TagFlip - AutoNLP

TagFlip AutoNLP enables automated training and deployment of language models for certain NLP tasks (currently only Token Classification is implemented) based on TagFlip annotated data or external Hugging Face datasets.

train_deploy

This project uses netJS for the main backend and mlFlow for the machine learning workflows. The frontend is built using React and Ant Design.

Try it!

TagFlip AutoNLP is available at autonlp.informatik.fh-swf.de.

Building & Deploying

Coverage

Docker

Currently, installing via Docker Compose is the easiest way to deploy the deploy the application.

Prerequisites

At least one Linux-based Docker host in version 20 or newer.
NVIDIA Container Toolkit per Docker host on which NLP models are trained (optional).
A Traefik Edge Router installation on each Docker host which should to be used for deploying trained models. If only a single Docker host is used in total the Traefik installation is done automatically when the core application is deployed via the installation script described later. For any additional Docker host on which model deployments should be deployed, the following Docker Compose deployment must be applied per host:

version: '3.6'
services:
  auto-nlp-traefik:
    image: 'traefik:v2.4'
    container_name: 'auto-nlp-traefik'
    command:
      - '--api.insecure=true'
      - '--providers.docker=true'
      - '--providers.docker.exposedbydefault=false'
      - '--providers.http=true'
      - '--entrypoints.web.address=:80'
    ports:
      - '${AUTONLP_TRAEFIK_ENTRYPOINT_PORT}:80'
      - '8086:8080'
    volumes:
      - '/var/run/docker.sock:/var/run/docker.sock:ro'
    networks:
      - auto-nlp-deployments
    restart: always

networks:
  auto-nlp-deployments:
    name: 'auto-nlp-deployments'
    driver: 'bridge'

1. Creating the configuration file `config.docker.yaml`.

TagFlip AutoNLP installation using Docker requires a configuration file config.docker.yaml in root directory of the project. In the following an example of this file is shown and described.

dataset-providers:
  tagflip.annotations:
    type: 'tagflip'
    config:
      api: 'https://url-to-tagflip-v1-api'

  huggingface:
    type: 'huggingface'

training-environments:
  docker:
    - name: 'some-docker-host'
      description: Docker training on some-docker-host
      ssh:
        username: 'some-user'
        host: 'some-docker-host'
        port: 22
      parameters:
        gpu_id: { choice: [0, 1] }

    - name: 'localhost'
      parameters:
        gpu_id: { choice: [0] }

deployment-environments:
  docker:
    - name: 'localhost'
      entrypoint: 'http://host.docker.internal:8085'
      parameters:
        worker_count: { range: [1, 12] }

    - name: 'some-deployment-host'
      ssh:
        username: 'some-user'
        host: 'some-deployment-host'
        port: 22
      entrypoint: 'https://public-url-to-some-deployment-host'
      parameters:
        worker_count: { range: [1, 16] }

dataset-providers

The key dataset-providers defines the data sources for datasets of the application. Currently two types of dataset providers are supported.

tagflip : Dataset providers of type tagflip can refer to a TagFlip-V1 backend. TagFlip-V1 is the previous TagFlip project, which can be found here: https://github.com/fhswf/tagflip-backend - In the future, this dataset provider will be replaced by this repository's TagFlip project once the old functionality is integrated.

huggingface: Dataset providers of type huggingface allow access to hugging face datasets. No further configuration is required. The application takes care that valid datasets are provided.

While there are multiple tagflip dataset-providers possible (multiple instances of TagFlip on different servers),, there should only be one dataset-provider of type huggingface defined at max.

training-environments

Under the key training-environments the runtime environments for the training are being defined. Currently, only Docker environments are being supported properly for training. If a defined Docker runtime is a remote Docker environment, an SSH configuration must be specified under the ssh key, as shown. For authentication, a corresponding key-file is expected to be found in the user directory ~/.ssh then.

Per Docker runtime environment the parameter configuration

parameters:
  gpu_id: { choice: [0, 1] }

can be configured to specify a list of GPU IDs that can be selected when scheduling a training. In this example, the runtime environment has two GPUs (ID 0, ID 1) which are available as selection options during training scheduling. The prerequisite for accelerated training is of course, that the NVIDIA Container Toolkit is installed on the Docker system. The GPU IDs of a Docker host can be determined via the command nvidia-smi.

deployment-environments

Under the key deployment-environments runtime environments for deployments can be defined. Currently, only Docker environments are supported.

If a defined Docker runtime is a remote Docker environment, an SSH configuration must be specified under the ssh key, as shown. For authentication, a corresponding key-file is expected to be found in the user directory ~/.ssh then.

