Clara Train SDK Container in NVIDIA GPU Cloud

March 15, 2022

NVIDIA Clara is an open, innovative computing framework that helps developers build and deploy medical imaging applications into a blended computing environment.

The Clara Train SDK container is an NVIDIA package. It is programmed in Python, allowing programmers to deploy industry-specific deep learning machines quickly. It helps NVIDIA improve deep learning models to train in an optimised way. These models are wrapped up as Medical Model Archives. NVIDIA Clara Train SDK container lowers production costs of such AI-enabled machines.

NVIDIA Clara SDK container provides back-to-back job flow to maximise deep learning training. It ultimately helps deploy the trained model for Medical Imaging cases. The machines are used in cases like organ and tumour classification and segmentation.

Essential concepts:

Some essential concepts to understand NVIDIA Clara Train SDK are:

  1. Components in NVIDIA Clara Train SDK: MONAI is a free, PyTorch-based framework for deep learning in healthcare imaging. The framework architecture has many components. Same way, NVIDIA Clara Train SDK container architecture is supported by MONAI components. The components are like:
  • The Pipeline of Training Data
  • The Pipeline of Validation Data
  • Applications taken from MONAI
  • Transforms of MONAI
  • Data from MONAI
  • Multi GPU parallel Engines
  • Inference methods
  • Event Handlers
  • Network Architectures
  • Loss functions
  • Optimisers from MONAI
  • Metrics
  • Visualisations (Tensorboard Visuals)
  • Utilities 
  1. Data pipelines: These are responsible for producing a group of data elements at the time of training. Generally, two data pipelines are used – one to generate training data and another to generate validation data.

The data channel contains a collection of transforms that is put into the input image and characterised data. It helps to generate the data in the format required by the model. 

Getting started with NVIDIA Clara container

NVIDIA Clara Train SDK container framework improves the training of classification and segmentation models in deep learning and medical imaging. The framework is used to analyse and evaluate the models as well. 

How to use Clara Train SDK is discussed below:

Supervised training:

NVIDIA Clara applies an algorithm for supervised training to get the best model based on training and validation datasets. Training datasets contain data elements used to minimise loss, and validation datasets contain data to validate during the training.

A single iteration through the full dataset is called an epoch. Because a full dataset can not be executed in a single run, it is broken into different collections of data elements. And for each collection of data, a loss function works to balance the weights of the model with the help of an optimiser. Validation metrics are computed to determine the quality of the model.

Data converter:

Digital Imaging and Communications (DICOM) is the standard and internationally accepted format to store, display and process medical images. If input image data is in DICOM or its resolution is not isotropic, then a data converter is used to convert the input data to isotropic NIfTI format.

To start working with the trained models, data conversion is performed into 1x1x1 mm NIfTI format.

Working with classification models:

Data is used in transfer learning to train the models for 2D classification jobs, and if required, data formats need to change. After that, Multi-GPU training is performed on the models. Tensorboard visualisation technique also needs to apply to the model.

Working with segmentation models:

Data is used in transfer learning to train the models for 2D classification jobs, and if required, data formats need to change. After that, Multi-GPU training is performed on the models. Tensorboard visualisation technique also needs to apply to the model.

NVIDIA Clara Framework in the field of healthcare and life sciences:

NVIDIA Clara train SDK container in Medical Imaging: Clara Train SDK is a domain for the developers upgraded for working on the framework with good APIs. Because of the application interfaces, AI-Assisted Annotation works perfectly. It makes any medical observer powered with AI and a MONAI and PyTorch-based training environment.

AI-Assisted Annotation application interfaces and an Annotation server can be easily incorporated into any Medical Viewer. The framework trains machines with Federated learning and Transfer learning tools and techniques. It also has methods like Automated Machine Learning (AutoML) for continuous evaluation. The SDK provides trained models as Model Applications packaged as MMARS (Medical Model ARchive) to users, providing an environment for data analytics and experts to start working with AI Development. 

NVIDIA Clara is a framework used in healthcare areas that are empowered with Artificial Intelligence. It is used in healthcare imaging, genetic data, and also in the development and use of efficient acceptors. It consists of multiple GPU-powered libraries, SDKs, and other programs for developers, data analytics, and researchers to create real-time, safe, and adaptable solutions.

  1. NVIDIA Clara used in medical devices:

●       Manage AI applications and arrange them to deploy medical devices and instruments.

●       To develop an end-to-end smooth and active workflow for medical imaging. 

  1. NVIDIA Clara SDK to discover drugs:

●       To meet the power of accelerated computing, Artificial Intelligence, and machine learning to amplify the entire drug discovery and production process.

●       Unlock the potential to bring life-saving drugs to market faster. 

  1. Clara SDK container to build smart hospitals:

●       To purchase high-performing AI-trained machines and use them, trained with transfer learning tools. 

  1. NVIDIA Clara for medical imaging:

●       To build a strong AI model with techniques like AutoML, privacy-preserving Federated Learning, and Transfer Learning.

●       To deploy an AI-enabled machine into an application to make it possible to interface in a hospital-like environment. 

  1. NVIDIA Clara used in genomics:

●       Powerful acceleration to primary, secondary, and tertiary analyses of genomic data.

●       Turnkey software designed for high-throughput labs on-premises or in the cloud. 

How to get started?

E2E clouds provide NVIDIA GPU cloud services. Experts in AI projects not having access to a local GPU workstation or server can use the NVIDIA Clara Train SDK with a GPU-enabled instance from a Cloud Service Provider.

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