Patent ID: 12206748

DETAILED DESCRIPTION

Data centers can store and process data for various purposes. For example, data centers can be used for networks that facilitate object (e.g., pedestrian) detection for an automotive where the automotive is trained based on a large input data set from training data stored at the data center. In other examples, the data center can store output data from medical instruments which can later be used for machine learning inferences. In these and other examples, the data center can process and store large amounts of data using multiple nodes, each node including multiple processing units (e.g., GPUs) to store and process data. Typically, the data center can utilize a job scheduler to select locations within the data center when the data center receives a job request. For example, the data center can receive a job request that takes four (4) graphics processing units (GPUs) to execute. The data center can use the job scheduler to select or find four available GPUs within the data center to execute the job request. The job scheduler can place job requests in a queue if there are not enough resources to execute the respective job request.

In conventional solutions, the job scheduler is primarily looking for available nodes and does not consider other factors. For example, the job scheduler can select the first available node, irrespective of other jobs or operations being executed in the data center. In data centers, the temperature can be maintained for each GPU, each node, and for the data center as a whole. Typically, the data center can cool a data center rack (e.g., a structure housing multiple nodes, servers, cables, networking devices, and other data center computing equipment) uniformly. Accordingly, the data center or node temperature is dependent on a job request that is generating the most heat. The job request that generates a maximum amount of heat can also dictate a cooling rate at a data center rack. If the job scheduler allocates a respective job request to the same data center rack, the temperature can increase and take longer to cool off—e.g., the data center rack can be unavailable for additional job requests for a longer duration as the cooling period takes longer. Similarly, if the job scheduler allocates the respective job request to nodes that are adjacent to other nodes already executing a job request, the temperature can increase for both nodes and take longer to cool off Allocating based on just the availability can also negatively impact water cooling, heat dissipation, air flow, a thermal equilibrium of a node, electricity costs, etc. Therefore the data center can perform inefficiently and increase costs.

Advantageously, aspects of the present disclosure can address the deficiencies above and other challenges by scheduling jobs in a data center using reinforcement learning with a machine learning model. For example, a processing device (e.g., job scheduler or executing agent) can provide a machine learning model with a condition (e.g., a thermal condition) and a job request. The processing device can receive or determine the condition based on user input or other requirements of the data center. The processing device can receive a condition representing a thermal or energy constraint or target for the data center—e.g., the processing device can receive a condition that indicates to reduce a temperature of the data center as a whole as much as possible when executing a respective job request. The processing device can receive a condition that represents at least one of a cooling condition for a node or GPU of the data center, a total energy consumption of the data center, water cooling conditions, heat dissipation conditions, air flow conditions, temperature conditions, location (e.g., location of adjacent operations, or location of all operations currently being executed at the data center) conditions, thermal equilibrium conditions, electricity consumption conditions, electricity costs conditions, a time (e.g., time of day or a duration associated with performing the job request) condition, etc. The processing device can also receive information regarding the location of other operations being executed across the data center. The machine learning model can determine (e.g., predict) a location within the data center that satisfies (or is the closest to satisfying) a target parameter associated with the condition. For example, the machine learning model can determine a location within the data center that will reduce energy consumption or energy costs the most. In other examples, the machine learning model can determine a location within the data center that will cause the least temperature increase or least cooling time. In that, the job scheduler can use the machine learning model to select locations for a job request that reduces a carbon footprint of the data center—e.g., the job scheduler can factor in conditions other than availability when selecting the location for the job request.

The processing device can generate a reward (e.g., parameter) that indicates a net change in a respective condition for a respective job—e.g., the processing device can generate a reward that indicates a net difference in temperature from executing the job request at a location rather than a second location. The reward can be used to update a training of the machine learning model—e.g., the machine learning model can undergo reinforcement learning based on the received reward. For example, the machine learning model can receive the reward, determine the selected location decreases energy consumption, and the processing device can update the machine learning model accordingly. The updated machine learning model can then predict a different location to execute the job request at, receive a second reward, update accordingly, and so forth. This process can be repeated, and the reinforcement learning of the location selection using the machine learning model can continue until a determination is made that no further improvements are being output (e.g., there are no other locations that will be closer to the target than a previous location predicted by the machine learning model)—e.g., until the job scheduler is optimized to select a location given the condition and job request. Additionally, the processing device can select the location based on the output of the machine learning model, determine an actual parameter (e.g., reward), and update the machine learning model if the actual results differ from the machine learning model prediction.

As the data center can receive any number of job requests, each having a different condition, the job scheduler can select one or more reinforcement learning techniques for each unique condition received—e.g., the job scheduler can select a first reinforcement learning technique for optimizing thermal conditions and a second reinforcement learning for a optimizing energy consumption. Accordingly, the data center can be optimized for any number of conditions as requested by a user. For example, the job scheduler can receive a condition associated with a predetermined water temperature, where the water can be used for other tasks such as melting snow on roads leading to the data center. In such examples, the processing device can select one or more reinforcement learning models associated with selecting a node in the data center that satisfies the predetermined water temperature.

By scheduling jobs using reinforcement learning (e.g., machine learning), the data center can conform to any number of conditions provided by a user—e.g., the job scheduler is not limited to selecting a location based on just availability. Additionally, the data center can perform better when temperature and energy consumption are considered and optimized for while selecting locations for jobs. For example, the job scheduler can avoid selecting adjacent nodes for job requests to reduce temperature and cooling times, allowing the data center to process more job requests faster.

FIG.1Ais a block diagram of a system100implementing machine learning in job scheduling, according to at least one embodiment. The system100can include a data center110coupled to a network103. In some embodiments, the system100can include a client device124coupled with the network103.

The data center110can include a rack112of one or more computing systems114(1)-114(N), where N is a positive integer equal to or greater than zero. Each computing system114can include a computing device116and a service processor120. In at least one embodiment, the computing device116can be considered a node. In other embodiments, multiple computing devices116can be considered a node—e.g., a node can include one or more computing devices116. In some embodiments, the computing device116can be an example of a graphics processing unit (GPU) or central processing unit (CPU). Although one computing device116is shown for each computing system116, it should be noted each computing system114can include any number of computing devices116greater than one (1). In at least one embodiment, the service processor120is a baseboard management controller (BMC). The BMC can be part of an IPMI type interface and can be located on a circuit board (e.g., motherboard) of the computing device116being monitored. The BMC can include one or more sensors that are operatively coupled to the computing device116or integrated within the computing device116. The sensors of a BMC measure internal physical variables such as temperature, humidity, power-supply voltage, fan speeds, communications parameters, and operating system (OS) functions. The BMC can provide a way to manage a computer that may be powered off or otherwise unresponsive. The service processor120provides out-of-band functionality by collecting the power consumption data of the computing device116independently from the computing device's CPU, firmware, and OS. The service processor120can provide the power consumption data via a network connection122independent from a primary network connection118of the computing device116. The service processor120can use the network connection122to the hardware itself rather than the OS or login shell to manage the computing device116, even if the computing device116is powered off or otherwise unresponsive. Although one rack112is illustrated, the data center110can include any number of racks112equal or greater than one (1).

In at least one embodiment, the rack112can be coupled with or include a data center manager128—e.g., the data center manager128can be coupled with multiple racks112or each rack112can include a data center manager128. In some embodiments, the data center manager128can manage computing device116of the rack112and computing systems114. In some embodiments, the data center manager128can include a service processor130and be connected to the network via network connection132. In at least one embodiment, the data center manager128includes a scheduler160with a machine learning (ML) model104—e.g., the data center manager128includes a ML supported scheduler160.

FIG.1Bis a block diagram of a data center manager128in a data center110implementing machine learning to schedule operations or jobs, according to at least one embodiment. The data center manager128can include an interface150, a network manager155, a scheduler160, and a data store165. In at least one embodiment, the data center manager128is configured to manage the racks112—e.g., schedule operations. In some embodiments, the data center manager128can detect and respond to events occurring at the racks112—e.g., starting a new cluster for an incoming operation or detecting a current cluster or rack112is failing.

In at least one embodiment, the interface150can enable the data center manager128to communicate commands, operations, information, etc., to the racks112. For example the interface150can enable the data center manager128to schedule operations at the racks112. In at least one embodiment, the data center manager128can monitor and detect when nodes (e.g., computing devices116) are down or creates clusters or pods to execute incoming operations. In some embodiments, the interface150is an example of an application programming interface (API) server. The interface can manage the network manager155, the scheduler160, and the data store165.

In at least one embodiment, network manager155can link the data center manager128and the racks112to a cloud network or cloud environment. For example, the network manager155can link a cluster to a cloud provider API. In some embodiments, the network manager155can check nodes in the cloud network, set up routes in the cloud network, or create, update, and delete cloud network load balancers.

In some embodiments, scheduler160is configured to schedule operations at nodes or computing device116of the racks112. In at least one embodiment, the scheduler160includes code, a component, or a configurable plug-in that indicates how to allocate resources—e.g., indicates a basis for selecting nodes for performing a job. In some embodiments, the scheduler160includes a processing device102that executes a machine learning (ML) model104trained to schedule jobs according to thermal conditions provided. For example, the processing device102can execute an ML model104to schedule operations in a manner that reduces energy consumption, reduces a temperature of a respective node, rack112, or the data center110or increases cooling efficiency. That is, the ML model104can schedule operations that optimize the thermal conditions of the data center110. For example, the scheduler160can receive an operation that will execute at three (3) nodes of the data center110. The ML model104can be trained to select three (3) available nodes that will execute the operation with the least amount of energy consumption or cause the least amount of temperature increase in the data center110. In some embodiments, the ML model104can be trained to optimize for at least one of the following thermal conditions; water cooling, heat dissipation, or air flow direction—e.g., where airflow direction describes whether a respective compute nodes are using air that is recirculated datacenter air or filtered air from outside the datacenter. Additional thermal conditions and constraints are discussed with reference toFIGS.2-4. In at least one embodiment, the ML model104can be trained by a set of training data that includes at least one of a water cooling parameter, heat dissipation parameter, air flow direction parameter, a type of operation to execute, an amount of nodes corresponding to executing the operation, and an amount of energy consumed. That is, the ML model104can be trained by mapping between the parameters and the thermal conditions and energy consumed or cooling provided. In one embodiment, the processing device102can calculate floating point operations per second (e.g., FLOPS) to measure computer performance using the parameters—e.g., the processing device102can calculate performance of the nodes given respective parameter values (e.g., given the node is operating with a first water cooling parameter (e.g., the node is cooling water at a respective rate). The ML model104can be trained to determine the energy consumed or cooling provided (e.g., for the node or for the data center) for the respective parameter—e.g., the energy consumed if the node is operating with the first water cooling parameter. In at least one embodiment, the processing device102can receive information regarding historical performance of a respective node—e.g., information regarding how much energy is consumed or a carbon footprint of utilizing the node. In at least one embodiment, the processing device102can receive information regarding historical performance of a respective GPU in a node. Additional details regarding the training is described with reference toFIGS.2-4. It should be noted that the ML model104can utilize any machine learning model—e.g., a deep (sequential) network, a single level of linear or non-linear operations, reinforcement learning, etc. For convenience, the remainder of the disclosure will refer to the implementation as a reinforcement learning model, even though some implementations might employ other types of learning machine models instead of, or in addition to, a reinforcement learning model. In some embodiments, the reinforcement learning model or algorithm can include a deep Q-network (DQN), a deep deterministic policy gradient (DDPG), Markov decision process (MDP), etc. In at least one embodiment, the ML model104is trained in a simulated environment as described with reference toFIG.2.

In at least one embodiment, ML model104can be trained to enable the scheduler160to make energy aware decisions. In at least one embodiment, the ML model104can be trained to use an outside air temperature (e.g., a temperature parameter) to schedule operations. For example, if the outside air temperature is less than 35 degrees Celsius (35° C.), that air could be filtered and directed to the compute nodes for cooling. In at least one embodiment, the node is configured to emit exhaust that is directed to outside the data center110. In such embodiments, the data center110consumes energy (e.g., there is an energy charge) to filter and blow the air from outside to a front of the racks112. In at least one embodiment, the data center110also consumes energy at the compute node based on the inlet air temperature provided. For example, as the input air temperature increases, racks112and nodes can consume additional energy due to increased fan power and additional leakage of the silicon at elevated temperatures. In at least one embodiment, the data center110can compare the energy consumed for filtering outside air with energy consumed to create 20 degrees Celsius (20° C.) Air with a traditional data center CRAH(Computer Room Air Handler) and compressor unit. For example, the data center110can consume additional power at a potentially lower temperature. Accordingly, the ML model104can be trained to select a node for an operation based on a performance of a respective node filtering outside air or using the CRAH.

