Question 80965 in Docker Question posted in
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Job-based cloud processing solution – Docker

I would like to do some cloud processing on a very small cluster of machines (<5).
This processing should be based on ‘jobs’, where jobs are parameterized scripts that run in a certain docker environment.
As an example for what a job could be:

  • Run in docker image "my_machine_learning_docker"
  • Download some machine learning dataset from an internal server
  • Train some neural network on the dataset
  • Produce a result and upload it to a server again.

My use cases are not limited to machine learning however.
A job could also be:

  • Run in docker image "my_image_processing_docker"
  • Download a certain amount of images from some folder on a machine.
  • Run some image optimization algorithm on each of the images.
  • Upload the processed images to another server.

Now what I am looking for is some framework/tool, that keeps track of the compute servers, that receives my jobs and dispatches them to an available server. Advanced priorization, load management or something is not really required.
It should be possible to query the status of jobs and of the servers via an API (I want to do this from NodeJS).
Potentially, I could imagine this framework/tool to dynamically spin up these compute servers in in AWS, Azure or something. That would not be a hard requirement though.

I would also like to host this solution myself. So I am not looking for a commercial solution for this.

Now I have done some research, and what I am trying to do has similarities with many, many existing projects, but I have not "quite" found what I am looking for.
Similar things I have found were (selection):

  • CI/CD solutions such as Jenkins/Gitlab CI. Very similar, but it seems to be tailored very much towards the CI/CD case, and I am not sure whether it is such a good idea to abuse a CI/CD solution for what I am trying to do.
  • Kubernetes: Appears to be able to do this somehow, but is said to be very complex. It also looks like overkill for what I am trying to do.
  • Nomad: Appears to be the best fit so far, but it has some proprietary vibes that I am not very much a fan of. Also it still feels a bit complex…

In general, there are many many different projects and frameworks, and it is difficult to find out what the simplest solution is for what I am trying to do.

Can anyone suggest anything or point me in a direction?

Thank you

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3

Answers


  1. 0

    I would use Jenkins for this use case even if it appears to you as a “simple” one. You can start with the simplest pipeline which can also deal with increasing complexity of your job. Jenkins has API, lots of plugins, it can be run as container for a spin up in a cloud environment.

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  2. 0

    Its possible you’re looking for something like AWS Batch flows: https://aws.amazon.com/batch/ or google datalflow https://cloud.google.com/dataflow. Out of the box they do scaling, distribution monitoring etc.

    But if you want to roll your own ….

    Option A: Queues

    For your job distribution you are really just looking for a simple message queue that all of the workers listen on. In most messaging platforms, a Queue supports deliver once semantics. For example

    Using queues for load distribution is a common pattern.
    A queue based solution can use both with manual or atuomated load balancing as the more workers you spin up, the more instances of your workers you have consuming off the queue. The same messaging solution can be used to gather the results if you need to, using message reply semantics or a dedicated reply channel. You could use the resut channel to post progress reports back and then your main application would know the status of each worker. Alternatively they could drop status in database. It probably depends on your preference for collecting results and how large the result sets would be. If large enough, you might even just drop results in an S3 bucket or some kind of filesystem.

    You could use something quote simple to mange the workers – Jenkins was already suggested is in defintely a solution I have seen used for running multiple instances accross many servers as you just need to install the jenkins agent on each of the workers. This can work quote easily if you own or manage the physical servers its running on. You could use TeamCity as well.

    If you want something cloud hosted, it may depend on the technology you use. Kubernetties is probably overkill here, but certiabnly could be used to spin up N nodes and increase/decrease those number of workers. To auto scale you could publish out a single metric – the queue depth – and trigger an increase in the number of workers based on how deep the queue is and a metric you work out based on cost of spinning up new nodes vs. the rate at which they are processed.

    You could also look at some of the lightweight managed container solutions like fly.io or Heroku which are both much easier to setup than K8s and would let you scale up easily.

    Option 2: Web workers

    Can you design your solution so that it can be run as a cloud function/web worker?
    If so you could set them up so that scaling is fully automated. You would hit the cloud function end point to request each job. The hosting engine would take care of the distribution and scaling of the workers. The results would be passed back in the body of the HTTP response … a json blob.

    Your workload may be too large for these solutions, but if its actually fairly light weight quick it could be a simple option.

    I don’t think these solutions would let you query the status of tasks easily.

    If this option seems appealing there are quite a few choices:

    Option 3: Google Cloud Tasks

    This is a bit of a hybrid option. Essentially GCP has a queue distribution workflow where the end point is a cloud function or some other supported worker, including cloud run which uses docker images. I’ve not actually used it myself but maybe it fits the bill.

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  3. 0

    When I look at a problem like this, I think through the entirity of the data paths. The map between source image and target image and any metadata or status information that needs to be collected. Additionally, failure conditions need to be handled, especially if a production service is going to be built.

    I prefer running Python, Pyspark with Pandas UDFs to perform the orchestration and image processing.

    S3FS lets me access s3. If using Azure or Google, Databricks’ DBFS lets me seamlessly read and write to cloud storage without 2 extra copy file steps.

    Pyspark’s binaryFile data source lets me list all of the input files to be processed. Spark lets me run this in batch or an incremental/streaming configuration. This design optimizes for end to end data flow and data reliability.

    For a cluster manager I use Databricks, which lets me easily provision an auto-scaling cluster. The Databricks cluster manager lets users deploy docker containers or use cluster libraries or notebook scoped libraries.

    The example below assumes the image is > 32MB and processes it out of band. If the image is in the KB range then dropping the content is not necessary and in-line processing can be faster (and simpler).

    Pseudo code:

    df = (spark.read
    .format("binaryFile")
    .option("pathGlobFilter", "*.png")
    .load("/path/to/data")
    .drop("content")
)

from typing import Iterator
def do_image_xform(path:str):
  # Do image transformation, read from dbfs path, write to dbfs path
  ...
  # return xform status
  return "success"

@pandas_udf("string")
def do_image_xform_udf(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
    for path in iterator:
        yield do_image_xform(path)

df_status = df.withColumn('status',do_image_xform_udf(col(path)))
df_status.saveAsTable("status_table") # triggers execution, saves status.
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