Patent Application
20230107221
The present disclosure relates generally to computer networks, and, more particularly, to simplifying machine learning workload composition.
Machine learning is becoming increasingly ubiquitous in the field of computing. Indeed, machine learning is now used across a wide variety of use cases, from analyzing sensor data from sensor systems to performing future predictions for controlled systems.
Unfortunately, running a machine learning workload is a complex and cumbersome task, today. This is because expressing a machine learning workload is not only tightly coupled with infrastructure resource management, but also embedded into the machine learning library that supports the workload. Consequently, users responsible for machine learning workloads are often faced with time-consuming source code updates and error-prone configuration updates in an ad-hoc fashion for different types of machine learning workloads.
The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:
According to one or more embodiments of the disclosure, a device receives, via a user interface, definition data for a machine learning workload. The user interface represents the machine learning workload as a set of roles and channels, each role representing a task to be performed by a node in a network and each channel representing a communication channel between roles. The device identifies, based on the definition data, groups of training nodes in the network that store training datasets, to perform training roles for the machine learning workload by training machine learning models on their respective training datasets. The device selects a set of intermediate aggregator nodes for the groups of training nodes, each intermediate aggregator node performing an aggregation role to aggregate those machine learning models trained by an assigned group of training nodes into intermediate models. The device provisions the machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes and by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node.
A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.
Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.
In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:
1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/5G/LTE backup connection). For example, a particular CE router 110 shown in network 100 may support a given customer site, potentially also with a backup link, such as a wireless connection.
2.) Site Type B: a site connected to the network by the CE router via two primary links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). A site of type B may itself be of different types:
2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).
2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For example, a particular customer site may be connected to network 100 via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.
2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/5G/LTE connection).
Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).
3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup link). For example, a particular customer site may include a first CE router 110 connected to PE-2 and a second CE router 110 connected to PE-3.
Servers 152-154 may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.
In some embodiments, the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc.
According to various embodiments, a software-defined WAN (SD-WAN) may be used in network 100 to connect local network 160, local network 162, and data center/cloud environment 150. In general, an SD-WAN uses a software defined networking (SDN)-based approach to instantiate tunnels on top of the physical network and control routing decisions, accordingly. For example, as noted above, one tunnel may connect router CE-2 at the edge of local network 160 to router CE-1 at the edge of data center/cloud environment 150 over an MPLS or Internet-based service provider network in backbone 130. Similarly, a second tunnel may also connect these routers over a 4G/5G/LTE cellular service provider network. SD-WAN techniques allow the WAN functions to be virtualized, essentially forming a virtual connection between local network 160 and data center/cloud environment 150 on top of the various underlying connections. Another feature of SD-WAN is centralized management by a supervisory service that can monitor and adjust the various connections, as needed.
The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.
The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a machine learning (ML) workload composition process 248, as described herein, any of which may alternatively be located within individual network interfaces.
It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.
In various embodiments, as detailed further below, ML workload composition process 248 may also include computer executable instructions that, when executed by processor(s) 220, cause device 200 to perform the techniques described herein. To do so, in some embodiments, ML workload composition process 248 may utilize machine learning. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators), and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function would be the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.
In various embodiments, ML workload composition process 248 may employ, or be responsible for the deployment of, one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample image data that has been labeled as depicting a particular condition or object. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes or patterns in the behavior of the metrics. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data.
Example machine learning techniques that ML workload composition process 248 can employ, or be responsible for deploying, may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), singular value decomposition (SVD), multi-layer perceptron (MLP) artificial neural networks (ANNs) (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like.
Unfortunately, running a machine learning workload is a complex and cumbersome task, today. This is because expressing a machine learning workload is not only tightly coupled with infrastructure resource management, but also embedded into the machine learning library that supports the workload. Consequently, users responsible for machine learning workloads are often faced with time-consuming source code updates and error-prone configuration updates in an ad-hoc fashion for different types of machine learning workloads
The techniques introduced herein mitigate the complexity of planning, updating, and execution of a machine learning workload by engineers through the representation of machine learning workloads through a certain layer of abstraction. In some aspects, the techniques herein also enable a cluster environment with a capability of managing low-level, non-core related tasks seamlessly without any intervention from engineers.
Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with ML workload composition process 248, which may include computer executable instructions executed by the processor 220 (or independent processor of interfaces 210) to perform functions relating to the techniques described herein.
