Deep Learning with GPUs in Production - AI By the BayAdam Gibson
This document discusses deep learning with GPUs in production environments. It describes different types of GPU clusters for research, cloud, and enterprise production. It also outlines the key considerations for running deep learning jobs on a GPU cluster, including memory management, throughput, resource provisioning, and runtime. Finally, it presents Deeplearning4j as a tool that addresses these challenges by allowing models to be trained on Spark and deployed in Java/Scala applications, with an integrated workflow for data scientists and data engineers.
20170721 future of reactive architecturesJamie Allen
Some knowledge will be difficult to scale across an entire team. How do we build applications that are reactive while still delivering business value quickly?
The document discusses using the Raspberry Pi GPU for deep neural network prediction on end devices. It provides an overview of the Raspberry Pi GPU architecture and benchmarks convolutional neural network models like GoogLeNet, ResNet50, and YOLO on the Raspberry Pi 3 and Zero. Optimization techniques discussed include specialized convolution implementations, instruction golfing to reduce operations, removing wasteful computations, and improving data locality.
37. 範囲型
Swift #2
let range1:CountableRange = 1..<4
//半開区間
for value in range1 {
print(value)
}//1,2,3
let range2:CountableClosedRange = 1...4
//閉区間
for value in range2 {
print(value)
}//1,2,3,4
38. 範囲型
Swift #3
let range3:Range = 1.1..<4.0
let range4:ClosedRange
= 1.1…4.0
//Coutableではない
for value in range3 {
print(value)
}//コンパイルエラー
40. 範囲型
Scala #2
val range1:Range = 1 until 4
//半開区間
for (i <- range1) {
println(i)
}//1,2,3
val range2:Range.Inclusive = 1 to 4
//閉区間
for (i <- range2) {
println(i)
}//1,2,3,4