments; Figure 4B illustrates an exemplary process for updating a feature value using a bidirectional connector in accordance with various embodiments; Figure 5 illustrates an exemplary process for updating a feature value using a bidirectional connector in accordance with various embodiments; Figure 6 illustrates components of a distributed system that can be used to update or perform inference using a machine learning model, according to at least one embodiment; FigureFig. 7A Inference and / or training logic according to at least one embodiment is illustrated; Fig. 7B Inference and / or training logic according to at least one embodiment is illustrated; Fig. 8 An exemplary data center system according to at least one embodiment is illustrated; Fig. 9 A computer system according to at least one embodiment is illustrated; Fig. 10 A computer system according to at least one embodiment is illustrated; Fig. 11 At least parts of a graphics processor according to one or more embodiments are illustrated; Fig. 12 At least parts of a graphics processor according to one or more embodiments are illustrated; Fig. 13 An exemplary data flow diagram for an advanced computing pipeline according to at least one embodiment is illustrated; Fig.Figure 14 shows a system diagram for an exemplary system for training, adapting, instantiating and deploying machine learning models in an advanced computing pipeline according to at least one embodiment; and Figures 15A and 15B illustrate a data flow diagram for a process for training a machine learning model and a client-server architecture for improving annotation tools with pre-trained annotation models according to at least one embodiment. DETAILED DESCRIPTION The invention is defined by the claims. To illustrate the invention, aspects and embodiments that may or may not be within the scope of the claims are described herein. This paper discloses approaches that provide systems and procedures including bidirectional connectors for transferring information between different address locations associated with different data sources. One or more features can have associated values ​​corresponding to one or more parameters of the feature. These features can be stored in several different data sources, which may have different properties or functionality. A bidirectional connector can be established to link the respective address locations for common features, enabling changes to one data source to be detected, evaluated, and implemented in the other connected data sources. The following description details various embodiments. For explanatory purposes, specific configurations and details are presented to provide a thorough understanding of these embodiments. However, a person skilled in the art will also recognize that the embodiments can be implemented practically without these specific details. Furthermore, well-known features may be omitted or simplified to avoid complicating the understanding of the described embodiments. The systems and procedures described herein may be used by, among others, non-autonomous vehicles or machines, semi-autonomous vehicles or machines (e.g., in a cabin infotainment or digital or virtual driver assistance application), autonomous vehicles or machines, guided and unguided robots or robotic platforms, warehouse vehicles, all-terrain vehicles, vehicles coupled to one or more trailers, flying vehicles, boats, shuttle vehicles, emergency vehicles, motorcycles, electric or motorized bicycles, aircraft, construction vehicles, trains, underwater vehicles, remotely controlled vehicles such as drones, and / or other types of vehicles.Furthermore, the systems and methods described herein can be used for a number of purposes, for example, but not limited to, machine control, machine locomotion, machine propulsion, synthetic data generation, model training or updating, perception, augmented reality, virtual reality, mixed reality, robotics, security and monitoring, simulation and digital twinning, autonomous or semi-autonomous machine applications, deep learning, environmental simulation, object or actuator simulation and / or digital twinning, data center processing, conversational artificial intelligence (AI), generative AI with large language models (LLMs) and / or vision language models (VLMs), light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, and / or other suitable applications. Disclosed embodiments may be included in a range of different systems, such as 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, antenna systems, media systems, boat systems, intelligent area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twinning operations, systems implemented using an edge device, systems involving one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least partially in a data center, and systems for performing conversational AI operations.Systems for performing generative AI operations using LLMs and / or VLMs, systems for performing light transport simulation, systems for performing collaborative content creation for 3D assets, systems that are implemented at least partially using cloud computing resources, and / or other types of systems. Approaches in accordance with various embodiments relate to establishing a bidirectional connector between a distributed content creation environment (such as an interactive three-dimensional rendering environment (3D rendering environment)) and one or more data sources. Various embodiments refer to a bidirectional connection that has a specific, targeted scope of responsibility for one or more features or components within the distributed content creation environment. As a result, the bidirectional connector can be tailored or specifically configured to monitor a certain component or feature without attempting to parse or synchronize an entire scene or data source. The bidirectional connector can be used to provide listeners at different address locations that are representative of specifically selected components or features.Addresses can be defined at either end of the connector (e.g., one for the scene description environment and a second for the data source), and changes to one or the other can then trigger a notification or a workflow to synchronize the associated change with the other address. This allows a specific feature to be modified or tracked independently of other components within the scene or other data within the data source. Approaches in accordance with various implementations can be used to enable collaboration between two or more different data sources, which may be represented within an interaction environment. For example, the interaction environment may provide a geometric / graphical representation of one or more objects or a scene, such as a 3D representation of a part and various sub-parts that comprise the part. A non-geometric data source may include specific information relating to an appearance of one or more sub-parts, such as the part's color, size, etc. However, because the data sources may be in different configurations or formats, changes in one may not be reflected in another without manual tracking and input.As a non-limiting example, a content creation program (CCP) can include various computer-aided drafting (CAD) programs that generate 3D representations of different objects. The objects can include sub-parts, which are also individually tracked and represented within the CAD file, or they can be linked to another CAD file in an array, among other options. The 3D representation can also be tracked or monitored in non-geometric data sources, such as database files, spreadsheets, and the like. For example, a part corresponding to a coffee cup array might include a body, a handle, and a lid. An associated CAD file might contain representative information about these parts, such as dimensions, color, finish material, and / or the like.The information from the CAD file can be processed in the interaction environment to provide a representation within that environment. Simultaneously, a parts list for the coffee cup can be generated, listing each component along with certain attributes, such as different colors or materials. If the CAD file is updated to change the color of the body, the associated spreadsheet must also be manually updated to reflect this change. Embodiments of the present disclosure address and overcome this problem by providing listeners for a bidirectional connector that can determine that a change has been made to one or more elements of the CAD file or the spreadsheet at a given address, and then perform the corresponding update at the other address.Returning to the example of changing the body's color in the interaction environment, the bidirectional connector can detect this change and then pass it to an associated spreadsheet linked to the color, updating the value in a given cell. In this way, changes within one or more data sources for a connected geometric data source can be updated and reflected within the interaction environment. Systems and methods can be used to establish a connected, collaborative environment using a set of data sources. Various embodiments involve one or more bidirectional connectors that "listen" at a specified address with respect to one or more features associated with the set of data sources. The bidirectional connectors can be used to: identify a change to a specified or connected component in a first data source, determine the value of the change, identify the corresponding address for the component in a connected second data source, and then modify the corresponding address in the connected second data source in accordance with the change in the first data source.In this way, one or more specific components or objects can be selected and updated for connection, instead of either manually updating related components or attempting to track and update an entire scene. Various other such functions may also be used within the scope of the different embodiments, as an average person skilled in the art will recognize in view of the teachings and recommendations contained herein. The revelation extends to any novel aspects or features described and / or illustrated herein. Further features of the disclosure are characterized by the independent and dependent claims. Any feature in one aspect of the disclosure can be applied to other aspects of the disclosure in any suitable combination. In particular, procedural aspects can be applied to device or system aspects and vice versa. Furthermore, features implemented in hardware can also be implemented in software, and vice versa. Any reference to software and hardware features herein should be interpreted accordingly. Each system or device feature as described herein can also be provided as a process feature, and vice versa. Functionally described system and / or device aspects (including means plus functional features) can alternatively be expressed in terms of their corresponding structure, such as a suitably programmed processor and associated memory. It should also be understandable that certain combinations of the various features described and defined in any aspect of the revelation can be implemented and / or provided and / or used independently of one another. This disclosure also provides computer programs and computer program products comprising software code adapted, when executed on a data processing device, to perform any of the procedures described herein and / or to embody any of the device and system features described herein, including any or all of the partial steps of a procedure. The disclosure also provides a computer or computing system (including networked or distributed systems) that includes an operating system supporting a computer program for performing any of the procedures described herein and / or embodying any of the device or system features described herein. The revelation also provides a computer-readable medium on which one or more of the aforementioned computer programs are stored. The revelation also provides a signal that carries one or more of the aforementioned computer programs. The disclosure extends to processes and / or devices and / or systems as described herein with reference to the accompanying drawings. Aspects and embodiments of the disclosure will now be described exclusively by way of example with reference to the accompanying drawings. Fig. 1 illustrates an exemplary environment 100 that can be used with embodiments of the present disclosure. In this example, the environment 100 includes an interaction environment 102, which can be a distributed platform capable of receiving data from one or more data sources 104A-104N, extracting information from the one or more data sources 104A-104N, and then generating a representation 106 associated with the one or more data sources 104A-104N for display. The interaction environment 102 can be a distributed platform that allows multiple users to view and interact with the representation 106, which in this example includes a 3D representation of a car. The car can be an arrangement of a number of different components and / or subcomponents, such as tires 108, taillights 110, seats 112, and / or the like. The one or more data sources 104A-104N can correspond to a different "type" or format of data source. As a non-restrictive example, data source 104A can correspond to a CCP used for modeling, such as a CAD program. The information provided by data source 104A can include feature information for various parts of the representation 106, such as geometric information that allows the interaction environment 102 to render the representation 106 of the car. Additional data sources 104B, 104C can provide other types of information, such as background information or scene information for other objects (not shown) that may be included in the environment.For example, the representation 106 can be positioned within an environment, such as a road, which includes other objects to provide more detail for the scene, such as road markings, signs, trees, other cars, pedestrians, buildings, and / or the like. Additionally, in at least one embodiment, one of the data sources 104A-104N can be associated with geometric information but can include data corresponding to one or more features associated with the representation 106. For example, a spreadsheet or database file can store information relating to a physical appearance of the representation 106, an orientation of the representation 106, and / or the like. Furthermore, the spreadsheet or database can be used to track and adjust a number of objects within the scene.For example, a spreadsheet or database file might have a value that specifies a number of trees to position within a field, such as five, so that if the data source is provided to the interaction environment as 102, five trees will be rendered. The systems and methods of this disclosure can include one or more bidirectional connectors between the different data sources 104A-104N and the interaction environment 102, such that connected values ​​between data sources 104A-104N are automatically updated in response to an update to a specific data source and / or the interaction environment 102. For example, if data source 104N includes information associated with the taillight 110, such as a shape, and the value in data source 104N is changed from "rectangle" to "circle", the associated component part in data source 104A is updated to change the geometry of the taillight 110, and the change would further be represented within the interaction environment 102.Accordingly, changes and modifications can be made between connected areas of different data sources 104A-104N and automatically distributed to data sources 104A-104N via the interaction environment 102. In certain embodiments, one or more data sources 104A-104N can be associated with one or more CCPs and / or one or more repository locations for files generated by the one or more CCPs. Different CCPs may have different suites or tools and features for content generation, and as a result, linking information between other types of data sources may not be possible. For example, one CCP may lack the capabilities of another, such as a 2D CAD program that is unable to render 3D representations of objects. Systems and methods of the present disclosure can be used to identify features represented between different data sources 104A-104N and then link these specific features and their associated values ​​without attempting to link entire files or all of the features, which could lead to errors.Instead, specific bidirectional connections can be established for a subset of possible features, where the subset can be selected by a user or chosen based on compatibility, among various other options. In at least one embodiment, the interaction environment can convert different input information or otherwise align it to a common format, such as Universal Scene Description (USD). Various embodiments identify one or more elements or features that can be linked between different data sources 104A-104N and provide a bidirectional connector to update values ​​indicating these linked features. In certain embodiments, the one or more elements can be predefined. For example, a specific CCP can be evaluated, and a chart or table can include one or more elements or features and their associated compatibility with various other potential data sources, such as other CCPs, database programs, spreadsheets, and the like.Accordingly, systems and procedures can be used to generate a collaborative multi-data-source system, whereby individual updates made to a data source or the interaction environment without directly modifying the data sources can be used to propagate changes via the connected values ​​associated with one or more bidirectional connectors. Fig. 2A illustrates an exemplary environment 200 that can be used with embodiments of the present disclosure. In this example, the interaction environment 102 includes the representation 106 of the car from Fig. 1, the car including various features such as the tires 108, the taillights 110, and the seats 112. Additionally, the data sources 104A and 104B are illustrated. As mentioned herein, there may be more data sources 104, as shown in Fig. 1, but for the sake of simplicity and clarity, only two data sources are discussed in this example. The illustrated data sources 104A and 104B each include feature lists 202, which represent a certain element within the representation 106. For example, the feature lists 202 may include specific part numbers, specific components, specific textures or colors, and / or the like. Additionally, it should be clear that feature lists 202 may also include metadata and other descriptive information, which may constitute a large portion of the information associated with the representation 106. That is, while the output of the various data sources 104A–104N may provide a graphical representation in 3D, most of the information is not geometric but rather in the form of metadata. As shown, there is an identifier 204 and a value 206 for each feature. The value 206 may correspond to a single number or multiple numbers. Additionally, the value 206 may be a binary value (e.g., yes / no, on / off, etc.).) for a given feature, such as a toggled checkbox. In at least one embodiment, the value 206 can be a series of numbers, such as dimensional values ​​corresponding to positions within the environment and / or relative to other features. Additionally, the value can refer to the size or appearance of an object, with a certain value corresponding to a color or texture, or with different values ​​making objects smaller or larger. Furthermore, the value can also specify a number of features that repeat within a scene, such as a pattern of bolt holes, which can include a value of "4" to represent four bolt holes. The illustrated feature lists 202 include different identifiers 204 in this example because the associated data sources 104A and 104B can have different capabilities for the respective feature lists 202A and 202B. For example, if data source 104A is a 3D CAD program and data source 104B is a spreadsheet, the 3D CAD program can include various features that do not correspond to or are not correlated with the spreadsheet, such as scene lighting information, among others. Data source 204A includes feature list 202A, which contains features with respective identifiers 204. Similarly, data source 204B includes feature list 202B, which contains features with respective identifiers 204. Each of these identifiers 204 also includes a value 206.In this example, "Feature A", "Feature K", and "Feature N" are included in both Feature List 202A and Feature List 202B. However, "Feature B" is not shown in Feature List 202B, and "Feature C" is not shown in Feature List 202A. Therefore, there can be no bidirectional connectors between these features, as discussed herein. Systems and methods can be used to connect specific value addresses for specific features within given feature lists. For example, a user may not want a full restore and / or a full update for a given file and instead want to restrict changes to specific elements of interest in order to increase speed and reduce complexity in both use and implementation. Furthermore, a user may want to block or otherwise prevent changes to specific features and remove connectors to restrict access to or modification of values ​​associated with different features. Accordingly, various embodiments of the present disclosure can include specific, targeted bidirectional connectors 208 to connect specific features between different data sources 104.Various embodiments may include connecting via the interaction environment 102, but it should be clear that other embodiments can provide connections between data sources 104A-104N without using the interaction environment 102 as an intermediary. In this example, a first bidirectional connector 208A is used to connect feature A between the first data source 104A and the second data source 104B. Likewise, a second bidirectional connector 