Extract an archive file (zip file or tar file) stored on AWS S3

Overview

S3 Extract

Extract an archive file (zip file or tar file) stored on AWS S3.

Details

Downloads archive from S3 into memory, then extract and re-upload to given destination.

The following S3 information is expected to be given as Environment Variables:

  • AWS_ENDPOINT_URL
  • AWS_ACCESS_KEY_ID
  • AWS_SECRET_ACCESS_KEY
  • CERT_PATH (optional)
  • CERT_DL_URL (optional)
  • SIGNATURE_VERSION (optional, defaults to "s3v4")
  • REGION_NAME (optional, defaults to "us-east-1")

Additionally, these information needed for ClearML remote execution can also be given as Env Variable (optional, args will override env var):

  • DEFAULT_DOCKER_IMG
  • DEFAULT_QUEUE

For those not familiar, environment variables can be set through various ways, some being:

  • export = in terminal
  • can be set in ~/.bashrc as well for more permanence

Iteratively extracting a folder of zips/tars is supported as well, through --src-is-dir flag.

Usage

usage: run.py [-h] [--src-is-dir] [--dst-bucket DST_BUCKET] [--verbose] [--remote] [--clml-proj CLML_PROJ] [--clml-task-name CLML_TASK_NAME] [--clml-task-type CLML_TASK_TYPE]
              [--docker-img DOCKER_IMG] [--queue QUEUE]
              src_bucket src_path dst_path

positional arguments:
  src_bucket            Source bucket
  src_path              Source path
  dst_path              Destination path

optional arguments:
  -h, --help            show this help message and exit
  --src-is-dir          Flag to indicate that given src path is a directory. Will iteratively extract any files in it ending with .zip or .tar.
  --dst-bucket DST_BUCKET
                        Destination bucket (optional), will default to Source bucket.
  --verbose             print out current upload filename as it progresses
  --remote              use clearml to remotely run job
  --clml-proj CLML_PROJ
                        ClearML Project Name
  --clml-task-name CLML_TASK_NAME
                        ClearML Task Name
  --clml-task-type CLML_TASK_TYPE
                        ClearML Task Type, e.g. training, testing, inference, etc
  --docker-img DOCKER_IMG
                        Base docker image to pull for ClearML remote execution
  --queue QUEUE         ClearML remote execution queue

Example usage:

python run.py my-bucket dataset/coco/images.tar dataset/coco/ --verbose --remote --clml-proj coco --clml-task-name coco_extraction --docker-img ubuntu/20.04 --queue 1xGPU
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