Executives are the Simon Cowell of the business world: impatient, critical, often caustic. But they're also desperately searching for talent. How do you make the right impression? These 5 tips will get you started
This document provides an overview of diabetes mellitus (DM), including the three main types (Type 1, Type 2, and gestational diabetes), signs and symptoms, complications, pathophysiology, oral manifestations, dental management considerations, emergency management, diagnosis, and treatment. DM is caused by either the pancreas not producing enough insulin or cells not responding properly to insulin, resulting in high blood sugar levels. The document compares and contrasts the characteristics of Type 1 and Type 2 DM.
2018年11月3日にパナソニックスタジアム吹田で開催されたイベント「JAWS FESTA 2018 OSAKA ~Passionate~」のセッション「AWSとDockerで実現するAI研究のためのPipeline as Code」で使った資料です。
来栖川電算ではAWS BatchやAmazon SageMaker的なことをオンプレ環境やハイブリッドクラウド環境で実現し、その上で研究プロエスをコード化しているという話です。研究プロセスを工夫すればもっと良い成果がだせるようになるはずです。
gcp ja night #31 での発表資料です。
http://gcpja.connpass.com/event/23874/
[補足記事]
http://qiita.com/na_ga/items/d89b320ba098a0941043
http://qiita.com/na_ga/items/7c3cc3f52dd4068fd319
PFN福田圭祐による東大大学院「融合情報学特別講義Ⅲ」(2022年10月19日)の講義資料です。
・Introduction to Preferred Networks
・Our developments to date
・Our research & platform
・Simulation ✕ AI
PFN Summer Internship 2021 / Kohei Shinohara: Charge Transfer Modeling in Neu...Preferred Networks
The document discusses techniques for modeling charge transfer in neural network potentials (NNPs) for materials simulation. It presents a graph neural network (GNN) baseline architecture called NequIP that predicts short-range atomic energies. Additional techniques are explored to model long-range electrostatic interactions, including adding an electrostatic correction term (Eele) using Ewald summation and using charge equilibration (Qeq) to predict atomic charges. Results show that while Qeq improves charge prediction accuracy, the baseline GNN achieves comparable or better overall accuracy in most datasets tested, possibly because the GNN can already learn electrostatic effects. The document also discusses PyTorch implementations of Ewald summation and Qeq for efficient evaluation.
This presentation was given at the Green500 BoF at SC21, in which PFN's VP of Computing Infrastructure Yusuke Doi discussed the power measurement for PFN's MN-3 supercomputer with MN-Core™ accelerators and how the company improved MN-3's power efficiency from 29.7GF/W to 39.38GF/W in 5 months.
More about MN-Core: https://projects.preferred.jp/mn-core/en/
More about MN-3: https://projects.preferred.jp/supercomputers/en/
9. 9
@everpeace
● 基盤系キーワード
○ Volcano(旧 kube-batch)
○ Kubernetes Batch Working Group
■ Kueue
● 事例はあまり多くない
○ HPC
■ Kubernetes as a Substrate for ATLAS Compute (Univ. of Texas, TU München)
■ KubeFlux: An HPC Scheduler Plugin for Kubernetes (IBM, LLNL)
○ Batch
■ Spark on Kubernetes: The Elastic Story (Apple)
■ Supporting Long-Lived Pods Using a Simple Kubernetes Webhook (Slack)
● Scheduler拡張系結構多い→このあと特集します
Batch/HPC on Kubernetes 最新潮流
10. 10
@everpeace
● 基盤系
○ Volcano: Intro & Deep Dive (Huawei)
○ Introduction to the Kubernetes WG Batch (Google, Alibaba)
○ Kueue: A Kubernetes-native Job Queueing (Google)
● 事例系
○ Kubernetes as a Substrate for ATLAS Compute (CERN)
○ KubeFlux: An HPC Scheduler Plugin for Kubernetes (IBM, Lawrence
Livermore National Laboratory)
Selected Sessions: Batch/HPC on k8s 最新潮流
21. 21
@everpeace
Kubernetes as a Substrate for ATLAS Compute
ATLASはCERNの大型ハドロン衝突型加速器にある素粒子物理実験装置
全体で600PBytesのデータ
700K+ vCPUs(一部クラウド有)
2020年に始めたMiniK8s Gridは現在は
Googleでバーストさせてトータル 100k vCPU
22. 22
@everpeace
Kubernetes as a Substrate for ATLAS Compute
CernVMFS
PanDA
Production and
Distributed Analysis
Jupyter+Daskの部分は
デモもあったので是非
ビデオ見てください!
23. 23
@everpeace
KubeFlux: An HPC Scheduler Plugin for Kubernetes
Lawrence Livermore National Laboratoryの
ElCapitan (2023予定) は >2 exaFLOPS!!
