# Creating a Sequence Pipeline A Sequence Pipeline is used to treat the data and Machine Learning or Deep Learning model in a series of steps from pre-processing to model serving and post-processing on the output product. In this example, the `CLI` command specifies on `konduit config` is used to configure the configuration file to serve the models on `Konduit-Serving`. You'll be able to follow this example on your local terminal on any directory. If deploying the model does not need pre- nor post-processing, only one step, a deep learning model is needed. This configuration is defined using a single Step to serve a model, and the command for creating the configuration file is like the following. ``` $ konduit config --pipeline dl4j --output config_dl4j.yaml --yaml ``` The YAML configuration is as follows. ``` --- 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: "" inputNames: - "1" - "2" outputNames: - "11" - "22" ``` The Steps are included in the `--pipeline` based on the model's requirement and how output should represent. For example, the model fetches an image input, so the `image_to_ndarray` should be pre-processing step to convert the image into an array. The table below shows all steps that can be used in the Sequence Pipeline. | Pre-processing Step | Model/Python Step | Post-processing Step | Logging | | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------- | | |

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| Here is another example with a series of step in Sequence Pipeline (`image_to_ndarray` to `nd4jtensorflow` to `classifier_output`). The input image needs to convert into an *n-*D array before feeding into the model and produce the classification output. A command likes below: ``` $ konduit config -p image_to_ndarray,nd4jtensorflow,classifier_output -o config.json ``` The command would give the configuration file in JSON with the complete 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" : "IMAGE_TO_NDARRAY", "config" : { "height" : 100, "width" : 100, "dataType" : "FLOAT", "includeMinibatchDim" : true, "aspectRatioHandling" : "CENTER_CROP", "format" : "CHANNELS_FIRST", "channelLayout" : "RGB", "normalization" : { "type" : "SCALE" }, "listHandling" : "NONE" }, "keys" : [ "key1", "key2" ], "outputNames" : [ "output1", "output2" ], "keepOtherValues" : true, "metadata" : false, "metadataKey" : "@ImageToNDArrayStepMetadata" }, { "@type" : "ND4JTENSORFLOW", "inputNames" : [ "1", "2" ], "outputNames" : [ "11", "22" ], "modelUri" : "" }, { "@type" : "CLASSIFIER_OUTPUT", "inputName" : "inputName (optional)", "returnLabel" : true, "returnIndex" : true, "returnProb" : true, "labelName" : "label", "indexName" : "index", "probName" : "prob", "labels" : [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ], "allProbabilities" : false } ] } } ``` {% hint style="info" %} Here is the example which use almost similar steps. You can find the JSON file on {% endhint %} Every Step in the Pipeline needs to modify based on the input characteristics, model configurations and how output should looks like in the end. Using a configuration file allows you to serve the model with Konduit-Serving.