| #!/usr/bin/env Rscript | |
| update.packages(ask = F) | |
| install.packages(c( | |
| "shiny", | |
| "plotly", | |
| "gridlayout", | |
| "bslib", |
| ARG RSTUDIO_VERSION=2022.07.2-576 | |
| ARG SHA256=6dc6a71c7a4805e347ab88d9d9574f8898191dfd0bc3191940ee3096ff47fbcd | |
| RUN apt-get update && \ | |
| apt install -y --no-install-recommends software-properties-common dirmngr && \ | |
| wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | sudo tee -a /etc/apt/trusted.gpg.d/cran_ubuntu_key.asc && \ | |
| add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/" && \ | |
| apt install -y --no-install-recommends r-base r-base-core r-recommended r-base-dev && \ | |
| apt-get update && apt-get -y dist-upgrade |
I will compare the logs from the successful RStudio build with one of the failed builds.
This can be the "base" of the AAW images. I noticed there is an environmental variable DEFAULT_JUPYTER_URL=/lab and it might be possible to have the SAME image for each of our offerings, but simply change that variable. So the RStudio image would be identical to the JupyterLab image but we'd change DEFAULT_JUPYTER_URL=/lab to DEFAULT_JUPYTER_URL=/rstudio. This may result in slightly less optmized (for size) images but it would reduce maintenence burden and as I've discovered already the below image does not take long to build.
ARG BASE_VERSION=2023-08-07
FROM jupyter/datascience-notebook:$BASE_VERSION
# Must be set in deepest image
ENV DEFAULT_JUPYTER_URL=/lab
| resource "azurerm_kubernetes_cluster" "cluster" { | |
| image_cleaner_enabled = true | |
| image_cleaner_interval_hours = var.azure_image_cleaner.image_cleaner_interval_hours | |
| } |
| FROM jupyter/datascience-notebook:ed2908bbb62e | |
| ############################### | |
| ### docker-bits/∞_CMD.Dockerfile | |
| ############################### | |
| # Configure container startup | |
| USER root | |
| WORKDIR /home/$NB_USER |
Data analysis starts with the data so lets look at tools for importing datasets and inspecting them for quality and interactions.
ydata has a Python tool that works great in JupyterLab called ydata-profiling. This can be used with the following code to obtain an overview of your data.
| # The Zone | |
| ## Features | |
| ### AI | |
| #### Vanna | |
| [Vanna](https://github.com/vanna-ai/vanna) is an AI framework that offers tools for natural language processing (NLP) and machine learning model deployment. It provides pre-built NLP models, custom model training, and an API for integrating these models into applications. | |
| **Key Features:** |
| #!/usr/bin/env bash | |
| # Install Homebrew | |
| /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" | |
| test -d ~/.linuxbrew && eval "$(~/.linuxbrew/bin/brew shellenv)" | |
| test -d /home/linuxbrew/.linuxbrew && eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)" | |
| echo "eval \"\$($(brew --prefix)/bin/brew shellenv)\"" >> ~/.bashrc | |
| # Install tools | |
| sudo apt install build-essential procps curl file git unzip |
| #!/bin/bash | |
| RSTUDIO_URL="http://localhost:8787" | |
| curl -f -s "$RSTUDIO_URL" || { echo "RStudio not responding"; exit 1; } | |
| echo "RStudio is running correctly." |