In at least one embodiment, the ML model104can be trained to enable the scheduler160to select nodes for an operation to create or warm water temperatures. For example, the scheduler160can schedule more operations at a rack112if there is request or indication to have warm water for district heating applications. In at least one embodiment, water control is typically at a rack112or at a CDU (Coolant Distribution Unit) level. In at least one embodiment, it is difficult to isolate water flow at a compute node level. In some embodiments, the data center can generate higher output water temperature if all nodes in a rack112are active. Accordingly, if a scheduler160can optimize warm water for energy reuse, the scheduler160can schedule operations at one rack112to increase a number of active node at the rack112or CDU domain.

In at least one embodiment, the processing device102can train the ML model104using data from data store165. For example, the processing device102can utilize reinforcement learning techniques to train the ML model104. In at least one embodiment, the processing logic102can provide training data and a current indication of a current location (e.g., a current state) for performing a respective operation and train the ML model104to predict thermal and energy usage of performing the respective operation at a different location. In such embodiments, the processing logic102can also train the ML model to select an optimal location (e.g., a desired location) based on the thermal and energy conditions provided—e.g., the ML model104can be trained to predict an optimal location that will consume a least amount of energy to perform the respective operation. In some embodiments, the data store165can be outside the data center manager128—e.g., the data store165can be included in a rack112or be remote and coupled to the data center manager128via network103. The processing device102can analyze the thermal information data at data store165and utilize the ML model104to predict a node to execute an operation at that is the most optimal for a given thermal condition. The data center manager128can include a collection service for collecting the thermal information for one or more computing devices116or nodes in the data center110. In at least one embodiment, the collection service is used by the data center manager128for collecting thermal information for training the ML model104and for use by the ML model104after training. In some embodiments, the processing device102can receive the thermal information from the racks112. The processing device102or the interface150can store the thermal information in the data store165. By utilizing the ML model104, the scheduler160can schedule operations that optimize for thermal conditions and improve the efficiency of the data center110.

FIG.2illustrates a system200for performing reinforcement learning to predict a node most optimal to execute an operation at based on thermal conditions provided, according to at least one embodiment. In some embodiments, the system200includes an agent202(e.g., an actor) and an environment204—e.g., a simulated environment. The agent202trains one or more machine learning models (e.g., ML model(s)104) which can be examples of a Q-network (e.g., or a deep Q-network (DQN)). The system200can be located in the processing device102as described with reference toFIG.1—e.g., within the scheduler160.

Reinforcement learning (RL) is a class of algorithms applicable to sequential decision making tasks. In some embodiments, the RL makes use of the Markov Decision Process (MDP) formalism where an agent202attempts to optimize a function in its environment204. An MDP can be completely described by a state space S (with states s∈S), an action space A (a∈A), a transition function T: S×A→S and a reward function R: S×A→In an MDP, an episode evolves over discrete time steps t=0, 1, 2, . . . , n, where the agent202observes a state st(206) and responds with an action at(210) using a policy π(at|st). The environment204provides to the agent202the next state st+1˜T (st, at)212and the reward rt=R(st, at)214. The agent202is tasked with maximizing the return (cumulative future rewards) by learning an optimal policy π*.

A Q-network may be trained via a process referred to as Q-learning. Q learning is a reinforcement learning process that causes a Q-network to perform a sequence of actions that will eventually generate a maximum total reward. This total reward is also called the Q-value. A function for computing the Q value may be as follows:
Q(st,at)←rt+γmaxaQ(st+1,a)

The above equation states that the Q-value yielded from being at state st(206) and performing action at(210) is the immediate reward r(st, at) (214) plus the highest Q-value possible from the next state st+1(212), state where γ is the discount factor which controls the contribution of rewards further in the future. The recursive definition of Q-functions allows the expression of Q(st+1, at+1) to be unrolled into future states, as follows:
Q(st,at)=rt+γrt+1+ . . . +γn−1rt+n−1+γnQ(st+n,at+n)
The Q-network learns to predict Q(st, at) by performing the following update step:

Q⁡(st,at)←Q⁡(st,at)+α[rt+1+γmaxaQ⁡(St+1,a)-Q⁡(st,at)]
where α represents learning rate or step size, which controls to what extent newly acquired information overrides old information.

In embodiments, the Q value of a state-action pair (st, at) under a policy π is defined to be the expected return if the action at210is taken at state st206and future actions are taken using the policy π, as set forth below:
Qπ(st,at)=rt+γrt+1+γ2rt+2+ . . . |,γ∈[0,1]

In embodiments, the discount factor γ∈[0,1] balances short-term versus long-term rewards. The Q-learning algorithm may start the agent202with a random policy and uses the experience gathered during its interaction with the environment (st, at, rt, st+1)204to iterate towards an optimal policy by updating Q with a learning rate α∈[0, 1]:

Q⁡(st,at)←1⁢(1-α)*Q⁡(st,at)+α*(rt+γmaxa′Q⁡(st+1,a′))

The policy for a Q-learning agent202may be represented as π(⋅|st)=argmaxQ(st, a). In one embodiment, a ∈-greedy policy, where random actions a are chosen with a probability E to increase exploration in the state space is used. In one embodiment, ∈ is annealed to zero during the course of training and is zero when performing evaluation. In one embodiment, multiple explorations can be done in parallel with a range of ∈ values.

Deep Q-learning is an extension of Q-learning that implements one or more machine learning models (e.g., machine learning model104) such as neural networks to essentially approximate the aforementioned Q values. In deep Q-learning, one or more artificial neural networks (e.g., machine learning model104) may be used to approximate the aforementioned Q-value function. Artificial neural networks generally include a feature representation component with a classifier or regression layers that map features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. Pooling is performed, and non-linearities may be addressed, at lower layers, on top of which a multi-layer perceptron is commonly appended, mapping top layer features extracted by the convolutional layers to decisions (e.g. modifications to design state of prefix circuits). Deep learning is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks may learn in a supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, where the different layers learn different levels of representations that correspond to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. Notably, a deep learning process can learn which features to optimally place in which level on its own. The “deep” in “deep learning” refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs may be that of the network and may be the number of hidden layers plus one. For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

In embodiments, training of a deep Q-network (DQN) may be stabilized using a second target network to estimate the Q values of (st+1, a′). The second target network may be updated less frequently than a first network. In embodiments, the DQN may sample an experience replay buffer. In one embodiment, a first machine learning model104is used to determine a prediction and a second machine learning model104is used to determine a target. The second machine learning model104may have a same architecture as the first machine learning model104in embodiments. However, in an embodiment the second machine learning model104may have frozen parameters while the first machine learning model104may have variable parameters. In an embodiment, the second machine learning model104is updated less frequently than the first machine learning model104. In one embodiment, a double-DQN algorithm is used, which may further improve training by reducing overestimations in the DQN. In at least one embodiment, the first machine learning model104can be used to predict thermal and energy usage associated with executing an operation at a respective location—e.g. the first machine learning model104can predict thermal and energy usage of performing the operation at a respective node. In some embodiments, the second machine learning model104can determine which location is closet to target thermal and energy conditions—e.g., the second machine learning model104could determine which node would use a least amount of energy to perform the operation.

In some examples, system200can be utilized to select or designate a node to execute an operation, where the node is selected to optimize a thermal condition provided. For example, the scheduler160can receive a request to execute an operation. In some embodiments, the request can include a location to execute the operation at. In other embodiments, the request can refrain from providing a location. In some embodiments, the request can also include a thermal condition that should be optimized for—e.g., the request can indicate to select a location that enables efficient cooling or a location that utilizes a least amount of energy. In some embodiments, the scheduler160or agent202can be programmed to optimize for the thermal condition—e.g., the scheduler160or agent202can be programmed to increase cooling efficiency or select a node that causes a least amount of temperature increase in a rack or the data center as a whole. In at least one embodiment, the agent202is configured to modify the location of the operation (e.g., select a second location) by taking an action210and train the machine learning model104based on the modifications and action210. In some embodiments, agent202can execute on a processing device such as a graphical processing unit (GPU) or a central processing unit (CPU). In some embodiments, the system200can include multiple agents202that may operate in parallel and share learning. Each agent202may execute on the same or a different processing device and/or the same or a different core of a processing device.

In at least one embodiment, the environment204can be configured to receive action210. In some embodiments, the environment204can simulate the action210to determine a next state212and next reward214. For example, the environment204can simulate the action210generate state212and predict a thermal condition at the second location responsive to the action210. The environment204can compare a value of the thermal condition for the state206and a value of the thermal condition for the next state212to generate the next reward214. For example, the environment204can output a positive next reward214if the value for the thermal condition for the next state212is closer to a target than the value for the thermal condition for state206. On the other hand, the environment204can output a negative next reward214if the value for the thermal condition associated with the next state212is further from the target than the value of the thermal condition for the state206. The environment204can output the next state212and next reward214. Accordingly, the agent202can receive the next reward214and update the machine learning model204. The agent202can also receive the next state212and take another action210to send to the environment204.

For example, the scheduler160can receive a request or be programmed to optimize for temperature of the node—e.g., the scheduler160can train ML model204to select nodes for an operation in a manner that causes a least amount of increase in temperate at the node, at a rack, or at a data center when executing the operation. In such embodiments, the agent202can take an action210and select a first node to execute the operation at. The environment204can receive the action and simulate running the operation at the first node to predict a first temperature associated with performing the operation at the first node. In response to predicting the first temperature, the environment204can provide a next state212to the agent202. In some embodiments, the agent202can receive the next state212and perform a second action210to select a second node to execute the operation at. The environment204can receive the second action210and simulate executing the operation at the second node to predict a second temperature associated with performing the operation at the second node. In such embodiments, the environment204can compare the first temperature with the second temperature. If the environment204determines the second temperature is less than the first temperature, the environment204can generate a positive next reward214. If the environment determines the second temperature is greater than the first temperature, the environment204can generate a negative next reward214. Accordingly, the agent202can update (e.g., re-train) the machine learning model104in response to receiving the next reward214—e.g., the agent202can re-train the machine learning model104to take similar actions210if the next reward214is positive or re-train the machine learning model104to take different actions210if the next reward214is negative. The system200can continue to simulate actions210until the actions210no longer result in positive rewards214(e.g., the system is fully optimized) or until actions210result in a temperature that satisfies a target temperature threshold. In some embodiments, actions210can include selecting multiple graphics processing units (GPUs) within a same node, selecting multiple GPUs across different nodes (e.g., across adjacent nodes or nodes that not adjacent), selecting multiple GPUs across different racks, etc.

In another example, the scheduler160can receive a request or be programmed to optimize for a water temperature—e.g., the scheduler160can train ML model204to select nodes for an operation in a manner that causes the water temperate to reach a certain threshold. For example, in some embodiments the water within the data center can be utilized for downstream tasks—e.g., for melting snow surrounding the data center. In such embodiments, agent202can train the ML model104such that executing operations in the data center enable the water temperature to reach a threshold value sufficient for melting snow. As described above, the agent202can take an action210and select a first node to execute the operation at. The environment204can receive the action and simulate running the operation at the first node to predict a first water temperature associated with performing the operation at the first node. In response to predicting the first water temperature, the environment204can provide a next state212to the agent202. In some embodiments, the agent202can receive the next state212and perform a second action210to select a second node to execute the operation at. The environment204can receive the second action210and simulate executing the operation at the second node to predict a second water temperature associated with performing the operation at the second node. In such embodiments, the environment204can compare the first water temperature with the second water temperature. If the environment204determines the second water temperature is closer to the target threshold than the first water temperature, the environment204can generate a positive next reward214. If the environment determines the second water temperature is further away from the target threshold than the first water temperature, the environment204can generate a negative next reward214. Accordingly, the agent202can update (e.g., re-train) the machine learning model104in response to receiving the next reward214—e.g., the agent202can re-train the machine learning model104to take similar actions210if the next reward214is positive or re-train the machine learning model104to take different actions210if the next reward214is negative.

