Deep Learning in the Cloud at Scale: A Data Orchestration Story

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Data Orchestration Summit 2020 organized by Alluxio https://www.alluxio.io/data-orchestration-summit-2020/ Deep Learning in the Cloud at Scale: A Data Orchestration Story Mickey Zhang, Software Engineer (Microsoft) About Alluxio: alluxio.io Engage with the open source community on slack: alluxio.io/slack

Software

Deep Learning in the Cloud at Scale:
A Data Orchestration Story
Chao Wang, Mickey Zhang, Qianjun Xu

Overview Machine Learning Requirements
End-to-End lifecycle and processes
Data Scientist Workﬂow
Deep Learning on Azure Machine Learning
Deep Learning: Additional Requirements
Distributed Training with Azure ML Compute
Kubernetes + Alluxio

Machine Learning
Typical E2E Process
Prepare
Data
Register and
Manage Model
Train &
Test Model
Build
Image
…
Build Model
(your favorite IDE)
Deploy Service
Monitor Model
Prepare Experiment Deploy
Orchestrate

DevOps loop for data science
Prepare
Data
Prepare
Register and
Manage Model
Build
Image
…
Build Model
(your favorite IDE)
Deploy Service
Monitor Model
Train &
Test Model

Characteristics of Deep Learning
Massive amounts of training data
Excels with raw, unstructured data
Automatic feature extraction
Computationally expensive

Distributed training mode: Data parallelism
Subset 1 CNN model
Worker 1
Subset 2 CNN model
Job manager
Worker 2
Dataset
CNN model

Distributed training mode: Model parallelism
Dataset
CNN model
CNN model CNN modelSubset 1 Subset 2
Worker 1 Worker 2
Job manager

Challenges of distributed training
Dependencies and Containers
Provision clusters of VMs
Schedule jobs
Distribute data
Gather results
Handling failures
Scale resources
Secure Access

Deep Learning Scenarios
1. Standard ImageNet
2. BERT-Large
3. Checkpoint Save/Load

Typical Data Consumption Model
Storage/NFS
Deep Learning Training Platform
RAM
SSD
GPU
CPU
Azure Kubernetes Service
RAM
SSD
GPU
CPU
RAM
SSD
GPU
CPU

Why Alluxio?
Scalable
Performance is scalable based on the
cluster size
Lower Cost
Leverage idle resources in the cluster
Performance
Improve data access throughput by
distributing across nodes
Flexibility
Manage multiple data sources in a
uniﬁed namespace

Side-Car Model With Alluxio
Storage
Deep Learning Training Platform
RAM
SSD
GPU
CPU
Azure Kubernetes Service
RAM
SSD
GPU
CPU
RAM
SSD
GPU
CPU
Data Preloaded in Cluster

ImageNet
PyTorch resnet50
1.3M images, 50~200 KB each

Bert-Large Training Job
100 partitions, 1.8 GB each

Checkpoint Save/Load
Single ﬁle, 4.7 GB
Load: 24 processes
Save: rank0 process

Looking Forward
• Build pilot experience for customers
• Continuing performance improvement

© Copyright Microsoft Corporation. All rights reserved.
Thank you!

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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