Maestro Worker Python

This module scaffolds the creation of a standard inference worker to run on the Moises/Maestro infrastructure.

Installation

To install the main branch:

pip install git+https://github.com/moises-ai/maestro-worker-python.git

To install a version (recommended):

pip install git+https://github.com/moises-ai/maestro-worker-python.git@3.5.0

Maestro init

Run the init script to scaffold a maestro worker in the current directory.

To create a different one, use the flag --folder

maestro-init

This will create a starter Maestro worker project, including:

A models folder to include your models
A docker-compose.yamlfile
A DockerFile
A requirements.txt file including this package
A worker.py with a worker example

Testing your worker

Using `maestro-cli`:

Run the CLI passing your worker file as the first param, then, any parameters exposed by your class. In this example, input_1 will be sent to the worker, with the value Hello.

maestro-cli ./worker.py --input_1=Hello

Using `maestro-server`:

Run the maestro server with the path to your worker. To see all options, use maestro-server --help

maestro-server --worker=./worker.py

Send a request to the server inference endpoint:

curl --request POST --url http://localhost:8000/worker-example/inference  --header 'Content-Type: application/json' \
    --data '{"input_1": "Hello"}'

Upload/Download server for development purposes

In order to avoid using signedurls for uploading/downloading files, you can use the maestro-upload-server command. This will start a server in the default 9090 port that will upload/download files in the local ./uploads folder.

Examples:

maestro-upload-server --port=9090

After server is running, you can upload files to it:

curl http://localhost:9090/upload-file/your_file_name

Then retrieve it:

curl http://localhost:9090/get-file/your_file_name

You can clean the files using:

curl http://localhost:9090/clean

You can also list files using:

curl http://localhost:9090/list-files

Worker Utils

Download a file from URL:

from maestro_worker_python.download_file import download_file

file_name = download_file("https://url_to_download_file")

Upload files to signed_url:

from maestro_worker_python.upload_files import upload_files, UploadFile

files_to_upload = []
files_to_upload.append(UploadFile(file_path="test_upload1.txt", file_type="text/plain", signed_url="https://httpbin.org/put"))
files_to_upload.append(UploadFile(file_path="test_upload2.txt", file_type="text/plain", signed_url="https://httpbin.org/put"))
upload_files(files_to_upload)

Convert media files:

from maestro_worker_python.convert_files import convert_files, FileToConvert

files_to_convert = []
files_to_convert.append(FileToConvert(input_file_path="input.mp3", output_file_path="output.wav", file_format="wav", max_duration=1200))
files_to_convert.append(FileToConvert(input_file_path="input.mp3", output_file_path="output.m4a", file_format="m4a", max_duration=1200))
convert_files(files_to_convert)

Get file duration in seconds

from maestro_worker_python.get_duration import get_duration
get_duration('./myfile.mp3')

Using Docker Compose

Build image

docker-compose build

Run the server

docker-compose run --service-ports worker

Developing this package

Install poetry

You can run it in development mode:

poetry install
poetry run maestro-init

If you get a keyring error (Ubuntu), you may need to run the following:

export PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring

To bump the package version:

poetry version (major|minor|patch)

Running tests:

poetry run python -m pytest

moises-ai/maestro-worker-python