In some embodiments, the agent202can train the ML model104for any number of thermal conditions. For example, the agent202can train the ML model104to optimize for any of the following thermal or energy conditions (or for any combination of the following thermal conditions); a cooling condition associated with the operation, total data center energy consumption condition, water cooling condition, heat dissipation condition, air flow condition, condition associated with temperature, condition associated with weather forecasts, condition associated with location of adjacent operations, condition associated with location of all operations currently executing at the data center, thermal equilibrium conditions, electricity consumption condition, condition associated with electricity costs, a condition associated with a time associated with the operation, condition associated with a type of operation, or a condition associated with an amount of energy associated with executing the job. For example, the ML model104can take into account outside weather and temperature conditions with multiday forecasting and publish confidence levels (e.g., using Bayesian methods) for energy levels. For example, the ML model104can predict that if a first operation is executed with these forecast conditions, an “X” amount of energy can be used in cooling with the confidence level of 60%. In examples where the forecast or prediction is wrong, the ML model104can be re-trained. In other examples, the ML model104can take into account electricity costs at different times through the day. In such examples, the ML model104can be trained to select times to execute operations when electricity is cheapest (e.g., at night).

In some embodiments, the processing device102can utilize a different reinforcement learning model or algorithm associated with each thermal condition or each possible combination of thermal conditions. For example, the processing device102can utilize a first reinforcement learning technique for optimizing for temperature and a second reinforcement learning technique for optimizing for electricity costs.

FIG.3illustrates a flow diagram of a method300for utilizing machine learning for job scheduling in a data center, according to at least one embodiment. The method300can be performed by processing logic comprising hardware, software, firmware, or any combination thereof. In at least one embodiment, the method300is performed by system100or system200as described with reference toFIGS.1-2. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other diagrams illustrating a method utilizing machine learning for job scheduling in a data center are possible.

At operation305, processing logic can request information pertaining to thermal parameters. In some embodiments, processing logic can train a machine learning (ML) model (e.g., machine learning model104as described with reference toFIG.1). In such embodiments, the processing logic can request or receive thermal parameters or thermal information to train the ML model. In some embodiments, the processing logic can request at least one of the following thermal parameters or thermal information; a cooling parameters associated with the operation, total data center energy consumption parameters, water cooling parameters, heat dissipation parameters, air flow parameters, parameters associated with temperature, parameters associated with weather forecasts, parameters associated with location of adjacent operations, parameters associated with location of all operations currently executing at the data center, thermal equilibrium parameters, electricity consumption parameters, parameters associated with electricity costs, parameters associated with a time associated with the operation, parameters associated with a type of operation, or a parameter associated with an amount of energy associated with executing the job. In some embodiments, the information requested can also include how many steps are in the operation (e.g., number of individual operations in a job request), types of operations to be performed (e.g., visualization, graphics, deep learning training, inference, computer-aided design (CAD), etc), an estimation of how much energy an operation should take, or how much data will be transferred (e.g., including which locations the data will be retrieved from). Accordingly, the ML model104can be trained for not only thermal conditions, but also to efficiently distribute parallel job requests. Additionally, the ML model104can be trained to factor in data locality—e.g., the ML model104can be trained to reduce a distance data travels or transfers within the data center. In some embodiments, the processing logic can train the ML model104to map between the thermal conditions and the energy consumed (e.g., cooling provided). That is, the ML model104can be trained to predict how much energy is consumed or cooling is provided at a respective node. By learning the energy consumed or cooling provided, the ML model104can select a node to execute an operation at in response to receiving a thermal condition.

At operation310, the processing logic can perform preprocessing on the information pertaining to the thermal conditions and generate one or more inputs to the machine learning model. In at least one embodiment, the processing logic can be programmed to optimize for a particular thermal condition (or for a combination of certain thermal conditions). Accordingly, the processing logic can preprocess the thermal information received and provide the appropriate thermal conditions as inputs to the ML model104. For example, the processing logic can be programmed to optimize for heat dissipation in the data center. In such examples, the processing logic can filter the information received and select information related to heat dissipation. This can enable the processing logic to generate one or more inputs to provide the machine learning model. In some embodiments, the processing logic can also select a reinforcement technique for the ML model104to utilize based on the thermal condition. For example, the processing logic can select a reinforcement learning technique associated with optimizing heat dissipation. By using a number of reinforcement learning techniques corresponding to different thermal conditions (or combinations of thermal conditions), the system100can schedule jobs in a flexible manner. That is, the scheduler160can select the optimal node(s) for any number of scenarios. For example, the scheduler160could select a node for performance, for saving electric costs, based on weather conditions, for downstream tasks, to maintain temperatures, etc.

At operation315, the processing logic provides the machine learning model a first input that includes one or more thermal conditions associated with an operation. For example, the processing logic can indicate to the ML model104to optimize for cooling and air flow. In some embodiments, the processing logic can also provide all input all relevant information associated with the one or more thermal conditions. For example, the processing logic can provide relevant information associated with air flow to the ML model104—e.g., current air flow rate, current nodes executing a job, number of nodes associated with the received job request, available nodes etc. In other embodiments, the processing logic could provide current electricity costs, future predicted electricity costs, etc., if the thermal condition is related to electricity costs.

At operation320, the processing logic can obtain one or more outputs identifying a node to perform the operation320. That is, the ML model104can output an optimal node to perform the operation based on the thermal conditions provided. In at least one embodiment, the processing logic can inform the scheduler of the node selected by the ML model104. In such embodiments, the job scheduler can schedule the operation to be executed at the node selected by the ML model104.

At operation325, the processing logic can provide the machine learning model a second input that includes one or more thermal values associated with the operation at the node. In some embodiments, after the operation is performed at the node selected by the ML model104, the processing logic can request information pertaining to the operation. For example, the processing logic can request the actual heat dissipation rate or the actual temperature of the node during the operation. In such examples, the processing logic can compare the actual thermal values (e.g., thermal or energy results) with the predicted results of the ML model104generated at operation320. If the processing logic determines the predicted results are different than the actual results, the processing logic can provide the second input to the ML model104and proceed to operation330.

At operation330, the processing logic can update the machine learning model responsive to providing the machine learning model the second input. In some embodiments, the processing logic can re-train the ML model104if the predicted results differ from the actual results—e.g., the processing logic can re-train the ML model104in response to comparing actual results with the predicted results of the ML model104. In at least one embodiment, the processing logic can update the ML model104as described with reference toFIG.2, e.g., provide a negative reward to the agent to incentivize different actions by the ML model104. By utilizing reinforcement techniques, the ML model104can continually improve and select the most optimal node based on the thermal condition provided.

FIG.4illustrates a flow diagram of a method400for utilizing machine learning for job scheduling in a data center, according to at least one embodiment. The method400can be performed by processing logic comprising hardware, software, firmware, or any combination thereof. In at least one embodiment, the method400is performed by system100or system200as described with reference toFIGS.1-2. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel. Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every embodiment. Other diagrams illustrating a method utilizing machine learning for job scheduling in a data center are possible.

At operation405, processing logic receives a first condition associated with an operation at a data center, the operation at the data center pertaining to a first location and corresponding to a first parameter value. In some embodiments, the processing logic can receive a job request or operation request to execute at the data center. In at least one embodiment, the request can include the first condition. In some embodiments, the processing logic can be programmed with a plurality of conditions and select the first condition in response to the request—e.g., the processing logic can be programmed to optimize for heat dissipation and air flow and select air flow based on the type of request. In at least one embodiment, the first condition represents at least one of a cooling condition associated with the operation, total data center energy consumption condition, water cooling condition, heat dissipation condition, air flow condition, condition associated with temperature, condition associated with weather forecasts, condition associated with location of adjacent operations, condition associated with location of all operations currently executing at the data center, thermal equilibrium conditions, electricity consumption condition, condition associated with electricity costs, a condition associated with a time associated with the operation, condition associated with a type of operation, or a condition associated with an amount of energy associated with executing the job.

In at least one embodiment, the processing logic can train a machine learning model prior to receiving the condition or job request. In such embodiments, the processing logic can generate training data comprising an amount of energy associated with executing the operation and at least one of a cooling parameter associated with the operation, total data center energy consumption parameter, water cooling parameter, heat dissipation parameter, air flow parameter, parameter associated with temperature, parameter associated with weather forecasts, parameter associated with location of adjacent operations, parameter associated with location of all operations currently executing at the data center, thermal equilibrium parameter, electricity consumption parameter, parameter associated with electricity costs, a parameter associated with a time associated with the operation, data transfer associated with the operation, or type of operation and train the machine learning model with the training data. That is, the processing logic can map a respective thermal input (e.g., parameter) to how much energy is consumed, cooling is provided, or a temperature corresponding to the operation (e.g., water temperature or temperature at a node). In some embodiments, the processing logic can map the respective parameter to additional thermal values including but not limited to air flow rate, thermal equilibrium conditions, energy costs, heat dissipation rates, total energy consumed, etc.

At operation410, the processing logic can provide the first condition as an input to a machine learning model. In some embodiments, the processing logic can also provide one or more additional constraints of a plurality of conditions (the plurality including the first condition) to the machine learning model. That is, the processing logic can provide a combination of thermal conditions or energy conditions to the machine learning model. In some embodiments, the machine learning model is trained to output a final location based on receiving the first condition and the one or more additional conditions of the plurality of conditions.

At operation415, the processing logic can perform one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output a indication of a final location associated with the operation. In at least one embodiment, the indication of the final location could be a location, a specific node to execute the operation at, a distance from a current location selected, etc. In such embodiments, the final location can correspond to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center. In some embodiments, the processing logic is to select the one or more reinforcement learning techniques from a plurality of reinforcement learning techniques, where the plurality of reinforcement learning techniques are associated with a plurality of conditions.

In at least on embodiment, the processing logic performs the one or more reinforcement learning techniques by processing the first condition associated with the operation using the machine learning model, where the machine learning model is trained output a second location to execute the operation that is a modification of the first location. In such embodiments, the processing logic can determine the first parameter value for the first location and a second parameter value for the second location. For example, the first condition can be energy consumption—e.g., the machine learning model can be trained to optimize for energy consumption. In such embodiments, the processing logic can determine an amount of energy consumed at the first location and an amount of energy consumed at the second location. In some embodiments, the processing logic can re-train (e.g., update) the machine learning model based on a comparison of the first parameter value and the second parameter value—e.g., the processing logic can update the machine learning model based making the comparison between the first location and the second location to determine which of the first location or the second location is closer to the target. In some embodiments, the processing logic can process the first location or the second location using the re-trained machine learning model, where the re-trained machine learning model outputs a third location of the operation that is a modification of the first location or the second location. That is, the processing logic can select the first location or the second location based on whether the first parameter value or the second parameter value is closer to the target.

In some embodiments, the processing logic can execute the operation (e.g., job request) at the final location in response to the machine learning model outputting the final location. In such embodiments, the processing logic can receive a second parameter value associated with executing the operation at the final location. In some embodiments, the processing logic can determine the second parameter value is different than the final parameter value in response to receiving the second parameter value. In at least one embodiment, the processing logic can re-train the machine learning model based on determining that the second parameter value is different than the final parameter value. In at least one embodiment, the final parameter value is a prediction of what parameter value will be associated with the final location. In some embodiments, the second parameter value is an actual parameter value associated with executing the operation at the final location. That is, the processing logic can update the machine learning model based on actual results differing from predicted results as described with reference toFIG.3.

FIG.5Aillustrates inference and/or training logic515used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B.

