Content Moderation for Comments

This repo does content moderation for text data in general

Set-up .env file for testing comment moderation in local

There needs to be a .env file with following parameters.

ProcessNumCPUPerReplica=0.1
ProcessNumReplicas=1
ProcessMaxCon=100

PromotionNumCPUPerReplica=0.1
PromotionNumReplicas=1
PromotionMaxCon=100

LeetNumCPUPerReplica=0.1
LeetNumReplicas=1
LeetMaxCon=100

ModerateNumCPUPerReplica=0.01
ModerateNumGPUPerReplica=0.33
ModerateNumReplicas=1
ModerateMaxCon=100

CombineNumCPUPerReplica=0.1
CombineNumReplicas=1
CombineMaxCon=100

SnowflakeResultsQueue=content_moderation_text_comment-results_dev
SnowflakeProfileResultsQueue=content_moderation_text_profile-results_dev
AiModelBucket=datalake-dev

Additional variables for internal testing

For DS Team internal testing, we also need to add the following env vars to the .env file:

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_DEFAULT_REGION=us-east-2

For use in g4dn.2xlarge instance, use the following variables instead

TBC

Instructions (Docker)

1. Ensure there are environment variables or .env file, see section above for environment variables.
2. Ensure GPU for docker is enabled. See section below.
3. Once the container is able to detect the GPU, we can follow the normal process of

docker-compose build
docker-compose up

Enabling GPU for Docker

To enable the GPU for Docker, make sure Nvidia drivers for the system are installed. Refer link for details

Commands which can help install Nvidia drivers are:

unbuntu-drivers devices
sudo ubuntu-drivers autoinstall

Then nvidia-docker2 tools needs to be installed. To install follow the below instructions. Refer link for details

curl https://get.docker.com | sh   && sudo systemctl --now enable docker
   distribution=$(. /etc/os-release;echo $ID$VERSION_ID)    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

Testing the code locally

1. Test if the code is working as expected. Firstly on terminal, do:

ray start --head --port=6300

2. Then, deploy the ray services:

python serve_tasks/tasks.py

3. Run this:

python serve_demo.py