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