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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
Requirements of an
advanced ML Platform
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
Deep Learning on
Azure Machine Learning
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
Kubernetes and Alluxio
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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