Clamsapp

Using CLAMS App

This document provides general instructions for installing and using CLAMS Apps. App developers may provide additional information specific to their app, hence it’s advised also to look up the app website (or code repository) to get the additional information.

Requirements

Generally, an CLAMS App requires

To run the app locally, Python3 with the clams-python module installed.
To run the app in a container (as an HTTP server), container management software such as docker or podman.
- (the CLAMS team is using docker for development and testing)
To invoke and execute analysis, HTTP client utility (such as curl).

For Python dependencies, usually CLAMS Apps come with requirements.txt files that list up the Python library. However, there could be other non-Python software/library that are required by the app.

Installation

CLAMS Apps available on the CLAMS App Directory. Currently all CLAMS Apps are all open-source projects and are distributed as

source code downloadable from code repository
pre-built container image

Please visit the app-directory to see which apps are available and where you can download them.

In most cases, you can “install” a CLAMS App by either

downloading source code from the app code repository and manually building a container image
downloading pre-built container image directly

Build image from source code

To download the source code, you can either use git clone command or download a zip file from the source code repository. The source code repository address can be found on the App Directory entry of the app.

From the locally downloaded project directory, run the following in your terminal to build an image from the included container specification file.

(Assuming you are using docker as your container manager)

docker build . -f Containerfile -t <image_name_you_pick>

Download prebuilt image

Alternatively, the app maybe already be available on a container registry.

docker pull <prebulit_image_name>

The image name can be found on the App Directory entry of the app.

Running CLAMS App

Running as a container

Once the image is built (by build) or downloaded (by pull), to create a container, run:

docker run -v /path/to/data/directory:/data -p <port>:5000 <image_name>

where /path/to/data/directory is the local location of your media files or MMIF objects and is the *host* port number you want your container to be listening to. The HTTP inside the container will be listening to 5000 by default.

Running as a local HTTP server

To run the app as a local HTTP server without containerization, you can run the following command from the source code directory.

python app.py

By default, a CLAMS App will be listening to port 5000, but you can change the port number by passing --port <number> option.
Be default, the app will be running in debugging mode, but you can change it to production mode by passing --production option to support larger traffic volume.

Running as a CLI program

(Not all CLAMS Apps can be run as a CLI program)

Many CLAMS Apps are written to work as a CLI-based Python Program, although they are primarily designed to be run as an HTTP server. CLI-based execution is useful for testing and debugging purposes, and varies from app to app. To see the command line interface, please visit the app source code repository and look for additional instructions from the app developer.

Invoking the app server

App metadata

Once the app is running as an HTTP server, visit the server address (localhost:5000)to get the app metadata. App metadata is also available at the App Directory entry of the app. App metadata contains important information about the app that we will use in the following sections.

Processing input media

To actually run the app and process input media through computational analysis, simply send a POST request to the app with a MMIF input as the request body.

MMIF input files can be obtained from outputs of other CLAMS apps, or you can create an empty MMIF only with source media locations using clams source command. See the help message for a more detailed instructions. (Make sure you have installed clams-python package version from PyPI.)

pip install clams-python
clams source --help

For example; by running

clams source audio:/data/audio/some-audio-file.mp3

You will get

{
  "metadata": {
    "mmif": "http://mmif.clams.ai/X.Y.Z"
  },
  "documents": [
    {
      "@type": "http://mmif.clams.ai/vocabulary/AudioDocument/v1",
      "properties": {
        "mime": "audio",
        "id": "d1",
        "location": "file:///data/audio/some-audio-file.mp3"
      }
    }
  ],
  "views": []
}

If an app requires just Document inputs (see input section of the app metadata), an empty MMIF with required media file locations will suffice.

However, some apps only works with input MMIF that already contains some annotations of specific types. To run such apps, you need to run different apps in a sequence.

(TODO: added CLAMS workflow documentation link here.)

When an input MMIF is ready, you can send it to the app server Here’s an example of using curl command, and store the response in a file output.mmif.

curl -H "Accept: application/json" -X POST -d@input.mmif -s http://localhost:5000 > output.mmif

# or using a bash pipeline 
clams source audio:/data/audio/some-audio-file.mp3 | curl -X POST -d@- -s http://localhost:5000 > output.mmif

Configuring the app

Running as an HTTP server, CLAMS Apps are stateless, but can be configured for each HTTP reqeust by providing configuration parameters as query string.

For configuration parameters, please look for parameter section of the app metadata.

CLAMS Team