From 51d6afeb4a58f8deef41072838e67db2e99295a3 Mon Sep 17 00:00:00 2001 From: jrasero Date: Thu, 22 Aug 2024 13:06:31 -0400 Subject: [PATCH] Update documentation --- _sources/chapters/module-1/tech_stack.md | 9 +++++---- chapters/module-1/tech_stack.html | 10 ++++++---- searchindex.js | 2 +- 3 files changed, 12 insertions(+), 9 deletions(-) diff --git a/_sources/chapters/module-1/tech_stack.md b/_sources/chapters/module-1/tech_stack.md index 4f16b31..7d85936 100644 --- a/_sources/chapters/module-1/tech_stack.md +++ b/_sources/chapters/module-1/tech_stack.md @@ -1,14 +1,15 @@ # Tech Stack +This section provides a brief introduction to the different technologies we will use throughout the course. + ## Open OnDemand -Open OnDemand is a graphical user interface that allows access to HPC via a web browser. The Open OnDemand access point is ood.hpc.virginia.edu. Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters. Let's have a quick look at their detailed documentation +Open OnDemand is a graphical user interface that allows access to HPC via a web browser. **We will use it to run all our code without requiring any local installations**. The Open OnDemand access point is ood.hpc.virginia.edu. Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters. Let's have a quick look at their detailed documentation ## JupyterLab -In this course we are going to work with **Jupyter notebooks**, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. You can get familiar to the use of jupyter notebook in our future lesson. - - +In this course we will work with **Jupyter notebooks**, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. We will become familiar with Jupyter notebooks in our future lesson. ## Canvas +Canvas will be used to share all course materials, including Jupyter notebooks for each lesson, assignments, and in-class activities. Quizzes will also be completed on this platform, and assignments will be submitted through it. Stay tuned on Canvas for announcements! diff --git a/chapters/module-1/tech_stack.html b/chapters/module-1/tech_stack.html index 23c4f0a..81750a3 100644 --- a/chapters/module-1/tech_stack.html +++ b/chapters/module-1/tech_stack.html @@ -32,9 +32,9 @@ - + - + @@ -351,16 +351,18 @@

Contents

Tech Stack#

+

This section provides a brief introduction to the different technologies we will use throughout the course.

Open OnDemand#

-

Open OnDemand is a graphical user interface that allows access to HPC via a web browser. The Open OnDemand access point is ood.hpc.virginia.edu. Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters. Let’s have a quick look at their detailed documentation

+

Open OnDemand is a graphical user interface that allows access to HPC via a web browser. We will use it to run all our code without requiring any local installations. The Open OnDemand access point is ood.hpc.virginia.edu. Within the Open OnDemand environment users have access to a file explorer; interactive applications like JupyterLab, RStudio Server & FastX Web; a command line interface; and a job composer and job monitor to submit jobs to the Rivanna and Afton clusters. Let’s have a quick look at their detailed documentation

JupyterLab#

-

In this course we are going to work with Jupyter notebooks, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. You can get familiar to the use of jupyter notebook in our future lesson.

+

In this course we will work with Jupyter notebooks, which are open-source web applications widely used in data science, machine learning, and scientific computing for writing and sharing documents combining live code, equations, visualizations, and narrative text. They support multiple programming languages, most notably Python and R, and integrate rich text formatting with Markdown and LaTeX for detailed explanations. Notebooks facilitate interactive coding, enabling step-by-step execution and debugging, and are ideal for visual data analysis. Their portability and support for reproducible research make them a valuable tool in both educational and research settings. We will become familiar with Jupyter notebooks in our future lesson.

Canvas#

+

Canvas will be used to share all course materials, including Jupyter notebooks for each lesson, assignments, and in-class activities. Quizzes will also be completed on this platform, and assignments will be submitted through it. Stay tuned on Canvas for announcements!

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