Laminoid is a stupid simple instance manager for Google Compute to run machine learning models.
Laminoid provides a reverse proxy for "authentication".
This system powers https://ai.featurebase.com/, which runs the SlothAI framework for building model pipelines.
Laminoid deploys boxes onto Google Cloud. These boxes contain graphics cards, which can run models.
A controller box is used. It may list, start or stop a box which runs models. The controller box is not allowed to create instances, however.
Currently supports Instructor Large or Instructor XL. Instructor also has a whitepaper you can read.
Supports keyterm extraction using a model ensemble. It uses BERT, H2-keywordextractor, and Fasttext.
Subsequent improvements to this repo will add support for other models, like Llama 2.
This deployment uses a simple token assigned to the network tags on the box when it starts. These tokens are assigned from a file.
You'll create a secrets.sh file with the box token in it before you do the deployment below.
Using a box's endpoints requires a username/password via a reverse proxy. The username is in nginx.conf.sloth and is sloth.
You could possibly move this to your own repo, changing things. If you do, change the deploy_sloth.sh and deploy_controller.sh scripts to reflect the Github repo.
Change all deploy scripts to use your Google service account and project names. You can change zones, but the ones listed are known to have the L4s for boxes.
You may want to change the number of GPUs attached if you like spending money.
Run this to deploy a controller box, that can start, stop and list the model boxes:
./controller/deploy_controller.sh --zone us-central1-a --prod
Run this to deploy a box that runs models:
./sloth/deploy_sloth.sh --zone us-central1-a
If you are using the SlothAI pipeline project, You will need to add a static IP to the controller box to ensure it can be contactd by the app.
Setup is done automatically on the model boxes by the deployment script. You may verify the service is running by SSH'ing to the box and issuing:
sudo screen -x
Detatch from the screen using ctrl-A, D. Alternately, you can ctrl-C the process to exit and it will start a new gunicorn.
Be sure to configure the firewall for remote calls using the token and the http authentiation provided by NGINX:
gcloud compute firewall-rules create beast --target-tags beast --allow tcp:9898
Use the controller's API to list all instances:
curl -X GET \
-u sloth:f00bar \
-H "Content-Type: application/json" \
http://sloth:<token>@box-ip:9898/api/instance/list?token=<token>
Get the status of a box:
curl -X GET \
-u sloth:f00bar \
-H "Content-Type: application/json" \
http://sloth:<token>@box-ip:9898/api/instance/<zone>/<instance_id>/status?token=<token>
Stop a box:
curl -X GET \
-u sloth:f00bar \
-H "Content-Type: application/json" \
http://sloth:<token>@box-ip:9898/api/instance/<zone>/<instance_id>/stop?token=<token>
Start a box:
curl -X GET \
-u sloth:f00bar \
-H "Content-Type: application/json" \
http://sloth:<token>@box-ip:9898/api/instance/<zone>/<instance_id>/start?token=<token>
These boxes are set to be spot instances. They will eventually be terminated by Google. Use the start method above to start the instance if it is not running.
To embed something from the APIs, use curl:
curl -X POST \
-u sloth:f00bar \
-H "Content-Type: application/json" \
-d '{"text": ["The sun rises in the east.", "Cats are curious animals.", "Rainbows appear after the rain."]}' \
http://sloth:<token>@box-ip:9898/embed
Do something similar to extract keyterms:
curl -X POST \
-u sloth:f00bar \
-H "Content-Type: application/json" \
-d '{"text": ["The sun rises in the east.", "Cats are curious animals.", "Rainbows appear after the rain."]}' \
http://sloth:<token>@box-ip:9898/keyterms
CUDA runs out of memory sometimes, likely due to gunicorn threading loading a seperate model into the GPU. I have no idea what's going on with it so don't make that number bigger unless you want to figure it out.