Academy Documentation
  • Usage of Academy Documentation
  • Getting Started
    • Background Information
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    • For Titan Users
    • For Scientists
    • For HPC Users
    • For Experienced Users
  • Cloud Computing
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  • Overview of the Platform
    • Overview of the Platform User Interface
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    • Introduction
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    • Introduction to CLI
    • Advanced CLI
  • Building Applets
    • Introduction
    • Bash
      • Example 1: Word Count (wc)
      • Example 2: fastq_quality_trimmer
      • Example 3: samtools
      • Example 4: cnvkit
    • Python
      • Example 1: Word Count (wc)
      • Example 2: fastq_quality_trimmer
      • Example 3: cnvkit
  • Building Workflows
    • Native Workflows
    • WDL
      • Example 1: hello
      • Example 2: Word Count (wc)
      • Example 3: fastq_trimmer
      • Example 4: cnvkit
      • Example 5: workflow
    • Nextflow
      • Resources To Learn Nextflow
      • Overview of Nextflow
      • Nextflow Setup
      • Importing Nf-Core
      • Building Nextflow Applets
      • Error Strategies for Nextflow
      • Job Failures
      • Useful Information
  • Interactive Cloud Computing
    • Cloud Workstation
    • TTYD
    • TTYD vs Cloud Workstation
    • JupyterLab
      • Introduction
      • Running a JupyterLab Notebook
  • Docker
    • Using Docker
    • Creating Docker Snapshots
    • Running Docker with Swiss Army Knife
  • Portals
    • Overview of JSON files for Portals
    • Branding JSON File
    • Home JSON File
    • Navigation JSON File
    • Updating Your Portal
  • AI/ ML Accelerator
    • Data Profiler
      • Introduction to Data Profiler
      • Utilizing Data Profiler Navigator
      • Dataset Level Screen
      • Table Level Screen
      • Column Level Screen
      • Explorer Mode
      • Accessing Data Profiler in ML JupyterLab
    • ML JupyterLab
      • Introduction to ML JupyterLab
      • Getting Started in ML JupyterLab
      • In App Features
      • Getting Started with ML JupyterLab
    • MLflow
      • Introduction to MLflow
      • Getting Started with MLflow
      • Using MLflow Tracking Server
      • Model Registry
      • Using Existing Model
      • Utilizing MLflow in JupyterLab
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  • Why Docker?
  • Docker Terms
  • Snapshots vs Images on the Platform
  • Docker and Security
  • Resources

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  1. Docker

Using Docker

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Last updated 8 months ago

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Why Docker?

  • Portability - Run code and scripts on any machine that has Docker installed

    • Avoids installation headaches on multiple machines (dependencies are installed with software)

    • Make it easy to run batch jobs on multiple instances

  • Reproducibility - be able to run code and generate the same outputs given a set of input files

    • Tie all software to specific versions

    • Utilize Docker images with multiple bioinformatics software installed

    • Examples: ,

Docker Terms

  • Docker Registries

    • Collection of repositories that hold container images

    • docker pull: pulls the images from a registry to a container on our machine

    • docker commit: When we commit changes, these changes are saved to the image in registry

Snapshots vs Images on the Platform

There are hard limits for using Docker Images.

  • DockerHub and other registries have a pull limit of 200 pulls/user/day

  • Saving a snapshot file to your project lets you scale without these limits

  • Especially helpful in batch processing

Docker and Security

  • Use images from trusted vendors whenever possible

    • Examples: Official Ubuntu Image, Amazon Linux Image, Biocontainers

    • Avoid "kitchen sink" images - hard to manage vulnerabilities

  • In general: pay attention to possible vulnerabilities and whether they affect your containers

  • Use dockerfiles to uninstall/patch possible vulnerabilities in images

Resources

To create a support ticket if there are technical issues:

  1. Go to the Help header (same section where Projects and Tools are) inside the platform

  2. Select "Contact Support"

  3. Fill in the Subject and Message to submit a support ticket.

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