[](https://github.com/psf/black)
# Marie-AI
Integrate AI-powered document pipeline into your applications
## Documentation
See the [MarieAI docs](https://docs.marieai.co).
## 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:
```sh
pip install --upgrade marieai
```
Install from source with:
```sh
pip install -e .
```
Build docker container:
```sh
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
```shell
docker run --rm -it marieai/marie:3.0.19-cuda
```
## Run the server with custom entrypoint
```shell
docker run --rm -it --entrypoint /bin/bash marieai/marie:3.0.19-cuda
```
## Telemetry
https://telemetry.marieai.co/
TODO :MOVE TO DOCS
# S3 Cloud Storage
```shell
docker compose -f docker-compose.s3.yml --project-directory . up --build --remove-orphans
```
CrossFTP
## Configure AWS CLI Credentials.
```shell
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.
```shell
docker pull zenko/cloudserver
```
## Create and start the container.
```sh
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.
```shell
aws s3 mb s3://mybucket --profile marie --endpoint-url http://localhost:8000 --region us-west-2
```
```shell
aws s3 ls --profile marie --endpoint-url http://localhost:8000
```
```shell
aws s3 cp some_file.txt s3://mybucket --profile marie --endpoint-url http://localhost:8000
```
```shell
aws s3 --profile marie --endpoint-url=http://127.0.0.1:8000 ls --recursive s3://
```
# 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
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