In at least one embodiment, inference and/or training logic515may include, without limitation, code and/or data storage501to store forward and/or output weight and/or input/output data, and/or other parameters to configure neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, training logic515may include, or be coupled to code and/or data storage501to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). 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, code and/or data storage501stores weight parameters and/or input/output data of each layer of a neural network trained or used in conjunction with one or more embodiments during forward propagation of input/output data and/or weight parameters during training and/or inferencing using aspects of one or more embodiments. In at least one embodiment, any portion of code and/or data storage501may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, any portion of code and/or data storage501may be internal or external to one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or code and/or data storage501may be 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, choice of whether code and/or code and/or data storage501is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, inference and/or training logic515may include, without limitation, a code and/or data storage505to store backward and/or output weight and/or input/output data corresponding to neurons or layers of a neural network trained and/or used for inferencing in aspects of one or more embodiments. In at least one embodiment, code and/or data storage505stores weight parameters and/or input/output data of each layer of a neural network 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 inferencing using aspects of one or more embodiments. In at least one embodiment, training logic515may include, or be coupled to code and/or data storage505to store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic, including integer and/or floating point units (collectively, arithmetic logic units (ALUs). 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 portion of code and/or data storage505may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. In at least one embodiment, any portion of code and/or data storage505may be internal or external to on one or more processors or other hardware logic devices or circuits. In at least one embodiment, code and/or data storage505may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, choice of whether code and/or data storage505is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors.

In at least one embodiment, code and/or data storage501and code and/or data storage505may be separate storage structures. In at least one embodiment, code and/or data storage501and code and/or data storage505may be same storage structure. In at least one embodiment, code and/or data storage501and code and/or data storage505may be partially same storage structure and partially separate storage structures. In at least one embodiment, any portion of code and/or data storage501code and/or data storage505may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory.

In at least one embodiment, inference and/or training logic515may include, without limitation, one or more arithmetic logic unit(s) (“ALU(s)”)510, including integer and/or floating point units, to perform logical and/or mathematical operations based, at least in part on, or indicated by, training and/or inference code (e.g., graph code), a result of which may produce activations (e.g., output values from layers or neurons within a neural network) stored in an activation storage520that are functions of input/output and/or weight parameter data stored in code and/or data storage501and/or code and/or data storage505. In at least one embodiment, activations stored in activation storage520are generated according to linear algebraic and or matrix-based mathematics performed by ALU(s)510in response to performing instructions or other code, wherein weight values stored in code and/or data storage505and/or code and/or data storage501are used as operands along 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 code and/or data storage505or code and/or data storage501or another storage on or off-chip.

In at least one embodiment, ALU(s)510are included within one or more processors or other hardware logic devices or circuits, whereas in another embodiment, ALU(s)510may be external to a processor or other hardware logic device or circuit that uses them (e.g., a co-processor). In at least one embodiment, ALUs510may be included within a processor's execution units or otherwise within a bank of ALUs accessible by a processor's execution units either within same processor or distributed between different processors of different types (e.g., central processing units, graphics processing units, fixed function units, etc.). In at least one embodiment, code and/or data storage501, code and/or data storage505, and activation storage520may be on same processor or other hardware logic device or circuit, whereas in another embodiment, they may be in different processors or other hardware logic devices or circuits, or some combination of same and different processors or other hardware logic devices or circuits. In at least one embodiment, any portion of activation storage520may be included with other on-chip or off-chip data storage, including a processor's L1, L2, or L3 cache or system memory. Furthermore, inferencing and/or training code may be stored with other code accessible to a processor or other hardware logic or circuit and fetched and/or processed using a processor's fetch, decode, scheduling, execution, retirement and/or other logical circuits.

In at least one embodiment, activation storage520may be cache memory, DRAM, SRAM, non-volatile memory (e.g., Flash memory), or other storage. In at least one embodiment, activation storage520may be completely or partially within or external to one or more processors or other logical circuits. In at least one embodiment, choice of whether activation storage520is internal or external to a processor, for example, or comprised of DRAM, SRAM, Flash or some other storage type may depend on available storage on-chip versus off-chip, latency requirements of training and/or inferencing functions being performed, batch size of data used in inferencing and/or training of a neural network, or some combination of these factors. In at least one embodiment, inference and/or training logic515illustrated inFIG.5Amay be used in conjunction with an application-specific integrated circuit (“ASIC”), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic515illustrated inFIG.5Amay be used in conjunction with central processing unit (“CPU”) hardware, graphics processing unit (“GPU”) hardware or other hardware, such as data processing unit (“DPU”) hardware, or field programmable gate arrays (“FPGAs”).

FIG.5Billustrates inference and/or training logic515, according to at least one or more embodiments. In at least one embodiment, inference and/or training logic515may include, without limitation, hardware logic in which computational resources are dedicated or otherwise exclusively used 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, inference and/or training logic515illustrated inFIG.5Bmay be used in conjunction with an application-specific integrated circuit (ASIC), such as Tensorflow® Processing Unit from Google, an inference processing unit (IPU) from Graphcore™, or a Nervana® (e.g., “Lake Crest”) processor from Intel Corp. In at least one embodiment, inference and/or training logic515illustrated inFIG.5Bmay be used in conjunction with central processing unit (CPU) hardware, graphics processing unit (GPU) hardware or other hardware, such as data processing unit (“DPU”) hardware, or field programmable gate arrays (FPGAs). In at least one embodiment, inference and/or training logic515includes, without limitation, code and/or data storage501and code and/or data storage505, which may 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 inFIG.5B, each of code and/or data storage501and code and/or data storage505is associated with a dedicated computational resource, such as computational hardware502and computational hardware506, respectively. In at least one embodiment, each of computational hardware502and computational hardware506comprises one or more ALUs that perform mathematical functions, such as linear algebraic functions, only on information stored in code and/or data storage501and code and/or data storage505, respectively, result of which is stored in activation storage520.

In at least one embodiment, each of code and/or data storage501and505and corresponding computational hardware502and506, respectively, correspond to different layers of a neural network, such that resulting activation from one “storage/computational pair501/502” of code and/or data storage501and computational hardware502is provided as an input to “storage/computational pair505/506” of code and/or data storage505and computational hardware506, in order to mirror conceptual organization of a neural network. In at least one embodiment, each of storage/computational pairs501/502and505/506may correspond to more than one neural network layer. In at least one embodiment, additional storage/computation pairs (not shown) subsequent to or in parallel with storage computation pairs501/502and505/506may be included in inference and/or training logic515.

FIG.6illustrates an example data center600, in which at least one embodiment may be used. In at least one embodiment, data center600includes a data center infrastructure layer610, a framework layer620, a software layer630, and an application layer1240.

In at least one embodiment, as shown inFIG.6, data center infrastructure layer610may include a resource orchestrator612, grouped computing resources614, and node computing resources (“node C.R.s”)616(1)-616(N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s616(1)-616(N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field programmable gate arrays (FPGAs), data processing units, graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s616(1)-616(N) may be a server having one or more of above-mentioned computing resources.

In at least one embodiment, grouped computing resources614may include separate groupings of node C.R.s housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s within grouped computing resources614may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s including CPUs or processors may grouped within one or more racks to provide compute resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination.

In at least one embodiment, resource orchestrator612may configure or otherwise control one or more node C.R.s616(1)-616(N) and/or grouped computing resources614. In at least one embodiment, resource orchestrator612may include a software design infrastructure (“SDI”) management entity for data center600. In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof.

In at least one embodiment, as shown inFIG.6, framework layer620includes a job scheduler622, a configuration manager624, a resource manager626and a distributed file system628. In at least one embodiment, framework layer620may include a framework to support software632of software layer630and/or one or more application(s)642of application layer640. In at least one embodiment, software632or application(s)642may respectively 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, framework layer620may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system628for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler622may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center600. In at least one embodiment, configuration manager624may be capable of configuring different layers such as software layer630and framework layer620including Spark and distributed file system628for supporting large-scale data processing. In at least one embodiment, resource manager626may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system628and job scheduler622. In at least one embodiment, clustered or grouped computing resources may include grouped computing resource614at data center infrastructure layer610. In at least one embodiment, resource manager626may coordinate with resource orchestrator612to manage these mapped or allocated computing resources.

In at least one embodiment, software632included in software layer630may include software used by at least portions of node C.R.s616(1)-616(N), grouped computing resources614, and/or distributed file system628of framework layer620. The one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software.

In at least one embodiment, application(s)642included in application layer640may include one or more types of applications used by at least portions of node C.R.s616(1)-616(N), grouped computing resources614, and/or distributed file system628of framework layer620. One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager624, resource manager626, and resource orchestrator612may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center600from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center.

In at least one embodiment, data center600may include tools, services, software, or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, 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 described above with respect to data center600. In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center600by using weight parameters calculated through one or more training techniques described herein.

In at least one embodiment, data center may use CPUs, application-specific integrated circuits (ASICs), GPUs, DPUs FPGAs, or other hardware to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services.

Inference and/or training logic515are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B. In at least one embodiment, inference and/or training logic515may be used in systemFIG.6for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.7is 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 some combination thereof700formed with a processor that may include execution units to execute an instruction, according to at least one embodiment. In at least one embodiment, computer system700may include, without limitation, a component, such as a processor702to employ execution units including logic to perform algorithms for process data, in accordance with present disclosure, such as in embodiment described herein. In at least one embodiment, computer system700may include processors, such as PENTIUM® Processor family, Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel® Nervana™ microprocessors available from Intel Corporation of Santa Clara, California, although other systems (including PCs having other microprocessors, engineering workstations, set-top boxes and like) may also be used. In at least one embodiment, computer system700may execute a version of WINDOWS' operating system available from Microsoft Corporation of Redmond, Wash., although other operating systems (UNIX and Linux for example), embedded software, and/or graphical user interfaces, may also be used.

Embodiments may be used in other devices such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, embedded applications may include a microcontroller, a digital signal processor (“DSP”), system on a chip, network computers (“NetPCs”), set-top boxes, network hubs, wide area network (“WAN”) switches, edge devices, Internet-of-Things (“IoT”) devices, or any other system that may perform one or more instructions in accordance with at least one embodiment.

In at least one embodiment, computer system700may include, without limitation, processor702that may include, without limitation, one or more execution units708to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system700is a single processor desktop or server system, but in another embodiment computer system700may be a multiprocessor system. In at least one embodiment, processor702may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor702may be coupled to a processor bus710that may transmit data signals between processor702and other components in computer system700.

In at least one embodiment, processor702may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”)704. In at least one embodiment, processor702may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor702. Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file706may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register.

In at least one embodiment, execution unit708, including, without limitation, logic to perform integer and floating point operations, also resides in processor702. In at least one embodiment, processor702may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit708may include logic to handle a packed instruction set709. In at least one embodiment, by including packed instruction set709in an instruction set of a general-purpose processor702, along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor702. In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor's data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor's data bus to perform one or more operations one data element at a time.

In at least one embodiment, execution unit708may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system700may include, without limitation, a memory720. In at least one embodiment, memory720may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory720may store instruction(s)719and/or data721represented by data signals that may be executed by processor702.

In at least one embodiment, system logic chip may be coupled to processor bus710and memory720. In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”)716, and processor702may communicate with MCH716via processor bus710. In at least one embodiment, MCH716may provide a high bandwidth memory path718to memory720for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH716may direct data signals between processor702, memory720, and other components in computer system700and to bridge data signals between processor bus710, memory720, and a system I/O722. In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH716may be coupled to memory720through a high bandwidth memory path718and graphics/video card712may be coupled to MCH716through an Accelerated Graphics Port (“AGP”) interconnect714.

In at least one embodiment, computer system700may use system I/O722that is a proprietary hub interface bus to couple MCH716to I/O controller hub (“ICH”)730. In at least one embodiment, ICH730may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory720, chipset, and processor702. Examples may include, without limitation, an audio controller729, a firmware hub (“flash BIOS”)728, a wireless transceiver726, a data storage724, a legacy I/O controller723containing user input and keyboard interfaces725, a serial expansion port727, such as Universal Serial Bus (“USB”), and a network controller734, which may include in some embodiments, a data processing unit. Data storage724may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device.

In at least one embodiment,FIG.7illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.7may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of computer system700are interconnected using compute express link (CXL) interconnects.

Inference and/or training logic515are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B. In at least one embodiment, inference and/or training logic515may be used in systemFIG.7for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.8is a block diagram illustrating an electronic device800for utilizing a processor810, according to at least one embodiment. In at least one embodiment, electronic device800may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, an edge device, an IoT device, or any other suitable electronic device.

