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Build and Configure a Sandbox
- Start a sandbox building. In the options, you should see the following:
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Update the branches and check the boxes. If you're planning on actually encoding videos, you'll need video_pipeline and video_encode_worker, however if you're just testing API endpoints, video_pipeline is sufficient.
Configure edX Studio
In studio>advanced settings, scroll to the bottom to find the "Video Upload Credentials" dialogue.
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{
"course_video_upload_token": "xxxxshared_course_token"
} |
If you haven't, create an edxapp superuser (The provided example sets 'staff' as a superuser, which is fine).:
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sudo su -s /bin/bash edxapp cd /edx/bin/python.edxapp /edx/bin/manage.edxapp lms manage_user staff staff@example.com --staff --superuser --settings=aws exit # deactivate env |
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# Activate Frontend Env. sudo su veda -s /bin/bash; source ~/veda_env; cd /edx/app/veda/veda; # Run mgmt command python manage.py createsuperuser # follow prompts |
Check and configure VAL access
Log in to the django admin (usually ${YOUR_SANDBOX_URL}/admin). Go to Oauth2 > Clients. There should be an extant client with a suffix like 'api/val/v0'. Click through.
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This should be FK-associated with a user, click the magnifying glass icon and associate this with the 'veda_service_user' user. (PK=8 as of this writing, but that may change)
Running
You should now be able to upload a video file (here's a sample) and have it process.
You might need to get a snack, this can be slow. Eventually it should resolve to look like this:
Debugging
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sudo /edx/bin/supervisorctl # then, for example tail -f veda_encode_worker stderr # for veda_encode_worker errlog |
Execute the tail command for other processes to see their logs