208B is used to connect feature N between the first data source 104A and the second data source 104B. However, as discussed herein, not every compatible feature may include a bidirectional connector, and for example, feature K is listed in both feature lists 202A and 202B, but there is no bidirectional connector 208A.This can be a deliberate decision by a user and / or one or more rules implemented with the Interaction Environment 102. For example, feature K may correspond to an element that is connected to a number of other elements, and a change to feature K may trigger a domino effect across various components. As a result, it may be desirable to delay updating connected data sources for this feature until it can be verified that the change has no adverse effects on other components. As another example, feature K may correspond to a component or element that is computationally intensive to modify and can therefore be "locked" for modification to provide a faster, lower-latency experience in the Interaction Environment 102.Additionally, in various embodiments, the quantity of data between the different feature lists 202A, 202B can be high, and therefore certain data can be prioritized to reduce the number of bidirectional connectors used, and as a result, information that can be connected is not necessarily connected. Fig. 2B illustrates an exemplary environment 220 that can be used with embodiments of the present disclosure. In this example, the functionality of the bidirectional connector 208A between a first state 222 and a second state 224 is illustrated. For example, in the first state 222, feature A corresponds to the taillight 110 and includes respective values ​​206 associated with the first feature list 202A and the second feature list 202B, which, as discussed herein, are each associated with different data sources 104A and 104B. In the first feature list 202A, feature A has a value of XX and is connected to feature A in the second feature list 202B via the bidirectional connector 208A. In this example, a change to the associated value 206 for feature A in the second feature list 202B is made by changing from XX to DD.In at least one embodiment, a listener is connected to the bidirectional connector 208A and can listen at the associated addresses with respect to the value 206 in the first and second data sources 104A and 104B and determine whether a change has occurred. A change can be compared with one or more thresholds or other parameters to determine whether the change is significant enough to update associated data sources 104. In this example, a change can be detected and it can be determined that it exceeds a threshold. Additionally, it can be determined that the change is "important" or relevant to the given scene. For example, with the representation 106 being a car, a change to the taillights, when only the rear view is considered, might only become relevant when a different camera view of the car is presented.In this example, the change occurs to an element within the camera's field of view associated with representation 106, and it may be considered important enough to apply the update to the other connected data sources 104. Furthermore, in certain embodiments, a delay or duration may be incorporated to determine whether the change to value 206 is intended and / or will be sustained. For example, a user might mistakenly update a value and then quickly revert the change. To avoid unnecessary updates and traffic, a delay may be imposed after a change before applying the change to other connected data sources 104. Additionally, changes may be evaluated to determine their validity.For example, if a value for a given feature corresponded to a numeric value and a user changed the value to a letter, this change may be considered invalid, as implementing the change might cause an error. In the illustrated embodiment, the change applied to feature A may be considered significant, exceed a threshold, be intentional, and / or exceed a threshold duration after application. Accordingly, various embodiments of the present disclosure can use the bidirectional connector 208A to automatically apply the change to the second data source 104A, which is associated with the first feature list 202A, and then render an updated representation 106 that incorporates the change. In this example, changing the value of feature A from XX to DD changes the taillights 110 from a rectangular shape to an elliptical shape. Accordingly, systems and methods can allow collaborators to select which data source is used to make modifications and then automatically apply these changes to other data sources using the bidirectional connector 208. Figure 3A illustrates an exemplary environment 300 that can be used with embodiments of the present disclosure. The environment 300 can be integrated into and / or communicate with one or more distributed computing environments, such as an interaction environment, allowing one or more users to collaborate and / or view objects and / or scenes. A bidirectional connector engine 302 can be used as one or more tools or features within the interaction environment, which can be provided as a service by a distributor of the interaction environment and / or be accessible by one or more additional providers. Furthermore, various features can be described as independent tools or modules, but the tools and the features executed by the tools can be integrated into a common tool or workflow.Additionally, each of the tools or modules can be supported by underlying hardware, such as graphics processing units (GPUs) and / or memory, that executes stored software instructions in response to one or more commands. The commands can be provided as input, such as from a user performing operations on one or more devices, and / or as part of an automated workflow, where the command can be received by one or more devices without direct user interaction. For example, a user might choose to load a software program that can initiate a workflow to grant permissions to the interaction environment, select an object to view, and execute one or more features of the 302 bidirectional connector engine. In this example, an input 304 is provided to data source(s) 104A-104N, which may be connected to or otherwise associated with another data source(s) 104A-104N to render or present one or more objects within the interaction environment 102. The data sources 104A-104N receiving the input 304 may include one or more data files or models, such as a model representative of an object or scene, which may be in 2D or 3D. The object may be an image or a volumetric model, and it may also be extracted from one or more video feeds, such as by selecting one or more frames from the video. In at least one embodiment, the input 304 corresponds to a specific file type, which may be unique to a CCP used to generate the input 304 or unique to an associated data source.For example, if input 304 was a volumetric model from NX, the file could be in the form of a ".prt" file, among various other options such as ".sim", ".afm", ".udf", ".igs", ".asm", ".stp", ".model", and / or the like. Additionally, information associated with the different file types can include metadata, not just geometric information associated with the input. Furthermore, data sources 104A-104N can be non-geometric data sources, such as spreadsheets, database files, and the like. In this example, a manager 306 can determine that the input 304 has been provided to the different data sources 104A-104N. For example, the manager 306 can be associated with one or more bidirectional connectors 208 (e.g., connectors) that can link certain features between the respective data sources 104A-104N, as discussed herein. In at least one embodiment, the one or more bidirectional connectors 208 can be generated by a user and / or can be generated automatically based on various properties of the data sources 104A-104N and / or the output of the data sources 104A-104N. For example, different bidirectional connectors 208 can be created if the output is a 3D representation of an object as opposed to a 2D representation. Additionally, different bidirectional connectors 208 can be formed if there are multiple objects within a scene.Systems and procedures can allow any geometry and associated non-geometric features, such as metadata, to be introduced into the interaction environment from one or more compatible sources, and then allow associated values ​​for different components in a data source to be edited, and allow this change to be propagated to another connected data source using one or more bidirectional connectors 208. The various bidirectional connectors 208 can connect a pair of data sources 104A-104N or can connect multiple data sources 104A-104N. In at least one embodiment, the connected data sources 104A-104N can connect individual features based on their respective address locations. That is, entire arrays or feature lists may not be connected, and specific elements can be selected to reduce latency when attempting to render or otherwise process an entire file as opposed to a subset. Additionally, the various bidirectional connectors 208 may be limited based on the capabilities of the associated data sources 104A-104N, and therefore computational resources may be wasted when attempting to update components that are not connected between data sources 104A-104N due to these limitations.As a non-restrictive example, a spreadsheet cannot include values ​​associated with scene lighting. As another example, a 2D rendering program cannot include volumetric information for an object. Systems and procedures provide the bidirectional connectors 208 to allow rapid modifications of specifically selected features in data sources 104A-104N and to automatically update changes made in one data source in other connected data sources. A listener 308 can be used to determine whether a value has been changed and / or modified. For example, the listener 308 can be associated with different address locations at each "end" of a bidirectional connector 208. Ends can refer to locations within the data sources and / or the interaction environment. It should be understood that the bidirectional connectors described herein may not be limited to connecting pairs of data sources 104A-104N and may include connections between three or more different data sources. As a result, the "ends" can refer to address locations within a given data source that includes a value connected to one or more additional data sources.In various embodiments, the bidirectional connectors 208 can use the interaction environment 102 as an intermediary, such that one end can be formed at the representation within the interaction environment for a connection to a first data source, and then this end, or another end, can be formed at the representation within the interaction environment with a second data source. During operation, the listener 308 can be used to determine whether a connected value has been modified. The listener 308 can be executed at intervals (e.g., every X seconds) or continuously. In at least one embodiment, the listener 308 can use one or more rules to delay notification of a change, such as by waiting to determine whether a change was intentional or an error, or by waiting for a certain period of time before providing an indication that a change has occurred.Listener 308 can also execute rules to determine that modifications are valid and will not cause rendering or operational errors if used to modify other connected locations. Additionally, Listener 308 can identify changes between an initial data file and a modified data file. For example, a user might interact with an object within the interaction environment and request an update or re-render by uploading a different version of one or more of the data files 104A-104N associated with the rendering. Accordingly, Listener 308 can be used to identify changes in some or all connections when it is determined that an updated file has been provided to the interaction environment. In another embodiment, the receiver 308 can receive a command or notification when a user interacts with a bidirectional connector, for example, by modifying one or more values ​​associated with the bidirectional connector. In certain embodiments, the receiver 308 can also delay different inputs or otherwise impose an order on them. For example, if two different users interact with the same value of an attribute, the receiver 308 can provide an indication that the value has been modified by multiple users, provide an instruction to perform an update according to the order in which the commands were received, disregard the first command in favor of the second command, disregard the second command in favor of the first command, or various combinations thereof.For example, different inputs received within a threshold time period can be evaluated, and then one or more rules can be applied, such as ignoring the first command because the probability that the second command will be rendered before the result of the first command is seen might be greater than a threshold. Additionally, in various implementations, a messenger service can be provided to notify users of rapid changes to different attributes, which can be used to moderate or otherwise encourage communication between users working in the same interaction environment. In at least one embodiment, a subset of features or attributes can be selected from the various bidirectional connectors. The selected features can be based on user preferences, data source properties, and / or combinations thereof. In at least one embodiment, an identifier service 310 can be used to evaluate input data sources 104A-104N to determine which features might be connected using one or more bidirectional connectors 208. For example, the identifier service 310 can scan associated entries regarding geometric and / or non-geometric features in each of the data sources 104A-104N and determine which features are the same in two or more of the input data sources 104A-104N.In certain embodiments, a feature data store 312 can be used during evaluation to restrict or otherwise target specific features or information. For example, a user can use one subset of information for a particular set of inputs and another subset for a different set of inputs, thereby targeting and matching how many bidirectional connectors to create for different types of input options. Systems and procedures can also use one or more trained machine learning systems associated with the identifier service 310 to evaluate and identify different features for creating bidirectional connectors. For example, the machine learning systems can parse data and metadata for each of the data sources 104A-104N and determine a match across different data sources 104A-104N. Additionally, the machine learning systems can incorporate information from the feature data store 312 to target or search for different types of features.Different sets of features can be configured for different types of input, as mentioned herein. As a result, the machine learning systems can scan the input files, determine that the output will be a 3D scene with a variety of different objects, and then, based on this determination, identify a certain number of features to generate the bidirectional connectors. Different parameters can also be used to limit or control the number of bidirectional connectors used, such as having a threshold maximum to reduce latency, or similar considerations. Systems and procedures can incorporate an evaluation service 314, which uses one or more rules stored in a rule data store 316, together with or in addition to the identifier service 310, to select different features for a connection using one or more bidirectional connectors 208. The rules can be based on file type, user preferences, resource capabilities, and / or the like. For example, if it is determined that the user is working with hardware that has limited processing capabilities, the rules can specify a maximum number of bidirectional connectors or restrict the bidirectional connectors to features that would not use significant processing capabilities. On the other hand, a user with excessive processing capabilities can be provided with additional bidirectional connectors or additional options. As a non-restrictive example of the operation of the 302 bidirectional connector engine, a user can use an initial CCP to generate an object. This object can be imported into an interaction environment and integrated into a scene generated by a different CCP. Furthermore, it can be associated with two different spreadsheets that track information for both the object and the scene. For example, the object might be a part positioned within a warehouse, and its associated spreadsheet might control the part's size, orientation, quantity, and so on. Additionally, the scene might include geometric features of the warehouse with associated position tracking, size, orientation, quantities, and so forth.An input can be provided to the spreadsheet associated with the object to rotate it and position it within a given location in the scene. The listener can specify that a change be made to one or more connected values ​​associated with the object's position and orientation, and this information can be used to adjust the object's location within the scene in the interaction environment. These changes can then cause additional changes to the scene. For example, if the object is moved to a new location, the appearance of certain objects in the scene may change; for instance, a tile on the floor may no longer be displayed, or a shadow may be cast over a different region.Another listener can then detect these changes in the rendering within the interaction environment and trigger updates to the spreadsheet associated with the scene to track revised values ​​for different connected features. In this way, using the various bidirectional connectors, modifications to one data source can be automatically tracked and represented in another. Various embodiments can generate bidirectional connectors and / or execute the listener, among other features, according to different workflows associated with certain data source types. Different workflows can be created based on the types of received input files, the types of data sources, and / or the like. For example, a workflow can be created to identify and generate bidirectional connectors upon detection of a file type. In at least one embodiment, the workflow can be based, at least partially, on compatibilities between features within dissimilar data sources. For example, workflows can be used to identify different types of features that are incompatible between two different data sources and then stop or block attempts to generate bidirectional connectors between these incompatible features. Fig. 3B illustrates an exemplary environment 350 that can be used with embodiments of the present disclosure. In this example, a bidirectional connector manager 352 is illustrated, which can be a subset of the bidirectional connector engine 302, for example, as part of the manager 306. In at least one embodiment, the bidirectional connector manager 352 can be used to identify and establish different address locations for desired connections and to execute changes based on identified modifications to different values ​​at connected addresses. For example, an address locator 354 can evaluate different features identified as desired for a connection and determine their address or location, such as within a feature list, associated with metadata and / or the like.The address locator 354 can also store different addresses and their associated feature identifiers within a connection data store 356, which can be used to track various bidirectional connectors 208 used with a given application. The address locator 354 can work with the listener 308 to identify changes at a connected address location. When a change is detected, an execution service 358 can be used to determine whether the change satisfies one or more rules stored within a rule data store 360. For example, a change might need to exceed a threshold quantity, such as being a percentage greater than a given value, or the like.As another example, a rule can be created to determine if a certain time elapsed after a change exceeds a threshold, thus reducing instances where a user mistakenly makes a change and then quickly reverts once the error is identified. The execution service 358 can interact with the bidirectional connector 208 to identify the associated address location linked to the value and then perform the update. As a result, bidirectional connectors can be established, monitored, and used to quickly and automatically update information across different data sources. Fig. 4A illustrates an exemplary flowchart for an exemplary process 400 for updating feature values ​​using a bidirectional connector, which can be used with embodiments of the present disclosure. It should be understood that, within the scope of the various embodiments, unless specifically stated otherwise, there may be additional, fewer, or alternative operations for this and other processes presented herein, which are performed in a similar or alternative sequence or at least partially in parallel. In this example, a notification is received that a modification to a first feature value has been performed at a first selected address location 402. The selected address location may correspond to an address within a data storage that is associated with an appearance or property of a feature.In at least one embodiment, the first feature value is a geometric value. In another embodiment, the first feature value is a non-geometric value. In at least one embodiment, the information is received after a bidirectional connection has been established between two heterogeneous content creation applications. As used herein, the bidirectional connection can be established for one or more feature values ​​corresponding