In at least one embodiment, system800may include, without limitation, processor810communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor810coupled 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.8illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,FIG.8may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated inFIG.8may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components ofFIG.8are interconnected using compute express link (CXL) interconnects.

In at least one embodiment,FIG.8may include a display824, a touch screen825, a touch pad830, a Near Field Communications unit (“NFC”)845, a sensor hub840, a thermal sensor846, an Express Chipset (“EC”)835, a Trusted Platform Module (“TPM”)838, BIOS/firmware/flash memory (“BIOS, FW Flash”)822, a DSP860, a drive820such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”)850, a Bluetooth unit852, a Wireless Wide Area Network unit (“WWAN”)856, a Global Positioning System (GPS)855, a camera (“USB 3.0 camera”)854such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)815implemented in, for example, LPDDR3standard. These components may each be implemented in any suitable manner.

In at least one embodiment, other components may be communicatively coupled to processor810through components discussed above. In at least one embodiment, an accelerometer841, Ambient Light Sensor (“ALS”)842, compass843, and a gyroscope844may be communicatively coupled to sensor hub840. In at least one embodiment, thermal sensor839, a fan837, a keyboard836, and a touch pad830may be communicatively coupled to EC835. In at least one embodiment, speaker863, headphones864, and microphone (“mic”)865may be communicatively coupled to an audio unit (“audio codec and class d amp”)862, which may in turn be communicatively coupled to DSP860. In at least one embodiment, audio unit864may include, for example and without limitation, an audio coder/decoder (“codec”) and a class D amplifier. In at least one embodiment, SIM card (“SIM”)857may be communicatively coupled to WWAN unit856. In at least one embodiment, components such as WLAN unit850and Bluetooth unit852, as well as WWAN unit856may be implemented in a Next Generation Form Factor (“NGFF”).

Inference and/or training logic515are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B. In at least one embodiment, inference and/or training logic515may be used in systemFIG.8for inferencing or predicting operations based, at least in part, on weight parameters calculated using neural network training operations, neural network functions and/or architectures, or neural network use cases described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.9is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system900includes one or more processors902and one or more graphics processors908, and may be a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors902or processor cores907. In at least one embodiment, system900is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, edge, or embedded devices.

In at least one embodiment, system900may include, or be incorporated within a server-based gaming platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system900is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system900may also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, or virtual reality device. In at least one embodiment, processing system900is a television or set top box device having one or more processors902and a graphical interface generated by one or more graphics processors908.

In at least one embodiment, one or more processors902each include one or more processor cores907to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores907is configured to process a specific instruction set909. In at least one embodiment, instruction set909may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores907may each process a different instruction set909, which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core907may also include other processing devices, such a Digital Signal Processor (DSP).

In at least one embodiment, processor902includes cache memory904. In at least one embodiment, processor902may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor902. In at least one embodiment, processor902also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores907using known cache coherency techniques. In at least one embodiment, register file906is additionally included in processor902which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file906may include general-purpose registers or other registers.

In at least one embodiment, one or more processor(s)902are coupled with one or more interface bus(es)910to transmit communication signals such as address, data, or control signals between processor902and other components in system900. In at least one embodiment, interface bus910, in one embodiment, may be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface910is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s)902include an integrated memory controller916and a platform controller hub930. In at least one embodiment, memory controller916facilitates communication between a memory device and other components of system900, while platform controller hub (PCH)930provides connections to I/O devices via a local I/O bus.

In at least one embodiment, memory device920may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device920may operate as system memory for system900, to store data922and instructions921for use when one or more processors902executes an application or process. In at least one embodiment, memory controller916also couples with an optional external graphics processor912, which may communicate with one or more graphics processors908in processors902to perform graphics and media operations. In at least one embodiment, a display device911may connect to processor(s)902. In at least one embodiment display device911may include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device911may include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In at least one embodiment, platform controller hub930enables peripherals to connect to memory device920and processor902via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller946, a network controller934, a firmware interface928, a wireless transceiver926, touch sensors925, a data storage device924(e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device924may connect 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, touch sensors925may include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver926may be a Wi-Fi 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, firmware interface928enables communication with system firmware, and may be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller934may enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus910. In at least one embodiment, audio controller946is a multi-channel high definition audio controller. In at least one embodiment, system900includes an optional legacy I/O controller940for coupling legacy (e.g., Personal System 2 (PS/2)) devices to system. In at least one embodiment, platform controller hub930may also connect to one or more Universal Serial Bus (USB) controllers942connect input devices, such as keyboard and mouse943combinations, a camera944, or other USB input devices.

In at least one embodiment, an instance of memory controller916and platform controller hub930may be integrated into a discreet external graphics processor, such as external graphics processor911. In at least one embodiment, platform controller hub930and/or memory controller916may be external to one or more processor(s)902. For example, in at least one embodiment, system900may include an external memory controller916and platform controller hub930, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s)902.

Inference and/or training logic515are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B. In at least one embodiment portions or all of inference and/or training logic515may be incorporated into graphics processor908. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in a graphics processor. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.5A or5B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of a graphics processor to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.10is a block diagram of a processor1000having one or more processor cores1002A-1002N, an integrated memory controller1013, and an integrated graphics processor1008, according to at least one embodiment. In at least one embodiment, processor1000may include additional cores up to and including additional core1002N represented by dashed lined boxes. In at least one embodiment, each of processor cores1002A-1002N includes one or more internal cache units1004A-1004N. In at least one embodiment, each processor core also has access to one or more shared cached units1006.

In at least one embodiment, internal cache units1004A-1004N and shared cache units1006represent a cache memory hierarchy within processor1000. In at least one embodiment, cache memory units1004A-1004N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units1006and1004A-1004N.

In at least one embodiment, processor1000may also include a set of one or more bus controller units1016and a system agent core1010. In at least one embodiment, one or more bus controller units1016manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core1010provides management functionality for various processor components. In at least one embodiment, system agent core1010includes one or more integrated memory controllers1013to manage access to various external memory devices (not shown).

In at least one embodiment, one or more of processor cores1002A-1002N include support for simultaneous multi-threading. In at least one embodiment, system agent core1010includes components for coordinating and operating cores1002A-1002N during multi-threaded processing. In at least one embodiment, system agent core1010may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores1002A-1002N and graphics processor1008.

In at least one embodiment, processor1000additionally includes graphics processor1008to execute graphics processing operations. In at least one embodiment, graphics processor1008couples with shared cache units1006, and system agent core1010, including one or more integrated memory controllers1013. In at least one embodiment, system agent core1010also includes a display controller1011to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller1011may also be a separate module coupled with graphics processor1008via at least one interconnect, or may be integrated within graphics processor1008.

In at least one embodiment, a ring based interconnect unit1012is used to couple internal components of processor1000. In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor1008couples with ring interconnect1012via an I/O link1013.

In at least one embodiment, I/O link1013represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module1018, such as an eDRAM module. In at least one embodiment, each of processor cores1002A-1002N and graphics processor1008use embedded memory modules1018as a shared Last Level Cache.

In at least one embodiment, processor cores1002A-1002N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores1002A-1002N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores1002A-1002N execute a common instruction set, while one or more other cores of processor cores1002A-1002N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores1002A-1002N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor1000may be implemented on one or more chips or as an SoC integrated circuit.

Inference and/or training logic515are used to perform inferencing and/or training operations associated with one or more embodiments. Details regarding inference and/or training logic515are provided below in conjunction withFIGS.5A and/or5B. In at least one embodiment portions or all of inference and/or training logic515may be incorporated into processor1000. For example, in at least one embodiment, training and/or inferencing techniques described herein may use one or more of ALUs embodied in graphics processor1008, graphics core(s)1002A-1002N, or other components inFIG.10. Moreover, in at least one embodiment, inferencing and/or training operations described herein may be done using logic other than logic illustrated inFIG.5A or5B. In at least one embodiment, weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor1000to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

FIG.11is an example data flow diagram for a process1100of generating and deploying an image processing and inferencing pipeline, in accordance with at least one embodiment. In at least one embodiment, process1100may be deployed for use with imaging devices, processing devices, and/or other device types at one or more facilities1102. Process1100may be executed within a training system1104and/or a deployment system1106. In at least one embodiment, training system1104may be used to perform training, deployment, and implementation of machine learning models (e.g., neural networks, object detection algorithms, computer vision algorithms, etc.) for use in deployment system1106. In at least one embodiment, deployment system1106may be configured to offload processing and compute resources among a distributed computing environment to reduce infrastructure requirements at facility1102. In at least one embodiment, one or more applications in a pipeline may use or call upon services (e.g., inference, visualization, compute, AI, etc.) of deployment system1106during execution of applications.

In at least one embodiment, some of applications used in advanced processing and inferencing pipelines may use machine learning models or other AI to perform one or more processing steps. In at least one embodiment, machine learning models may be trained at facility1102using data1108(such as imaging data) generated at facility1102(and stored on one or more picture archiving and communication system (PACS) servers at facility1102), may be trained using imaging or sequencing data1108from another facility(ies), or a combination thereof. In at least one embodiment, training system1104may be used to provide applications, services, and/or other resources for generating working, deployable machine learning models for deployment system1106.

In at least one embodiment, model registry1124may be backed by object storage that may support versioning and object metadata. In at least one embodiment, object storage may be accessible through, for example, a cloud storage (e.g., cloud1226ofFIG.12) compatible application programming interface (API) from within a cloud platform. In at least one embodiment, machine learning models within model registry1124may uploaded, listed, modified, or deleted by developers or partners of a system interacting with an API. In at least one embodiment, an API may provide access to methods that allow users with appropriate credentials to associate models with applications, such that models may be executed as part of execution of containerized instantiations of applications.

In at least one embodiment, training pipeline1204(FIG.12) may include a scenario where facility1102is training their own machine learning model, or has an existing machine learning model that needs to be optimized or updated. In at least one embodiment, imaging data1108generated by imaging device(s), sequencing devices, and/or other device types may be received. In at least one embodiment, once imaging data1108is received, AI-assisted annotation1110may be used to aid in generating annotations corresponding to imaging data1108to be used as ground truth data for a machine learning model. In at least one embodiment, AI-assisted annotation1110may include one or more machine learning models (e.g., convolutional neural networks (CNNs)) that may be trained to generate annotations corresponding to certain types of imaging data1108(e.g., from certain devices). In at least one embodiment, AI-assisted annotations1110may then be used directly, or may be adjusted or fine-tuned using an annotation tool to generate ground truth data. In at least one embodiment, AI-assisted annotations1110, labeled clinic data1112, or a combination thereof may be used as ground truth data for training a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1116, and may be used by deployment system1106, as described herein.

In at least one embodiment, training pipeline1204(FIG.12) may include a scenario where facility1102needs a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1106, but facility1102may 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 may be selected from a model registry1124. In at least one embodiment, model registry1124may include machine learning models trained to perform a variety of different inference tasks on imaging data. In at least one embodiment, machine learning models in model registry1124may have been trained on imaging data from different facilities than facility1102(e.g., facilities remotely located). 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, when being trained on imaging data from a specific location, training may take place at that location, or at least in a manner that protects confidentiality of imaging data or restricts imaging data from being transferred off-premises. In at least one embodiment, once a model is trained—or partially trained—at one location, a machine learning model may be added to model registry1124. In at least one embodiment, a machine learning model may then be retrained, or updated, at any number of other facilities, and a retrained or updated model may be made available in model registry1124. In at least one embodiment, a machine learning model may then be selected from model registry1124—and referred to as output model1116—and may be used in deployment system1106to perform one or more processing tasks for one or more applications of a deployment system.

In at least one embodiment, training pipeline1204(FIG.12), a scenario may include facility1102requiring a machine learning model for use in performing one or more processing tasks for one or more applications in deployment system1106, but facility1102may 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 model registry1124may not be fine-tuned or optimized for imaging data1108generated at facility1102because of differences in populations, robustness of training data used to train a machine learning model, diversity in anomalies of training data, and/or other issues with training data. In at least one embodiment, AI-assisted annotation1110may be used to aid in generating annotations corresponding to imaging data1108to be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, labeled data1112may be used as ground truth data for training a machine learning model. In at least one embodiment, retraining or updating a machine learning model may be referred to as model training1114. In at least one embodiment, model training1114—e.g., AI-assisted annotations1110, labeled clinic data1112, or a combination thereof—may be used as ground truth data for retraining or updating a machine learning model. In at least one embodiment, a trained machine learning model may be referred to as output model1116, and may be used by deployment system1106, as described herein.

