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YAML
In YAML format
Apart from JSON, the configuration file also can be in YAML format. It is identical to JSON but more human-readable data representation and pretty straightforward. Difference to JSON, YAML's hierarchy is denoted by using double space characters as an example below.
---
host: "localhost"
port: 0
use_ssl: false
protocol: "HTTP"
static_content_root: "static-content"
static_content_url: "/static-content"
static_content_index_page: "/index.html"
kafka_configuration:
start_http_server_for_kafka: true
http_kafka_host: "localhost"
http_kafka_port: 0
consumer_topic_name: "inference-in"
consumer_key_deserializer_class: "io.vertx.kafka.client.serialization.JsonObjectDeserializer"
consumer_value_deserializer_class: "io.vertx.kafka.client.serialization.JsonObjectDeserializer"
consumer_group_id: "konduit-serving-consumer-group"
consumer_auto_offset_reset: "earliest"
consumer_auto_commit: "true"
producer_topic_name: "inference-out"
producer_key_serializer_class: "io.vertx.kafka.client.serialization.JsonObjectSerializer"
producer_value_serializer_class: "io.vertx.kafka.client.serialization.JsonObjectSerializer"
producer_acks: "1"
mqtt_configuration: {}
custom_endpoints: []
pipeline:
steps:
- '@type': "DEEPLEARNING4J"
modelUri: "<path_to_model>"
inputNames:
- "1"
- "2"
outputNames:
- "11"
- "22"
- '@type': "LOGGING"
logLevel: "INFO"
log: "KEYS_AND_VALUES"
Example of default YAML configuration file with custom Graph Pipeline Steps:
---
host: "localhost"
port: 0
use_ssl: false
protocol: "HTTP"
static_content_root: "static-content"
static_content_url: "/static-content"
static_content_index_page: "/index.html"
kafka_configuration:
start_http_server_for_kafka: true
http_kafka_host: "localhost"
http_kafka_port: 0
consumer_topic_name: "inference-in"
consumer_key_deserializer_class: "io.vertx.kafka.client.serialization.JsonObjectDeserializer"
consumer_value_deserializer_class: "io.vertx.kafka.client.serialization.JsonObjectDeserializer"
consumer_group_id: "konduit-serving-consumer-group"
consumer_auto_offset_reset: "earliest"
consumer_auto_commit: "true"
producer_topic_name: "inference-out"
producer_key_serializer_class: "io.vertx.kafka.client.serialization.JsonObjectSerializer"
producer_value_serializer_class: "io.vertx.kafka.client.serialization.JsonObjectSerializer"
producer_acks: "1"
mqtt_configuration: {}
custom_endpoints: []
pipeline:
outputStep: "4"
steps:
"1":
'@type': "LOGGING"
'@input': "input"
logLevel: "INFO"
log: "KEYS_AND_VALUES"
"2":
'@type': "TENSORFLOW"
'@input': "1"
input_names:
- "1"
- "2"
output_names:
- "11"
- "22"
model_uri: "<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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