Palestra focada em visão computacional, com o intento de demonstrar a necessidade e importância da aritmética matricial
para Deep Learning e resoluções de problemas na área utilizando TensorFlow e CNTK.
29. 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
36. 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 Azure 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