- Alluxio provides a POSIX-compatible API that allows machine learning workloads running on Kubernetes to access distributed data sources like HDFS, S3, and GCS as if they were local. - Alluxio can be deployed on Kubernetes using kubectl, Helm charts, or the Alluxio CSI driver to provide data caching and access control. - Recent developments include JNI-Fuse enhancements for improved performance, ongoing work on the Alluxio CSI driver, and collaborations to support Alluxio on Kubernetes.

1. Deploying Alluxio in the Cloud for
Machine Learning
Lu Qiu @ Alluxio
06/24/2021
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2. About Me – Lu Qiu
● Software Engineer @ Alluxio
● Email: lu@alluxio.com
● Master Data Science @ GWU
● Areas: Alluxio fault tolerant system, journal
system, metrics system, and POSIX API.
Alluxio integration with Cloud
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3. Agenda
● What is Alluxio POSIX API
● Deploy Alluxio in K8S for ML workloads
● Updates in Alluxio POSIX Developments
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4. What is Alluxio POSIX API
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5. Apps Connecting to Alluxio via POSIX API
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6. Accessing Remote/Distributed Data as
Local Directories
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HDFS #1
Obj Store
NFS
HDFS #2
Connecting to
• HDFS
• Amazon S3
• Azure
• Google Cloud
• Ceph
• NFS
• Many more

7. Alluxio
Server
Alluxio
Server
Model Training
Distributed Caching w/ Unified Namespace
Alluxio
Server
A
B
/path1/file1
/path2/file2
C
A
B C A
Model Training Model Training
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8. Deploy Alluxio in K8s for ML
Workloads
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9. Use Alluxio POSIX API in ML/AI Training
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Alluxio
Server
Alluxio
Server
9
...
GPU Instance

10. Train in Cloud Kubernetes with Alluxio
- Provision the whole Kubernetes cluster on cloud
- Deploy Alluxio services via Docker and Kubernetes
- Launch distributed training job via Kubeflow and Arena in one command
- Accelerate training speed by caching data locally
- Focusing on the training logics instead of worry about the data
preparation (No need to modify training script, use remote data like
local data)

11. Deployment Variance - Kubectl
- The default way of launching Alluxio cluster on Kubernetes
- Use in testing environment, especially when your deployment is simple
- If you have special requirements that is not fulfilled by other deploy ways,
kubectl is your chose.
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$ kubectl create -f alluxio-configmap.yaml
$ kubectl create -f ./master/
$ kubectl create -f ./worker/

12. Deployment Variance - Helm
- Package Manager for Kubernetes, preferred over Kubectl
- Use Alluxio shared Helm charts, provide your desired configuration, and launch
the Alluxio services in one command.
- Define a common template for Alluxio services and deploy the same/similar
applications across different environments without copy-pasting.
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$ helm repo add alluxio-charts
https://alluxio-charts.storage.googleapis.com/openSource/2.6.0
$ helm install alluxio -f config.yaml alluxio-charts/alluxio
Deploy Alluxio on Kubernetes

13. Deployment Variance - Alluxio CSI
Container Storage Interface
- Expose Alluxio to container applications (e.g. Docker, Kubernetes) as
persistent storage.
- Support customizing storage features without the need to integrate Alluxio
driver into Kubernetes package and wait for kubernetes releases.
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14. Alluxio CSI
- Mount point level access control
- Separate mount points for different users or use
cases
- Access control via POSIX filesystem user group
- Different configuration for different mount points
- Separate read/write path
- Enable metadata cache for input data folder
- Disable metadata cache and set write option to Through
for output folder.
Customized mount
option
Path in
Alluxio

15. Alluxio CSI
Reference
- Microsoft: Speed up large-scale ML/DL offline inference job with Alluxio
- Alluxio/alluxio-csi (github.com)
- Alluxio CSI integration and improvements (#13435)
Special thanks to Binyang from Microsoft and Baolong from Tencent
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16. Deployment Variance - Fluid
Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and
Accelerator for data-intensive applications, such as big data and AI applications.
Apart from accelerating training, Fluid also supports
- Access control in dataset level for data scientists
- Auto launching and scaling internal Alluxio services. Users are not needed to
worry about the deployment pain points of Alluxio services.
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17. 17

18. Fluid
Reference
- Fluid: When Alluxio Meets Kubernetes
- Speeding up Atlas deep learning platform with Alluxio + Fluid
- cheyang/fluid (github.com)
Special thanks to Cheyang from Alibaba
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19. Updates in Alluxio POSIX
Developments
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20. Community Collaboration
Community-driven collaboration
- Contributors from NJU, Alibaba, Tencent, Microsoft, Alluxio
Already in used by Microsoft, Analytics Aspects, BOSS
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21. JNI-Fuse enhancements
(available in 2.6.0)
- Local RPC elimination by integrating Fuse into Worker process
- Modularized JNI-Fuse library (github.com/maobaolong/jnifuse)
- Implement Fuse flush() operation in write path (#13103)
- Implement Fuse utimens() operation to support linux touch command (#13218)
- Implement Fuse symlink() operation to avoid execution errors (#13429)
- Support open file for overwrite (#13236)
- Fix Fuse write then read problem in Fuse async release() operation (#13160)
- Add metrics for Fuse operations (#13201)
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22. JNI-Fuse enhancements
(ongoing projects)
- Alluxio CSI improvements (#13435)
- Improve data loading speed by dynamically allocating loading jobs (#13485)
- Support updating configuration during runtime to better support Alluxio on
Kubernetes (#13643)
- Support libfuse 3 (#12758)
- Remote RPC optimization
Join Alluxio weekly community sync to create solutions together!
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23. We Are Hiring! (contact careers@alluxio.com)
Join the Alluxio Community today
www.alluxio.io/slack | @alluxio
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