Merge pull request #97 from edx/sofiya/docker

MakeFile and docker compose file to run VEDA locally. 

docker/Makefile

+http-shell:
+	docker exec -it veda.http /bin/bash
+
+up:
+	docker-compose up -d
+
+down:
+	docker stop veda.rabbitmq veda.http veda.encode veda.pipeline veda.deliver
+	docker rm veda.rabbitmq veda.http veda.encode veda.pipeline veda.deliver

docker/README.md

+## Running VEDA locally using docker
+
+This directory is the current state of the work to docker-ize VEDA so it can be run locally. 
+
+## Setup to run
+
+The [edx-video-pipeline](http://github.com/edx/edx-video-pipeline), [edx-video-worker](github.com/edx/edx-video-worker), and [v_videocompile](http://github.com/yro/v_videocompile) should all be downloaded in one directory and an environment variable ```VEDA_WORKSPACE``` set to point to the directory:  
+
+```
+export VEDA_WORKSPACE=/Users/sofiyasemenova/veda
+```
+
+### Images
+
+The images can be found on docker hub:
+- [Http](https://hub.docker.com/r/ssemenova/veda-http/)
+- [Pipeline](https://hub.docker.com/r/ssemenova/veda-pipeline/)
+- [Deliver](https://hub.docker.com/r/ssemenova/veda-deliver/)
+- [Encode](https://hub.docker.com/r/ssemenova/veda-encode/)
+- [Rabbit MQ](https://hub.docker.com/r/ssemenova/veda-rabbitmq/)
+
+## Work to be done
+
+This is probably not an exhaustive list:
+
+- The http endpoint works, but work needs to be done to connect all the containers together and have them speak over local ports instead of calling ec2 containers.
+- The database settings file contains the right database configuration, but we need to actually make sure the database works and is set up locally.
+- I have done very little with the Rabbit MQ container. The image is based off the base veda docker image, but has nothing rabbit-specific. I'm not sure about what work needs to be done here.

docker/docker-compose.yml

+version: "0.1"
+
+services:
+  rabbitmq:
+    image: "veda-rabbitmq"
+    container_name: "veda.rabbitmq"
+    volumes:
+      - ${VEDA_WORKSPACE}/rabbitmq:/rabbitmq
+    tty: true
+
+  pipeline:
+    image: "veda-pipeline"
+    container_name: "veda.pipeline"
+    volumes:
+      - ${VEDA_WORKSPACE}/edx-video-pipeline:/edx-video-pipeline
+    tty: true
+
+  http:
+    image: "veda-http"
+    command: bash -c 'cd /edx-video-pipeline/; python manage.py runserver 0.0.0.0:8000 --settings=VEDA.settings.local'
+    container_name: "veda.http"
+    volumes:
+       - ${VEDA_WORKSPACE}/edx-video-pipeline:/edx-video-pipeline
+    ports:
+       - "8000:8000"
+    tty: true
+    stdin_open: true
+
+  encode:
+    image: "veda-encode"
+    container_name: "veda.encode"
+    volumes:
+      - ${VEDA_WORKSPACE}/edx-video-worker:/edx-video-worker
+    tty: true
+
+  deliver:
+    image: "veda-deliver"
+    container_name: "veda.deliver"
+    volumes:
+      - ${VEDA_WORKSPACE}/edx-video-pipeline:/edx-video-worker
+    tty: true

instance_config.yaml

@@ -2,6 +2,7 @@
 # ---
 # Database information
 # ---
+# PRODUCTION
 DATABASES:
     default:
         ENGINE: django.db.backends.sqlite3
@@ -13,6 +14,13 @@ FERNET_KEYS: ["a_random_string"]
 
 debug: True
 
+# LOCAL DOCKER
+#  default:
+#        ENGINE: 'django.db.backends.sqlite3'
+#        NAME: 'veda-docker'
+#        HOST: 'localhost'
+#        PORT: 3306
+
 # Frontend S3 Auth
 veda_secret_access_key: A_RANDOM_STRING
 veda_access_key_id: A_RANDOM_STRING