International Business Machines Corporation

Jayaram Kallapalayam Radhakrishnan

Hui Lei

Shu Tao

Ashish Verma

Charles Otto Schulz

Vinod Muthusamy

Yogish Sabharwal

Vatche Isahagian

Vaibhav Saxena

Anshul Gandhi

Scott Boag

Benjamin W Herta

Saurav Basu

Atin Sood

Dimitrios Pendarakis

Andrzej P Kochut

Alexei A Karve

Minkyong Kim

David R Safford

Kenneth Alan Goldman

Vijay Krishnarao Naik

Han Chen

Gegi Thomas

Zhe Zhang

Stefan Berger

Simon J Kofkin-Hansen

Zhiming Shen

Colin Sutcher-Shepard

Techniques regarding determining hyperparameters for a differentially private federated learning process are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a hyperparameter advisor component that determines a hyperparameter for a model of a differentially private federated learning process based on a defined numeric relationship between a privacy budget, a learning rate schedule, and a batch size.

Hyperparameter determination for a differentially private federated learning process

One embodiment provides a method, including: receiving at least one deep learning job for scheduling and running on a distributed system comprising a plurality of nodes; receiving a batch size range indicating a minimum batch size and a maximum batch size that can be utilized for running the at least one deep learning job; determining a plurality of runtime estimations for running the at least one deep learning job; creating a list of optimal combinations of (i) batch sizes and (ii) numbers of the plurality of nodes for running both (a) the at least one deep learning job and (b) current deep learning jobs; and scheduling the at least one deep-learning job at the distributed system, responsive to identifying, by utilizing the list, that the distributed system has necessary processing resources for running both (iii) the at least one deep learning job and (iv) the current deep learning jobs.

Application aware resource allocation for deep learning job scheduling

A lifecycle management method, system, and computer program product include coordinating hardware, platform and application-level health checks for framework-independent and application-specific monitoring, failure detection, and recovery, coordinating the hardware, the platform, and the application-level health check by state-specific aggregation of distributed atomic status events, and creating a recovery policy based on the state-specific aggregation of the distributed atomic status events.

Scalable multi-framework multi-tenant lifecycle management of deep learning applications

Methods, systems, and computer program products for determining optimal compute resources for distributed batch based optimization applications are provided herein. A method includes obtaining a size of an input dataset, a size of a model, and a set of batch sizes corresponding to a job to be processed using a distributed computing system; computing, based at least in part on the set of batch sizes, one or more node counts corresponding to a number of nodes that can be used for processing said job; estimating, for each given one of the node counts, an execution time to process the job based on an average computation time for a batch of said input dataset and an average communication time for said batch of said input dataset; and selecting, based at least in part on said estimating, at least one of said node counts for processing the job.

Determining optimal compute resources for distributed batch based optimization applications

A lifecycle management method, system, and computer program product include establishing a public key infrastructure (PKI) for end-to-end encryption of control plane and data plane communications by providing encryption between arbitrary components for applicant execution where an interaction pattern is isolated, secure, and a multi-tenant environment.

Examples of techniques for deploying an application on a cloud environment satisfying integrity and geo-fencing constraints are disclosed herein. A computer implemented method may include: receiving a guest application for deployment on a cloud environment; receiving the integrity constraints on the integrity of each of the plurality of host where the application is to be deployed; receiving geo-fencing constraints identifying a geographic location where the guest application is to be deployed; determining for which of the plurality of hosts the integrity constraints and the geo-fencing constraints are satisfied; and deploying the guest application on at least one of the plurality of hosts that satisfy the integrity constraints and the geo-fencing constraints.

Application deployment and monitoring in a cloud environment to satisfy integrity and geo-fencing constraints

Examples of GPU resource sharing among applications are disclosed. In one example, a method includes receiving a first request from a first application of the plurality of applications for first requested GPU resources, and receiving a second request from a second application of the plurality of applications for second GPU resources. The method also includes, responsive to determining that the first requested GPU resources are available, allocating a first slice of the GPU resources with a first requested amount of resources to the first application and, responsive to determining that the second requested GPU resources are available, allocating a second slice of the GPU resources with a second requested amount of resources to the second application. Further, the method includes enabling the first application and the second application to execute concurrently within the first slice of the GPU and the second slice of the GPU respectively.

Graphics processing unit resource sharing

Examples of GPU resource sharing among distributed applications in a distributed computing environment are disclosed. In one example, a method includes receiving a first request from a first distributed application of the plurality of distributed applications for first requested GPU resources. The method may further include receiving a second request from a second distributed application of the plurality of distributed applications for second requested GPU resources. The method may also include receiving response from each of the plurality of computing nodes indicating an availability of GPU resources for each of the plurality of computing nodes. Additionally, the method may include, responsive to determining that at least one of the first and second requests can be fulfilled by at least one of the plurality of computing nodes, allocating a first set of GPU slices for the first application and allocating a second set of GPU slices for the second application.

A system and an article of manufacture for de-duplicating virtual machine image accesses include identifying one or more identical blocks in two or more images in a virtual machine image repository, generating a block map for mapping different blocks with identical content into a same block, deploying a virtual machine image by reconstituting an image from the block map and fetching any unique blocks remotely on-demand, and de-duplicating virtual machine image accesses by storing the deployed virtual machine image in a local disk cache.

Virtual machine image access de-duplication

Growing community of inventors

Yorktown Heights, NY, United States of America

Jayaram Kallapalayam Radhakrishnan