to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform. Accordingly, in at least one embodiment, the information can be received via the bidirectional connection, and the first feature value can be one of the one or more feature values. It can be determined that a modification to the first feature value exceeds a threshold 404. For example, changes that are smaller than a certain percentage or below a minimum specified value can be considered non-errors or insignificant with respect to rendering or modifying a different source. Additionally, the threshold can also correspond to a duration associated with the change, such as a time period after the change has been made. A second selected address location associated with the first feature value can be identified 406. The second selected address location can be at a different end of the bidirectional connector and can be associated with a different data source than the first selected address location. In at least one embodiment, a second feature value at the second address location can be updated based on the modification 408.Accordingly, changes made at the first address can be automatically made at the second address. Fig. 4B illustrates an exemplary flowchart for an exemplary process 420 for updating feature values ​​using a bidirectional connector that can be used with embodiments of the present disclosure. In this example, a first address corresponding to a selected feature in a first data source is identified 422. The first address may be associated with a value that modifies one or more properties of the selected feature. As discussed herein, in at least one embodiment, the selected feature may correspond to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform. A second address corresponding to the selected feature in a second data source may be identified 424.In at least one embodiment, a bidirectional connection can be generated between the first address and the second address 426. The bidirectional connection can include a listener that monitors the respective addresses for changes. In at least one embodiment, the first address can correspond to a first content creation application and the second address can correspond to a second content creation application. The first and second content creation applications can be heterogeneous applications. It can be determined that a modification to a first value for at least one of the first address or the second address has been made 428. Based on the modification, a second value for the other of the first address or the second address can be modified 430. For example, the second value can be modified to be the same as the first value.Accordingly, changes to one address can be transferred to another address. Fig. 5 illustrates an exemplary flowchart of an exemplary process 500 for updating feature values ​​using a bidirectional connector that can be used with embodiments of the present disclosure. In this example, a bidirectional connector 502 is monitored. The bidirectional connector can be associated with a first address location at a first data source and a second address location at a second data source. As described herein, the first and second data sources can be different data sources, such as a CAD file and a spreadsheet, among various other options. It can be determined that a modification has been made to a value at either the first or the second address location 504.For example, a listener can evaluate the address location(s) to determine whether a change has occurred in a value associated with one or more features in the content. A determination can be made as to whether the modification exceeds a threshold (506). The threshold can be associated with the amount of the change in the value (e.g., a fixed amount, a percentage, a minimum / maximum value, etc.), a time period after the change was made, and the like. If not, the modification is ignored (508). If the modification exceeds the threshold, the value can then be updated at the other address location, either the first or the second, to match a modified value that corresponds to the value after the modification (510). In other words, the values ​​at the first address location and the second address location are the same.Subsequently, a rendering corresponding to one or more features associated with the first address location and the second address location can be updated. 512 Accordingly, modifications between different data sources can be synchronized and the results of these modifications can be rendered for viewing. As discussed, aspects of various approaches presented herein may be light enough to be executed in real time on a device such as a client device, like a personal computer or a game console. Such processing may be performed on or for content generated by or received from the client device, or received from an external source, such as streaming data or other content received over at least one network. In some cases, the processing and / or determination of this content may be performed by one of these other devices, systems, or entities, which then makes it available to the client device (or such receiver) for presentation or other such use. As an example, Fig. 6 illustrates an exemplary network configuration 600 that can be used to provide, generate, modify, encode, process, and / or transmit image data or other such content. In at least one embodiment, a client device 602 can generate or receive data for a session using components of a control application 604 on the client device 602 and data stored locally on that client device. In at least one embodiment, a content application 624 running on a server 620 (e.g., a cloud server or edge server) can initiate a session associated with at least one client device 602, using a session manager and user data stored in a user database 636, and can cause content, such as one or more digital assets (e.g., images, videos, etc.), to be transmitted to the client device 602.Object representations) from an asset repository 634 are determined by a content manager 626. A content manager 626 can work with an image synthesis module 628 to generate or synthesize new objects, digital assets, or other such content for presentation via the client device 602. In at least one embodiment, this image synthesis module 628 can use one or more neural networks, or machine learning models, which can be trained or updated using a training module 632 or system located on or communicating with the server 620.This may include training and / or using a diffusion model 630 to generate content tiles that can be used by an image synthesis module 628 to, for example, apply a non-repeating texture to a region of an environment for which image or video data is presented via a client device 602. At least some of the generated content can be transmitted to the client device 602 using a suitable transmission manager 622 for sending via download, streaming, or another such transmission channel. An encoder may be used to encode and / or compress at least some of this data before it is transmitted to the client device 602.In at least one embodiment, the client device 602, which receives such content, can provide this content to a corresponding control application 604, which may also or alternatively include a graphical user interface 610, a content manager 612, and an image synthesis or diffusion module 614 for use in providing, synthesizing, modifying, or using content for presentation (or other purposes) on or by the client device 602. A decoder can also be used to decode data received over the network(s) 640 for presentation via the client device 602, such as image or video content through a display 606 and audio, such as sounds and music, through at least one audio playback device 608, such as a loudspeaker or headphones.In at least one embodiment, at least part of this content may already be stored, rendered, or accessible on the client device 602, so that transmission over the network 640 is not necessary for at least this part of the content, for example, if this content may have previously been downloaded or stored locally on a hard disk or optical disc. In at least one embodiment, a transmission mechanism, such as data streaming, may be used to transfer this content from the server 620 or the user database 636 to the client device 602. In at least one embodiment, part of this content may be obtained, enhanced, and / or streamed from another source, such as a third-party service 660 or another client device 650, which may also include a content application 662 for generating, enhancing, or providing content.In at least one embodiment, parts of this functionality can be performed using multiple computing devices, or multiple processors within one or more computing devices, which may, for example, include a combination of CPUs and GPUs. In this example, these client devices can include any suitable computing devices, such as a desktop computer, a laptop, a set-top box, a streaming device, a game console, a smartphone, a tablet, a VR headset, AR glasses, a wearable computer, or a smart TV. Each client device can send a request over at least one wired or wireless network, which can include, for example, the internet, Ethernet, a local area network (LAN), or a cellular network, among other such options. In this example, these requests can be sent to an address associated with a cloud provider, which can operate or control one or more electronic resources in a cloud provider environment, such as a data center or server farm.In at least one embodiment, the request can be received or processed by at least one edge server located at a network edge and outside of at least one security layer associated with the cloud provider environment. This reduces latency by allowing client devices to interact with servers located in close proximity, while simultaneously improving the security of resources within the cloud provider environment. In at least one embodiment, such a system can be used to perform graphic rendering operations. In other embodiments, such a system can be used for other purposes, such as providing image or video content for testing or validating autonomous machine applications or for performing deep learning operations. In at least one embodiment, such a system can be implemented using an edge device or involve one or more virtual machines (VMs). In at least one embodiment, such a system can be implemented at least partially in a data center or at least partially using cloud computing resources. INFERENCE AND TRAINING LOGIC Fig. 7A illustrates inference and / or training logic 715, which is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Fig. 7A and / or 7B. In at least one embodiment, the inference and / or training logic 715 may, without limitation, include a code and / or data storage device 701 to store forward and / or output weights and / or input / output data and / or other parameters for configuring neurons or layers of a neural network that is trained and / or used for inferencing in aspects by one or more embodiments. In at least one embodiment, the training logic 715 may include or be coupled to a code and / or data storage device 701 to store graph code or other software for controlling the timing and / or sequencing, into which weight and / or other parameter information for configuring the logic, including integer and / or floating-point units (collectively, arithmetic logic units, or ALUs), is loaded.In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture from a neural network to which the code corresponds. In at least one embodiment, the code and / or data storage 701 stores weight parameters and / or input / output data from each layer of a neural network that is trained or used in conjunction with one or more embodiments during the forward propagation of input / output data and / or weight parameters during training and / or inference using aspects of one or more embodiments. In at least one embodiment, any part of the code and / or data storage 701 can be enclosed with other on-chip or off-chip data storage, including processor L1, L2, or L3 cache or system memory. In at least one embodiment, any part of the code and / or data storage 701 can be located internally or externally of one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 701 can be a cache memory, dynamic randomly addressable memory (“DRAM”), static randomly addressable memory (“SRAM”), non-volatile memory (e.g., flash memory), or other storage.In at least one embodiment, the choice of whether the code and / or data storage 701 is, for example, internal or external to a processor or consists of a DRAM, SRAM, Flash or other storage type, may depend on the available on-chip versus off-chip storage, the latency requirements of training and / or inference functions performed, the batch size of data used in inferencing and / or training a neural network, or a combination of these factors. In at least one embodiment, the inference and / or training logic 715 may, without limitation, include a code and / or data storage 705 to store backward and / or output weights and / or input / output data corresponding to neurons or layers of a neural network that is trained and / or used for inference in aspects of one or more embodiments. In at least one embodiment, the code and / or data storage 705 stores weight parameters and / or input / output data from each layer of a neural network that is trained or used in conjunction with one or more embodiments during backward propagation of input / output data and / or weight parameters during training and / or inference using aspects of one or more embodiments.In at least one embodiment, the training logic 715 can include or be coupled to a code and / or data storage 705 to store graph code or other software for controlling timing and / or sequencing, into which weight and / or other parameter information for configuring the logic, including integer and / or floating-point units (collectively, arithmetic logic units, or ALUs), is loaded. In at least one embodiment, code, such as graph code, loads weight or other parameter information into processor ALUs based on an architecture of a neural network to which the code corresponds. In at least one embodiment, any part of the code and / or data storage 705 can be included with other on-chip or off-chip data storage, including processor L1, L2, or L3 cache or system memory.In at least one embodiment, any part of the code and / or data storage 705 can be located internally or externally of one or more processors or other hardware logic devices or circuits. In at least one embodiment, the code and / or data storage 705 can be cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or another type of storage. In at least one embodiment, the choice of whether the code and / or data storage 705 is located internally or externally of a processor, or consists of a DRAM, SRAM, flash, or other type of storage, can depend on the available on-chip versus off-chip storage, the latency requirements of training and / or inference functions performed, the batch size of data used in inferencing and / or training a neural network, or a combination of these factors. In at least one embodiment, the code and / or data storage 701 and the code and / or data storage 705 can be separate storage structures. In at least one embodiment, the code and / or data storage 701 and the code and / or data storage 705 can be the same storage structure. In at least one embodiment, the code and / or data storage 701 and the code and / or data storage 705 can be partly the same storage structure and partly separate storage structures. In at least one embodiment, any part of the code and / or data storage 701 and the code and / or data storage 705 can be included with other on-chip or off-chip data storage, including processor L1, L2, or L3 cache or system memory. In at least one embodiment, the inference and / or training logic 715 may, without limitation, include one or more arithmetic logic unit(s) (“ALU(s)”) 710, including integer and / or floating-point units, for performing logical and / or mathematical operations at least partly based on or specified by a training and / or inference code (e.g., graph code), wherein a result thereof may produce activations (e.g., output values ​​of layers or neurons within a neural network), stored in an activation memory 720, which are functions of input / output and / or weight parameter data, stored in the code and / or data memory 701 and / or the code and / or data memory 705.In at least one embodiment, activations stored in the activation memory 720 are generated according to linear algebraic and / or matrix-based mathematics, performed by ALU(s) 710 in response to the execution of instructions or other code, wherein weight values ​​stored in the code and / or data memory 705 and / or the code and / or data memory 701 are used as operands together with other values, such as bias values, gradient information, momentum values ​​or other parameters or hyperparameters, any or all of which may be stored in the code and / or data memory 705 or the code and / or data memory 701 or any other on- or off-chip memory. In at least one embodiment, ALU(s) 710 are enclosed within one or more processors or other hardware logic devices or circuits, while in another embodiment, ALU(s) 710 may be external to a processor or other hardware logic device or circuit that uses them (e.g., a coprocessor). In at least one embodiment, ALU(s) 710 may be enclosed within execution units of a processor or otherwise within a series of ALUs accessible by execution units of a processor, either in the same processor or distributed among different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.).In at least one embodiment, the code and / or data storage 701, the code and / or data storage 705, and the activation storage 720 can be located on the same processor or other hardware logic device or circuit, while in another embodiment they can be located on different processors or other hardware logic devices or circuits, or a combination of the same and different processors or other hardware logic devices or circuits. In at least one embodiment, any part of the activation storage 720 can be included with other on-chip or off-chip data storage, including processor L1, L2, or L3 cache or system memory.Furthermore, inference and / or training code can be stored with other code accessible to a processor or other hardware logic or circuitry and retrieved and / or processed using fetch, decode, scheduling, execution, sleep, and / or other logic circuitry of a processor. In at least one embodiment, the activation memory 720 can be a cache memory, DRAM, SRAM, non-volatile memory (e.g., flash memory), or other storage. In at least one embodiment, the activation memory 720 can be located wholly or partially inside or outside one or more processors or other logic circuits. In at least one embodiment, the choice of whether the activation memory 720 is located, for example, internally or externally from a processor or consists of a DRAM, SRAM, flash, or other storage type, can depend on the available on-chip versus off-chip storage, the latency requirements of training and / or inference functions performed, the batch size of data used in inferencing and / or training a neural network, or a combination of these factors. In at least one embodiment, the activation memory shown in Fig.Figure 7A illustrates the inference and / or training logic 715 in conjunction with an application-specific integrated circuit (“ASIC”), such as Google’s TensorFlow® Processing Unit, a Graphcore™ Inference Processing Unit (IPU), or an Intel Corp. Nervana® processor (e.g., “LakeCrest” processor). In at least one embodiment, the inference and / or training logic 715 illustrated in Figure 7A can be used in conjunction with central processing unit (“CPU” hardware), graphics processing unit (“GPU” hardware), or other hardware, such as field-programmable gate arrays (“FPGAs”). Fig. 7B illustrates inference and / or training logic 715 according to at least one or more embodiments. In at least one embodiment, the inference and / or training logic 715 may include, among other things, hardware logic in which computing resources are dedicated or otherwise used exclusively in conjunction with weight values ​​or other information corresponding to one or more layers of neurons within a neural network. In at least one embodiment, the inference and / or training logic 715 illustrated in Fig. 7B may be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Google’s TensorFlow® Processing Unit, a Graphcore™ Inference Processing Unit (IPU), or an Intel Corp. Nervana® processor (e.g., “LakeCrest” processor). In at least one embodiment, the inference and / or training logic 715 illustrated in Fig.Figure 7B illustrates the use of inference and / or training logic 715 in conjunction with central processing unit hardware (“CPU” hardware), graphics processing unit hardware (“GPU” hardware), or other hardware, such as field programmable gate arrays (“FPGAs”). In at least one embodiment, the inference and / or training logic 715 includes, without limitation, code and / or data storage 701 and code and / or data storage 705, which can be used to store code (e.g., graph code), weight values, and / or other information, including bias values, gradient information, momentum values, and / or other parameter or hyperparameter information. In at least one embodiment illustrated in Fig. 7B, the code and / or data storage 701 and the code and / or data storage 705 are each connected to a dedicated computer resource, such as computer hardware 702 or 705 respectively.Computer hardware 706, associated. In at least one embodiment, the computer hardware 702 and the computer hardware 706 each comprise one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in the code and / or data storage 701 or the code and / or data storage 705, respectively, wherein the results thereof are stored in the activation storage 720. In at least one embodiment, the code and / or data storage 701 and 705, respectively, and