> For the complete documentation index, see [llms.txt](https://serving.konduit.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://serving.konduit.ai/examples/cli/use-cases/creating-a-sequence-pipeline.md). # 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 | | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------- | | |

| |

| 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://serving.konduit.ai/examples/cli/use-cases/creating-a-sequence-pipeline.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.