In at least one embodiment, deployment system1106may include software1118, services1120, hardware1122, and/or other components, features, and functionality. In at least one embodiment, deployment system1106may include a software “stack,” such that software1118may be built on top of services1120and may use services1120to perform some or all of processing tasks, and services1120and software1118may be built on top of hardware1122and use hardware1122to execute processing, storage, and/or other compute tasks of deployment system1106. In at least one embodiment, software1118may include any number of different containers, where each container may execute an instantiation of an application. In at least one embodiment, each application may perform one or more processing tasks in an advanced processing and inferencing pipeline (e.g., inferencing, object detection, feature detection, segmentation, image enhancement, calibration, etc.). In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data1108, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility1102after processing through a pipeline (e.g., to convert outputs back to a usable data type). In at least one embodiment, a combination of containers within software1118(e.g., that make up a pipeline) may be referred to as a virtual instrument (as described in more detail herein), and a virtual instrument may leverage services1120and hardware1122to execute some or all processing tasks of applications instantiated in containers.

In at least one embodiment, a data processing pipeline may receive input data (e.g., imaging data1108) in a specific format in response to an inference request (e.g., a request from a user of deployment system1106). In at least one embodiment, input data may be representative of one or more images, video, and/or other data representations generated by one or more imaging devices. In at least one embodiment, data may undergo pre-processing as part of data processing pipeline to prepare data for processing by one or more applications. In at least one embodiment, post-processing may be performed on an output of one or more inferencing tasks or other processing tasks of a pipeline to prepare an output data for a next application and/or to prepare output data for transmission and/or use by a user (e.g., as a response to an inference request). In at least one embodiment, inferencing tasks may be performed by one or more machine learning models, such as trained or deployed neural networks, which may include output models1116of training system1104.

In at least one embodiment, tasks of data processing pipeline may be encapsulated in a container(s) that each represents a discrete, fully functional instantiation of an application and virtualized computing environment that is able to reference machine learning models. In at least one embodiment, containers or applications may be published into a private (e.g., limited access) area of a container registry (described in more detail herein), and trained or deployed models may be stored in model registry1124and associated with one or more applications. In at least one embodiment, images of applications (e.g., container images) may be available in a container registry, and once selected by a user from a container registry for deployment in a pipeline, an image may be used to generate a container for an instantiation of an application for use by a user's system.

In at least one embodiment, developers (e.g., software developers, clinicians, doctors, etc.) may develop, publish, and store applications (e.g., as containers) for performing image processing and/or inferencing on supplied data. In at least one embodiment, development, publishing, and/or storing may be performed using a software development kit (SDK) associated with a system (e.g., to ensure that an application and/or container developed is compliant with or compatible with a system). In at least one embodiment, an application that is developed may be tested locally (e.g., at a first facility, on data from a first facility) with an SDK which may support at least some of services1120as a system (e.g., system1200ofFIG.12). In at least one embodiment, because DICOM objects may contain anywhere from one to hundreds of images or other data types, and due to a variation in data, a developer may be responsible for managing (e.g., setting constructs for, building pre-processing into an application, etc.) extraction and preparation of incoming data. In at least one embodiment, once validated by system1200(e.g., for accuracy), an application may be available in a container registry for selection and/or implementation by a user to perform one or more processing tasks with respect to data at a facility (e.g., a second facility) of a user.

In at least one embodiment, developers may then share applications or containers through a network for access and use by users of a system (e.g., system1200ofFIG.12). In at least one embodiment, completed and validated applications or containers may be stored in a container registry and associated machine learning models may be stored in model registry1124. In at least one embodiment, a requesting entity—who provides an inference or image processing request—may browse a container registry and/or model registry1124for an application, container, dataset, machine learning model, etc., select a desired combination of elements for inclusion in data processing pipeline, and submit an imaging processing request. In at least one embodiment, a request may include input data (and associated patient data, in some examples) that is necessary to perform a request, and/or may include a selection of application(s) and/or machine learning models to be executed in processing a request. In at least one embodiment, a request may then be passed to one or more components of deployment system1106(e.g., a cloud) to perform processing of data processing pipeline. In at least one embodiment, processing by deployment system1106may include referencing selected elements (e.g., applications, containers, models, etc.) from a container registry and/or model registry1124. In at least one embodiment, once results are generated by a pipeline, results may be returned to a user for reference (e.g., for viewing in a viewing application suite executing on a local, on-premises workstation or terminal).

In at least one embodiment, to aid in processing or execution of applications or containers in pipelines, services1120may be leveraged. In at least one embodiment, services1120may include compute services, artificial intelligence (AI) services, visualization services, and/or other service types. In at least one embodiment, services1120may provide functionality that is common to one or more applications in software1118, so functionality may be abstracted to a service that may be called upon or leveraged by applications. In at least one embodiment, functionality provided by services1120may run dynamically and more efficiently, while also scaling well by allowing applications to process data in parallel (e.g., using a parallel computing platform1230(FIG.12)). In at least one embodiment, rather than each application that shares a same functionality offered by a service1120being required to have a respective instance of service1120, service1120may be shared between and among various applications. In at least one embodiment, services may include an inference server or engine that may be used for executing detection or segmentation tasks, as non-limiting examples. In at least one embodiment, a model training service may be included that may provide machine learning model training and/or retraining capabilities. In at least one embodiment, a data augmentation service may further be included that may 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 may be used that may add image rendering effects—such as ray-tracing, rasterization, denoising, sharpening, etc.—to add realism to two-dimensional (2D) and/or three-dimensional (3D) models. In at least one embodiment, virtual instrument services may be included that provide for beam-forming, segmentation, inferencing, imaging, and/or support for other applications within pipelines of virtual instruments.

In at least one embodiment, where a service1120includes an AI service (e.g., an inference service), one or more machine learning models may be executed by calling upon (e.g., as an API call) an inference service (e.g., an inference server) to execute machine learning model(s), or processing thereof, as part of application execution. In at least one embodiment, where another application includes one or more machine learning models for segmentation tasks, an application may call upon an inference service to execute machine learning models for performing one or more of processing operations associated with segmentation tasks. In at least one embodiment, software1118implementing advanced processing and inferencing pipeline that includes segmentation application and anomaly detection application may be streamlined because each application may call upon a same inference service to perform one or more inferencing tasks.

In at least one embodiment, hardware1122may include GPUs, CPUs, DPUs, 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 hardware1122may be used to provide efficient, purpose-built support for software1118and services1120in deployment system1106. In at least one embodiment, use of GPU processing may be implemented for processing locally (e.g., at facility1102), within an AI/deep learning system, in a cloud system, and/or in other processing components of deployment system1106to improve efficiency, accuracy, and efficacy of image processing and generation. In at least one embodiment, software1118and/or services1120may 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 some of computing environment of deployment system1106and/or training system1104may be executed in a datacenter one or more supercomputers or high performance computing systems, with GPU optimized software (e.g., hardware and software combination of NVIDIA's DGX System). In at least one embodiment, hardware1122may include any number of GPUs that may be called upon to perform processing of data in parallel, as described herein. In at least one embodiment, cloud platform may further include GPU processing for GPU-optimized execution of deep learning tasks, machine learning tasks, or other computing tasks. In at least one embodiment, cloud platform may further include DPU processing to transmit data received over a network and/or through a network controller or other network interface directly to (e.g., a memory of) one or more GPU(s). In at least one embodiment, cloud platform (e.g., NVIDIA's NGC) may be executed using an AI/deep learning supercomputer(s) and/or GPU-optimized software (e.g., as provided on NVIDIA's DGX Systems) as a hardware abstraction and scaling platform. In at least one embodiment, cloud platform may integrate an application container clustering system or orchestration system (e.g., KUBERNETES) on multiple GPUs to enable seamless scaling and load balancing.

FIG.12is a system diagram for an example system1200for generating and deploying an imaging deployment pipeline, in accordance with at least one embodiment. In at least one embodiment, system1200may be used to implement process1100ofFIG.11and/or other processes including advanced processing and inferencing pipelines. In at least one embodiment, system1200may include training system1104and deployment system1106. In at least one embodiment, training system1104and deployment system1106may be implemented using software1118, services1120, and/or hardware1122, as described herein.

In at least one embodiment, system1200(e.g., training system1104and/or deployment system1106) may implemented in a cloud computing environment (e.g., using cloud1226). In at least one embodiment, system1200may be implemented locally with respect to a healthcare services facility, or as a combination of both cloud and local computing resources. In at least one embodiment, access to APIs in cloud1226may be restricted to authorized users through enacted security measures or protocols. In at least one embodiment, a security protocol may include web tokens that may be signed by an authentication (e.g., AuthN, AuthZ, Gluecon, etc.) service and may carry appropriate authorization. In at least one embodiment, APIs of virtual instruments (described herein), or other instantiations of system1200, may be restricted to a set of public IPs that have been vetted or authorized for interaction.

In at least one embodiment, various components of system1200may communicate between and among one another using any of a variety 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 facilities and components of system1200(e.g., for transmitting inference requests, for receiving results of inference requests, etc.) may be communicated over data bus(ses), wireless data protocols (Wi-Fi), wired data protocols (e.g., Ethernet), etc.

In at least one embodiment, training system1104may execute training pipelines1204, similar to those described herein with respect toFIG.11. In at least one embodiment, where one or more machine learning models are to be used in deployment pipelines1210by deployment system1106, training pipelines1204may be used to train or retrain one or more (e.g. pre-trained) models, and/or implement one or more of pre-trained models1206(e.g., without a need for retraining or updating). In at least one embodiment, as a result of training pipelines1204, output model(s)1116may be generated. In at least one embodiment, training pipelines1204may include any number of processing steps, such as but not limited to imaging data (or other input data) conversion or adaption In at least one embodiment, for different machine learning models used by deployment system1106, different training pipelines1204may be used. In at least one embodiment, training pipeline1204similar to a first example described with respect toFIG.11may be used for a first machine learning model, training pipeline1204similar to a second example described with respect toFIG.11may be used for a second machine learning model, and training pipeline1204similar to a third example described with respect toFIG.11may be used for a third machine learning model. In at least one embodiment, any combination of tasks within training system1104may be used depending on what is required for each respective machine learning model. In at least one embodiment, one or more of machine learning models may already be trained and ready for deployment so machine learning models may not undergo any processing by training system1104, and may be implemented by deployment system1106.

In at least one embodiment, output model(s)1116and/or pre-trained model(s)1206may include any types of machine learning models depending on implementation or embodiment. In at least one embodiment, and without limitation, machine learning models used by system1200may include machine learning model(s) using linear regression, logistic regression, decision trees, support vector machines (SVM), Naïve Bayes, k-nearest neighbor (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 pipelines1204may include AI-assisted annotation, as described in more detail herein with respect to at leastFIG.11B. In at least one embodiment, labeled data1112(e.g., traditional annotation) may be generated by any number of techniques. In at least one embodiment, labels or other annotations may 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 may be hand drawn, in some examples. In at least one embodiment, ground truth data may be synthetically produced (e.g., generated from computer models or renderings), real produced (e.g., designed and produced from real-world data), machine-automated (e.g., using feature analysis and learning to extract features from data and then generate labels), human annotated (e.g., labeler, or annotation expert, defines location of labels), and/or a combination thereof. In at least one embodiment, for each instance of imaging data1108(or other data type used by machine learning models), there may be corresponding ground truth data generated by training system1104. In at least one embodiment, AI-assisted annotation may be performed as part of deployment pipelines1210; either in addition to, or in lieu of AI-assisted annotation included in training pipelines1204. In at least one embodiment, system1200may include a multi-layer platform that may include a software layer (e.g., software1118) of diagnostic applications (or other application types) that may perform one or more medical imaging and diagnostic functions. In at least one embodiment, system1200may be communicatively coupled to (e.g., via encrypted links) PACS server networks of one or more facilities. In at least one embodiment, system1200may be configured to access and referenced data from PACS servers to perform operations, such as training machine learning models, deploying machine learning models, image processing, inferencing, and/or other operations.