the corresponding computer hardware 702 and 706, respectively, correspond to different layers of a neural network, such that a resulting activation from a "storage / computer pair 701 / 702" of the code and / or data storage 701 and computer hardware 702 is provided as input to the "storage / computer pair 705 / 706" of the code and / or data storage 705 and computer hardware 706, in order to reflect the conceptual organization of a neural network. In at least one embodiment, each of the storage / computer pairs 701 / 702 and 705 / 706 can correspond to more than one neural network layer. In at least one embodiment, additional storage / computer pairs (not shown) can be included after or in parallel to the storage / computer pairs 701 / 702 and 705 / 706 in the inference and / or training logic 715. DATA CENTER Fig. 8 illustrates an exemplary data center 800, in which at least one embodiment can be used. In at least one embodiment, the data center 800 includes a data center infrastructure layer 810, a framework layer 820, a software layer 830, and an application layer 840. In at least one embodiment, as shown in Fig. 8, the data center infrastructure layer 810 can include a resource orchestrator 812, clustered compute resources 814, and node compute resources (“node CRs”) 816(1)-816(N), where “N” is a positive integer. In at least one embodiment, node CRs 816(1)-816(N) can include, but are not limited to, any number of central processing units (CPUs) or other processors (including accelerators, in-field programmable gate arrays (FPGAs), graphics processing units, etc.), memory devices (e.g., dynamic read-only memories), storage devices (e.g., solid-state or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules, and cooling modules, etc. In at least one embodiment, one or more node CRs can be controlled by the node CRs.s 816(1)-816(N) be a server that has one or more of the computing resources mentioned above. In at least one embodiment, grouped compute resources 814 can include separate groupings of node CRs located in one or more racks (not shown), or many racks located in data centers at different geographic locations (also not shown). Separate groupings of node CRs within grouped compute resources 814 can include grouped compute, network, storage, or memory resources that can be configured or allocated to support one or more workloads. In at least one embodiment, multiple node CRs, including CPUs or processors, can be grouped in one or more racks to provide compute resources to support one or more workloads.In at least one embodiment, one or more racks can also include any number of power modules, cooling modules, and network switches in any combination. In at least one embodiment, the resource orchestrator 812 can configure or otherwise control one or more node CRs 816(1)-816(N) and / or grouped computing resources 814. In at least one embodiment, the resource orchestrator 812 can include a software design infrastructure (SDI) management entity for the data center 800. In at least one embodiment, the resource orchestrator 812 can include hardware, software, or a combination thereof. In at least one embodiment, as shown in Fig. 8, a framework layer 820 includes a job scheduler 822, a configuration manager 824, a resource manager 826, and a distributed file system 828. In at least one embodiment, the framework layer 820 can include a framework to support software 832 of software layer 830 and / or one or more application(s) 842 of application layer 840. In at least one embodiment, the software 832 or application(s) 842 can each include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure.In at least one embodiment, the framework layer 820 can be, but is not limited to, a type of free and open-source software web application framework, such as Apache Spark™ (hereinafter "Spark"), which can utilize the distributed file system 828 for large-scale data processing (e.g., "big data"). In at least one embodiment, the job scheduler 822 can include a Spark driver to facilitate the scheduling of workloads supported by various layers of the data center 800. In at least one embodiment, the configuration manager 824 can be capable of configuring various layers, such as the software layer 830 and the framework layer 820, including Spark and the distributed file system 828, to support large-scale data processing.In at least one embodiment, the resource manager 826 is capable of managing clustered or grouped computing resources that are allocated or assigned to support the distributed file system 828 and the job scheduler 822. In at least one embodiment, the clustered or grouped computing resources can include a grouped computing resource 814 at the data center infrastructure layer 810. In at least one embodiment, the resource manager 826 can coordinate with the resource orchestrator 812 to manage these allocated or assigned computing resources. In at least one embodiment, software 832, which is enclosed in software layer 830, can include software used by at least parts of the node CRs 816(1)-816(N), grouped computing resources 814, and / or distributed file system 828 of framework layer 820. The one or more types of software can include, but are not limited to, internet web page search software, email virus scanning software, database software, and streaming video content software. In at least one embodiment, one or more application(s) 842 enclosed in the application layer 840 may include one or more types of applications used by at least parts of the node CRs 816(1)-816(N), grouped compute resources 814, and / or distributed file system 828 of the framework layer 820. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive computation application, and a machine learning application, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), or other machine learning applications used in conjunction with one or more embodiments. In at least one embodiment, any configuration manager 824, resource manager 826, and resource orchestrator 812 can implement any number and any type of self-modifying operations based on any set and any type of data acquired in any technically feasible manner. In at least one embodiment, self-modifying operations can relieve a data center operator of the data center 800 of potentially making poor configuration decisions and potentially avoiding underutilized and / or poorly performing parts of a data center. In at least one embodiment, the Data Center 800 may include tools, services, software, and other resources for training one or more machine learning models or for predicting or inferring information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources as described above in relation to the Data Center 800.In at least one embodiment, trained machine learning models corresponding to one or more neural networks can be used to infer and predict information regarding Data Center 800 using the resources described above, by using weight parameters calculated via one or more training techniques described herein. In at least one embodiment, the data center can use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, or other hardware to perform training and / or inference using the resources described above. Additionally, one or more of the software and / or hardware resources described above can be configured as a service to allow users to train or perform information inference, such as image recognition, speech recognition, or other artificial intelligence services. The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Figures 7A and / or 7B. In at least one embodiment, the inference and / or training logic 715 can be used in the system of Figure 8 to infer or predict operations at least partially based on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein. Such components can be used for data synchronization. COMPUTER SYSTEMS Fig. 9 is a block diagram illustrating an exemplary computer system, which may be a system with interconnected devices and components, a system-on-a-chip (SoC), or any other combination thereof 900, formed with a processor, which may include execution units for carrying out an instruction, according to at least one embodiment. In at least one embodiment, the computer system 900 may, without limitation, include a component, such as a processor 902, to employ execution units, including logic for carrying out algorithms on process data, according to the present disclosure, such as in the embodiment described herein.In at least one embodiment, the Computer System 900 may include processors such as the PENTIUM® processor family, Xeon™, Itanium®, XScale™ and / or StrongARM™, Intel® Core™ or Intel® Nervana™ microprocessors, available from Intel Corporation, Santa Clara, California, although other systems (including PCs with other microprocessors, engineering workstations, set-top boxes and the like) may also be used. In at least one embodiment, the Computer System 900 may run a version of a WINDOWS operating system available from Microsoft Corporation, Redmond, Washington, although other operating systems (for example, UNIX and Linux), embedded software and / or graphical user interfaces may also be used. Embodiments can be used in other devices, such as handheld devices and embedded applications. Some examples of handheld devices include mobile phones, Internet Protocol devices, digital cameras, personal digital assistants (PDAs), and handheld PCs. In at least one embodiment, embedded applications can include a microcontroller, a digital signal processor (DSP), a system-on-a-chip, network computers (NetPCs), set-top boxes, network hubs, wide area network (WAN) switches, or any other system capable of executing one or more instructions according to at least one embodiment. In at least one embodiment, the computer system 900 may, without limitation, include the processor 902, which may, without limitation, include one or more execution units 908 for performing machine learning model training and / or inference according to the techniques described herein. In at least one embodiment, the computer system 900 is a single-processor desktop or server system, but in another embodiment, the computer system 900 may be a multiprocessor system. In at least one embodiment, the processor 902 may, without limitation, include a complex instruction set computing (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) computing microprocessor, a processor implementing a combination of instruction sets, or another processing device, such as a digital signal processor.In at least one embodiment, the processor 902 can be coupled to a processor bus 910, which transmits data signals between the processor 902 and other components in the computer system 900. In at least one embodiment, the processor 902 can, without limitation, include an internal Level 1 ("L1") cache memory ("Cache") 904. In at least one embodiment, the processor 902 can have a single internal cache or multiple levels of an internal cache. In at least one embodiment, the cache memory can be located external to the processor 902. Other embodiments can also include a combination of both internal and external caches, depending on the specific implementation and requirements. In at least one embodiment, a register file 906 can store different types of data in different registers, including, without limitation, integer registers, floating-point registers, status registers, and instruction pointer registers. In at least one embodiment, the execution unit 908, including, without limitation, logic for performing integer and floating-point operations, is also contained in processor 902. In at least one embodiment, the processor 902 may also include a microcode ("ucode") read-only memory ("ROM") that stores microcode for certain macro instructions. In at least one embodiment, the execution unit 908 may include logic for handling a packaged instruction set 909. In one embodiment, by including a packaged instruction set 909 in an instruction set of a general-purpose processor 902, together with associated switching technology for executing instructions, operations used by many multimedia applications can be performed using packaged data in a general-purpose processor 902.In one or more embodiments, many multimedia applications can be accelerated and run more efficiently by using the full width of a processor data bus to perform operations on packaged data, thereby eliminating the need to transfer smaller units of data across the processor data bus to perform one or more operations on a single data element at a time. In at least one embodiment, the execution unit 908 can also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, the computer system 900 can include, without limitation, a memory 920. In at least one embodiment, the memory 920 can be implemented as a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, a flash memory device, or another storage device. In at least one embodiment, the memory 920 can store instruction(s) 919 and / or data 921, represented by data signals that can be executed by the processor 902. In at least one embodiment, the system logic chip can be coupled to the processor bus 910 and the memory 920. In at least one embodiment, the system logic chip can, without limitation, include a memory controller hub (MCH) 916, and the processor 902 can communicate with the MCH 916 via the processor bus 910. In at least one embodiment, the MCH 916 can provide a high-bandwidth memory path 918 to the memory 920 for instruction and data storage and for storing graphics instructions, data, and textures. In at least one embodiment, the MCH 916 can route data signals between the processor 902, the memory 920, and other components in the computer system 900, and for bridging data signals between the processor bus 910, the memory 920, and a system I / O 922. In at least one embodiment, the system logic chip can provide a graphics port for coupling with a graphics controller.In at least one embodiment, the MCH 916 can be coupled to the memory 920 via a high-bandwidth memory path 918, and the graphics / video card 912 can be coupled to the MCH 916 via an Accelerated Graphics Port (“AGP”) intermediate connection 914. In at least one embodiment, the computer system 900 can use the system I / O 922, which is a proprietary hub interface bus, to couple the MCH 916 to the I / O controller hub (“ICH”) 930. In at least one embodiment, the ICH 930 can provide direct connections to some I / O devices via a local I / O bus. In at least one embodiment, the local I / O bus can, without limitation, include a high-speed I / O bus for connecting peripheral devices to the memory 920, chipset, and processor 902. Examples may include, without limitation, an audio controller 929, a firmware hub (“Flash BIOS”) 928, a wireless transceiver 926, a data storage device 924, a legacy I / O controller 923 containing user input and keyboard interfaces 925, a serial expansion port 927 such as Universal Serial Bus (“USB”), and a network controller 934.Data storage 924 can include a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage devices. In at least one embodiment, Fig. 9 illustrates a system that includes interconnected hardware devices or “chips,” while in other embodiments, Fig. 9 may illustrate an exemplary system-on-a-chip (“SoC”). In at least one embodiment, devices can be interconnected using proprietary interconnects, standardized interconnects (e.g., PCIe), or a combination thereof. In at least one embodiment, one or more components of the Computer System 900 are interconnected using Compute Express Link (CXL) interconnects. The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Figures 7A and / or 7B. In at least one embodiment, the inference and / or training logic 715 can be used in the system of Figure 9 to infer or predict operations at least partially based on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein. Such components can be used for data synchronization. Fig. 10 is a block diagram illustrating an electronic device 1000 for using a processor 1010 according to at least one embodiment. In at least one embodiment, the electronic device 1000 can be, for example, and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop computer, a tablet, a mobile device, a telephone, an embedded computer, or any other suitable electronic device. In at least one embodiment, the system 1000 can, without limitation, include the processor 1010, which is communicatively coupled to any suitable number or type of components, peripherals, modules, or devices. In at least one embodiment, the processor 1010 is coupled using a bus or interface such as a 1°C bus, a system management bus (SMBus), a low-pin-count (LPC) bus, a serial peripheral interface (SPI), a high-definition audio (HDA) bus, a serial advance technology attachment (SATA) bus, a universal serial bus (USB) (versions 1, 2, 3), or a universal asynchronous receiver / transmitter (UART) bus. In at least one embodiment, Fig. 10 illustrates a system that includes interconnected hardware devices or “chips”, while in other embodiments, Fig.Figure 10 may illustrate an exemplary system-on-a-chip (“SoC”). In at least one embodiment, the devices illustrated in Figure 10 can be interconnected using proprietary interconnects, standardized interconnects (e.g., PCIe), or a combination thereof. In at least one embodiment, one or more components of Figure 10 are interconnected using Compute Express Link (CXL) interconnects. In at least one embodiment, Fig. 10 can show a display 1024, a touchscreen 1025, a touchpad 1030, a near field communication (“NFC”) 1045, a sensor hub 1040, a thermal sensor 1046, an Express chipset (“EC”) 1035, a Trusted Platform Module (“TPM”) 1038, a BIOS / firmware / flash memory (“BIOS, FW Flash”) 1022, a DSP 1060, a drive 1020 such as a solid-state drive (“SSD”) or a hard disk drive (“HDD”), a wireless local area network (“WLAN”) 1050, a Bluetooth unit 1052, a wireless wide area network (“WWAN”) 1056, a global positioning system (GPS) 1055, a camera (“USB 3.0 camera”) 1054 such as a Include a USB 3.0 camera and / or a Low-Power Double Data Rate ("LPDDR") memory unit ("LPDDR3") 1015, for example implemented in the LPDDR3 standard. These components can each be implemented in any suitable manner. In at least one embodiment, other components can be communicatively coupled to the processor 1010 via the components discussed above. In at least one embodiment, an accelerometer 1041, an ambient light sensor (ALS) 1042, a compass 1043, and a gyroscope 1044 can be communicatively coupled to the sensor hub 1040. In at least one embodiment, a thermal sensor 1039, a fan 1037, a keyboard 1036, and a touchpad 1030 can be communicatively coupled to the EC 1035. In at least one embodiment, a loudspeaker 1063, headphones 1064, and a microphone (“Mic”) 1065 can be communicatively coupled to an audio unit (“audio codec and Class-D amplifier”) 1062, which in turn can be communicatively coupled to the DSP 1060. In at least one embodiment, the audio unit 1062 can, for example and without limitation, include an audio encoder / decoder (“codec”) and a Class-D amplifier.In at least one embodiment, the SIM card (“SIM”) 1057 can be communicatively coupled with the WWAN unit 1056. In at least one embodiment, components such as the WLAN unit 1050 and Bluetooth unit 1052, as well as the WWAN unit 1056, can be implemented in a next-generation form factor (“NGFF”). The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Figures 7A and / or 7B. In at least one embodiment, the inference and / or training logic 715 can be used in the system of Figure 10 to infer or predict operations at least partially based on weight parameters computed using neural network training operations, neural network functions and / or architectures, or neural network use cases as described herein. Such components can be used for data synchronization. Fig. 11 is a block diagram of a processing system according to at least one embodiment. In at least one embodiment, the system 1100 includes one or more processor(s) 1102 and one or more graphics processor(s) 1108 and can be a single-processor desktop system, a multi-processor workstation system, or a server system with a large number of processors 1102 or processor cores 1107. In at least one embodiment, the system 1100 is a processing platform included in a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices. In at least one embodiment, the system 1100 can include or be contained within a server-based gaming platform, a gaming console (including a gaming and media console, a mobile gaming console, a handheld gaming console, or an online gaming console), or a mobile phone, a smartphone, a tablet computer, or a mobile internet device. In at least one embodiment, the system 1100 can also include, be coupled to, or be integrated with a wearable device, such as a wearable smartwatch, smart glasses, augmented reality, or virtual reality device.In at least one embodiment, the processing system 1100 is a television set or a set-top box device with one or more processor(s) 1102 and a graphical interface generated by one or more graphics processor(s) 1108. In at least one embodiment, the one or more processor(s) 1102 each include one or more processor core(s) 1107 for processing instructions that, when executed, perform operations for system and user software. In at least one embodiment, the one and the multiple processor core(s) 1107 are each configured to process a specific instruction set 1109. In at least one embodiment, the instruction set 1109 can facilitate complex instruction set computing (CISC), reduced instruction set computing (RISC), or very-long instruction word (VLIW) computing. In at least one embodiment, the processor core(s) 1107 can each process a different instruction set 1109, which may include instructions for facilitating the emulation of other instruction sets.In at least one embodiment, the processor core(s) 1107 may also include other processing devices, such as a digital signal processor (DSP). In at least one embodiment, the processor(s) 1102 can include a cache