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
    • 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
Powered by GitBook
On this page
  • New to JupyterLab?
  • Introduction
  • Why JupyterLab?
  • Requesting an Instance
  • Use Single DXJupyter Instance if:
  • Use Spark Cluster DXJupyter If:
  • JupyterLab with Use Cases
  • Spark JupyterLab with Use Cases
  • Starting an Instance
  • Monitoring Your Instance
  • Resources

Was this helpful?

Export as PDF
  1. Interactive Cloud Computing
  2. JupyterLab

Introduction

PreviousJupyterLabNextRunning a JupyterLab Notebook

Last updated 2 months ago

Was this helpful?

Before you begin, review the overview documentation and log onto the

New to JupyterLab?

If you have never used a JupyterLab notebook before, please view this information:

Introduction

We can interact with the platform in several different ways and install software packages in these different environments depending on what we are wanting to use and how we want to use it. Here is what we are explaining in these sections:

Why JupyterLab?

Many Data Science Tasks are Interactive

  • Notebook- based analysis

  • Exploratory Data Analysis (EDA)

  • Data Preprocessing/ Cleaning

  • Implementing new Machine Learning/ Model

  • Building Workflows

Requesting an Instance

Use Single DXJupyter Instance if:

  • Work can be done on a single machine instance

  • Main Use Cases:

    • Python/R

    • Image Processing

    • ML

    • Stata

Use Spark Cluster DXJupyter If:

  • Working with very large datasets that will not fit in memory on a single instance

  • Using Apollo and querying a large ingested dataset

  • Need to use Spark based tools such as dxdata, HAIL or GLOW

JupyterLab with Use Cases

Spark JupyterLab with Use Cases

Starting an Instance

  1. From Project List: Tools > JupyterLab

  2. New JupyterLab button (top right)

  3. Name your JupyterLab Environment

  4. Snapshot (if you have one): single_jupyterlab.tar.gz

  5. Project: Select Your Project

  6. Choose your configuration

  7. Choose your instance type

  8. Choose your duration

  9. Select your feature

  10. Click Start Environment

Monitoring Your Instance

  1. In the JupyterLab list, select the one you are wanting to monitor

  2. On the right had side, info about the job will appear

    It will look like this:

  1. You can also view this on the monitor tab by selecting in the info "View this Job in Monitor" (highlighted in gold above) or by using the Monitor Tab in the project space. It will look like this on the Monitor Tab

Running instances may take a while to load as the allocations become available.

  1. Once it says "ready" select open

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.

DXJupyterLab Quickstart Guide
Running DXJupyterLab
Full Documentation
DNAnexus Platform
Jupyter Notebook Documentation
Try Jupyter
Jupyter Architecture
Content Community