In at least one embodiment, a software layer may be implemented as a secure, encrypted, and/or authenticated API through which applications or containers may be invoked (e.g., called) from an external environment(s) (e.g., facility1102). In at least one embodiment, applications may then call or execute one or more services1120for performing compute, AI, or visualization tasks associated with respective applications, and software1118and/or services1120may leverage hardware1122to perform processing tasks in an effective and efficient manner.

In at least one embodiment, deployment system1106may execute deployment pipelines1210. In at least one embodiment, deployment pipelines1210may include any number of applications that may be sequentially, non-sequentially, or otherwise applied 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, as described herein, a deployment pipeline1210for an individual device may 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.). In at least one embodiment, for a single device, there may be more than one deployment pipeline1210depending on information desired from data generated by a device. In at least one embodiment, where detections of anomalies are desired from an MRI machine, there may be a first deployment pipeline1210, and where image enhancement is desired from output of an MRI machine, there may be a second deployment pipeline1210.

In at least one embodiment, an image generation application may include a processing task that includes use of a machine learning model. In at least one embodiment, a user may desire to use their own machine learning model, or to select a machine learning model from model registry1124. In at least one embodiment, a user may implement their own machine learning model or select a machine learning model for inclusion in an application for performing a processing task. In at least one embodiment, applications may be selectable and customizable, and by defining constructs of applications, deployment, and implementation of applications for a particular user are presented as a more seamless user experience. In at least one embodiment, by leveraging other features of system1200—such as services1120and hardware1122—deployment pipelines1210may be even more user friendly, provide for easier integration, and produce more accurate, efficient, and timely results.

In at least one embodiment, deployment system1106may include a user interface1214(e.g., a graphical user interface, a web interface, etc.) that may be used to select applications for inclusion in deployment pipeline(s)1210, arrange applications, modify, or change applications or parameters or constructs thereof, use and interact with deployment pipeline(s)1210during set-up and/or deployment, and/or to otherwise interact with deployment system1106. In at least one embodiment, although not illustrated with respect to training system1104, user interface1214(or a different user interface) may be used for selecting models for use in deployment system1106, for selecting models for training, or retraining, in training system1104, and/or for otherwise interacting with training system1104.

In at least one embodiment, pipeline manager1212may be used, in addition to an application orchestration system1228, to manage interaction between applications or containers of deployment pipeline(s)1210and services1120and/or hardware1122. In at least one embodiment, pipeline manager1212may be configured to facilitate interactions from application to application, from application to service1120, and/or from application or service to hardware1122. In at least one embodiment, although illustrated as included in software1118, this is not intended to be limiting, and in some examples (e.g., as illustrated inFIG.10) pipeline manager1212may be included in services1120. In at least one embodiment, application orchestration system1228(e.g., Kubernetes, DOCKER, etc.) may include a container orchestration system that may group applications into containers as logical units for coordination, management, scaling, and deployment. In at least one embodiment, by associating applications from deployment pipeline(s)1210(e.g., a reconstruction application, a segmentation application, etc.) with individual containers, each application may execute in a self-contained environment (e.g., at a kernel level) to increase speed and efficiency.

In at least one embodiment, each application and/or container (or image thereof) may be individually developed, modified, and deployed (e.g., a first user or developer may develop, modify, and deploy a first application and a second user or developer may develop, modify, and deploy a second application separate from a first user or developer), which may allow for focus on, and attention to, a task of a single application and/or container(s) without being hindered by tasks of another application(s) or container(s). In at least one embodiment, communication, and cooperation between different containers or applications may be aided by pipeline manager1212and application orchestration system1228. In at least one embodiment, so long as an expected input and/or output of each container or application is known by a system (e.g., based on constructs of applications or containers), application orchestration system1228and/or pipeline manager1212may facilitate communication among and between, and sharing of resources among and between, each of applications or containers. In at least one embodiment, because one or more of applications or containers in deployment pipeline(s)1210may share same services and resources, application orchestration system1228may orchestrate, load balance, and determine sharing of services or resources between and among various applications or containers. In at least one embodiment, a scheduler may be used to track resource requirements of applications or containers, current usage or planned usage of these resources, and resource availability. In at least one embodiment, a scheduler may thus allocate resources to different applications and distribute resources between and among applications in view of requirements and availability of a system. In some examples, a scheduler (and/or other component of application orchestration system1228) may determine resource availability and distribution based on constraints imposed on a system (e.g., user constraints), such as quality of service (QoS), urgency of need for data outputs (e.g., to determine whether to execute real-time processing or delayed processing), etc.

In at least one embodiment, services1120leveraged by and shared by applications or containers in deployment system1106may include compute services1216, AI services1218, visualization services1220, and/or other service types. In at least one embodiment, applications may call (e.g., execute) one or more of services1120to perform processing operations for an application. In at least one embodiment, compute services1216may be leveraged by applications to perform super-computing or other high-performance computing (HPC) tasks. In at least one embodiment, compute service(s)1216may be leveraged to perform parallel processing (e.g., using a parallel computing platform1230) for processing data through one or more of applications and/or one or more tasks of a single application, substantially simultaneously. In at least one embodiment, parallel computing platform1230(e.g., NVIDIA's CUDA) may enable general purpose computing on GPUs (GPGPU) (e.g., GPUs1222). In at least one embodiment, a software layer of parallel computing platform1230may provide access to virtual instruction sets and parallel computational elements of GPUs, for execution of compute kernels. In at least one embodiment, parallel computing platform1230may include memory and, in some embodiments, a memory may be shared between and among multiple containers, and/or between and among different processing tasks within a single container. In at least one embodiment, inter-process communication (IPC) calls may be generated for multiple containers and/or for multiple processes within a container to use same data from a shared segment of memory of parallel computing platform1230(e.g., where multiple different stages of an application or multiple applications are processing same information). In at least one embodiment, rather than making a copy of data and moving data to different locations in memory (e.g., a read/write operation), same data in same location of a memory may be used for any number of processing tasks (e.g., at a same time, at different times, etc.). In at least one embodiment, as data is used to generate new data as a result of processing, this information of a new location of data may be stored and shared between various applications. In at least one embodiment, location of data and a location of updated or modified data may be part of a definition of how a payload is understood within containers.

In at least one embodiment, AI services1218may be leveraged to perform inferencing services for executing machine learning model(s) associated with applications (e.g., tasked with performing one or more processing tasks of an application). In at least one embodiment, AI services1218may leverage AI system1224to execute machine learning model(s) (e.g., neural networks, such as CNNs) for segmentation, reconstruction, object detection, feature detection, classification, and/or other inferencing tasks. In at least one embodiment, applications of deployment pipeline(s)1210may use one or more of output models1116from training system1104and/or other models of applications to perform inference on imaging data. In at least one embodiment, two or more examples of inferencing using application orchestration system1228(e.g., a scheduler) may be available. In at least one embodiment, a first category may include a high priority/low latency path that may achieve higher service level agreements, such as for performing inference on urgent requests during an emergency, or for a radiologist during diagnosis. In at least one embodiment, a second category may include a standard priority path that may be used for requests that may be non-urgent or where analysis may be performed at a later time. In at least one embodiment, application orchestration system1228may distribute resources (e.g., services1120and/or hardware1122) based on priority paths for different inferencing tasks of AI services1218.

In at least one embodiment, shared storage may be mounted to AI services1218within system1200. In at least one embodiment, shared storage may operate as a cache (or other storage device type) and may be used to process inference requests from applications. In at least one embodiment, when an inference request is submitted, a request may be received by a set of API instances of deployment system1106, and one or more instances may be selected (e.g., for best fit, for load balancing, etc.) to process a request. In at least one embodiment, to process a request, a request may be entered into a database, a machine learning model may be located from model registry1124if not already in a cache, a validation step may ensure appropriate machine learning model is loaded into a cache (e.g., shared storage), and/or a copy of a model may be saved to a cache. In at least one embodiment, a scheduler (e.g., of pipeline manager1212) may be used to launch an application that is referenced in a request if an application is not already running or if there are not enough instances of an application. In at least one embodiment, if an inference server is not already launched to execute a model, an inference server may be launched. Any number of inference servers may be launched per model. In at least one embodiment, in a pull model, in which inference servers are clustered, models may be cached whenever load balancing is advantageous. In at least one embodiment, inference servers may be statically loaded in corresponding, distributed servers.

In at least one embodiment, inferencing may be performed using an inference server that runs in a container. In at least one embodiment, an instance of an inference server may 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 may be loaded. In at least one embodiment, when starting an inference server, a model may be passed to an inference server such that a same container may be used to serve different models so long as inference server is running as a different instance.

In at least one embodiment, during application execution, an inference request for a given application may be received, and a container (e.g., hosting an instance of an inference server) may be loaded (if not already), and a start procedure may be called. In at least one embodiment, pre-processing logic in a container may load, decode, and/or perform any additional pre-processing on incoming data (e.g., using a CPU(s) and/or GPU(s) and/or DPU(s)). In at least one embodiment, once data is prepared for inference, a container may perform inference as necessary on data. In at least one embodiment, this may include a single inference call on one image (e.g., a hand X-ray), or may require inference on hundreds of images (e.g., a chest CT). In at least one embodiment, an application may summarize results before completing, which may include, without limitation, 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 may be assigned different priorities. For example, some models may have a real-time (TAT<1 min) priority while others may have lower priority (e.g., TAT<12 min). In at least one embodiment, model execution times may be measured from requesting institution or entity and may include partner network traversal time, as well as execution on an inference service.

In at least one embodiment, transfer of requests between services1120and inference applications may be hidden behind a software development kit (SDK), and robust transport may be provided through a queue. In at least one embodiment, a request will be placed in a queue via an API for an individual application/tenant ID combination and an SDK will pull a request from a queue and give a request to an application. In at least one embodiment, a name of a queue may be provided in an environment from where an SDK will pick it up. In at least one embodiment, asynchronous communication through a queue may be useful as it may allow any instance of an application to pick up work as it becomes available. Results may be transferred back through a queue, to ensure no data is lost. In at least one embodiment, queues may also provide an ability to segment work, as highest priority work may go to a queue with most instances of an application connected to it, while lowest priority work may go to a queue with a single instance connected to it that processes tasks in an order received. In at least one embodiment, an application may run on a GPU-accelerated instance generated in cloud1226, and an inference service may perform inferencing on a GPU.

In at least one embodiment, visualization services1220may be leveraged to generate visualizations for viewing outputs of applications and/or deployment pipeline(s)1210. In at least one embodiment, GPUs1222may be leveraged by visualization services1220to generate visualizations. In at least one embodiment, rendering effects, such as ray-tracing, may be implemented by visualization services1220to generate higher quality visualizations. In at least one embodiment, visualizations may include, without limitation, 2D image renderings, 3D volume renderings, 3D volume reconstruction, 2D tomographic slices, virtual reality displays, augmented reality displays, etc. In at least one embodiment, virtualized environments may be used to generate a virtual interactive display or environment (e.g., a virtual environment) for interaction by users of a system (e.g., doctors, nurses, radiologists, etc.). In at least one embodiment, visualization services1220may include an internal visualizer, cinematics, and/or other rendering or image processing capabilities or functionality (e.g., ray tracing, rasterization, internal optics, etc.).

In at least one embodiment, hardware1122may include GPUs1222, AI system1224, cloud1226, and/or any other hardware used for executing training system1104and/or deployment system1606. In at least one embodiment, GPUs1222(e.g., NVIDIA's TESLA and/or QUADRO GPUs) may include any number of GPUs that may be used for executing processing tasks of compute services1216, AI services1218, visualization services1220, other services, and/or any of features or functionality of software1118. For example, with respect to AI services1218, GPUs1222may be used to perform pre-processing on imaging data (or other data types used by machine learning models), post-processing on outputs of machine learning models, and/or to perform inferencing (e.g., to execute machine learning models). In at least one embodiment, cloud1226, AI system1224, and/or other components of system1200may use GPUs1222. In at least one embodiment, cloud1226may include a GPU-optimized platform for deep learning tasks. In at least one embodiment, AI system1224may use GPUs, and cloud1226—or at least a portion tasked with deep learning or inferencing—may be executed using one or more AI systems1224. As such, although hardware1122is illustrated as discrete components, this is not intended to be limiting, and any components of hardware1122may be combined with, or leveraged by, any other components of hardware1122.