memory 1104. In at least one embodiment, the processor(s) 1102 can have a single internal cache or multiple levels of an internal cache. In at least one embodiment, the cache memory is shared by different components of the processor(s) 1102. In at least one embodiment, the processor(s) 1102 also use an external cache (e.g., a Level 3 (L3) cache or Last-Level Cache (LLC)) (not shown), which can be shared by processor cores 1107 using known cache coherence techniques. In at least one embodiment, the register file 1106 is additionally included in the processor(s) 1102, which contains different types of registers for storing different types of data (e.g.,The register file may include integer registers, floating-point registers, status registers, and an instruction pointer register. In at least one embodiment, the register file may include 1106 general-purpose registers or other registers. In at least one embodiment, one or more processors 1102 are coupled to one or more interface buses 1110 for transmitting communication signals, such as address, data, or control signals, between the processors 1102 and other components in the system 1100. In at least one embodiment, the interface bus(s) 1110 can be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, the interface bus(s) 1110 is not limited to a DMI bus and can include one or more peripheral component interconnect buses (e.g., PCI, PCI Express), memory buses, or other types of interface buses. In at least one embodiment, the processor(s) 1102 can include an integrated memory controller 1116 and a platform controller hub 1130.In at least one embodiment, the storage controller 1116 facilitates communication between a storage device and other components of the system 1100, while the platform controller hub (PCH) 1130 provides connections to I / O devices via a local I / O bus. In at least one embodiment, the storage device 1120 can be a dynamic random-access memory (DRAM) device, a static random-access memory (SRAM) device, a flash memory device, a phase-change memory device, or another storage device that has suitable performance to serve as process memory. In at least one embodiment, the storage device 1120 can be operated as system memory for the system 1100 to store data 1122 and instructions 1121 for use when one or more processor(s) 1102 are running an application or process. In at least one embodiment, the memory controller 1116 is also coupled with an optional external graphics processor 1112, which can communicate with one or more graphics processor(s) 1108 within the processor(s) 1102 to perform graphics and media operations.In at least one embodiment, a display device 1111 can be connected to the processor(s) 1102. In at least one embodiment, the display device 1111 can include one or more internal displays, such as in a mobile electronic device or a laptop device, or external displays connected via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, the display device 1111 can include a head-mounted display (HMD), such as a stereoscopic display for use in virtual reality (VR) or augmented reality (AR) applications. In at least one embodiment, the platform controller hub 1130 enables peripheral devices to be connected to the storage device 1120 and the processor(s) 1102 via a high-speed I / O bus. In at least one embodiment, I / O peripheral devices include, but are not limited to, an audio controller 1146, a network controller 1134, a firmware interface 1128, a wireless transceiver 1126, touch sensors 1125, and a data storage device 1124 (e.g., hard disk drive, flash memory, etc.). In at least one embodiment, the data storage device 1124 can be connected via a storage interface (e.g., SATA) or via a peripheral bus, such as a peripheral component interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, the touch sensors 1125 can include touchscreen sensors, pressure sensors, or fingerprint sensors.In at least one embodiment, the wireless transceiver 1126 can be a WiFi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long-Term Evolution (LTE) transceiver. In at least one embodiment, the firmware interface 1128 enables communication with the system firmware and can, for example, be a Unified Extensible Firmware Interface (UEFI). In at least one embodiment, the network controller 1134 enables a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) is coupled with an interface bus 1110. In at least one embodiment, the audio controller 1146 is a multi-channel high-definition audio controller. In at least one embodiment, the system 1100 includes an optional legacy I / O controller 1140 for coupling older devices (e.g.,Personal System 2 devices (PS / 2 devices) are integrated with the system. In at least one embodiment, the platform controller hub 1130 can also be connected to one or more Universal Serial Bus (USB) controller 1142 connection input devices, such as keyboard and mouse combinations 1143, a camera 1144, or other USB input devices. In at least one embodiment, an instance of the memory controller 1116 and platform controller hub 1130 can be integrated into a discrete external graphics processor, such as the external graphics processor 1112. In at least one embodiment, the platform controller hub 1130 and / or the memory controller 1116 can be external to one or more processor(s) 1102. For example, in at least one embodiment, the system 1100 can include an external memory controller 1116 and platform controller hub 1130, which can be configured as a memory controller hub and peripheral controller hub within a system chipset that communicates with the processor(s) 1102. The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Figures 7A and / or 7B. In at least one embodiment, parts or all of the inference and / or training logic 715 may be contained in the graphics processing unit 1500. For example, in at least one embodiment, training and / or inference techniques described herein may utilize one or more ALUs contained in a graphics processing unit. Furthermore, in at least one embodiment, inference and / or training operations described herein may be performed using logic that differs from the logic illustrated in Figures 7A and / or 7B.In at least one embodiment, weight parameters can be stored in on-chip or off-chip memories and / or registers (shown or not shown), thereby configuring graphics processor ALUs to perform one or more machine learning algorithms, neural network architectures, use cases or training techniques described herein. Such components can be used for data synchronization. Fig. 12 is a block diagram of a processor 1200 comprising one or more processor core(s) 1202A-1202N, an integrated memory controller 1214, and an integrated graphics processor 1208 according to at least one embodiment. In at least one embodiment, the processor 1200 can include additional cores up to and including additional core 1202N, represented by dashed boxes. In at least one embodiment, the processor core(s) 1202A-1202N each include one or more internal cache units 1204A-1204N. In at least one embodiment, each processor core also has access to one or more shared cache units 1206. In at least one embodiment, the internal cache unit(s) 1204A-1204N and shared cache unit(s) 1206 constitute a cache memory hierarchy within the processor 1200. In at least one embodiment, the cache unit(s) 1204A-1204N can include at least one level of an instruction and data cache within each processor core and one or more levels of a shared middle-level cache, such as Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of a cache, wherein a highest level of a cache prior to external memory is classified as LLC. In at least one embodiment, cache coherence logic maintains coherence between different cache units 1206 and 1204A-1204N. In at least one embodiment, the processor 1200 can also include a set of one or more bus controller units 1216 and a system agent core 1210. In at least one embodiment, one or more bus controller units 1216 manage a set of peripheral buses, such as one or more PCI or PCI Express buses. In at least one embodiment, the system agent core 1210 provides management functionality for various processor components. In at least one embodiment, the system agent core 1210 includes one or more integrated memory controllers 1214 for managing access to various external storage devices (not shown). In at least one embodiment, one or more processor core(s) 1202A-1202N include support for simultaneous multithreading. In at least one embodiment, the system agent core 1210 includes components for coordinating and one processor core(s) 1202A-1202N during multithreading processing. In at least one embodiment, the system agent core 1210 may additionally include a power control unit (PCU) that includes logic and components for regulating one or more power states of processor cores 1202A-1202N and graphics processor 1208. In at least one embodiment, the processor 1200 additionally includes the graphics processor 1208 for performing graphics processing operations. In at least one embodiment, the graphics processor 1208 is coupled with a shared cache unit(s) 1206 and the system agent core 1210, including one or more integrated memory controllers 1214. In at least one embodiment, the system agent core 1210 also includes a display controller 1211 for driving the graphics processor output to one or more coupled displays. In at least one embodiment, the display controller 1211 can also be a separate module that is coupled to the graphics processor 1208 via at least one intermediate connection or can be integrated into the graphics processor 1208. In at least one embodiment, a ring-based interconnect unit 1212 is used to couple internal components of the processor 1200. In at least one embodiment, an alternative interconnect unit can be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, the graphics processor 1208 is coupled to a ring-based interconnect unit 1212 via an I / O link 1213. In at least one embodiment, the I / O link 1213 represents at least one of several types of I / O intermediaries, including an on-package I / O intermediary, which facilitates communication between different processor components and an embedded high-performance memory module 1218, such as an eDRAM module. In at least one embodiment, the processor core(s) 1202A-1202N and the graphics processor 1208 each use embedded memory modules 1218 as a shared last-level cache. In at least one embodiment, the processor core(s) 1202A-1202N are homogeneous cores executing a common instruction set architecture. In at least one embodiment, the processor core(s) 1202A-1202N are heterogeneous with respect to the instruction set architecture (ISA), wherein one or more of the processor core(s) 1202A-1202N execute a common instruction set, while one or more of the other cores of the processor core(s) 1202A-1202N execute a subset of a common instruction set or a different instruction set. In at least one embodiment, the processor core(s) 1202A-1202N are heterogeneous with respect to a microarchitecture, wherein one or more cores exhibiting relatively higher power consumption are coupled with one or more performance cores exhibiting lower power consumption.In at least one embodiment, the 1200 processor can be implemented on one or more chips or as an integrated SoC circuit. The inference and / or training logic 715 is used to perform inference and / or training operations associated with one or more embodiments. Details regarding the inference and / or training logic 715 are provided below in conjunction with Figures 7A and / or 7B. In at least one embodiment, parts of or all of the inference and / or training logic 715 may be contained in the processor 1200. For example, in at least one embodiment, training and / or inference techniques described herein may use one or more ALUs contained in a graphics processor 1208, graphics core(s) 1202A-1202N, or other components shown in Figure 12. Furthermore, in at least one embodiment, inference and / or training operations described herein may be performed using logic different from that illustrated in Figures 7A and / or 7B.In at least one embodiment, weight parameters can be stored in on-chip or off-chip memories and / or registers (shown or not shown), thereby configuring ALUs of a graphics processor 1200 to perform one or more machine learning algorithms, neural network architectures, use cases or training techniques described herein. Such components can be used for data synchronization. VIRTUALIZED COMPUTING PLATFORM Figure 13 is an exemplary data flow diagram for a process 1300 of generating and deploying an image processing and inference pipeline according to at least one embodiment. In at least one embodiment, the process 1300 can be used with imaging devices, processing devices, and / or other types of devices on one or more facilities 1302. The process 1300 can be executed in a training system 1304 and / or a deployment system 1306. In at least one embodiment, the training system 1304 can be used to perform the training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in the deployment system 1306.In at least one embodiment, the deployment system 1306 can be configured to offload processing and computing resources into a distributed computing environment to reduce infrastructure requirements at the facility 1302. In at least one embodiment, one or more applications in a pipeline can use or call services (e.g., inference, visualization, computation, AI, etc.) of the deployment system 1306 during application execution. In at least one embodiment, some applications used in advanced processing and inference pipelines can use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models can be trained at the facility 1302 using data 1308 (such as imaging data) generated at the facility 1302 (and stored on one or more Picture Archiving and Communication System (PACS) servers at the facility 1302), can be trained using imaging or sequencing data 1308 from another facility(ies), or a combination thereof.In at least one embodiment, the training system 1304 can be used to provide applications, services and / or other resources for generating functional, deployable machine learning models for the deployment system 1306. In at least one embodiment, the model register 1324 can be backed up by object storage, which enables the support of version and object metadata. In at least one embodiment, the object storage can be accessible, for example, via cloud storage in a cloud platform compatible with an application programming interface (API). In at least one embodiment, machine learning models within the model register 1324 can be uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API can provide access to procedures that allow users with appropriate permissions to associate models with applications, enabling models to be executed as part of the execution of containerized application instantiations. In at least one embodiment, the training system 1304 (Fig. 13) can include a scenario in which a facility 1302 trains its own machine learning model or has / have an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data 1308 generated by an imaging device(s), sequencing devices, and / or other types of devices can be received. In at least one embodiment, after imaging data 1308 has been received, AI-assisted annotation 1310 can be used to assist in generating annotations in accordance with the imaging data 1308, which are to be used as ground truth data for a machine learning model. In at least one embodiment, AI-assisted annotation 1310 can be used to support one or more machine learning models (e.g.,This includes convolutional neural networks (CNNs) that can be trained to generate annotations according to certain types of imaging data 1308 (e.g., from certain devices). In at least one embodiment, AI-assisted annotation 1310 can then be used directly or adapted or fine-tuned using an annotation tool to generate ground-truth data. In at least one embodiment, AI-assisted annotation 1310, labeled data 1312, or a combination thereof can be used as ground-truth data to train a machine learning model. In at least one embodiment, a trained machine learning model can be designated as output model(s) 1316 and used by the deployment system 1306 as described herein. In at least one embodiment, a training pipeline can include a scenario in which a facility 1302 requires a machine learning model for use in performing one or more processing tasks for one or more applications in the deployment system 1306, but the facility 1302 may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, an existing machine learning model can be selected from a model register 1324. In at least one embodiment, the model register 1324 can contain machine learning models that are trained to perform a range of different inference tasks on imaging data.In at least one embodiment, machine learning models in model register 1324 may have been trained on imaging data from facilities that differ from facility 1302 (e.g., remote facilities). In at least one embodiment, machine learning models may have been trained on imaging data from one location, two locations, or any number of locations. In at least one embodiment, if training is performed on imaging data from a specific location, the training may take place at that location or at least be carried out in a manner that protects the confidentiality of imaging data or restricts the transfer of imaging data outside the business premises. In at least one embodiment, after a model has been trained—or partially trained—at a location, a machine learning model may be added to model register 1324.In at least one embodiment, a machine learning model can then be retrained or updated on any number of other devices, and a retrained or updated model can be made available in the model register 1324. In at least one embodiment, a machine learning model can then be selected from the model register 1324—and designated as output model(s) 1316—and can then be used in the deployment system 1306 to perform one or more processing tasks for one or more applications of a deployment system. In at least one embodiment, a scenario may include a facility 1302 that requires a machine learning model for use in performing one or more processing tasks for one or more applications in the deployment system 1306, but the facility 1302 may not currently have such a machine learning model (or may not have a model that is optimized, efficient, or effective for such purposes). In at least one embodiment, a machine learning model selected from the model register 1324 may not be fine-tuned or optimized for imaging data 1308 generated at the facility 1302 due to differences in populations, robustness of training data used to train a machine learning model, diversity of irregularities in training data, and / or other problems with training data.In at least one embodiment, AI-assisted annotation 1310 can be used to support the generation of annotations corresponding to the imaging data 1308, which are to be used as ground-truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data 1312 can be used as ground-truth data for training a machine learning model. In at least one embodiment, the retraining or updating of a machine learning model can be referred to as model training 1314. In at least one embodiment, model training 1314—e.g., AI-assisted annotation 1310, labeled data 1312, or a combination thereof—can be used as ground-truth data for retraining or updating a machine learning model.In at least one embodiment, a trained machine learning model can be designated as output model(s) 1316 and used by the deployment system 1306 as described herein. In at least one embodiment, the deployment system 1306 can include software 1318, services 1320, hardware 1322, and / or other components, features, and functionality. In at least one embodiment, the deployment system 1306 can include a software "stack" such that software 1318 can be built on top of services 1320 and use services 1320 to perform some or all of the processing tasks, and services 1320 and software 1318 can be built on top of hardware 1322 and use hardware 1322 to perform processing, storage, and / or other computing tasks of the deployment system 1306. In at least one embodiment, the software 1318 can include any number of distinct containers, each container capable of instantiating an application.In at least one embodiment, each application can perform one or more processing tasks in an advanced processing and inference pipeline (e.g., inference, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inference pipeline can be defined based on selections of different containers that are desired or required for processing imaging data 1308, in addition to containers that receive and configure imaging data for use by each container and / or for use by a device 1302 after processing by a pipeline (e.g., to convert outputs back into a usable data type). In at least one embodiment, a combination of containers within the software 1318 (e.g.,which constitute a pipeline) are referred to as a virtual instrument (as described in more detail herein), and a virtual instrument can use services 1320 and hardware 1322 to perform some or all of the processing tasks of applications instantiated in containers. In at least one embodiment, a data processing pipeline can receive input data (e.g., imaging data 1308) in a specific format in response to an inference request (e.g., a request from a user of a deployment system 1306). In at least one embodiment, input data can represent one or more images, video, and / or other data representations generated by one or more imaging devices. In at least one embodiment, data can undergo preprocessing as part of the data processing pipeline to prepare the data for processing by one or more applications.In at least one embodiment, post-processing can be performed on an output from one or more inference tasks or other processing tasks of a pipeline to prepare output data for a subsequent application and / or to prepare output data for transmission and / or use by a user (e.g., in response to an inference request). In at least one embodiment, inference tasks can be performed by one or more machine learning models, such as trained or deployed neural networks, which may include an output model(s) 1316 of the training system 1304. In at least one embodiment, tasks of the data processing pipeline can be encapsulated in a container(s), each representing a discrete, fully functional instantiation of an application and virtualized computing environment capable of referencing machine learning models. In at least one embodiment, containers or applications can be published in a private area (e.g., a restricted access area) from a container register (described in more detail herein), and trained or deployed models can be stored in model register 1324 and associated with one or more applications. In at least one embodiment, images of applications (e.g.,Container images) are available in a container registry, and after being selected by a user from a container registry for use in a pipeline, an image can be used to generate a container for instantiating an application for use by a user system. In at least one embodiment, developers (e.g., software developers, clinicians, physicians, etc.) can develop, publish, and store applications (e.g., as containers) for performing image processing and / or inference on supplied data. In at least one embodiment, the development, publication, and / or storage can be performed using a software development kit (SDK) associated with a system (e.g., to ensure that a developed application and / or container is consistent with or compatible with a system). In at least one embodiment, an application being developed can be tested locally (e.g., on a first facility, on data from a first facility) as a system (e.g., System 1200 of Fig. 12) using an SDK that can support at least some services.In at least one embodiment, because DICOM objects can contain between one and hundreds of images or other data types, and due to variations in the data, a developer may be responsible for managing (e.g., defining constructs, incorporating preprocessing into an application, etc.) the extraction and preparation of incoming data. In at least one embodiment, an application, after being validated by a System 1300 (e.g., for accuracy), may be available in a container register for selection and / or implementation by a user to perform one or more data processing tasks at a user's facility (e.g., a second facility). In at least one embodiment, developers can then share applications or containers over a network for access and use by users of a system (e.g., System 1300 of Fig. 13). In at least one embodiment, completed and validated applications or containers can be stored in a container register, and associated machine learning models can be stored in the model register 1324. In at least one embodiment, a requesting entity—providing an inference or image processing request—can search a container register and / or model register 1324 for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in a data processing pipeline, and send an image processing request.In at least one embodiment, a request can include input data (and in some examples, associated patient data) necessary to execute a request, and / or can include a selection of applications and / or machine learning models to be executed when processing a request. In at least one embodiment, a request can be forwarded to one or more components of the deployment system 1306 (e.g., a cloud) to perform processing in the data processing pipeline. In at least one embodiment, the processing by the deployment system 1306 can include referencing selected elements (e.g., applications, containers, models, etc.) from a container register and / or model register 1324. In at least one embodiment, after results have been generated by a pipeline, results can be sent to a user for reference (e.g.,(for viewing in a viewing application suite running on a local on-premise workstation or endpoint). In at least one embodiment, services 1320 can be used to support the processing or execution of applications or containers in pipelines. In at least one embodiment, services 1320 can include computing services, artificial intelligence (AI) services, visualization services, and / or other service types. In at least one embodiment, services 1320 can provide functionality common to one or more applications in software 1318, so that functionality can be abstracted to a service that can be called or used by applications. In at least one embodiment, functionality provided by services 1320 can run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform 1230 (Fig. 12)).In at least one embodiment, services 1320 can be shared between and by different applications, instead of each application sharing the same functionality offered by services 1320 requiring its own instance of services 1320. In at least one embodiment, services can include an inference server or inference engine that can be used as non-restrictive examples for performing detection or segmentation tasks. In at least one embodiment, a model training service can be included that can provide machine learning model training and / or retraining capabilities. Furthermore, in at least one embodiment, a data augmentation service can be included that can provide GPU-accelerated data (e.g., DICOM, RIS, CIS, REST-compliant, RPC, raw, etc.) extraction, resizing, scaling, and / or other augmentation.In at least one embodiment, a visualization service can be used that can add image rendering effects—such as ray tracing, rasterization, denoising, sharpening, etc.—to enhance realism in two-dimensional (2D) and / or three-dimensional (3D) models. In at least one embodiment, virtual instrument services can be included that provide beamforming, segmentation, inference, imaging, and / or support for other applications within pipelines of virtual instruments. In at least one embodiment, where services 1320 include an AI service (e.g., an inference service), one or more machine learning models can be executed by invoking an inference service (e.g., an inference server) (e.g., as an API call) to execute a machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, if another application includes one or more machine learning models for segmentation tasks, an application can invoke an inference service to execute machine learning models to perform one or more processing operations associated with segmentation tasks.In at least one embodiment, software 1318, which implements an advanced processing and inference pipeline that includes a segmentation application and an anomaly detection application, can be simplified because each application can call the same inference service to perform one or more inference tasks. In at least one embodiment, hardware 1322 can include GPUs, CPUs, graphics cards, an AI / deep learning system (e.g., an AI supercomputer such as NVIDIA's DGX), a cloud platform, or a combination thereof. In at least one embodiment, different types of hardware 1322 can be used to provide efficient, purpose-built support for software 1318 and services 1320 in the deployment system 1306. In at least one embodiment, the use of GPU processing can be implemented for local processing (e.g., at the facility 1302), within an AI / deep learning system, in a cloud system, and / or in other processing components of the deployment system 1306 to improve the efficiency, accuracy, and effectiveness of image processing and generation.In at least one embodiment, software 1318 and / or services 1320 can be optimized for GPU processing with respect to deep learning, machine learning, and / or high-performance computing, as non-limiting examples. In at least one embodiment, at least a portion of the computing environment of the deployment system 1306 and / or the training system 1304 can be run in a data center, in one or more supercomputers or high-performance computing systems, using GPU-optimized software (e.g., a hardware and software combination of NVIDIA's DGX system). In at least one embodiment, hardware 1322 can include any number of GPUs that can be called upon to perform parallel data processing as described herein. In at least one embodiment, a cloud platform can further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks.In at least one embodiment, the cloud platform (e.g., NVIDIA's NGC) can be run using one or more AI / deep learning supercomputers and / or GPU-optimized software (e.g., as deployed on NVIDIA's DGX systems) as a hardware abstraction and scaling platform. In at least one embodiment, the cloud platform can integrate an application container clustering or orchestration system (e.g., Kubernetes) across multiple GPUs to enable seamless scaling and load balancing. Figure 14 shows a system diagram for an exemplary system 1400 for generating and deploying an imaging pipeline according to at least one embodiment. In at least one embodiment, the system 1400 can be used to implement the process 1300 of Figure 13 and / or other processes that include advanced processing and inference pipelines. In at least one embodiment, the system 1400 can include a training system 1304 and a deployment system 1306. In at least one embodiment, the training system 1304 and the deployment system 1306 can be implemented using the software 1318, services 1320, and / or hardware 1322 as described herein. In at least one embodiment, System 1400 (e.g., Training System 1304 and / or Deployment System 1306) can be implemented in a cloud computing environment (e.g., using Cloud 1426). In at least one embodiment, System 1400 can be implemented locally with respect to a healthcare facility or as a combination of cloud and local computing resources. In at least one embodiment, access to APIs in Cloud 1426 can be restricted to authorized users by means of established security measures or protocols. In at least one embodiment, a security protocol can include web tokens that can be signed by an authentication service (e.g., AuthN, AuthZ, Gluecon, etc.) and can include corresponding authorization.In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of System 1400, can be restricted to a set of public IPs that have been audited or authorized for interaction. In at least one embodiment, various components of System 1400 can communicate between themselves and with each other using a range of different network types, including, but not limited to, local area networks (LANs) and / or wide area networks (WANs) via wired and / or wireless communication protocols. In at least one embodiment, communication between devices and components of System 1400 (e.g., for transmitting inference requests, receiving results of inference requests, etc.) can be conducted via a data bus, wireless data protocols (WiFi), wired data protocols (e.g., Ethernet), etc. In at least one embodiment, the training system 1304 can execute training pipelines 1404, similar to those described herein with reference to Fig. 13. In at least one embodiment, training pipelines 1404 can be used when one or more machine learning models in a deployment pipeline 1410 are to be used by the deployment system 1306 to train or retrain one or more (e.g., pre-trained) models and / or to implement one or more of the pre-trained models 1406 (e.g., without the need for retraining or updating). In at least one embodiment, an output model 1316 can be generated as a result of the training pipelines 1404.In at least one embodiment, training pipelines 1404 can include any number of processing steps, such as, but not limited to, the conversion or adaptation of imaging data (or other input data). In at least one embodiment, different training pipelines 1404 can be used for different machine learning models used by the deployment system 1306. In at least one embodiment, a training pipeline 1404, similar to a first example described with reference to Fig. 13, can be used for a first machine learning model; a training pipeline 1404, similar to a second example described with reference to Fig. 13, can be used for a second machine learning model; and a training pipeline 1404, similar to a third example described with reference to Fig. 13, can be used for a third machine learning model.In at least one embodiment, any combination of tasks can be used within a training system 1304, depending on the requirements of each machine learning model. In at least one embodiment, one or more machine learning models can already be trained and ready for use, so that machine learning models may not require processing by the training system 1304 and can be implemented by the deployment system 1306. In at least one embodiment, the output model(s) 1316 and / or pretrained models 1406 may include any type of machine learning model, depending on the implementation or embodiment. In at least one embodiment and without limitation, machine learning models used by the System 1400 may include machine learning model(s) that employ linear regression, logistic regression, decision trees, support vector machines (SVMs), Naive Bayes, k-nearest neighbors (Knn), K-means clustering, random forest, dimensionality reduction algorithms, gradient boosting algorithms, neural networks (e.g., auto-encoders, convolutional, recurrent, perceptrons, long / short-term / memory (LSTM), Hopfield, Boltzmann, deep-belief, deconvolutional, generative adversarial, liquid-state machine, etc.), and / or other types of machine learning models. In at least one embodiment, training pipelines can include AI-assisted annotation, as described in more detail herein, at least with reference to Fig. 14B. In at least one embodiment, labeled data (e.g., traditional annotation) can be generated by a variety of techniques. In at least one embodiment, labels or other annotations can be generated within a drawing program (e.g., an annotation program), a computer-aided design (CAD) program, a labeling program, another type of program suitable for generating annotations or labels for ground truth, and / or, in some examples, drawn by hand. In at least one embodiment, ground truth data can be produced as follows: synthetically (e.g., generated from computer models or renderings), real (e.g., conceived and produced from real-world data), machine-automated (e.g.,using feature analysis and learning to extract features from data and then generate labels), annotated by a human (e.g., a labeler or annotation expert defines the location of the labels), and / or a combination thereof. In at least one embodiment, for each instance of imaging data 1308 (or a type of data used by machine learning models), there can be corresponding ground-truth data generated by the training system 1304. In at least one embodiment, AI-assisted annotation can be performed as part of the deployment pipeline(s) 1410; either in addition to or instead of AI-assisted annotation included in training pipelines 1404. In at least one embodiment, the system 1400 can include a multi-layer platform that includes a software layer (e.g.,Software 1318) for diagnostic applications (or other application types) that can perform one or more medical imaging and diagnostic functions. In at least one embodiment, the System 1400 can be communicatively coupled (e.g., via encrypted links) to PACS server networks of one or more facilities. In at least one embodiment, the System 1400 can be configured to access and reference data from PACS servers to perform operations such as training machine learning models, deploying machine learning models, image processing, inference, and / or other operations. In at least one embodiment, a software layer can be implemented as a secure, encrypted, and / or authenticated API through which applications or containers from external environments (e.g., facility 1302) can be invoked (e.g., called). In at least one embodiment, applications can then call or execute one or more services 1320 to perform computational, AI, or visualization tasks associated with the respective applications, and software 1318 and / or services 1320 can utilize hardware 1322 to perform processing tasks in an effective and efficient manner. In at least one embodiment, communications sent to or received from a training system 1304 and a deployment system 1306 can take place using a pair of DICOM adapters 1402A, 1402B. In at least one embodiment, the deployment system 1306 can execute a deployment pipeline 1410. In at least one embodiment, the deployment pipeline 1410 can include any number of applications that can be applied sequentially, non-sequentially, or otherwise to imaging data (and / or other data types) generated by imaging devices, sequencing devices, genomics devices, etc. – including AI-assisted annotation, as described above. In at least one embodiment, the deployment pipeline 1410, as described herein, can be referred to as a virtual instrument for a device (e.g., a virtual ultrasound instrument, a virtual CT scan instrument, a virtual sequencing instrument, etc.) for a specific device.In at least one embodiment, there can be more than one input pipeline 1410 for a single device, depending on the information desired from the data generated by the device. In at least one embodiment, if detection of irregularities from an MRI machine is desired, there can be a first input pipeline 1410, and if image enhancement for the output of an MRI machine is desired, there can be a second input pipeline 1410. In at least one embodiment, an image generation application can include a processing task that involves the use of a machine learning model. In at least one embodiment, a user can choose to use their own machine learning model or select a machine learning model from a model register 1324. In at least one embodiment, a user can implement their own machine learning model or select a machine learning model for inclusion in an application to perform a processing task. In at least one embodiment, applications can be selectable and customizable, and by defining constructs of applications, the use and implementation of applications for a particular user are presented as a more seamless user experience.In at least one embodiment, by utilizing other features of the system 1400 - such as services 1320 and hardware 1322 - deployment pipelines 1410 can be even more user-friendly, provide simpler integration and produce more accurate, efficient and timely results. In at least one embodiment, the deployment system 1306 may include a user interface (“UI”) 1414 (e.g., a graphical user interface, a web interface, etc.) that can be used to select applications for inclusion in deployment pipelines 1410, to order applications, to modify or change applications or parameters or constructs thereof, to use and interact with deployment pipelines 1410 during setup and / or deployment, and / or to otherwise interact with the deployment system 1306. In at least one embodiment, the UI 1414 (or another user interface), although not illustrated with respect to the training system 1304, may be used to select models for use in the deployment system 1306, to select models for training or retraining in the training system 1304, and / or to otherwise interact with the training system 1304. In at least one embodiment, a pipeline manager 1412 can be used, in addition to an application orchestration system 1428, to manage the interaction between applications or containers of deployment pipelines 1410 and services 1320 and / or hardware 1322. In at least one embodiment, the pipeline manager 1412 can be configured to facilitate application-to-application, application-to-services 1320, and / or application-or-service-to-hardware 1322 interactions. In at least one embodiment, the pipeline manager 1412, although illustrated as being included in the software 1318 (which is not to be interpreted as limiting), can in some examples be included in services 1320. In at least one embodiment, the application orchestration system 1428 (e.g., Kubernetes, Docker, etc.) can be configured to facilitate application-to-services interactions 1320 and / or hardware 1322.) include a container orchestration system that can group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, each application can be run in a self-contained environment (e.g., at the kernel level) by associating applications from deployment pipelines 1410 (e.g., a reconstruction application, a segmentation application, etc.) with individual containers to increase speed and efficiency. In at least one embodiment, each application and / or container (or each image thereof) can be developed, modified, and deployed individually (e.g., a first user or developer can develop, modify, and deploy a first application, and a second user or developer can develop, modify, and deploy a second application separately from the first user or developer). This allows focus and attention to be concentrated on a single application and / or container's task without being hindered by tasks performed by other applications or containers. In at least one embodiment, communication and cooperation between different containers and applications can be supported by the pipeline manager 1412 and the application orchestration system 1428.In at least one embodiment, the application orchestration system 1428 and / or the pipeline manager 1412 can facilitate communication among and between, and resource sharing among, each of the applications or containers, provided that an expected input and / or output from each container or application is known to a system (e.g., based on constructs of applications or containers). In at least one embodiment, since one or more applications or containers in deployment pipelines 1410 can share the same services and resources, the application orchestration system 1428 can orchestrate, balance, and determine the load for sharing services or resources between and among different applications or containers.In at least one embodiment, a scheduler can be used to track resource requests from applications or containers, the current or planned use of these resources, and resource availability. In at least one embodiment, a