Academy Documentation
  • Usage of Academy Documentation
  • Getting Started
    • Background Information
    • For Apollo Users
    • For Titan Users
    • For Scientists
    • For HPC Users
    • For Experienced Users
  • Cloud Computing
    • General Information
    • Cloud Computing for Scientists
    • Cloud Computing for HPC Users
  • Overview of the Platform
    • Overview of the Platform User Interface
    • Tool Library and App Introduction
  • Billing Access and Orgs
    • Orgs and Account Management
    • Billing and Pricing
  • Cohort Browser
    • Apollo Introduction
    • Overview of the Cohort Browser
    • Combining Cohorts
    • Genomic Variant Browser
    • Somatic Variants
  • JSON
    • Introduction
    • JSON on the Platform
  • Command Line Interface (CLI)
    • 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
      • Example 5: samtools with a Docker Image
    • Python
      • Example 1: Word Count (wc)
      • Example 2: fastq_quality_trimmer
      • Example 3: cnvkit
    • Publishing Applets to Apps
  • 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
      • Launching a ML JupyterLab Job
      • 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
Powered by GitBook
On this page
  • Instance Type Overview
  • Naming
  • Choosing an Instance Type
  • Instance Classes and Cores
  • Choosing a Good Instance Type
  • Multistep Workflows
  • Resources

Was this helpful?

Export as PDF
  1. Cloud Computing

General Information

PreviousCloud ComputingNextCloud Computing for Scientists

Last updated 9 months ago

Was this helpful?

Instance Type Overview

Naming

AWS naming of instance types is broken down here:

Choosing an Instance Type

Question
Focus on this

Does the software utilize multiple cores?

mem2_ssd1_v2_x16

Is the software GPU optimized?

mem2_ssd1_gpu_x32

How much memory does the software use (per core)?

mem2_ssd1_v2_x16

How much disk space is needed for the software (per core)?

mem2_ssd1_v2_x16

Always use version 2 of an instance type!

mem2_ssd1_v2_x16

Instance Classes and Cores

Each class (like mem1) is scaled so that each core in an instance has access to the same amount of memory/disk space:

  • Example: mem1_ssd1_v2_x2:

    • 4 Gb total memory / 2 cores =

    • 2 Gb / Core

  • Example: mem1_ssd1_v2_x8:

    • 16 Gb total memory / 8 cores =

    • 2 Gb / core

Choosing a Good Instance Type

  • Scale usage/instance type according to usage statistics and dataset size

    • If it doesn't utilize all resources

      • Use a smaller instance type

    • Runs out of memory, or is slow

      • Consider using a larger instance type

Multistep Workflows

  • Each stage of a workflow is run by a different set of workers

  • Each stage can be customized in terms of instance type

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.

Instance Types Documentation
Full Documentation