Body: <p>I'm new to devops .</p>
  <p>I created a converter and want to deploy it . The converter converts a 3D model from a format to another and then you'll be able to visualize the outpout file on the platform and then you can download the file if you want to.</p>
  <p>when bench-marking the process , i found out that for now it runs up to 1 minute when the files are really big , I'm using azure just moved to aws this week . For now the converter converts one file at a time and uses blender's python library and a c++ library ( when trying to use docker i built these inside the container )</p>
  <p>I started by creating a docker container that will read a heavy blob and then output the converted file but figured out that docker is not designed to read local files .</p>
  <p>I'm searching for the right model to host this . Is docker a good solution ? If not , is there other ways to do this ?</p>
  
Comments: CC BY-SA 4.0
Comments: 2020-08-24T10:52:27.907Z
Comments: Seems like a job for AWS batch, ECS or Lambda? Can you provide some idea of how long the process lasts, what constraints on operations are?
Comments: Bruce Becker
Comments: CC BY-SA 4.0
Comments: 2020-08-24T11:00:42.753Z
Comments: Hi Bruce , when bench-marking the process , i found out that for now it runs up to 1 minute when the files are really big , I'm using azure just moved to aws this week . For now the converter converts one file at a time and uses blender's python library and a c++ library ( when trying to use docker i built these inside the container )
Comments: tawfikboujeh
Tags: <docker><performance><scalability>
Title: What is the best way to deploy a short living process when you have no machine?
UserDisplayName: tawfikboujeh
ThreadText: # What is the best way to deploy a short living process when you have no machine?
  
  (Asked by: username_0 on 2020-08-24)
  
  I'm new to devops .
  
  I created a converter and want to deploy it . The converter converts a 3D model from a format to another and then you'll be able to visualize the outpout file on the platform and then you can download the file if you want to.
  
  when bench-marking the process , i found out that for now it runs up to 1 minute when the files are really big , I'm using azure just moved to aws this week . For now the converter converts one file at a time and uses blender's python library and a c++ library ( when trying to use docker i built these inside the container )
  
  I started by creating a docker container that will read a heavy blob and then output the converted file but figured out that docker is not designed to read local files .
  
  I'm searching for the right model to host this . Is docker a good solution ? If not , is there other ways to do this ?
  
  **Tags:** docker, performance, scalability
  
  > **Comments:**
  > **username_1**: Seems like a job for AWS batch, ECS or Lambda? Can you provide some idea of how long the process lasts, what constraints on operations are?  
  > **username_0**: Hi username_1 , when bench-marking the process , i found out that for now it runs up to 1 minute when the files are really big , I'm using azure just moved to aws this week . For now the converter converts one file at a time and uses blender's python library and a c++ library ( when trying to use docker i built these inside the container )  
  
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  ## 0 answers
  
  