Specifically, according to various embodiments, a device receives, via a user interface, definition data for a machine learning workload. The user interface represents the machine learning workload as a set of roles and channels, each role representing a task to be performed by a node in a network and each channel representing a communication channel between roles. The device identifies, based on the definition data, groups of training nodes in the network that store training datasets, to perform training roles for the machine learning workload by training machine learning models on their respective training datasets. The device selects a set of intermediate aggregator nodes for the groups of training nodes, each intermediate aggregator node performing an aggregation role to aggregate those machine learning models trained by an assigned group of training nodes into intermediate models. The device provisions the machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes and by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node.
Operationally, as would be appreciated, a machine learning workload may be used to perform tasks such as aggregated model training, performing inferences on a certain dataset, or the like. However, defining a machine learning workload, especially across a distributed set of nodes/sites, can also be a very cumbersome and error-prone task.
According to various embodiments, the techniques herein propose decomposing machine learning workloads into primitives/building blocks and decoupling core building blocks (e.g., the AI/ML algorithm) of the workload from the infrastructure building blocks (e.g., network connectivity and communication topology). The infrastructure building blocks are abstracted so that the users can compose their workloads in a simple and declarative manner. In addition, scheduling the workloads is straightforward and foolproof, using the techniques herein.
In various embodiments, the techniques herein propose representing a machine learning workload using the following building block types:
Roles and channels may also have various properties associated with them, to control the provisioning of a machine learning workload. In some embodiments, these properties may be categorized as predefined ones and extended ones. Predefined properties may be essential to support the provisioning and set by default, whereas extended properties may be user-defined. In other words, to enrich the functionality of the roles and channels, the user/engineer may opt to customize extended properties.
By way of example, a role may have either or both of the following pre-defined properties:
For a channel, there may be the following property:
Using the above building blocks and properties, the system can greatly simplify the process for defining a machine learning workload for a user.
As shown, workload design template 300 consists of three roles: machine learning (ML) model trainer 302, intermediate model aggregator 304, and global model aggregator 306. Connecting them in template 300 may be three types of channels: trainer channel 308, parameter channel 310, and aggregation channel 312.
Trainer channels allows communication between peer trainer nodes at runtime. For instance, assume that the group by property is set to group trainer nodes into separate groups located in the western U.S. and the UK. In such a case, trainer channels may be provisioned between these nodes. Similarly, a parameter channel may enable communications between intermediate model aggregators, such as intermediate model aggregator 304 and trainer nodes in the various groups, such as model trainer 302. Finally, an aggregation channel may connect the intermediate model aggregator to global model aggregator 306.
To provision the machine learning workload across the different hospitals, a user may convey, via a user interface, definition data for the workload. For instance, the user may specify the type of model to be trained, values for the replica property, the number of datasets to use, tags for the group by property, any values for the load balancing property, combinations thereof, or the like.
Based on the definition data, the system may identify that the needed training datasets are located at nodes 402a-402e (e.g., the different hospitals). Note that the user does not need to know where the data is located during the design phase for machine learning workload 400, as the system may automatically identify nodes 402a-402e, automatically, using an index of their available data. In turn, the system may designate each of nodes 402a-402e as having training roles, meaning that each one is to train a machine learning model in accordance with the definition data and using its own local training dataset. In other words, once the system has identified nodes 402a-402e as each having training datasets matching the requisite type of data for the training, the system may provision and configure each of these nodes with a trainer role.
Assume now that the group by property has been set to group nodes 402a-402e by their geographic locations. Consequently, nodes 402a-402c may be grouped into a first group of trainer/training nodes, based on these hospitals all being located in the western US, by being tagged with a “us_west” tag. Similarly, nodes 402d-402e may be grouped into a second group of training nodes, based on these hospitals being located in the UK, by being tagged with a “uk tag.
For purposes of simplifying this example, also assume that the replica property is set to 1, by default, meaning that there is only one trainer role instance to be configured at each of nodes 402a-402e.
To connect the different sites/nodes 402a-402e in each group, the system may also provision and configure trainer channels between the nodes in each group. For instance, the system may configure trainer channels 408a between nodes 402a-402c within the first geographic group of nodes, as well as a trainer channel 408b between nodes 402d-402e in the second geographic group of nodes.
