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Running Docker
Chris Fregly edited this page Nov 5, 2015
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1 revision
local-macosx-or-windows-or-linux$ docker images
REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE
fluxcapacitor/pipeline latest b62e5d6e385f 36 hours ago 3.653 GB
- THE RUN COMMAND IS VERY LONG GIVEN ALL OF THE
-p
PORT MAPPINGS. BE SURE TO COPY/PASTE ALL OF IT! - If running on a large-memory host, you may want to increase the
-m 8g
value to something higher. - If you plan to mimic my live presentation demo, you may want to change the
-p 30080:80
http port mapping to-p 80:80
. ** Obviously, you still need to setup CNAME's and customizeconfig/apache2
andconfig/fluxcapacitor
Apache Httpd configs. - Contact me with any questions or create a Github issue and I'll answer as quickly as possible.
Ignore this message if you see it.
WARNING: Your kernel does not support swap limit capabilities, memory limited without swap.
local-macosx-or-linux$ docker run -it --privileged --name pipeline -h docker -m 8g -v ~/pipeline/notebooks:/root/pipeline/notebooks -p 30080:80 -p 34042:4042 -p 39160:9160 -p 39042:9042 -p 39200:9200 -p 37077:7077 -p 38080:38080 -p 38081:38081 -p 36060:6060 -p 36061:6061 -p 32181:2181 -p 38090:8090 -p 30000:10000 -p 30070:50070 -p 30090:50090 -p 39092:9092 -p 36066:6066 -p 39000:9000 -p 39999:19999 -p 36081:6081 -p 35601:5601 -p 37979:7979 -p 38989:8989 -p 34040:4040 -p 36379:6379 -p 38888:8888 -p 34321:54321 -p 38099:8099 fluxcapacitor/pipeline bash
local-windows$ docker run -it --privileged --name pipeline -h docker -m 8g -v %USERPROFILE%\notebooks:/root/pipeline/notebooks -p 30080:80 -p 34042:4042 -p 39160:9160 -p 39042:9042 -p 39200:9200 -p 37077:7077 -p 38080:38080 -p 38081:38081 -p 36060:6060 -p 36061:6061 -p 32181:2181 -p 38090:8090 -p 30000:10000 -p 30070:50070 -p 30090:50090 -p 39092:9092 -p 36066:6066 -p 39000:9000 -p 39999:19999 -p 36081:6081 -p 35601:5601 -p 37979:7979 -p 38989:8989 -p 34040:4040 -p 36379:6379 -p 38888:8888 -p 34321:54321 -p 38099:8099 fluxcapacitor/pipeline bash
Environment Setup
Demos
6. Serve Batch Recommendations
8. Streaming Probabilistic Algos
9. TensorFlow Image Classifier
Active Research (Unstable)
15. Kubernetes Docker Spark ML
Managing Environment
15. Stop and Start Environment