Marie-AI

Integrate AI-powered document pipeline into your applications

Documentation

See the MarieAI docs.

Installation

You don't need this source code unless you want to modify the package. If you just want to use the package, just run:

pip install --upgrade marieai

Install from source with:

pip install -e .

Build docker container:

DOCKER_BUILDKIT=1 docker build . --build-arg PIP_TAG="standard" -f ./Dockerfiles/gpu.Dockerfile  -t marieai/marie:3.0-cuda 

Command-line interface

This library additionally provides an marie command-line utility which makes it easy to interact with the API from your terminal. Run marie -h for usage.

Example code

Examples of how to use this library to accomplish various tasks can be found in the MarieAI documentation. It contains code examples for:

• Document cleanup
• Optical character recognition (OCR)
• Document Classification
• Document Splitter
• Named Entity Recognition
• Form detection
• And more

Run with default entrypoint

docker run --rm  -it marieai/marie:3.0.19-cuda

Run the server with custom entrypoint

docker run --rm  -it --entrypoint /bin/bash  marieai/marie:3.0.30-cuda

marie server --start --uses sample.yml  

Telemetry

https://telemetry.marieai.co/

TODO :MOVE TO DOCS

S3 Cloud Storage

docker compose -f  docker-compose.s3.yml --project-directory . up  --build --remove-orphans

CrossFTP

Configure AWS CLI Credentials.

vi ~/.aws/credentials
[marie] # this should be in the file
aws_access_key_id=your_access_key_id
aws_secret_access_key=your_secret_access_key

Pull the Docker image.

docker pull zenko/cloudserver

Create and start the container.

docker run --rm -it --name marie-s3-server -p 8000:8000 \
-e SCALITY_ACCESS_KEY_ID=MARIEACCESSKEY \
-e SCALITY_SECRET_ACCESS_KEY=MARIESECRETACCESSKEY \
-e S3DATA=multiple \
-e S3BACKEND=mem zenko/cloudserver

SCALITY_ACCESS_KEY_ID : Your AWS ACCESS KEY 
SCALITY_SECRET_ACCESS_KEY: Your AWS SECRET ACCESS KEY 
S3BACKEND: Currently using memory storage

Verify Installation.

aws s3 mb s3://mybucket  --profile marie --endpoint-url http://localhost:8000 --region us-west-2

aws s3 ls --profile marie --endpoint-url http://localhost:8000

aws s3 cp some_file.txt s3://mybucket  --profile marie --endpoint-url http://localhost:8000

aws s3 --profile marie --endpoint-url=http://127.0.0.1:8000 ls --recursive s3://

Remove files from the bucket

aws s3 rm  s3://marie --recursive --profile marie --endpoint-url http://localhost:8000

Production setup

Configuration for the S3 server will be stored in the following files: https://towardsdatascience.com/10-lessons-i-learned-training-generative-adversarial-networks-gans-for-a-year-c9071159628