(富岳は442 PFLOPS)
※現行設備は言及なし
紹介されたユースケースは生物系が多い
10%くらいしかcloud利用していないが
今後増えていく予定
26. 26
@everpeace
● Batch/HPCで登場したセッション
○ Working your Cluster: Smarter Scheduling Decisions for Your
Workloads (Intel)
→ Telemetry Aware Scheduling (Custom Metrics API連携)
○ Resource Orchestration of HPC on Kubernetes: Where We Are Now
and the Journey Ahead! (RedHat) → NUMA Aware Scheduling
○ KubeFlux: An HPC Scheduler Plugin for Kubernetes (IBM, LLNL)
→ HPC Scheduler & kube-scheduler連携
● 純粋にScheduler拡張系のセッション
○ Network-aware Scheduling in Kubernetes (Ghent University)
→Infrastructure Topology & Network Aware Scheduling
Selected Sessions: Scheduler最新拡張事例
27. 27
@everpeace
Telemetry Aware Scheduling
Working your Cluster: Smarter Scheduling Decisions for Your Workloads
Nodeメトリクスを
カスタムメトリクス
APIでexposeする
Scheduler Extender
として動作してPodの
TAS Policyをenforce
TAS Policy CR
(Telemetry Aware
Scheduling Policy)
28. 28
@everpeace
Telemetry Aware Scheduling
Working your Cluster: Smarter Scheduling Decisions for Your Workloads
dontschedule strategy:
health_metric メトリクスが1なNodeにはscheduleしない
scheduleonmetric strategy:
temperature メトリクスが少ないNodeにスケジュールされる
labeling strategy:
memory_used_card0メトリクスが100を超えたら card0=trueって
いうnode labelを付与
deschedule stragety:
tempertureメトリクスが80を超えたらdeschedule
freeRAMメトリクスが200を切ったらdeschedule
29. 29
@everpeace
NUMA Aware Scheduling
Resource Orchestration of HPC on Kubernetes: Where We Are Now and the Journey Ahead!
kube-schedulerはNodeのNUMA利用状況を
知らない
→ Topology Manager Policyがきついと
PodがScheduleされてもErrorで全然
上がらない
30. 30
@everpeace
NUMA Aware Scheduling
Resource Orchestration of HPC on Kubernetes: Where We Are Now and the Journey Ahead!
KubeletのPodResource APIを使って
resourceのassign状況を
NodeResourceTopology CRにexpose
Scheduler Pluginで
NodeResourceTopology CR
を見てschedule判断
Node毎に生成される
zone: NUMA, socket, die, etc.
cost: zone間の距離を表す指標
31. 31
@everpeace
HPC Scheduler & kube-scheduler連携
KubeFlux: An HPC Scheduler Plugin for Kubernetes
コレまでのnode-centricなmodelは
● monogenousな環境向け
● Heterogeneousな環境だと効率悪い
● リソースの包含関係をグラフとして表現
● リッチなグラフtraversal/allocaiton API
● 複雑なスケジューリングをcodeを
変更せずに実現可能
※SIG-Schedulingのsubprojectだった
Poseidonと少し違う感じがするが
詳細不明
39. 39
@everpeace
● Batch/HPC基盤系
○ [Keynote] High Performance Computing on Google Kubernetes Engine(Google)
○ Kueue: A Kubernetes-native Job Queueing (Google)
○ Volcano – Cloud Native Batch System for AI, BigData and HPC (Huawei)
○ Fast Data on-Ramp with Apache Pulsar on K8 (StreamNative)
○ Efficient Deep Learning Training with Ludwig AutoML, Ray, and Nodeless Kubernetes
(Elotl, Predibase)
● HPC系事例
○ [LT] How to Handle Fair Scheduling in a Private Academic K8s infrastructure (Masaryk
University, CESNET)
● Scheduler系
○ Resource Orchestration of HPC on Kubernetes: Where We Are Now and the Journey
Ahead! (RedHat)
○ Get More Computing Power by Helping the OS Scheduler (Intel)
○ Apache YuniKorn A Kubernetes Scheduler Plugin for Batch Workloads(Cloudera)
[参考] Kubernetes Batch + HPC Day
40. 40
@everpeace
● Batch/HPC基盤系
○ Volcano: Intro & Deep Dive (Huawei)
○ Introduction to the Kubernetes WG Batch (Google, Alibaba)
○ Unlimited Data Science Libraries, One Container Image, No Installation! (Red Hat, Ghent Univ.)
○ [LT]Secure Multi User HPC Jobs in Kubernetes with Kyverno (Ohio Supercomputer Center)
● Batch系事例
○ Spark on Kubernetes: The Elastic Story (Apple)
○ Supporting Long-Lived Pods Using a Simple Kubernetes Webhook (Slack)
● HPC系事例
○ Kubernetes as a Substrate for ATLAS Compute (CERN)
○ KubeFlux: An HPC Scheduler Plugin for Kubernetes (IBM, LLNL)
● Scheduler系
○ Working your Cluster: Smarter Scheduling Decisions for Your Workloads (Intel)
○ KubeFlux: An HPC Scheduler Plugin for Kubernetes (IBM, LLNL)
[参考]KubeCon + CloudNativeCon (Batch/HPC系)