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JSON
In JSON format
Below is the example of a default configuration file in JSON format that needs to serve the model in Konduit-Serving. This configuration file may need simple editing to set up your setup, especially in Pipeline Steps.
Example default Inference Configuration with Sequence Pipeline Steps:
{
"host" : "localhost",
"port" : 0,
"useSsl" : false,
"protocol" : "HTTP",
"staticContentRoot" : "static-content",
"staticContentUrl" : "/static-content",
"staticContentIndexPage" : "/index.html",
"kafkaConfiguration" : {
"startHttpServerForKafka" : true,
"httpKafkaHost" : "localhost",
"httpKafkaPort" : 0,
"consumerTopicName" : "inference-in",
"consumerKeyDeserializerClass" : "io.vertx.kafka.client.serialization.JsonObjectDeserializer",
"consumerValueDeserializerClass" : "io.vertx.kafka.client.serialization.JsonObjectDeserializer",
"consumerGroupId" : "konduit-serving-consumer-group",
"consumerAutoOffsetReset" : "earliest",
"consumerAutoCommit" : "true",
"producerTopicName" : "inference-out",
"producerKeySerializerClass" : "io.vertx.kafka.client.serialization.JsonObjectSerializer",
"producerValueSerializerClass" : "io.vertx.kafka.client.serialization.JsonObjectSerializer",
"producerAcks" : "1"
},
"mqttConfiguration" : { },
"customEndpoints" : [ ],
"pipeline" : {
"steps" : [ {
"@type" : "DEEPLEARNING4J",
"modelUri" : "<path_to_model>",
"inputNames" : [ "1", "2" ],
"outputNames" : [ "11", "22" ]
}, {
"@type" : "LOGGING",
"logLevel" : "INFO",
"log" : "KEYS_AND_VALUES"
} ]
}
}
Example default Inference Configuration with Graph Pipeline Steps:
{
"host" : "localhost",
"port" : 0,
"useSsl" : false,
"protocol" : "HTTP",
"staticContentRoot" : "static-content",
"staticContentUrl" : "/static-content",
"staticContentIndexPage" : "/index.html",
"kafkaConfiguration" : {
"startHttpServerForKafka" : true,
"httpKafkaHost" : "localhost",
"httpKafkaPort" : 0,
"consumerTopicName" : "inference-in",
"consumerKeyDeserializerClass" : "io.vertx.kafka.client.serialization.JsonObjectDeserializer",
"consumerValueDeserializerClass" : "io.vertx.kafka.client.serialization.JsonObjectDeserializer",
"consumerGroupId" : "konduit-serving-consumer-group",
"consumerAutoOffsetReset" : "earliest",
"consumerAutoCommit" : "true",
"producerTopicName" : "inference-out",
"producerKeySerializerClass" : "io.vertx.kafka.client.serialization.JsonObjectSerializer",
"producerValueSerializerClass" : "io.vertx.kafka.client.serialization.JsonObjectSerializer",
"producerAcks" : "1"
},
"mqttConfiguration" : { },
"customEndpoints" : [ ],
"pipeline" : {
"outputStep" : "4",
"steps" : {
"1" : {
"@type" : "LOGGING",
"@input" : "input",
"logLevel" : "INFO",
"log" : "KEYS_AND_VALUES"
},
"2" : {
"@type" : "TENSORFLOW",
"@input" : "1",
"inputNames" : [ "1", "2" ],
"outputNames" : [ "11", "22" ],
"modelUri" : "<path_to_model>"
},
"3" : {
"@type" : "DEEPLEARNING4J",
"@input" : "1",
"modelUri" : "<path_to_model>",
"inputNames" : [ "1", "2" ],
"outputNames" : [ "11", "22" ]
},
"4" : {
"@type" : "MERGE",
"@input" : [ "2", "3" ]
}
}
}
}
For more details on how to create the configuration file, please refer to the examples:
Last modified 1yr ago
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