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AI Library
An Open Source Machine Learning Framework
Prasanth Anbalagan, AI Center of Excellence, Red Hat
MLconf 2018
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Machine Learning
● Challenges faced in adopting Machine Learning
○ Implementation
■ Data Science expertise required in implementing the models
○ Infrastructure
■ Choice of Infrastructure vs Deployment vs Management.
○ Accessibility
■ Ease of use.
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AI Library
● AI-Library
○ an open source collection of AI components
■ machine learning algorithms
■ machine learning solutions to common use cases
■ part of Open Data Hub
● “machine learning-as-a-service” platform, built on top of
OpenShift and Kubernetes.
○ allows rapid prototyping of ideas.
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AI Components
Association Rule Learning
Correlation Analysis
Duplicate Bug Detection
Sentiment Analysis
Flake Analysis
Matrix Factorization
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Understanding the Workflow
Save
Data
Use
Results
OpenWhisk ML models
Run
Model
python
modules
Container Application Platform
aws
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AI Library
Association Rule Learning
Correlation Analysis
Duplicate Bug Detection
Sentiment Analysis
Flake Analysis
Matrix Factorization
Storage
Actions
Ansible
Object Storage (S3 Compatible)
OpenWhisk + OpenShift
Deployment using playbooks
AI Components
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Saving Data
RADOS
Command Line Interface
Open Data Hub
Reports
&
Visuals
ETL
Model
Training
APIs
Apache Kafka
Big
Data
Storage
Data
Streams
Open Data Hub
aws
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Models
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Duplicate Bug Detection
Product Percentage
Red Hat OpenStack 12%
Red Hat Enterprise Linux 13%
Red Hat Ceph Storage 10%
OpenShift Container Platform 10%
● Duplicate Bugs Statistics
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Duplicate Bug Detection
Topic Modeling
Existing bugs
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Duplicate Bug Detection
Topic
Modeling
Similarity Measure
score1 score2 score3 score n
sort
Top matches
New bug
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Duplicate Bug Detection
Duplicate Bug
Detection
Existing bugs
new bugs
Recommendation
on duplicates
Software Bot
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Flake Analysis
What are flakes?
● Test fails, but software functions correctly and there is no bug.
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Flake Analysis
Test Logs
Clustering
Test Logs
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Flake Analysis
Classification
Clusters of Test Logs
New Test Log
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Flake Analysis
Clusters of Test Logs
Probability of a test
being flake in the
chosen cluster
Flake
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Run a Model
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Container Application Platform
Project 1
OpenWhisk Jobs
Workflow
Read data
Invoke action (training, prediction, poll etc)
Save data
or store results
poll status
submit jobs
Project1 Project2
(ML models)
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{
"status": "failure",
"log": Traceback (most recent call last):
File "/build/cockpit/bots/../test/verify/check-networking-team", line 81, in testTeam
b.wait_present("#network-interface-slaves tr[data-interface='%s']" % iface1)
File "/build/cockpit/test/common/testlib.py", line 230, in wait_present
return self.wait_js_func('ph_is_present', selector)
raise Error(res['error'])
Error: timeoutnnWrote TestNetworking-testTeam-rhel-7-4-127.0.0.2-2301-FAIL.png
Wrote TestNetworking-testTeam-rhel-7-4-127.0.0.2-2301-FAIL.html
"test": "testTeam (check_networking_team.TestNetworking)",
"flake": true,
}
Sample Training Data - Flake Analysis
flag whether a test failure was a false
positive or not
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curl -u <Auth>
"https://openwhisk.openshift.com/api/v1/namespaces/_/actions/ai-library/ flake-analysis-training?"
-X POST -H "Content-Type: application/json" -d
'{
"name" : "flakes-training-10982" ,
"app_args" : "-s3Path=flake-analysis/datasets/training/records
-s3Destination= flake-analysis/models/testflakes.model"
}'
Model Training - Flake Analysis
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"key": "job_cpu",
"value": "8000m"
Model Training - Flake Analysis
"key": "docker_image",
"value": "docker.io/panbalag/ailibrary"
"key": "job_memory",
"value": "16000Mi"
[#]$ wsk action get /whisk.system/ai-library/flake-analysis-training
8 cores
16 GB
Apache Spark
Tensorflow
Scikit-learn
Scipy
Gensim ...
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# ----------------------------------------------------------------------
# testNotRemovingDisks (check_storage_mdraid.TestStorage)
# ----------------------------------------------------------------------
[0608/105412.651574:ERROR:gpu_process_transport_factory.cc(1019)] Lost UI shared context.
DevTools listening on ws://127.0.0.1:9406/devtools/browser/e6259bcd-d4d3-4014-ac9e-1d7526cd2771
Traceback (most recent call last):
File "/build/cockpit/bots/../test/verify/check-storage-mdraid", line 298, in testNotRemovingDisks
b.wait_not_in_text('#detail-sidebar', "DISK3")
File "/build/cockpit/test/common/testlib.py", line 261, in wait_js_cond
self.raise_cdp_exception("timeoutnwait_js_cond", cond, result["exceptionDetails"], trailer)
File "/build/cockpit/test/common/testlib.py", line 166, in raise_cdp_exception
raise Error("%s(%s): %s" % (func, arg, msg))
Error: timeout
Sample Prediction Data - Flake Analysis
Logs from test failures
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curl -u <Auth>
"https://openwhisk.openshift.com/api/v1/namespaces/_/actions/ai-library/ flake-analysis-prediction?"
-X POST -H "Content-Type: application/json" -d
'{
"name" : "flakes-prediction-012" ,
"app_args" : "-model = flake-analysis/models/testflakes.model
-s3Path = flake-analysis/prediction-data/failures/records
-s3Destination = flake-analysis/predictions"
Prediction - Flake Analysis
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# ----------------------------------------------------------------------
# testNotRemovingDisks (check_storage_mdraid.TestStorage)
# ----------------------------------------------------------------------
[0608/105412.651574:ERROR:gpu_process_transport_factory.cc(1019)] Lost UI shared context.
..
…
…..
…….
File "/build/cockpit/test/common/testlib.py", line 261, in wait_js_cond
self.raise_cdp_exception("timeoutnwait_js_cond", cond, result["exceptionDetails"], trailer)
File "/build/cockpit/test/common/testlib.py", line 166, in raise_cdp_exception
raise Error("%s(%s): %s" % (func, arg, msg))
Error: timeout
# Flake likely: 0.89
Sample Result - Flake Analysis
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Demo
Using OpenShift and Ceph Storage
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Conclusion
● Challenges in adopting Machine Learning
● AI-Library
● Open Data Hub
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References
AI-Library
https://gitlab.com/opendatahub/ai-library
Open Data Hub
https://opendatahub.io/
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THANK YOU
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