Once the system has identified nodes 402a-402e, it may also identify intermediate model aggregator nodes 404a-404b, to support the groups of nodes 402a-402c and 402d-402e, respectively. In turn, the system may configure model aggregator nodes 404a-404b with intermediate model aggregation roles. In addition, the system may configure parameter channels 410a-410b to connect the groups of nodes 402a-402c and 402d-402e with intermediate model aggregator nodes 404a-404b, respectively. These parameter channels 410a-410b, like their respective groups of nodes 402, may be tagged with the ‘us_west’ and ‘uk’ tags, respectively. In some instances, intermediate model aggregator nodes 404a-404b may be selected based on their distances or proximities to their assigned nodes among nodes 402a-402e. For instance, intermediate model aggregator node 404b may be cloud-based and selected based on it being in the same geographic region as nodes 402d-402e. Indeed, intermediate model aggregator node 404a may be provisioned in the Google cloud (gcp) in the western US, while intermediate model aggregator node 404b may be provisioned in the Amazon cloud (AWS) in the UK region.
During execution, each trainer node 402a-402e may train a machine learning model using its own local training dataset. In turn, nodes 402a-402e may send the parameters of these trained models to their respective intermediate model aggregator nodes 404a-404b via parameter channels 410a-410b. Using these parameters, each of intermediate model aggregator nodes 404a-404b may form an aggregate machine learning model. More specifically, intermediate model aggregator node 404a may aggregate the models trained by nodes 402a-402c into a first intermediate model and intermediate model aggregator node 404b may aggregate the models trained by nodes 402d-402e into a second aggregate model.
Finally, the system may also provision machine learning workload 400 in part by selecting and configuring global model aggregator node 406. Here, the system may configure a global aggregation role to global model aggregator node 406 and configure aggregation channels 412 that connect it to intermediate model aggregator nodes 404a-404b. Note that these aggregation channels may not be tagged with a geographic tag, either.
Once configured and provisioned, intermediate model aggregator nodes 404a-404b may send the parameters for their respective intermediate models to global model aggregator node 406 via aggregation channels 412. In turn, global model aggregator node 406 may use these model parameters to form a global, aggregated machine learning model that can then be distributed for execution. As a result of the provisioning by the system, the resulting global model will be based on the disparate training datasets across nodes 402a-402e, and in a way that greatly simplifies the definition process of the machine learning workload used to train the model.
At step 515, as detailed above, the device may identify, based on the definition data, groups of training nodes in the network that store training datasets, to perform training roles for the machine learning workload by training machine learning models on their respective training datasets. In one embodiment, the training datasets comprise private data not shared externally by the training nodes, such as personally identifiable information (PII) data, medical data, or the like. In various embodiments, the definition data specifies a type of training data and does not specify locations of the training datasets. In other words, based on the type of data specified, the device may search for nodes where data of that type is stored.
At step 520, the device may select a set of intermediate aggregator nodes for the groups of training nodes, as described in greater detail above. In various embodiments, each intermediate aggregator node performing an aggregation role to aggregate those machine learning models trained by an assigned group of training nodes into intermediate models. In some embodiments, the device selects the set of intermediate aggregator nodes for the groups of training nodes based in part on their distances to the groups of training nodes. In a further embodiment, at least one of the intermediate aggregator nodes is cloud-based.
At step 525, as detailed above, the device may provision the machine learning workload by configuring the groups of training nodes, the set of intermediate aggregator nodes, and a global aggregator node for the set of intermediate aggregator nodes. In addition, the device may do so in part by configuring channels between training nodes in a group, between the groups of training nodes and the set of intermediate aggregator nodes, and between the set of intermediate aggregator nodes and the global aggregator node. For instance, the device configures the channels by configuring APIs on the groups of training nodes, the set of intermediate aggregator nodes, and the global aggregator node, to form communication channels in the network. In some embodiments, the channels between the groups of training nodes and the set of intermediate aggregator nodes are parameter channels via which the groups of training nodes send parameters of the machine learning models that they train to their intermediate aggregator nodes. In some embodiments, the global aggregator node performs a global aggregation of the intermediate models into a global machine learning model. Procedure 500 then ends at step 530.
It should be noted that while certain steps within procedure 500 may be optional as described above, the steps shown in
The techniques described herein, therefore, allow for the simplified definition of a machine learning workload across different nodes. Doing so can greatly increase the productivity of the engineers responsible for defining such workloads, as well as helping to reduce errors.
While there have been shown and described illustrative embodiments that provide for simplifying machine learning workload composition, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to machine learning workloads directed towards model training, the techniques herein are not limited as such and may be used for other types of machine learning tasks, such as making inferences or predictions, in other embodiments. In addition, while certain protocols are shown, other suitable protocols may be used, accordingly.
The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.
