Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
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Alluxio Monthly Webinar Apr. 23, 2024 For more Alluxio Events: https://www.alluxio.io/events/ Speaker: - ChanChan Mao (Developer Advocate, Alluxio) - Shawn Sun (Tech Lead of Cloud Native, Alluxio) Cloud-native model training jobs require fast data access to achieve shorter training cycles. Accessing data can be challenging when your datasets are distributed across different regions and clouds. Additionally, as GPUs remain scarce and expensive resources, it becomes more common to set up remote training clusters from where data resides. This multi-region/cloud scenario introduces the challenges of losing data locality, resulting in operational overhead, latency and expensive cloud costs. In the third webinar of the multi-cloud webinar series, Chanchan and Shawn dive deep into: - The data locality challenges in the multi-region/cloud ML pipeline - Using a cloud-native distributed caching system to overcome these challenges - The architecture and integration of PyTorch/Ray+Alluxio+S3 using POSIX or RESTful APIs - Live demo with ResNet and BERT benchmark results showing performance gains and cost savings analysis
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Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
- 1. Cloud-Native Model Training on Distributed Data Shawn Sun, Cloud Native Tech Lead @ Alluxio - [email protected] ChanChan Mao, Developer Advocate @ Alluxio - [email protected] 1
- 2. Cloud Native Tech Lead @ Alluxio Shawn Sun Developer Advocate @ Alluxio ChanChan Mao
- 3. Tightly-Coupled Hadoop & HDFS On-Prem HDFS Single Region & Single Cloud 10yr Ago The Evolution of the Modern Data Stack
- 4. Tightly-Coupled Hadoop & HDFS Compute-Storage Separation On-Prem HDFS Cloud Data Lake Single Region & Single Cloud Multi-Region/ Hybrid/Multi-Cloud 10yr Ago Today More Elastic, Cheaper, More Scalable The Evolution of the Modern Data Stack
- 5. Compute-Storage Separation Cloud Data Lake Multi-Region/ Hybrid/Multi-Cloud Today Data is Remote from Compute; Locality is Missing I/O Challenges The Evolution of the Modern Data Stack
- 6. ● GET/PUT operation costs add up quickly ● Cross-region data transfer (egress) fees ● GPU cycles are wasted waiting for data ● Job failures ● Amazon S3 errors: 503 Slow Down 503 Service Unavailable I/O Challenges ● Analytics SQL: High query latency because of retrieving remote data ● Model Training: Training is slow because of loading remote data in each epoch (LISTing lots of small files is particularly slow) Performance Cost Reliability
- 7. 10% of your data is hot data
- 8. 10% of your data is hot data Data Caching Layer between compute & storage Add a Source: Alluxio
- 9. Reduce Latency I/O Compute I/O Compute Compute I/O (first time retrieving remote data) Compute I/O Compute Without Cache With Cache Total job run time is reduced I/O Compute Compute Compute I/O
- 10. Increase GPU Utilization I/O (data loading) Training I/O Training Training I/O (first time loading remote data) Training I/O Training Training I/O Training Training Without Cache With Cache GPU is idle idle I/O idle GPU is idle GPU is busy most of the time GPU utilization is greatly increased
- 11. Reduce Cloud Storage Cost Compute Compute AWS S3 us-east-1 Without Cache With Cache AWS S3 us-west-1 AWS S3 us-east-1 Frequently Retrieving Data = High GET/PUT Operations Costs & Data Transfer Costs Fast Access with Hot Data Cached AWS S3 us-west-1 Only Retrieve Data When Necessary = Lower S3 Costs … … … … Data Cache
- 12. Improve Reliability Prevent Network Congestion Relieve Overloaded Storage Prevent Job Failures like “503 Service Unavailable” …
- 13. DATA CACHING LAYER Observations So Far … ● The evolution of modern data stack poses challenges for data locality ● You should care about I/O in data lake because it greatly impacts the performance, cost & reliability of your data platform ● Having a data caching layer between compute and storage can solve the I/O challenges ● You can use cache for both analytics and AI workloads COMPUTE STORAGE
- 14. ALLUXIO 14 Accessing Data and Models In the Cloud 14
- 15. Hybrid/Multi-Cloud ML Platforms Online ML platform Serving cluster Models Training Data Models 1 2 3 Offline training platform Training cluster DC/Cloud A DC/Cloud B 15 Separation of compute and storage
- 16. 1. Read data directly from cloud storage 2. Copy data from cloud to local before training 3. Local cache layer for data reuse 4. Distributed cache system Existing Solutions 16
- 17. Option 1: Read From Cloud Storage ● Easy to set up ● Performance are not ideal ■ Model access: Models are repeatedly pulled from cloud storage ■ Data access: Reading data can take more time than actual training 82% of the time spent by DataLoader 17
