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Karolinska Institutet, Core Facility SMILE (Stockholm Medical Image Laboratory and Education)

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Karolinska Institutet, Core Facility SMILE (Stockholm Medical Image Laboratory and Education) Karolinska Institutet, Core facility SMILE (Stockholm Medical Image Laboratory and Education) http://smile.ki.se/ - New user registration guide - List of available educational Courses for SMILE Users - Further educational resources - Introduction to facility and services Facility Manager: Scientific Director: Andy Triantafyllos Paparountas Ph.D., MBA Professor Birgitta Janerot Sjöberg CLINTEC CLINTEC email: [email protected] phone: 08-524 83801 Address: SMILE, Karolinska University Hospital Huddinge, Medical Radiation Physics and Nuclear Medicine C2-76, 141 85 Stockholm New User registration Application + Free online courses 1. a: SMILE new user registration b: SMILE new user Computing Rules Circular 2. Only For non-KI academic users: KI affiliate account required! 3. Register in the KI invoicing system iLab (free online courses can be requested through this platform ) iLab invoicing system help connection details will be provided at the iLab registered email Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 3 http://smile.ki.se/ NVIDIA Online courses (page 1 of 2) free for SMILE Core Facility Users upon registering on iLab as SMILE’s users -students (BSc, MSc, PhD) only need to register on iLab as users to access the courses to access the GPU-Server for hands-on training addition of Project_ID in iLab is required contact your supervisor -post docs and employees need to register on iLab as users and register 1 or more chargeable Project_ID in iLab access to both NVIDIA courses + GPU Server (-prerequisites and list below-subject to change) time course title (click for more details) DEEP LEARNING FOR HEALTHCARE 2 Medical Image Classification Using the MedNIST Dataset 2 Image Classification with TensorFlow: Radiomics—1p19q Chromosome Status Classification 2 Data Augmentation and Segmentation with Generative Networks for Medical Imaging 2 Coarse-to-Fine Contextual Memory for Medical Imaging ACCELERATED COMPUTING COURSES 2 High-Performance Computing with Containers 2 Optimization and Deployment of TensorFlow Models with TensorRT 8 Fundamentals of Accelerated Computing with CUDA Python ACCELERATED DATA SCIENCE COURSES 2 Modeling Time-Series Data with Recurrent Neural Networks in Keras 2 Deep Learning at Scale with Horovod 6 Fundamentals of Accelerated Data Science with RAPIDS 28/08/2020 4 NVIDIA Courses (page 2 of 2) additional free courses - requirements as in page 1 Videos + lecture notes + online practicals a value of up to $180 per person per course Containing $125 credit on Amazon Web Services (AWS) GPU compute per person Deep Learning (syllabus) GPU Accelerated Computing (syllabus) . Introduction to Machine and Deep . Introduction to CUDA C Learning . Memory and Data Locality . Applied Image Classification . Thread Execution Efficiency . Applied Object Detection . Memory Access Performance . Convolutional Neural Networks . Parallel Computation Patterns . Applied Image Segmentation . Efficient Host-Device Data Transfer . Energy-based Learning . OpenACC, MPI, OpenCL . Super-and-Unsupervised Learning . New! Unified Memory . Generative Adversarial Networks . New! Dynamic Parallelism . Recurrent Neural Networks . New! Multi-GPU Systems . Natural Language Processing . New! CUDA Library Usage . And Many More! . And Many More! Acts as base for the course “Fundamentals of Acts as base for the course “Fundamentals of Deep Learning for Computer Vision” Accelerated Computing with CUDA C/C++” Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 5 http://smile.ki.se/ Free Educational Resources (page 1 of 3) (no registration required) Introduction to the NVIDIA DGX-1 GPU-Server . DGX-Fundamentals . Quick guide to the Nvidia DGX platform software architecture of the KI SMILE’s GPU Server . Introduction to Docker containers and Kubernetes . List of pre-made Docker containers harnessing the power of DGX-1 Ways to connect . Jupyter connection and use on DGX-1 . Remote CLI Using the job scheduler (SLURM) . CLARA connect . Connect to Matlab using your desktop license (Scheduled for a future release) . CLARA Bundled Analysis Pipelines Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 6 http://smile.ki.se/ Free Educational Resources (page 2 of 3) (no registration required) Introduction to Deep Learning . What is Deep Learning? . Deep Learning Examples . What is the difference between AI, machine learning and deep learning? . What is the difference between Deep Learning training and Inference? . Deep Learning in a Nutshell : Core Concepts . Deep Learning in a Nutshell : Sequence Learning . Deep Learning in a Nutshell : Reinforcement Learning . What can Deep Learning Do for You? . 