In at least one embodiment, AI system1224may include a purpose-built computing system (e.g., a super-computer or an HPC) configured for inferencing, deep learning, machine learning, and/or other artificial intelligence tasks. In at least one embodiment, AI system1224(e.g., NVIDIA's DGX) may include GPU-optimized software (e.g., a software stack) that may be executed using a plurality of GPUs1222, in addition to DPUs, CPUs, RAM, storage, and/or other components, features, or functionality. In at least one embodiment, one or more AI systems1224may be implemented in cloud1226(e.g., in a data center) for performing some or all of AI-based processing tasks of system1200.

In at least one embodiment, cloud1226may include a GPU-accelerated infrastructure (e.g., NVIDIA's NGC) that may provide a GPU-optimized platform for executing processing tasks of system1200. In at least one embodiment, cloud1226may include an AI system(s)1224for performing one or more of AI-based tasks of system1200(e.g., as a hardware abstraction and scaling platform). In at least one embodiment, cloud1226may integrate with application orchestration system1228leveraging multiple GPUs to enable seamless scaling and load balancing between and among applications and services1120. In at least one embodiment, cloud1226may tasked with executing at least some of services1120of system1200, including compute services1216, AI services1218, and/or visualization services1220, as described herein. In at least one embodiment, cloud1226may perform small and large batch inference (e.g., executing NVIDIA's TENSOR RT), provide an accelerated parallel computing API and platform1230(e.g., NVIDIA's CUDA), execute application orchestration system1228(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 cinematics), and/or may provide other functionality for system1200.

FIG.13Aillustrates a data flow diagram for a process1300to train, retrain, or update a machine learning model, in accordance with at least one embodiment. In at least one embodiment, process1300may be executed using, as a non-limiting example, system1200ofFIG.12. In at least one embodiment, process1300may leverage services1120and/or hardware1122of system1200, as described herein. In at least one embodiment, refined models1312generated by process1300may be executed by deployment system1106for one or more containerized applications in deployment pipelines1210.

In at least one embodiment, model training1114may include retraining or updating an initial model1304(e.g., a pre-trained model) using new training data (e.g., new input data, such as customer dataset1306, and/or new ground truth data associated with input data). In at least one embodiment, to retrain, or update, initial model1304, output or loss layer(s) of initial model1304may be reset, or deleted, and/or replaced with an updated or new output or loss layer(s). In at least one embodiment, initial model1304may have previously fine-tuned parameters (e.g., weights and/or biases) that remain from prior training, so training or retraining1114may not take as long or require as much processing as training a model from scratch. In at least one embodiment, during model training1114, by having reset or replaced output or loss layer(s) of initial model1304, parameters may be updated and re-tuned for a new data set based on loss calculations associated with accuracy of output or loss layer(s) at generating predictions on new, customer dataset1306(e.g., image data1108ofFIG.11).

In at least one embodiment, pre-trained models1206may be stored in a data store, or registry (e.g., model registry1124ofFIG.11). In at least one embodiment, pre-trained models1206may have been trained, at least in part, at one or more facilities other than a facility executing process1300. In at least one embodiment, to protect privacy and rights of patients, subjects, or clients of different facilities, pre-trained models1206may have been trained, on-premise, using customer or patient data generated on-premise. In at least one embodiment, pre-trained models1206may be trained using cloud1226and/or other hardware1122, but confidential, privacy protected patient data may not be transferred to, used by, or accessible to any components of cloud1226(or other off premise hardware). In at least one embodiment, where a pre-trained model1206is trained at using patient data from more than one facility, pre-trained model1206may have been individually trained for each facility prior to being trained on patient or customer data from another facility. In at least one embodiment, such as where a customer or patient data has been released of privacy concerns (e.g., by waiver, for experimental use, etc.), or where a customer or patient data is included in a public data set, a customer or patient data from any number of facilities may be used to train pre-trained model1206on-premise and/or off premise, such as in a datacenter or other cloud computing infrastructure.

In at least one embodiment, when selecting applications for use in deployment pipelines1210, a user may also select machine learning models to be used for specific applications. In at least one embodiment, a user may not have a model for use, so a user may select a pre-trained model1206to use with an application. In at least one embodiment, pre-trained model1206may not be optimized for generating accurate results on customer dataset1306of a facility of a user (e.g., based on patient diversity, demographics, types of medical imaging devices used, etc.). In at least one embodiment, prior to deploying pre-trained model1206into deployment pipeline1210for use with an application(s), pre-trained model1206may be updated, retrained, and/or fine-tuned for use at a respective facility.

In at least one embodiment, a user may select pre-trained model1206that is to be updated, retrained, and/or fine-tuned, and pre-trained model1206may be referred to as initial model1304for training system1104within process1300. In at least one embodiment, customer dataset1306(e.g., imaging data, genomics data, sequencing data, or other data types generated by devices at a facility) may be used to perform model training1114(which may include, without limitation, transfer learning) on initial model1304to generate refined model1312. In at least one embodiment, ground truth data corresponding to customer dataset1306may be generated by training system1104. In at least one embodiment, ground truth data may be generated, at least in part, by clinicians, scientists, doctors, practitioners, at a facility (e.g., as labeled clinic data1112ofFIG.11).

In at least one embodiment, AI-assisted annotation1110may be used in some examples to generate ground truth data. In at least one embodiment, AI-assisted annotation1110(e.g., implemented using an AI-assisted annotation SDK) may 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, user1310may use annotation tools within a user interface (a graphical user interface (GUI)) on computing device1308.

In at least one embodiment, user1310may interact with a GUI via computing device1308to edit or fine-tune (auto)annotations. In at least one embodiment, a polygon editing feature may be used to move vertices of a polygon to more accurate or fine-tuned locations.

In at least one embodiment, once customer dataset1306has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training1114to generate refined model1312. In at least one embodiment, customer dataset1306may be applied to initial model1304any number of times, and ground truth data may be used to update parameters of initial model1304until an acceptable level of accuracy is attained for refined model1312. In at least one embodiment, once refined model1312is generated, refined model1312may be deployed within one or more deployment pipelines1210at a facility for performing one or more processing tasks with respect to medical imaging data.

In at least one embodiment, refined model1312may be uploaded to pre-trained models1206in model registry1124to be selected by another facility. In at least one embodiment, his process may be completed at any number of facilities such that refined model1312may be further refined on new datasets any number of times to generate a more universal model.

FIG.13Bis an example illustration of a client-server architecture1332to enhance annotation tools with pre-trained annotation models, in accordance with at least one embodiment. In at least one embodiment, AI-assisted annotation tools1336may be instantiated based on a client-server architecture1332. In at least one embodiment, annotation tools1336in imaging applications may aid radiologists, for example, identify organs and abnormalities. In at least one embodiment, imaging applications may include software tools that help user1310to identify, as a non-limiting example, a few extreme points on a particular organ of interest in raw images1334(e.g., in a 3D MRI or CT scan) and receive auto-annotated results for all 2D slices of a particular organ. In at least one embodiment, results may be stored in a data store as training data1338and used as (for example and without limitation) ground truth data for training. In at least one embodiment, when computing device1308sends extreme points for AI-assisted annotation1110, a deep learning model, for example, may receive this data as input and return inference results of a segmented organ or abnormality. In at least one embodiment, pre-instantiated annotation tools, such as AI-Assisted Annotation Tool1336B inFIG.13B, may be enhanced by making API calls (e.g., API Call1344) to a server, such as an Annotation Assistant Server1340that may include a set of pre-trained models1342stored in an annotation model registry, for example. In at least one embodiment, an annotation model registry may store pre-trained models1342(e.g., machine learning models, such as deep learning models) that are pre-trained to perform AI-assisted annotation on a particular organ or abnormality. These models may be further updated by using training pipelines1204. In at least one embodiment, pre-installed annotation tools may be improved over time as new labeled clinic data1112is added.

Such components may be used to generate synthetic data imitating failure cases in a network training process, which may help to improve performance of the network while limiting the amount of synthetic data to avoid overfitting.

Other variations are within spirit of present disclosure. Thus, while disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the disclosure to a specific form or forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the disclosure, as defined in appended claims.

Use of terms “a” and “an” and “the” and similar referents in the context of describing disclosed embodiments (especially in the context of following claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context, and not as a definition of a term. Terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (meaning “including, but not limited to,”) unless otherwise noted. “Connected,” when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitations of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. In at least one embodiment, the use of the term “set” (e.g., “a set of items”) or “subset” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, the term “subset” of a corresponding set does not necessarily denote a proper subset of the corresponding set, but subset and corresponding set may be equal.

Conjunctive language, 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 context, is otherwise understood with the context as used in general to present that an item, term, etc., may be either A or B or C, or any nonempty subset of the set of A and B and C. For instance, in an illustrative example of a set having three members, conjunctive phrases “at least one of A, B, and C” and “at least one of A, B and C” refer to any of the following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of A, at least one of B and at least one of C each to be present. In addition, unless otherwise noted or contradicted by context, the term “plurality” indicates a state of being plural (e.g., “a plurality of items” indicates multiple items). In at least one embodiment, the number of items in a plurality is at least two, but can be more when so indicated either explicitly or by context. Further, unless stated otherwise or otherwise clear from context, the phrase “based on” means “based at least in part on” and not “based solely on.”

Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those processes described herein (or variations and/or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively 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-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission) but includes non-transitory data storage circuitry (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-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause a computer system to perform operations described herein. In at least one embodiment, a set of non-transitory computer-readable storage media comprises multiple non-transitory computer-readable storage media and one or more of individual non-transitory storage media of multiple non-transitory computer-readable storage media lack all of the code while multiple non-transitory computer-readable storage media collectively store all of the code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors.

Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that singly or collectively perform operations of processes described herein and such computer systems are configured with applicable hardware and/or software that enable the performance of operations. Further, a computer system that implements at least one embodiment of present disclosure is a single device and, in another embodiment, is a distributed computer system comprising multiple devices that operate differently such that distributed computer system performs operations described herein and such that a single device does not perform all operations.

Use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.

All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

In description and claims, terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may not be intended as synonyms for each other. Rather, in particular 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 yet still co-operate or interact with each other.

Unless specifically stated otherwise, it may be appreciated that throughout specification terms such as “processing,” “computing,” “calculating,” “determining,” or like, refer to action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within computing system's registers and/or memories into other data similarly represented as physical quantities within computing system's memories, registers or other such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device or portion of a device that processes electronic data from registers and/or memory and transform that electronic data into other electronic data that may be stored in registers and/or memory. A “computing platform” may comprise one or more processors. As used herein, “software” processes may include, for example, software and/or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently. In at least one embodiment, terms “system” and “method” are used herein interchangeably insofar as the system may embody one or more methods and methods may be considered a system.

In the present document, references may be made to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. In at least one embodiment, the process of obtaining, acquiring, receiving, or inputting analog and digital data can be accomplished in a variety of ways such as by receiving data as a parameter of a function call or a call to an application programming interface. In at least one embodiment, processes of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a serial or parallel interface. In at least one embodiment, processes of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a computer network from providing entity to acquiring entity. In at least one embodiment, references may also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, processes of providing, outputting, transmitting, sending, or presenting analog or digital data can be accomplished by transferring data as an input or output parameter of a function call, a parameter of an application programming interface or inter-process communication mechanism.

Although descriptions herein set forth example embodiments of described techniques, other architectures may be used to implement described functionality, and are intended to be within the scope of this disclosure. Furthermore, although specific distributions of responsibilities may be defined above for purposes of description, various functions and responsibilities might be distributed and divided in different ways, depending on circumstances.

Furthermore, although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that subject matter claimed in appended claims is not necessarily limited to specific features or acts described. Rather, specific features and acts are disclosed as exemplary forms of implementing the claims.