Cluster Inference

The Cluster Inference is a tool allowing to infer cluster classes on knowledge graph entries This tools is a rest service.

Cluster Inference API

This API is also available via the service itself on http://<service host>:<service port>/swagger

Cluster Inference configuration

Example of Configuration:

relation.json

{
    "logger": {
        "logging-level": "{{ project.loglevel }}"
    },
    "relations": {
        "cluster":{
            "algorithm":"kmeans",
            "number-of-classes":16,
            "number-of-iterations":16,
            "seed":123456,       
            "batch-size":4096, 
            "embeddings":
            {
                "server":{
                    "host":"0.0.0.0",
                    "port":10005,
                    "associate-environment": {
                        "host":"SENT_EMBEDDING_HOST",
                        "port":"SENT_EMBEDDING_PORT"
                    },
                    "use-ssl":false,
                    "no-verify-ssl":true
                },
                "aggregate":{
                    "configuration":"{{ project.path }}/configs/embeddings.json"
                }
            }
        },
        "clustering-model":{
            "semantic-quantizer-model":"{{ project.path }}/resources/modeling/relation_names.model.pkl",
            "train-if-not-exists":true
        },
        "network": {
            "host":"0.0.0.0",
            "port":10013,
            "associate-environment": {
                "host":"CLUSTER_INFERENCE_HOST",
                "port":"CLUSTER_INFERENCE_PORT"
            }
        },
        "runtime":{
            "request-max-size":100000000,
            "request-buffer-queue-size":100,
            "keep-alive":true,
            "keep-alive-timeout":5,
            "graceful-shutown-timeout":15.0,
            "request-timeout":60,
            "response-timeout":60,
            "workers":1
        }
    }
}

Configure cluster inference logger

Logger is configuration at top level of json in logger field.

Example of Configuration:

logger configuration

{
    "logger": {
        "logging-level": "debug"
    }    
}

The logger fields are:

• logging-file is the filename of the log file (notice that "-\" will be added to this name=

• logging-path is the path to the logfile (if it does not exist it will be created)

• logging-level contains two fields:

• file for the logging level of the file

• screen for the logging level on screen output

Both can be set to the following values:

• debug for the debug level and developper information
• info for the level of information
• warning to display only warning and errors
• error to display only error

Configure cluster inference Network

Example of Configuration:

network configuration

{
    "network": {
        "host":"0.0.0.0",
        "port":8080,
        "associate-environment": {
            "host":"HOST_ENVNAME",
            "port":"PORT_ENVNAME"
        },
        "ssl":
        {
            "certificate":"path/to/certificate",
            "key":"path/to/key"
        }
    }
}

The network fields:

• host : hostname

• port : port of the service

• associated-environement : is the "host" and "port" associated environment variables that allows to replace the default one. This field is not mandatory.

• "host" : associated "host" environment variable

• "port" : associated "port" environment variable

• ssl : ssl configuration IN PRODUCTION IT IS MANDATORY TO USE CERTIFICATE AND KEY THAT ARE *NOT* SELF SIGNED

• cert : certificate file

• key : key file

Configure cluster inference runtime

Example of Configuration:

network configuration

{
    "runtime":{
        "request-max-size":100000000,
        "request-buffer-queue-size":100,
        "keep-alive":true,
        "keep-alive-timeout":5,
        "graceful-shutown-timeout":15.0,
        "request-timeout":60,
        "response-timeout":60,
        "workers":1
    }    
}

The Runtime fields:

• request-max-size : how big a request may be (bytes)

• request-buffer-queue-size: request streaming buffer queue size

• request-timeout : how long a request can take to arrive (sec)

• response-timeout : how long a response can take to process (sec)

• keep-alive: keep-alive

• keep-alive-timeout: how long to hold a TCP connection open (sec)

• graceful-shutdown_timeout : how long to wait to force close non-idle connection (sec)

• workers : number of workers for the service on a node

• associated-environement : if one of previous field is on the associated environment variables that allows to replace the default one. This field is not mandatory.

• request-max-size : overwrite with environement variable

• request-buffer-queue-size: overwrite with environement variable
• request-timeout : overwrite with environement variable
• response-timeout : overwrite with environement variable
• keep-alive: overwrite with environement variable
• keep-alive-timeout: overwrite with environement variable
• graceful-shutdown_timeout : overwrite with environement variable
• workers : overwrite with environement variable

Cluster Inference service

To run the command type simply from tkeir directory:

python3 thot/clusterinfer_svc.py --config=<path to configuration file>

or if you install tkeir wheel:

tkeir-clusterinfer-svc.py --config=<path to configuration file>

A light client can be run through the command

python3 thot/clusterinfer_client.py --config=<path to configuration file> --input=<input directory> --output=<output directory>

or if you install tkeir wheel:

tkeir-clusterinfer-client.py --config=<path to configuration file> --input=<input directory> --output=<output directory>

Cluster Inference Tests

The converter service come with unit and functional testing.

Cluster Inference Unit tests

Unittest allows to test Cluster Inference classes only.

python3 -m unittest thot/tests/unittests/TestRelationClusterizerConfiguration.py
python3 -m unittest thot/tests/unittests/TestClusterInference.py

Cluster Inference Functional tests

python3 -m unittest thot/tests/functional_tests/TestClusterInferSvc.py