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‎.devcontainer/Dockerfile.debug

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# Comments are provided throughout this file to help you get started.
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# If you need more help, visit the Dockerfile reference guide at https://docs.docker.com/go/dockerfile-reference/
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# Create a docker conda image
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# Offical Available Dev Container Templates, see https://containers.dev/templates
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# NOTE: Non Dev Container *Conda Installations might lead to errors during building the container.
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FROM mcr.microsoft.com/devcontainers/miniconda
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USER root
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# Copy the environment.yml file to the container. Also
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# copy "noop.txt" so the COPY instruction does not fail if no environment.yml exists.
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COPY environment.yml* noop.txt /requirements/
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# Make RUN commands use `bash --login`:
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# SHELL ["/bin/bash", "--login", "-c"]
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# Install linux packages
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RUN apt-get update
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# Install git, process tools
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RUN apt-get update && apt-get -y install git procps build-essential curl swig
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# RUN apt-get update && \
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# apt-get upgrade -y && \
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# apt-get install -y git
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RUN git config --global --add safe.directory /workspace
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# Add the conda directory to your Linux PATH, since this is not done automatically after installation.
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RUN conda init bash
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# Update conda, install and set libmamba solver
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RUN conda update -n base conda -y
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RUN conda install -n base conda-libmamba-solver
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RUN conda config --set solver libmamba
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# Create conda env (for your data science work)
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RUN conda env create --file requirements/environment.yml
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RUN conda install ipython_genutils
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RUN conda install ipykernel
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RUN ipython kernel install --user --name=env1
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# RUN conda init
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# TODO: switch from mcr.microsoft.com/devcontainers/miniconda to conda/miniconda3:latest
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# Install Git, since not available in conda/miniconda3:latest
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# RUN apt-get update && \
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# apt-get upgrade -y && \
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# apt-get install -y git
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# RUN git config --global --add safe.directory /workspace
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# The code to run when container is started:
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# Common practice to keep the Docker container running without performing any significant action.
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ENTRYPOINT ["tail", "-f", "/dev/null"]

‎.devcontainer/devcontainer.json

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// For format details, see https://aka.ms/devcontainer.json. For config options, see the
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// README at: https://github.com/devcontainers/templates/tree/main/src/anaconda
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{
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"name": "dat.dev-container-conda",
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"dockerComposeFile": ["./docker-compose.yml"],
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"service": "project",
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"runServices": ["project"],
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"workspaceFolder": "/workspace",
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// Avoid dubious ownership in repository in dev container,
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// see https://github.com/microsoft/vscode-remote-release/issues/6810 or https://www.kenmuse.com/blog/avoiding-dubious-ownership-in-dev-containers/
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"postCreateCommand": {
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// "git": "sudo apt-get update && sudo apt-get install -y git",
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"git": "git config --global --add safe.directory /workspace",
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"conda_init": "conda init"
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// "conda_activate": "conda activate env1"
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},
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// "postStartCommand": {
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// // "git": "sudo apt-get update && sudo apt-get install -y git",
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// // "git": "git config --global --add safe.directory /workspace",
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// // "conda_init": "conda init"
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// "conda_activate": "conda activate env1"
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// },
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"customizations": {
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// When connecting to a docker container your local VS Code starts an instance without extensions to ensure isolation and consitency.
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// Therefore extensions can be specified here for automatic installation when connecting.
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"vscode": {
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"settings": {
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"terminal.integrated.profiles.linux": {
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"bash": {
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"path": "/bin/bash"
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}
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}
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},
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// Use the VS Code Extenstions "Identifier" to define extensions
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"extensions": [
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"ms-python.python",
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"ms-python.vscode-pylance",
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"ms-python.black-formatter",
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"ms-azuretools.vscode-docker",
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"ms-toolsai.jupyter"
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//"zainchen.json",
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//"tomoki1207.pdf",
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//"grapecity.gc-excelviewer"
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]
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}
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}
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}

‎.devcontainer/docker-compose.yml

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# version: "3.7"
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# networks:
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# app-net: null
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services:
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project:
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# tells Docker Compose to build the Docker image using the Dockerfile in the specified directory.
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build:
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context: .
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dockerfile: ./Dockerfile.debug
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image: dat/dev-container-conda
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# networks:
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# - app-net
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# Map local volume (parent dir) to container (/workspace)
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volumes:
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- ../:/workspace

‎.devcontainer/environment.yml

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name: env1
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channels:
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- conda-forge
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- pytorch
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dependencies:
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- python<=3.11
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- pip
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- ipython_genutils
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- ipykernel
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- jupyterlab
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- numpy>=1.22
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- matplotlib>=3.1
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- pandas>=1.4
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- pandas-datareader
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- scipy
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- scikit-learn>=1.1
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- seaborn
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- lightgbm
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- pytorch
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- lightning
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- imbalanced-learn
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- mlflow
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- autogluon.common
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- pip:
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- black
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- 'black[jupyter]'
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- optuna

‎.devcontainer/noop.txt

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This file copied into the container along with environment.yml* from the parent
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folder. This file is included to prevents the Dockerfile COPY instruction from
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failing if no environment.yml is found.

