The ability to create and manage model catalogs.The ability to pull models in from external stores such as NGC.The ability to store and manage models locally through user inputs.The following aspects of model management are available: The Clara Deploy SDK now offers management of AI models for instances of NVIDIA Triton Inference Server. Not only are there different models for different purposes, but there are also multiple model versions that must be maintained over time. With the rise of AI, it may only get more tedious. Managing AI models has been a manual process. When the system doesn’t have resources to fulfill the requirements of a queued job, the scheduler retains the pending job until enough resources become available. It is responsible for queuing and scheduling pipeline job requests based on available resources. The NVIDIA Clara platform has a scheduler that is responsible for managing resources allocated to the platform for executing pipeline jobs, and other resources such as render servers. Queuing gives the Clara Deploy SDK the resiliency necessary for you to build fault-tolerant hospital-grade systems that meet the needs of future AI. This concept has been introduced in the Clara Deploy SDK, where studies of higher urgency can be prioritized over processing other studies. Hospitals use priorities to triage patients appropriately based on severity of symptoms. Allocation of memory for the pipeline using Fast I/O through the CPDriver.Compatibility of concatenated operators in terms of data type (where specified).This enables the following functionality: The Clara Deploy SDK supports pipeline composition using operators that conform to a signature, or well-defined interface. Code comparison showing a strongly typed interface (on the right). Figure 2 shows the Clara Deploy SDK technology stack.įigure 3. The most pressing problem for deploying AI models is architecting an inference platform that can handle the rapidly changing AI ecosystem, including the increasing number of requests for processing, massive size of healthcare datasets, and diversity of the processing pipelines themselves that use a heterogeneous computing environment.ĭuring GTC Digital 2020, we released an update to the Clara Deploy SDK that included platform features and reference applications that enable developers and data scientists with a unified foundation for delivering intelligent workloads and realizing the vision of the smart hospital.Īt SIIM 2020, we have now made available the latest version for Clara Deploy SDK, which includes new reference pipelines for COVID-19 inference from CT datasets, a suite of tools for digital pathology (including a pipeline, operators, and visualization), and additional tools to accelerate developers (including shared memory for multi-AI pipelines and features like easier DICOM configuration over REST APIs). The NVIDIA Clara Deploy SDK answers this call by providing a reference framework for the deployment of multi-AI, multi-modality workflows in smart hospitals: one architecture orchestrating and scaling imaging, genomics, and video processing workloads. Each application demands different compute capabilities for HPC, AI, and visualization. Flexible compute capability, with “write once, run anywhere” capability makes it possible to deploy state-of-the-art applications at the edge in hospitals. Healthcare demands specific restrictions in how data is transmitted, and respecting patient data privacy is paramount. These smart hospitals adopting AI applications face big challenges in IT and infrastructure. Within the next five years, we will see the rise of the “smart hospital,” augmented by workflows incorporating thousands of AI models. With Moore’s law broken and computational capability ever increasing, models that save lives and make us more efficient and effective are becoming the norm. The adoption of AI in hospitals is accelerating rapidly. NVIDIA Clara Deploy SDK in the healthcare ecosystem.
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