scheduler can therefore allocate resources to different applications and distribute resources between and among applications with respect to the requirements and availability of a system. In some examples, a scheduler (and / or another component of the application orchestration system 1428) can determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of data output needs (e.g., to determine whether to perform real-time or delayed processing), etc. In at least one embodiment, services 1320, which are used and shared by applications or containers in the deployment system 1306, can include compute services 1416, AI services 1418, visualization services 1420, and / or other service types. In at least one embodiment, applications can call (e.g., execute) one or more services 1320 to perform processing operations for an application. In at least one embodiment, compute services 1416 can be used by applications to perform supercomputing or other high-performance computing (HPC) tasks. In at least one embodiment, one or more compute services 1416 can be used to perform parallel processing (e.g., using a parallel computing platform 1430) to process data across one or more applications and / or one or more tasks of a single application, essentially simultaneously.In at least one embodiment, the parallel computing platform 1430 (e.g., NVIDIA's CUDA) can enable general-purpose computing on GPUs (GPGPU) (e.g., GPUs / Graphics 1422). In at least one embodiment, a software layer of the parallel computing platform 1430 can provide access to virtual instruction sets and parallel computing elements of GPUs for executing computing kernels. In at least one embodiment, the parallel computing platform 1430 can include memory, and in some embodiments, memory can be shared between and among multiple containers and / or between and among different processing tasks within a single container.In at least one embodiment, interprocess communication (IPC) calls can be generated for multiple containers and / or for multiple processes within a container to use the same data from a shared segment of the memory of the Parallel Computing Platform 1430 (e.g., when multiple different stages of an application or multiple applications process the same information). In at least one embodiment, instead of copying data and moving it to different locations in memory (e.g., a read / write operation), the same data can be used at the same memory location for any number of processing tasks (e.g., at the same time, at different times, etc.).In at least one embodiment, when data is used to generate new data as a result of processing, this information can be stored in a new location and shared between different applications. In at least one embodiment, the location of data and a location of updated or modified data can be part of a definition of how a payload is understood within containers. In at least one embodiment, AI services 1418 can be used to perform inference services for executing machine learning models associated with applications (e.g., those tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services 1418 can utilize the AI ​​system 1424 to execute machine learning models (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and / or other inference tasks. In at least one embodiment, applications of deployment pipelines 1410 can use one or more output models 1316 from the training system 1304 and / or other application models to perform inference on imaging data. In at least one embodiment, two or more examples of inference using the application orchestration system 1428 (e.g.,a scheduler) may be available. In at least one embodiment, a first category may include a high-priority / low-latency path that can achieve higher quality-of-service agreements, such as performing inference on urgent requests in emergencies or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard-priority path that can be used for requests that may not be urgent or when the analysis can be performed at a later time. In at least one embodiment, the application orchestration system 1428 may distribute resources (e.g., services 1320 and / or hardware 1322) based on priority paths for different inference tasks of AI services 1418. In at least one embodiment, shared storage can be connected to AI services 1418 within the system 1400. In at least one embodiment, shared storage can be operated as a cache (or other storage device type) and used to process inference requests from applications. In at least one embodiment, when an inference request is sent, a request can be received by a set of API instances of the deployment system 1306, and one or more instances can be selected (e.g., for best fit, load balancing, etc.) to process a request.In at least one embodiment, to process a request, a request can be entered into a database; a machine learning model can be found from a model register 1324 if it is not already in a cache; a validation step can ensure that the appropriate machine learning model is loaded into a cache (e.g., shared storage); and / or a copy of a model can be stored in a cache. In at least one embodiment, a scheduler (e.g., a pipeline manager 1412) can be used to start an application referenced in a request if no application is already running or if there are not enough instances of an application. In at least one embodiment, an inference server can be started if no inference server has already been started to execute a model. A number of inference servers can be started per model.In at least one embodiment, models can be cached in a pull model where inference servers are clustered, if load balancing is advantageous. In at least one embodiment, inference servers can be statically loaded on corresponding distributed servers. In at least one embodiment, inference can be performed using an inference server running in a container. In at least one embodiment, an instance of an inference server can be associated with a model (and optionally a plurality of versions of a model). In at least one embodiment, if an instance of an inference server does not exist when a request to perform inference on a model is received, a new instance can be loaded. In at least one embodiment, a model can be submitted to an inference server when it is started, so that the same container can be used to serve different models, as long as an inference server is running as a different instance. In at least one embodiment, an inference request for a given application can be received during application execution, and a container (e.g., hosting an instance of an inference server) can be loaded (if it is not already) and a start operation can be invoked. In at least one embodiment, preprocessing logic within a container can load, decode, and / or perform other additional preprocessing on incoming data (e.g., using a CPU(s) and / or GPU(s)). In at least one embodiment, after data has been prepared for inference, a container can perform inference on data as needed. In at least one embodiment, this can involve a single inference call on a single image (e.g., a hand X-ray) or it can require inference on hundreds of images (e.g., a breast CT scan).In at least one embodiment, an application can summarize results before completion, which can include, without limitation, generating a single confidence score, pixel-level segmentation, voxel-level segmentation, generating a visualization, or generating text to summarize findings. In at least one embodiment, different models or applications can be assigned different priorities. For example, some models can have a real-time priority (TAT < 1 min), while others can have a lower priority (e.g., TAT < 10 min). In at least one embodiment, model execution times can be measured by a requesting institution or entity and can include partner network traversal time as well as execution on an inference service. In at least one embodiment, the transfer of requests between Services 1320 and inference applications can be hidden behind a software development kit (SDK), and robust transport through a queue can be provided. In at least one embodiment, a request is placed in a queue via an API for a single application / tenant ID combination, and an SDK will retrieve a request from the queue and pass it to an application. In at least one embodiment, a queue name can be provided in an environment from which an SDK will retrieve it. In at least one embodiment, asynchronous communication through a queue can be useful, as it allows each instance of an application to pick up work as it becomes available.Results can be transferred back via a queue to ensure no data is lost. In at least one embodiment, queues can also provide the ability to segment work, with highest-priority work going to a queue with the most associated instances of an application, while lowest-priority work going to a queue with a single associated instance that processes tasks in the order they were received. In at least one embodiment, an application can run on a GPU-accelerated instance generated in Cloud 1426, and an inference service can perform inference on a GPU. In at least one embodiment, visualization services 1420 can be used to generate visualizations for viewing application outputs and / or deployment pipelines 1410. In at least one embodiment, GPUs / graphics 1422 can be used by visualization services 1420 to generate visualizations. In at least one embodiment, rendering effects, such as ray tracing, can be implemented by visualization services 1420 to generate higher-quality visualizations. In at least one embodiment, visualizations can include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomography slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments can be used to provide a virtual interactive display or environment (e.g.,to generate a virtual environment for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services 1420 may include an internal visualizer, cinematography, and / or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.). In at least one embodiment, Hardware 1322 can include GPUs / Graphics 1422, AI System 1424, Cloud 1426, and / or other hardware used to run the Training System 1304 and / or Deployment System 1306. In at least one embodiment, GPUs / Graphics 1422 (e.g., NVIDIA's TESLA and / or QUADRO GPUs) can include any number of GPUs used to perform processing tasks of Computing Services 1416, AI Services 1418, Visualization Services 1420, other services, and / or any features or functionality of Software 1318. For example, in relation to AI services 1418, GPUs / Graphics 1422 can be used to perform preprocessing on imaging data (or other data types used by machine learning models), postprocessing on outputs of machine learning models and / or to perform inference (e.g. to run machine learning models).In at least one embodiment, the Cloud 1426, the AI ​​system 1424, and / or other components of the System 1400 can utilize GPUs / graphics 1422. In at least one embodiment, the Cloud 1426 can include a GPU-optimized platform for deep learning tasks. In at least one embodiment, the AI ​​system 1424 can utilize GPUs, and the Cloud 1426—or at least a portion intended for deep learning or inference—can be executed using one or more AI systems 1424. As such, although the Hardware 1322 is illustrated as discrete components, this is not to be construed as restrictive, and any components of the Hardware 1322 can be combined with or utilized by other components of the Hardware 1322. In at least one embodiment, the AI ​​system 1424 can include a dedicated computing system (e.g., a supercomputer or an HPC) configured for inference, deep learning, machine learning, and / or other artificial intelligence tasks. In at least one embodiment, the AI ​​system 1424 (e.g., NVIDIA's DGX) can include GPU-optimized software (e.g., a software stack) that can be run using a variety of GPUs / graphics 1422, in addition to CPUs, RAM, storage, and / or other components, features, or functionality. In at least one embodiment, one or more AI systems 1424 can be deployed in the cloud 1426 (e.g., in a data center) to perform some or all of the AI-based processing tasks of the system 1400. In at least one embodiment, the Cloud 1426 can include a GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that can provide a GPU-optimized platform for performing processing tasks of the System 1400. In at least one embodiment, the Cloud 1426 can include an AI System 1424 for performing one or more AI-based tasks of the System 1400 (e.g., as a hardware abstraction and scaling platform). In at least one embodiment, the Cloud 1426 can be integrated into an application orchestration system 1428 utilizing multiple GPUs to enable seamless scaling and load balancing between and among applications and services 1320. In at least one embodiment, the Cloud 1426 can be provided for running at least some services 1320 of the System 1400, including compute services 1416, AI services 1418, and / or visualization services 1420, as described herein.In at least one embodiment, the Cloud 1426 can perform small and large batch inference (e.g., running NVIDIA's TENSOR RT), provide an accelerated parallel computing API and platform 1430 (e.g., NVIDIA's CUDA), run an application orchestration system 1428 (e.g., KUBERNETES), provide a graphics rendering API and platform (e.g., for ray tracing, 2D graphics, 3D graphics, and / or other rendering techniques to produce higher-quality cinematography), and / or provide other functionality for the System 1400. Fig. 15A illustrates a data flow diagram for a process 1500 for training, retraining, or updating a machine learning model according to at least one embodiment. In at least one embodiment, the process 1500 can be executed using a non-restrictive exemplary system 1400 from Fig. 14. In at least one embodiment, the process 1500 can utilize services and / or hardware as described herein. In at least one embodiment, refined models 1512 generated by the process 1500 can be executed by a deployment system for one or more containerized applications in deployment pipelines. In at least one embodiment, the model training 1514 can include retraining or updating an initial model 1504 (e.g., a pre-trained model) using new training data (e.g., new input data, such as a customer record 1506, and / or new ground truth data associated with the input data). In at least one embodiment, in order to retrain or update the initial model 1504, one or more output or loss layers of the initial model 1504 can be reset, deleted, and / or replaced with updated or new output or loss layers. In at least one embodiment, an initial model 1504 can be used with previously fine-tuned parameters (e.g.,(weights and / or distortions) that remain from previous training, so that training or retraining 1514 will not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training 1514, by resetting or replacing output or loss layers of the initial model 1504, parameters for a new dataset can be updated and retuned based on loss calculations associated with the accuracy of the output or loss layers when generating forecasts on a new customer dataset 1506. In at least one embodiment, pre-trained models 1506 can be stored in a data store or register. In at least one embodiment, pre-trained models 1506 can have been trained, at least partially, at one or more facilities that are different from a facility that executes the process 1500. In at least one embodiment, to protect the privacy and rights of patients, subjects, or clients at different facilities, pre-trained models 1506 can have been trained on-premise using customer or patient data generated on-premise.In at least one embodiment, pre-trained models 1306 can be trained using a cloud and / or other hardware; however, confidential, privacy-protected patient data may not be transferred to, used by, or accessible by other components of a cloud (or other off-premises hardware). In at least one embodiment, if pre-trained models 1506 are trained using patient data from more than one institution, they may have been trained individually for each institution before being trained on patient or customer data from another institution. In at least one embodiment, for example, if customer or patient data has been cleared from privacy concerns (e.g., by waiver, for experimental use, etc.),) or if customer or patient data is included in a public dataset, customer or patient data is used by any number of facilities to train pre-trained models 1506 on-premise and / or off-premise, such as in a data center or other cloud computing infrastructure. In at least one embodiment, when selecting applications for use in deployment pipelines, a user can also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model to use, so a user can select a pre-trained model for use with an application. In at least one embodiment, the pre-trained model may not be optimized for generating accurate results on a customer dataset 1506 of a user's facility (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.).In at least one embodiment, before a pre-trained model is placed in a deployment pipeline for use with an application(s), pre-trained models can be updated, retrained and / or fine-tuned for use at a particular facility. In at least one embodiment, a user can select a pre-trained model that needs to be updated, retrained, and / or fine-tuned, and this pre-trained model can be referred to as the initial model 1504 for a training system within the process 1500. In at least one embodiment, a customer dataset 1506 (e.g., imaging data, genomic data, sequencing data, or other data types generated by devices at a facility) can be used to perform model training (which can include, without limitation, transfer learning) on ​​the initial model 1504 to generate the refined model 1512. In at least one embodiment, ground-truth data corresponding to the customer dataset 1506 can be generated by the training system 1304.In at least one embodiment, ground truth data can be generated at least in part by clinicians, scientists, doctors, and medical professionals at an institution. In at least one embodiment, AI-powered annotation can be used in some examples to generate ground-truth data. In at least one embodiment, AI-powered annotation (e.g., implemented using an AI-powered annotation SDK) can leverage machine learning models (e.g., neural networks) to generate suggested or predicted ground-truth data for a customer dataset. In at least one embodiment, a user can use annotation tools within a user interface (a graphical user interface (GUI)) on a computing device. In at least one embodiment, the user 1510 can interact with a GUI via the computing device 1508 to edit or fine-tune (auto)annotations. In at least one embodiment, a polygon editing feature can be used to move the vertices of a polygon to more accurate or fine-tuned locations. In at least one embodiment, once the customer dataset 1506 contains associated ground-truth data, ground-truth data (e.g., from AI-assisted annotation, manual labeling, etc.) can be used during model training to generate a refined model 1512. In at least one embodiment, the customer dataset 1506 can be applied to the initial model 1504 any number of times, and ground-truth data can be used to update parameters of the initial model 1504 until acceptable accuracy is achieved for the refined model 1512. In at least one embodiment, once the refined model 1512 has been generated, the refined model 1512 can be deployed within one or more deployment pipelines at a facility to perform one or more processing tasks related to medical imaging data. In at least one embodiment, the refined model 1512 can be uploaded to a model register of pre-trained models for selection by another facility. In at least one embodiment, this process can be performed at any number of facilities, allowing the refined model 1512 to be further refined on new datasets as often as desired to generate a more universal model. Figure 15B is an exemplary illustration of a client-server architecture 1532 for enhancing annotation tools with pre-trained annotation models according to at least one embodiment. In at least one embodiment, the AI-supported annotation tool 1536 can be instantiated based on a client-server architecture 1532. In at least one embodiment, the AI-supported annotation tool 1536 can assist radiologists in imaging applications, for example, in identifying organs and irregularities. In at least one embodiment, imaging applications can include software tools that help the user 1510, as a non-limiting example, to identify a few extreme points on a particular organ of interest in raw images 1534 (e.g., in a 3D MRI or CT scan) and automatically receive annotated results for all 2D sections of a particular organ.In at least one embodiment, results can be stored in a data store as training data 1538 and used (for example, and without limitation) as ground-truth data for training. In at least one embodiment, a deep learning model can, for example, when the computing device 1508 sends extreme points for AI-assisted annotation, receive this data as input and return inference results for a segmented organ or segmented irregularity. In at least one embodiment, pre-instantiated annotation tools, such as the AI-assisted annotation tool 1536 in Fig. 15B, can be enhanced by making API calls (e.g., API call 1544) to a server, such as an annotation assistant server 1540, which may include a set of pre-trained models 1542 stored, for example, in an annotation model register.In at least one embodiment, an annotation model registry can store pre-trained models (e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation on a specific organ or irregularity. These models can be further updated using training pipelines. In at least one embodiment, pre-installed annotation tools can be improved over time as new labeled data are added. The disclosure of this application also includes the following numbered clauses: 1. A computer-implemented method comprising: Establishing a bidirectional connection between two heterogeneous content creation applications for one or more feature values ​​corresponding to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform; Receiving, via the bidirectional connection, a notification that a modification has been made to a first feature value of the one or more feature values ​​at a first selected address location; Determining that the modification of the first feature value exceeds a threshold; Identifying a second selected address location,that is associated with the first feature value; updating a second feature value at the second selected address location based on the modification; and updating a representation of an object corresponding to the second feature value based on the modification. 