‎.gitignore

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# General
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.DS_Store
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.AppleDouble
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.LSOverride
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# Icon must end with two \r
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Icon
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# Thumbnails
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._*
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# Files that might appear in the root of a volume
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.DocumentRevisions-V100
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.fseventsd
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.Spotlight-V100
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.TemporaryItems
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.Trashes
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.VolumeIcon.icns
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.com.apple.timemachine.donotpresent
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# Directories potentially created on remote AFP share
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.AppleDB
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.AppleDesktop
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Network Trash Folder
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Temporary Items
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.apdisk

‎.vscode/extensions.json

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{
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"recommendations": [
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"ms-vscode-remote.remote-containers"
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]
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}

‎.vscode/launch.json

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{
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"version": "0.1.0",
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"configurations": [
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{
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"name": "Python: Current File",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"console": "integratedTerminal",
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"justMyCode": true,
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"cwd":"${fileDirname}"
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}
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]
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}

‎LICENSE

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Creative Commons Legal Code
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CC0 1.0 Universal
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CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
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LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
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ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
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INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
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REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS
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PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM
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THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED
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HEREUNDER.
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Statement of Purpose
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The laws of most jurisdictions throughout the world automatically confer
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exclusive Copyright and Related Rights (defined below) upon the creator
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and subsequent owner(s) (each and all, an "owner") of an original work of
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authorship and/or a database (each, a "Work").
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Certain owners wish to permanently relinquish those rights to a Work for
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the purpose of contributing to a commons of creative, cultural and
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scientific works ("Commons") that the public can reliably and without fear
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works, reuse and redistribute as freely as possible in any form whatsoever
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These owners may contribute to the Commons to promote the ideal of a free
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For these and/or other purposes and motivations, and without any
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limitation commercial, advertising or promotional purposes (the
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"License"). The License shall be deemed effective as of the date CC0 was
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applied by Affirmer to the Work. Should any part of the License for any
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reason be judged legally invalid or ineffective under applicable law, such
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partial invalidity or ineffectiveness shall not invalidate the remainder
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of the License, and in such case Affirmer hereby affirms that he or she
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will not (i) exercise any of his or her remaining Copyright and Related
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4. Limitations and Disclaimers.
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a. No trademark or patent rights held by Affirmer are waived, abandoned,
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c. Affirmer disclaims responsibility for clearing rights of other persons
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Further, Affirmer disclaims responsibility for obtaining any necessary
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‎README.md

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# VSCode dev-container (Docker) setup with Miniconda and Jupyter Notebooks
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This is a [**containerized dev setup**](https://code.visualstudio.com/docs/devcontainers/containers) respectively related for using [**remote containers**](https://code.visualstudio.com/docs/remote/containers) to work on data science / machine learning problems using **Anaconda with VSCode**.
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It installs [`conda/miniconda3:latest`](https://hub.docker.com/r/conda/miniconda3/) as a base image and installs ipykernel,
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so it supports Jupyter notebooks (installed as a VSCode extension), as well as debugging *.py files directly.
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A conda environment is created within the dev container and can easily be configured with the desired dependencies/packages.
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Various VSCode extensions are installed automatically in the container as well.
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Mac OS Install Instructions:
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- Install Docker Desktop `brew install --cask docker`
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- Install VSCode `brew install --cask visual-studio-code`
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- Open VSCode
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- Install VSCode remote container extension: [ms-vscode-remote.remote-containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
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- Start dev container: F1 + "Open folder in container ..."
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- Open demo.ipynb or demo.py to try the setup
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Optional Steps:
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- Add/remove VSCode extensions in .devcontainer/devcontainer.json
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- Change Anaconda environment in .devcontainer/environment.yml
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- Update gitignore to match your setup

‎demo.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
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"\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
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"\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
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"\u001b[1;31mor\n",
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"\u001b[1;31mconda install jupyter notebook -U'\n",
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"\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
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]
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}
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],
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"source": [
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"from autogluon.tabular import TabularDataset, TabularPredictor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_root = 'https://autogluon.s3.amazonaws.com/datasets/Inc/'\n",
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"train_data = TabularDataset(data_root + 'train.csv')\n",
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"test_data = TabularDataset(data_root + 'test.csv')\n",
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"\n",
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"predictor = TabularPredictor(label='class').fit(train_data=train_data)\n",
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"predictions = predictor.predict(test_data)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "a7109dcfa2d8cdf324fb89d9244b4f54d7419cdde71f559ec698c0c5cbbd65ee"
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},
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"kernelspec": {
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"display_name": "Python 3.7.13 ('env1')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎demo.py

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def main():
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print("Hello World!")
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if __name__ == "__main__":
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main()

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