Batch Processing with Seldon Core

Seldon Core provides a command line component that allows for highly parallelizable batch processing with the horizontally scalable seldon core kubernetes model deployments.

For stream processing with Seldon Core please see Stream Processing with KNative Eventing.

Horizontally Scalable Workers and Replicas

The parallelizable batch processor worker allows for high throughput as it is able to leverage the Seldon Core horizontal scaling replicas as well as autoscaling, and hence providing flexibility to the user to optimize their configuration as required.

The diagram below shows a standard workflow where data can be downloaded and then uploaded through an object store, and the Seldon model can be created and deleted when the job finishes successfully.

Integration with ETL & Workflow Managers

The Seldon Batch component has been built to be modular and flexible such that it can be integrated across any workflow managers.

This allows you to leverage Seldon on a large number of batch applications, including triggers that have to take place on a scheduled basis (e.g. once a day, once a month, etc), or jobs that can be triggered programmatically.

Hands on Examples

We have provided a set of examples that show you how you can use the Seldon batch processing component:

High Level Implementation Details

CLI Parameters

To get more insights on each of the commands available you can interact with the batch processor component as follows:

$ seldon-batch-processor --help

Usage: seldon-batch-processor [OPTIONS]

  Command line interface for Seldon Batch Processor, which can be used to
  send requests through configurable parallel workers to Seldon Core models.
  It is recommended that the respective Seldon Core model is also optimized
  with number of replicas to distribute and scale out the batch processing
  work. The processor is able to process data from local filestore input
  file in various formats supported by the SeldonClient module. It is also
  suggested to use the batch processor component integrated with an ETL
  Workflow Manager such as Kubeflow, Argo Pipelines, Airflow, etc. which
  would allow for extra setup / teardown steps such as downloading the data
  from object store or starting a seldon core model with replicas. See the
  Seldon Core examples folder for implementations of this batch module with
  Seldon Core.

  -d, --deployment-name TEXT      The name of the SeldonDeployment to send the
                                  requests to  [required]

  -g, --gateway-type [ambassador|istio|seldon]
                                  The gateway type for the seldon model, which
                                  can be through the ingress provider
                                  (istio/ambassador) or directly through the
                                  service (seldon)

  -n, --namespace TEXT            The Kubernetes namespace where the
                                  SeldonDeployment is deployed in

  -h, --host TEXT                 The hostname for the seldon model to send
                                  the request to, which can be the ingress of
                                  the Seldon model or the service itself

  -t, --transport [rest|grpc]     The transport type of the SeldonDeployment
                                  model which can be REST or GRPC

  -a, --data-type [data|json|str]
                                  Whether to use json, strData or Seldon Data
                                  type for the payload to send to the
                                  SeldonDeployment which aligns with the
                                  SeldonClient format

  -p, --payload-type [ndarray|tensor|tftensor]
                                  The payload type expected by the
                                  SeldonDeployment and hence the expected
                                  format for the data in the input file which
                                  can be an array

  -w, --workers INTEGER           The number of parallel request processor
                                  workers to run for parallel processing

  -r, --retries INTEGER           The number of retries for each request
                                  before marking an error

  -i, --input-data-path PATH      The local filestore path where the input
                                  file with the data to process is located

  -o, --output-data-path PATH     The local filestore path where the output
                                  file should be written with the outputs of
                                  the batch processing

  -m, --method [predict]          The method of the SeldonDeployment to send
                                  the request to which currently only supports
                                  the predict method

  -l, --log-level [debug|info|warning|error]
                                  The log level for the batch processor
  -b, --benchmark                 If true the batch processor will print the
                                  elapsed time taken to run the process

  -u, --batch-id TEXT             Unique batch ID to identify all datapoints
                                  processed in this batch, if not provided is
                                  auto generated

  --help                          Show this message and exit.


Each data point that is sent to the Seldon Core model contains the following identifiers in the request metadata:

  • Batch ID - A unique identifier which can be provided through CLI or is automatically generated

  • Batch Instance ID - A generated unique identifier for each datapoint processed

  • Batch Index - The local ordered descending index for the datapoint relative to the input file location

These identifiers are added on each request as follows:

seldon_request = {
    <data>: <current_batch_instance>,
    "meta": {
        "tags": {
            "batch_id": <BATCH_ID>
            "batch_instance_id": <BATCH_INSTANCE_ID>
            "batch_index": <BATCH_INDEX>

This allows the requests to be identified and matched against the initial request in the data.


The implementation of the module is done leveraging Python’s Threading system.

Benchmarking was carried out using vanilla Python requests module to assess performance of Threading vs Twisted vs AsyncIO. The results showed better performance with Asyncio, however given that the logic in the worker is quite minimal (ie sending a request) and most of the time is waiting for the response, the implementation with Python’s native threading was able to perform at speeds that were efficient enough to very easily scale to thousands of workers.

However currently the implementation uses the Seldon Client which does not leverage quite a few optimization requirements to increase the performance of processing, such as re-using a session. However even without these optimisations the worker will still reach a highly concurrent performance, and these optimizations will be introduced as adoption of this component (and feedback) grows.