LUIS Containers is a really interesting (preview) feature. One of the issues with LUIS, and machine learning in general, is that each endpoint learns differently. E.g. if you create two LUIS endpoints, JANET and JOHN, and import the exact same JSON model then Janet and John will likely give you different scores for exactly the same utterance. Whilst that is just something you need to appreciate it, it does make a lot of workflow scenarios very difficult, E.g. testing. One potential solution to this is to export the LUIS model as a Container. In my initial tests this seems to clone the server, i.e. you get JANET2 rather than daughter of JANET – you get the same scores for the same utterance. Here is my add-on help to the documentation.
The main documentation is currently located at Install and run LUIS docker containers. If you don’t want to follow my guide then my advice is to read that but before you do anything read the next document along as that has a better explanation of the settings Configure Language Understanding Docker containers
My guide to get up an running with LUIS Containers (for Windows 10)
I’m assuming you’ll be using the Command prompt and not a Bash terminal (as used in the official guide).
- Install Docker – if you’re new to Docker then I’d recommend at least following the first page of Get Started. Install Docker for Windows 10.
- Create a folder on your local disk that will container the LUIS model, e.g. C:\LUIS\Input and a folder for the output, e.g. C:\LUIS\Output
- Open the Docker settings panel (from the taskbar tools) and ensure you are sharing the local disk where you created the folders in (2)
- Go to LUIS and export the version of the model you want to use. Remember to select export as a Container.
- Get the Docker image for LUIS,
docker pull mcr.microsoft.com/azure-cognitive-services/luis:latest
- Now create the container, remember this is all one line in CMD, so consider writing a batch file for it.
docker run --rm -it -p 5000:5000 --memory 4g --cpus 2 --mount type=bind,src=c:\luis\input,target=/input --mount type=bind,src=c:\luis\output,target=/output mcr.microsoft.com/azure-cognitive-services/luis Eula=Accept Billing=https://YOUR_REGION.api.cognitive.microsoft.com/luis/v2.0 ApiKey=YOUR_API_KEY
Where the Billing endpoint can be take from the first part of the Endpoint address in LUIS ‘Keys and Endpoint settings’ and the ApiKey is only the set of digits from the key of the same page. E.g. 3q919c439w2445f217b3w262622331c1
- You can then use your REST client of choice. You may use http://localhost:5000/swagger/index.html but be warned you’ll need to convert the AppId to a GUID (online coverter)…I know, right?? You should get an error saying something like; No model found with the given Application ID. If you do, then look at the output from your Docker console window. It should say something like could not find file xyz.gz.
- Copy the file you downloaded in (4) and put it into the input folder you created in (2). Carefully examine the name of the file. It needs to perfectly match the name of the file in the error message in (7)
- Ctrl+c the Docker container. Re-run the command from (6). Retry your REST call. You should now be working 🙂
NB, when you finish with this and try it again at a later date you may get errors when restarting your container, step (6). It may say something like, Error starting userland proxy. This appears to be a problem with Docker on Windows 10. You need to reopen the Docker desktop from the Taskbar and select Restart. This can take a little time but keep hovering over the icon and it will show you when it’s running again. Then you can re-issue step (6) and everything should be fine again.
One last note is that you need to stay online when using the Container. You can temporarily go offline but any prolonged absence and the LUIS Container will take itself offline with failed to reach metering endpoint, resource temporarily unavailable. Shame, that scuppers offline use. Oh well, can’t have everything you want 🙂