- 18. Option 2: Copy Data To Local Before Training ● Data is now local ■ Faster access + less cost ● Management is hard ■ Must manually delete training data after use ● Local storage space is limited ■ Dataset is huge - limited benefits 18
- 19. Option 3: Local Cache for Data Reuse Examples: S3FS built-in local cache, Alluxio Fuse SDK ● Reused data is local ■ Faster access + less cost ● Cache layer provider helps data management ■ No manual deletion/supervision ● Cache space is limited ■ Dataset is huge - limited benefits 19
- 20. Option 4: Distributed Cache System Clients Worker Worker Worker … ● Training data and trained models can be kept in cache - distributed. ● Typically with data management functionalities. 20
- 21. Challenges 1. Performance ● Pulling data from cloud storage is hurting training/serving. 2. Cost ● Repeatedly requesting data from cloud storage is costly. 3. Reliability ● Availability is the key for every service in cloud. 4. Usability ● Manual data management is unfavorable. 21
- 22. ALLUXIO 22 Alluxio as an example 22
- 23. Clients Worker Worker … Masters Worker ● Use consistent hashing to cache both data and metadata on workers. ● Worker nodes have plenty space for cache. Training data and models only need to be pulled once from cloud storage. Cost -- ● No more single point of failure. Reliability ++ ● No more performance bottleneck on masters. Performance ++ ● Data management system. Consistent Hashing for caching 23
- 24. By the numbers ● High Scalability ■ One worker supports 30 - 50 million files ■ Scale linearly - easy to support 10 billions of files ● High Availability ■ 99.99% uptime ■ No single point of failure ● High Performance ■ Faster data loading ● Cloud-native K8s Operator and CSI-FUSE for data access management 24
- 25. 25 Cloud Storage Alluxio Operator Training Framework Training Framework Cloud VMs Alluxio Operator Kubernetes Alluxio Cluster Training Framework Manages the life cycle of Alluxio clusters and datasets
- 26. 26 Alluxio Cluster CRD Alluxio Operator follows the Kubernetes Operator pattern 1.Create AlluxioCluster, Dataset CRs 2.Inform CR User K8s Api Server Alluxio Operator Alluxio Cluster Dataset 3.Manage k8s resources 4.Reconcile ● Zero-downtime Upgrade ● High-availability ● Auto-scaling
- 27. Alluxio FUSE ● Expose the Alluxio file system as a local file system. ● Can access the cloud storage just as accessing local storage. ○ cat, ls ○ f = open(“a.txt”, “r”) ● Very low impact for end users 27
- 28. Alluxio CSI on K8s x Alluxio FUSE for Data Access ● FUSE: Turn remote dataset in cloud into local folder for training ● CSI: Launch Alluxio FUSE pod only when dataset is needed Alluxio Fuse pod Fuse Container Host Machine Application pod Application Container Persistent volume + claim mount mount 28
- 29. ALLUXIO 29 Data Access Management for PyTorch 29
- 30. Under Storage Integration with PyTorch Training (Alluxio) Training Node Get Task Info Alluxio Client PyTorch Get Cluster Info Send Result Cache Cluster Service Registry Cache Worker Cache Worker Execute Task Cache Worker Cache Client Find Worker(s) Affinity Block Location Policy Client-side load balance 1 2 3 4 5 Cache miss - Under storage task 30
- 31. Data Loading Performance ImageNet (subset) 31 Yelp review
- 32. 32 Training Directly from Storage (S3-FUSE) - > 80% of total time is spent in DataLoader - Result in Low GPU Utilization Rate (<20%) GPU Utilization Improvement
- 33. Training with Alluxio-FUSE - Reduced DataLoader Rate from 82% to 1% (82X) - Increase GPU Utilization Rate from 17% to 93% (5X) GPU Utilization Improvement
- 34. ALLUXIO 34 How to enable Python Applications 34
- 35. Use Alluxio - Ray Integration as an example 35 Ray Dataloader fsspec - Alluxio impl Alluxio Python client Ray etcd Alluxio Worker REST API server Alluxio Worker REST API server PyArrow Dataset loading Registration Get worker addresses
- 36. Alluxio+Ray Benchmark – Small Files ● Dataset ○ 130GB imagenet dataset ● Process Settings ○ 4 train workers ○ 9 process reading ● Active Object Store Memory ○ 400-500 MiB 36
- 37. Alluxio+Ray Benchmark – Large Parquet files ● Dataset ○ 200MiB files, adds up to 60GiB ● Process Settings ○ 28 train workers ○ 28 process reading ● Active Object Store Memory ○ 20-30 GiB 37
- 38. Cost Saving – Egress/Data Transfer Fees 38
- 39. Cost Saving – API Calls/S3 Operations (List, Get) List/Get API calls only access Alluxio 39
- 40. Any Questions? Scan the QR code for a Linktree including great learning resources, exciting meetups & a community of data & AI infra experts! 40
- 41. Thank you! 41 Up Next: AI/ML Infra Meetup Thur May 9 @ Uber Sunnyvale https://lu.ma/AIMLinfra Speak at an Alluxio event: https://forms.gle/iJX9GTMaAVQdzKc28