Como o Azure me ajudou a trabalhar com Deep Learning
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This document discusses how Azure can help with deep learning work. It provides a timeline of artificial intelligence, machine learning, and deep learning from the 1950s to the future. It then discusses deep learning architectures like neural networks, GANs, and RNNs. It presents Azure Batch, a service for large-scale parallel training with GPUs and networking. Finally, it discusses challenges like data explosion, the need for scale, low latency, and throughput for AI and the role of CPUs, GPUs, FPGAs and other technologies in addressing these challenges.
Como o Azure me ajudou a trabalhar com Deep Learning
- 1. Como o Azure me ajudou a trabalhar com Deep Learning Vitor Meriat
- 2. Microsoft MVP Artificial Intelligence vitormeriat.com.br linkedin.com/in/vitormeriat twitter.com/vitormeriat github.com/vitormeriat youtube.com/vitormeriat
- 3. Agenda
- 5. 1950s BECOMES A REALITY 1970s EMERGES AND GAINS GROUND 1990s EXPANDS AI T I M E L I N E AI Artificial Intelligence Machine Learning Deep Learning
- 6. FUTURE IS WE !!! T I M E L I N E AI 1950s 1970s 1990s Artificial Intelligence Machine Learning Deep Learning
- 8. Computer Vision and Natural Language Processing
- 13. Word Embeddings
- 18. Perceptron
- 21. Nc FP32/64 TRAINING A/D SERIES AZURE BATCH ND2 INT32/64 TRAINING ND1 INT32 TRAINING INFINIBAND HIGH SPEED NETWORK FPGA1 ACCELERATED APPS FPGA MICROSERVICES HAAS MGMT Rendering Service HPC for AI Training Service • What is it? • Easy to run parallel large-scale training with GPU and InfiniBand high speed networking • Experimentation and simulation with any framework • Built on Azure Batch, Docker & Python • Azure Compliant Infrastructure • Designed with and supported by AIR Philly Team • • Key Capabilities • Open- TensorFlow, CNTK, Caffe, MXNet support • Submit and go parameterized job configuration • Experiment management and monitoring • Support teams with sharing of reserved capacity • Re-image VMs between users for compliance • Python API to integrate with experimentation tools • Publish models for eval with AML • H1 CY 2017 • Preview mid semester, summer GA –In Preview • Good support for internal and external early adopters AZURE BATCH DEEP LEARNING JOB SERVICE
- 22. A faster, more efficient, more intelligent cloud Data explosion: 2013 4.4 ZB - 2020 44 ZB ML, DNN, AI are driving requirements up faster Autonomous decision making Real-time insights into connected devices Interactive user experiences Cloud-scale services Searches and recommendations (Indexing the Internet!) The need for SCALE The need for LOW-LATENCY The need for THROUGHPUT 1 0 0 1 1 0 1 0 1 0 2013 1 0 0 1 1 0 1 0 1 0 2020 4.4 ZB 44 ZB 0 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 1 1 Source: IDC 2014
- 24. Azure Batch AI Training AI Training At Scale with Azure
- 25. Autodesk 3ds Max / Maya VM Renderer VM Renderer VM Renderer Integrated Client Plugin Azure Batch Monitoring Reporting Single bill
- 26. FPGAs EVALUATION CPUs and FPGAs, ASICs under investigation EFFICIENCY TRAINING CPUs and GPUs, limited FPGAs, ASICs under investigation Control Unit (CU) Registers Arithmetic Logic Unit (ALU) + + + + + + + FLEXIBILITY CPUs GPUs ASICs
- 27. Storage Input Serverless Process Log Process High availability Database Server Server Monitor ARCHITECTURE API AI - Scalable and Distributable Models CLIENTE Reports Storage Output Batch Set