3 steps to kick off your Deep Learning Project . Deep Learning Glossary : Concepts defined . Webinar: Deep Learning and Beyond Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 7 http://smile.ki.se/ Free Educational Resources (page 3 of 3) (no registration required) Introduction to Basic tools for AI CUDA TensorFlow (deep learning framework) Monai (Medical open network for AI) PyTorch (deep learning framework) CLARA (medical imaging) KERAS (neural-network building blocks) ParaBricks (NGS) Pre-trained Models and LIST Apache Spark (Data analytics, machine RAPIDS (data science and analytics on learning algorithms) GPU) Other links . Accelerating Deep Learning Research in Medical Imaging Using MONAI . MONAI v0.2 Brings Domain Specialized Best Practices to Medical Imaging AI Researchers . Accelerating Apache Spark 3.0 with GPUs and RAPIDS . Running Python UDFs in Native NVIDIA CUDA Kernels with the RAPIDS cuDF Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 8 http://smile.ki.se/ AI infrastructure at Karolinska Institutet Core Facility SMILE . founded in 2001 as a core facility at Karolinska Institutet & Karolinska University Hospital. SMILE re-established as core facility in 2019, with the purpose to facilitate evaluation of medical digital images, irrespective of source. Installation and deployment of AI infrastructure to enable accelerated AI model processing for 3 Universities, 1 Institute and the Medical Research Lab of Karolinska Hospital in the area of Stockholm Karolinska Institutet, Stockholm University, KTH, Science for Life, Karolinska Hospital . SMILE is taking steps towards enabling KI to become the Stockholm hub of the Swedish National Infrastructure for Computing. Current nodes in the picture. Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 9 http://smile.ki.se/ Overall Machine learning architecture SMILE strategy Focuses on highlighted areas ANALYTICS SERVICE GPU SERVER + DATA STORAGE INFRASTRUCTURE 28/08/2020 10 Technical details of NVIDIA DGX-1 Server What is the DGX-1 Remote Computational Analytics System based on Graphics Processing Units (GPUs) able to run 100x times faster than CPUs . 8 x Tesla V100 32GB GPUs Reference design at KI interconnected through NVLINK . 1PT-Flops (at half precision) 1015 calculations/sec up to 3 decimals . SMILE’s contract includes free 5 years maintenance directly by NVIDIA +FREE local NVIDIA training courses Andy Paparountas, Karolinska Institute Core Facility SMILE http://smile.ki.se/ 28/08/2020 11 KI - DGX installation Currently Installed Docker images Survey to identify clients’ needs . CLARA-train-sdk -seminars . MATLAB r2019b -new software + support . RAPIDS . TENSORFLOW: . PYTORCH: . kipoi (KERAS - collection of bioinformatics models) . Julia (newer-better than python) . python Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 12 http://smile.ki.se/ Software isolation ensures cybersecurity at machine level . Only NVIDIA-certified software can run on this DGX-1 system. Need green light for other software, ensuring optimization, compatibility and security. Academic software gets certified and runs as a container . Every software and job process is isolated (container-ized=small Virtual Machines) and cannot interact with other running jobs Cybersecurity ensured within the system. Example applications & full list . NGS Sequencing -> Parabricks . Medical Imaging and segmentation-> Clara . AI processing -> Pytorch acceleration -> Dali . Machine Learning -> TensorFlow . Computer vision -> Autonomous vehicles . Script Versioning system -> Jupyter / Git . Data Science -> Rapids . Further uses we plan for: . - microscopy image denoising . - molecular dynamics . - fluid simulations . - GPU accelerated statistics Andy Paparountas, Karolinska Institute Core Facility SMILE http://smile.ki.se/ 28/08/2020 13 Example Projects: D-PACS for Medical Imaging (planning phase) KS KI Data Storage Data analytics service Service preprocess + filtering RIS-PACS Pseudonymization Preprocess TAG Data / BFT + Delivery service (KI-eHealth Data Visualization + Dataset KI-IT/SNIC) Selection KS Data Storage (KI->SMILE) (Place-Holder) Data analytics service SMILE’s preprocess + filtering GPU SERVER Preprocess TAG Data Data Visualization + Dataset Selection Processing + Programmatically NVIDIA CLARA Computer Process Services: Cluster Service + Filter • Automated DICOM GPU + CPU + Annotate Annotation (eHealth/GRID) + Versioning • Medical image Visualization • Others + External Consortia + SEPARATE SERVICE for + Collaborators KS projects, insulated from every other service Name Surname + Clients 28/08/2020 14 Examples on how to use the GPU Server Medical application - radiology visualizations Cinematic Rendering Ultrasound model creation Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 15 http://smile.ki.se/ Medical application – AI-assisted medical research Reconstruction Speed increase AI – MRI and visualization enhancement Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 16 http://smile.ki.se/ Medical application - Medical imaging automated segmentation pre-trained + (re-)training models . Auto-Segmentation (MIT-K module) of CT-Scans Andy Paparountas, Karolinska Institute Core Facility SMILE 28/08/2020 17 http://smile.ki.se/.
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