Deployments of trained models deployed in a Docker environment are being accessed via a Traefik Edge Router. Therefore, a Traefik must be installed, as described in the prerequisites, on each Docker host that is being configured under deployment-environments. If there is only one Docker host for the application and all it's trainings and deployments, the Taefik Edge Router installation will be done automatically by applying the installation script as described below.

However, a public URL must be specified via the entrypoint key, that references or redirects to the Traefik entrypoint, so that the deployments can be accessed via that URL.

The configuration statement

parameters:
  worker_count: { range: [1, 16] }

can be used to specify the minimum and maximum number of worker processes per deployment on the Docker host. The weaker the system the more restrictive the values should be, since model deployments generally require a lot of resources and one model instance is loaded per worker.

2. Creating the .env file `docker-build.env`

The installation requires the existence of certain Environment variables. The following environment variables must be set for the application:

COMPOSE_PROJECT_NAME=auto-nlp # Docker Compose project name

  # TagFlip AutoNLP
AUTONLP_ENV=docker # (don't chance since we use docker as installation target)
AUTONLP_CORE_PORT=3000 # Port for the deployment of the AutoNLP Core component
AUTONLP_TRAEFIK_ENTRYPOINT_PORT=8085 # Port for deployment of the central Traefik proxy
AUTONLP_MINIO_PORT=3006 # Port for the deployment of the MinIO-S3 server
AUTONLP_CORE_PUBLIC_URL=http://public-url-to-auto-nlp-core:3000 # A public URL for accessing the AutoNLPCore component
AUTONLP_DEPLOYMENT_PROXY_ENTRYPOINT_URL=http://public-url-to-traefik-proxy:8085 # A public URL for accessing the central Traefik Proxy

#MongoDB
# if not mongodb://localhost:27017 without authentication (standard) should be used
MONGODB_URI=
MONGODB_USER=
MONGODB_PASSWORD=

#REDIS
# if not localhost:6379 without authentication (standard) should be used
REDIS_HOST=
REDIS_USER=
REDIS_PASSWORD=

# MLflow specific variables
MLFLOW_TRACKING_PORT=3005 # Port for the deployment of the MLflow Tracking Servers
MLFLOW_TRACKING_URI=http://public-url-to-mlflow-tracking-server:3005 # A public URL for accessing the MLflow-Tracking-Server
MLFLOW_TRACKING_USERNAME= # A Username for accessing the MLflow Tracking Server
MLFLOW_TRACKING_PASSWORD= # A Password for accessing the MLflow Tracking Server
MLFLOW_S3_ENDPOINT_URL=http://public-url-to-s3-endpoint:3006 # A public URL for accessing the S3-Server (MinIO)
AWS_ACCESS_KEY_ID=minioadmin # S3 Bucket Access Key
AWS_SECRET_ACCESS_KEY=minioadmin # S3 Bucket Secret Key

BACKEND_STORE_URI=sqlite:///data/sqlite.db #  Database file, which is being used inside MLflow Tracking-Servers verwendet wird (don't change)
BUCKET_URI_OR_LOCAL_PATH=s3://tagflip-autonlp # Name of the S3-Bucket where artifacts will be stored (don't change)

3. Running the installation

If the root directory of the project contains a configuration file config.docker.yaml and an .env file docker-build.env are present in the root directory, the installation script can be executed.

The installation can be started via

./build.sh

Running the script will perform a Docker Compose deployment. The associated Docker Compose file is located in the path ./packages/docker-compose.yaml.

Notes regarding CI builds

Originally, yalc was used to manage dependencies between auto-nlp-core , auto-nlp-shared-js, and auto-nlp-ui. However, this approach complicates CI builds. The build now uses yarn workspaces and yarn PnP for this purpose.

To build the backend, run

yarn install
yarn workspaces foreach -t -v run build

The backend can be started using

yarn workspace auto-nlp-core run start

Credits

Main Author: Timo Neuhaus (neuhaus.timo@fh-swf.de)

fhswf / tagflip-autonlp

readme

TagFlip - AutoNLP

Try it!

Building & Deploying

Docker

Prerequisites

1. Creating the configuration file `config.docker.yaml`.

dataset-providers

training-environments

deployment-environments

2. Creating the .env file `docker-build.env`

3. Running the installation

Notes regarding CI builds

Credits

fhswf / tagflip-autonlp

readme

TagFlip - AutoNLP

Try it!

Building & Deploying

Docker

Prerequisites

1. Creating the configuration file config.docker.yaml.

dataset-providers

training-environments

deployment-environments

2. Creating the .env file docker-build.env

3. Running the installation

Notes regarding CI builds

Credits

1. Creating the configuration file `config.docker.yaml`.

2. Creating the .env file `docker-build.env`