2. Computer-implemented procedure according to clause 1, further comprising: receiving a notification corresponding to the modification via the bidirectional connection; and determining that a modified first feature value corresponds to a modification type. 3. Computer-implemented procedure according to clause 1 or 2, wherein the first selected address location is associated with a first data source and the second selected address location is associated with a second data source. 4. Computer-implemented procedure according to clause 3,wherein the second data source provides a three-dimensional representation of the first feature value. 5. Computer-implemented method according to a preceding clause, wherein establishing a bidirectional connection comprises: generating a receiver between the first selected address location and the second selected address location. 6. Computer-implemented method according to a preceding clause, further comprising: selecting, from a first data source, one or more addresses corresponding to a selected feature. 7. Computer-implemented method according to a preceding clause, wherein the threshold is at least one of a minimum value, a maximum value, a percentage, or a duration. 8. Computer-implemented method according to clause 7, further comprising: determining a first output format from a first data source.that is associated with the first address location; Determining a second output format of a second data source associated with the second address location; Determining one or more common features between the first data source and the second data source; and Determining at least one of the first address location or the second address location based on the determined one or more common features. 9. A processor comprising: one or more circuits for: identifying a first address corresponding to a selected feature in a first data source, wherein the selected feature corresponds to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform; identifying a second address,which corresponds to the selected feature in a second data source; generating a bidirectional connection between the first address and the second address, wherein the first address corresponds to a first content creation application and the second address corresponds to a second content creation application, the first and second content creation applications comprising heterogeneous applications; determining a modification to a first value for at least one of the first address or the second address; and modifying, at least based on the modification, a second value for the other of the first address or the second address. 10. Processor according to clause 9, wherein the first data source stores a two-dimensional representation of the selected feature and the second data source stores a three-dimensional representation of the feature. 11. Processor according to clause 9 or 10,wherein the one or more circuits further serve to: determine that the modification exceeds a threshold before modifying the second value. 12. Processor according to any one of clauses 9 to 11, wherein the one or more circuits further serve to: identify a modification type for the first value; and determine that the modification type corresponds to one or more selected modification types. 13. Processor according to any one of clauses 9 to 12, wherein the one or more circuits further serve to: determine a content type associated with the first address and the second address; identify the selected feature from a list of features, wherein the selected feature has a corresponding value less than the value corresponding to each feature for the first data source and the second data source; and provide a recommendation,to generate the bidirectional connection. 14. Processor according to any of clauses 9 to 13, wherein the processor consists of at least one of the following: a system for performing simulation operations; a system for performing simulation operations for testing or validating autonomous machine applications; a system for performing digital twinning operations; a system for performing a light transport simulation; a system for rendering a graphical output; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system,that contains one or more virtual machines (VMs); a system for performing operations for a conversational AI application; a system for performing operations for a generative AI application; a system for performing operations using a language model; a system for performing one or more generative content operations using a large language model (LLM); a system for performing one or more generative content operations using a vision language model (VLM); a system that is at least partially implemented in a data center; a system for performing hardware tests using simulation; a system for performing one or more generative content operations using a language model; a synthetic data generation system; a collaborative content creation platform for 3D assets; or a system,which is implemented at least partially using cloud computing resources. 15. A system comprising: one or more processing units that perform the following functions: identifying a change in one or more feature values ​​from a first data set, updating one or more corresponding feature values ​​in a second data set based on the change, and updating a three-dimensional representation of an object associated with the one or more feature values ​​based on the one or more feature values ​​in the second data set, wherein the object corresponds to a scene of synthetically generated graphical data maintained in a distributed content creation platform. 16. System according to clause 15, wherein the first data set is associated with a first file type and the second data set is associated with a second file type. 17. System according to clause 16,where the first file type includes geometric three-dimensional data (3D data) and metadata for the object. 18. System according to clause 17, wherein the second file type includes the metadata for the object. 19. System according to any of clauses 15 to 18, wherein the system further serves to: identify one or more feature values ​​based on a trained neural network evaluation of components associated with the first data set and the second data set. 20. System according to any of clauses 15 to 19, wherein the system is one of the following: a system for performing simulation operations; a system for performing simulation operations for testing or validating autonomous machine applications; a system for performing digital twinning operations; a system for performing a light transport simulation; a system for rendering a graphical output; a system for performing deep learning operations; a system,that is implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system that contains one or more virtual machines (VMs); a system for performing operations for a conversational AI application; a system for performing operations for a generative AI application; a system for performing operations using a language model; a system for performing one or more generative content operations using a large language model (LLM); a system for performing one or more generative content operations using a vision language model (VLM); a system,that is implemented at least partially in a data center; a system for performing hardware tests using simulation; a system for performing one or more generative content operations using a language model; a synthetic data generation system; a collaborative content creation platform for 3D assets; or a system that is implemented at least partially using cloud computing resources. Other variations are consistent with the nature of the present disclosure. Therefore, while the disclosed techniques are receptive to various modifications and alternative constructions, certain illustrated embodiments are shown in the drawings and have been described in detail above. It should be understood, however, that there is no intention to limit the disclosure to any specific form or forms that are disclosed; on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents that are consistent with the nature and scope of the disclosure as defined in the appended claims. The use of the terms "a," "an," "the," and "a," and reference terms in the context of describing the disclosed embodiments (particularly in the context of the following claims) is to be interpreted as covering both singular and plural forms, unless otherwise specified herein or clearly contrary to the context, and not as a definition of a term. The terms "comprising," "having," "including," and "containing" are to be interpreted as open terms (meaning "including but not limited to") unless otherwise noted. The term "connected," unless modified and referring to physical connections, is to be interpreted as partially or wholly contained within, attached to, or joined with, even if something is in between.The mention of values ​​herein is intended as a quick method of individual reference to each separate value falling within the scope, unless otherwise specified herein, and each separate value is incorporated into the patent specification as if it were individually named herein. The use of the term "sentence" (e.g., "a set of things") or "substance," unless otherwise noted or contradicted by context, is to be interpreted as a non-empty collection comprising one or more elements. Furthermore, unless otherwise noted or contradicted by context, the term "substance" of a corresponding sentence does not necessarily denote a proper substance of the corresponding sentence; however, the substance and the corresponding sentence may be the same. A conjunctive expression, such as phrases of the form "at least one of A, B, and C" or "at least one of A, B, and C," unless specifically stated otherwise or otherwise clearly contradicted by the context, is to be understood otherwise in the context as it is generally used to present that a thing, concept, etc., can be either A or B or C, or a non-empty clause of the sentence of A, B, and C. For example, in an illustrative example of a sentence containing three elements, the conjunctive expressions "at least one of A, B, and C" and "at least one of A, B, and C" refer to any one of the following sentences: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Such a conjunctive expression therefore generally does not intend to imply that certain embodiments require that at least one of A, at least one of B and at least one of C be present.Furthermore, unless otherwise noted or contradicted by the context, the term "plurality" indicates that a state is plural (e.g., "a multitude of things" indicates multiple things). A multitude is at least two things, but may be more if this is either explicitly stated or indicated by the context. Additionally, unless otherwise stated or clear from the context, the phrase "based on" means "at least partly based on" and not "exclusively based on". The operations of processes described herein may be performed in any suitable order unless otherwise specified herein or otherwise clearly contradicted by the context. In at least one embodiment, a process such as those described herein (or variants and / or combinations thereof) is carried out under the control of one or more computer systems configured with executable instructions and implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), executed together on one or more processors, by hardware, or combinations thereof. In at least one embodiment, code is stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors.In at least one embodiment, a computer-readable storage medium is a non-transient computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electrical or electromagnetic transmission) but includes a non-transient data storage circuit (e.g., buffers, cache, and queues) within transceivers of transitory signals. In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transient computer-readable storage media that have executable instructions stored thereon (or on other memory to store executable instructions) which, when executed by one or more processors of a computer system (i.e., as a result of execution), cause the computer system to perform the operations described herein.In at least one embodiment, a set of non-transient machine-readable storage media comprises multiple non-transient machine-readable storage media. One or more individual non-transient storage media within a set of multiple non-transient machine-readable storage media lack all code, while multiple non-transient machine-readable storage media together store all code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors—for example, a non-transient machine-readable storage medium stores instructions, and a central processing unit (CPU) executes some instructions, while a graphics processing unit (GPU) executes other instructions.In at least one embodiment, different components of a computer system have separate processors, and different processors execute different subsets of instructions. Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that, alone or together, perform operations of the processes described herein, and such computer systems are configured with applicable hardware and / or software that enables the performance of operations. Furthermore, a computer system implementing at least one embodiment of the present disclosure is a single device, and in another embodiment, it is a distributed computer system comprising several devices that operate differently, such that distributed computer systems perform the operations described herein and such that a single device does not perform all operations. The use of an example and all examples or exemplary phrasing (e.g., "such as"), as provided herein, is intended solely to make embodiments of the disclosure more readily understandable and does not constitute a limitation of the scope of the disclosure, unless otherwise claimed. No expression in the patent specification shall be construed as implying that an unclaimed element is essential for the exercise of the disclosure. All references, including publications, patent applications, and patents mentioned herein, are hereby incorporated by reference to the same extent as if each reference had been individually and specifically incorporated by reference and set forth herein in its entirety. In the description and claims, the terms "coupled" and "connected," along with their derivatives, may be used. It is understood that these terms may not be intended as synonyms. Rather, in specific examples, "connected" or "coupled" may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. "Coupled" may also mean that two or more elements are not in direct contact with each other but nevertheless work together or interact. Unless specifically stated otherwise, it may be apparent throughout the patent specification that terms such as "processing", "calculating", "calculating", "determining" or the like refer to operations and / or processes of a computer or computing system, or a similar electronic computing device, which manipulate data represented as physical quantities, such as electronic quantities, within the registers and / or memory of the computing system and / or transform them into other data represented likewise as physical quantities within the memory, registers or other such information storage, transmission or display devices of the computing system. Similarly, the term "processor" can refer to a device or part of a device that processes electronic data from registers and / or memories and transforms that electronic data into other electronic data that can be stored in registers and / or memories. As non-restrictive examples, the "processor" can be a CPU or a GPU. A "computing platform" can include one or more processors. As used herein, "software" processes can include, for example, software and / or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Furthermore, each process can refer to multiple processes for executing instructions sequentially or in parallel, continuously or intermittently.The terms “system” and “procedure” are used synonymously here insofar as the system may contain one or more procedures and procedures may be considered as a system. This document may refer to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Obtaining, acquiring, receiving, or inputting analog and digital data can be implemented in various ways, such as receiving data as parameters of a function call or an application programming interface (API) call. In some implementations, the process of obtaining, acquiring, receiving, or inputting analog and digital data can be implemented by transferring data over a serial or parallel interface. In another implementation, the process of obtaining, acquiring, receiving, or inputting analog and digital data can be implemented by transferring data over a computer network from a providing entity to a receiving entity.References can also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, the process for providing, outputting, transmitting, sending, or presenting analog or digital data can be implemented by transferring data as input or output parameters of a function call, a parameter of an application programming interface, or an inter-process communication mechanism. Although the discussion above presents exemplary implementations of the described techniques, other architectures may be used to implement the described functionality and are intended to be within the scope of this disclosure. Furthermore, although specific distributions of responsibilities are defined above for the purpose of discussion, various functions and responsibilities may be distributed and subdivided in different ways depending on the circumstances. Furthermore, although the subject matter has been described in terms specific to structural features and / or methodological actions, it is understood that the subject matter claimed in the attached patent claims is not necessarily limited to the specific features or actions described. Rather, specific features and actions are disclosed as exemplary forms for implementing the claims. It is understood that the aspects and embodiments described above are merely examples and that modifications in detail may be made within the scope of the claims. Each device, method and feature disclosed in the description and (if applicable) in the claims and drawings can be provided independently or in any suitable combination. Reference numerals appearing in the claims serve only for illustration and are not intended to have any limiting effect on the scope of the claims.

Claims

A computer-implemented method comprising: establishing a bidirectional connection between two heterogeneous content creation applications for one or more feature values ​​corresponding to an object in a virtual scene of synthetically generated graphical data maintained in a distributed content creation platform; receiving, via the bidirectional connection, a notification that a modification has been made to a first feature value of the one or more feature values ​​at a first selected address location; determining that the modification of the first feature value exceeds a threshold; and, after determining that the modification of the first feature value exceeds a threshold: identifying a second selected address location associated with the first feature value; and updating a second feature value at the second selected address location based on the modification.and updating a representation of an object that corresponds to the second feature value, based on the modification. Computer-implemented method according to claim 1, further comprising: receiving a notification corresponding to the modification via the bidirectional connection; and determining that a modified first feature value corresponds to a modification type. Computer-implemented method according to claim 1 or 2, wherein the first selected address location is associated with a first data source and the second selected address location is associated with a second data source. Computer-implemented method according to claim 3, wherein the second data source provides a three-dimensional representation of the first feature value. Computer-implemented method according to a preceding claim, wherein establishing a bidirectional connection comprises: generating a receiver between the first selected address location and the second selected address location. A computer-implemented method according to a preceding claim, further comprising: selecting, from a first data source, one or more addresses corresponding to a selected feature. Computer-implemented method according to a preceding claim, wherein the threshold corresponds to at least one of a minimum value, a maximum value, a percentage or a time period. A computer-implemented method according to claim 7, further comprising: determining a first output format of a first data source associated with the first address location; determining a second output format of a second data source associated with the second address location; determining one or more common features between the first data source and the second data source; and determining at least one of the first address location or the second address location based on the determined one or more common features.

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