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Merge branch 'main' into dependabot/github_actions/dawidd6/action-download-artifact-9
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‎.github/runs-on.yml‎

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images:
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quantecon_ubuntu2404:
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platform: "linux"
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arch: "x64"
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ami: "ami-09baf66e396fa7cfd"

‎.github/workflows/cache.yml‎

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name: Build Cache [using jupyter-book]
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on:
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push:
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branches:
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- main
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schedule:
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# Execute cache weekly at 3am on Monday
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- cron: '0 3 * * 1'
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workflow_dispatch:
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jobs:
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cache:
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runs-on: quantecon-gpu
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container:
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image: ghcr.io/quantecon/lecture-python-container:cuda-12.6.0-anaconda-2024-10-py312-b
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options: --gpus all
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runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
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steps:
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro
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shell: bash -l {0}
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run: |
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pip install --upgrade "jax[cuda12-local]"
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pip install numpyro
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python scripts/test-jax-install.py
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- name: Check nvidia drivers
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shell: bash -l {0}
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run: |

‎.github/workflows/ci.yml‎

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name: Build Project [using jupyter-book]
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on: [pull_request]
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jobs:
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deploy-runner:
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runs-on: ubuntu-latest
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steps:
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- uses: iterative/setup-cml@v3
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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- name: Deploy runner on EC2
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env:
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REPO_TOKEN: ${{ secrets.QUANTECON_SERVICES_PAT }}
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AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
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run: |
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cml runner launch \
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--cloud=aws \
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--cloud-region=us-west-2 \
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--cloud-type=p3.2xlarge \
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--labels=cml-gpu \
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--cloud-hdd-size=50
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preview:
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needs: deploy-runner
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runs-on: [self-hosted, cml-gpu]
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container:
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image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
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options: --gpus all
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runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
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steps:
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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# Check nvidia drivers
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro
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shell: bash -l {0}
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run: |
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pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
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pip install pyro-ppl
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pip install --upgrade "jax[cuda12-local]"
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pip install numpyro
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python scripts/test-jax-install.py
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# Check nvidia drivers
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- name: nvidia Drivers
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shell: bash -l {0}
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run: nvidia-smi

‎.github/workflows/collab.yml‎

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name: Build Project on Google Collab (Execution)
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on: [pull_request]
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jobs:
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execution-checks:
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runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=ubuntu24-gpu-x64/disk=large"
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container:
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image: docker://us-docker.pkg.dev/colab-images/public/runtime
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options: --gpus all
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steps:
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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- name: Check nvidia drivers
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shell: bash -l {0}
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run: |
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nvidia-smi
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- name: Check python version
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shell: bash -l {0}
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run: |
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python --version
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- name: Display Pip Versions
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shell: bash -l {0}
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run: pip list
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- name: Download "build" folder (cache)
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uses: dawidd6/action-download-artifact@v3
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with:
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workflow: cache.yml
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branch: main
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name: build-cache
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path: _build
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# Install build software
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- name: Install Build Software
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shell: bash -l {0}
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run: |
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pip install jupyter-book==1.0.3 quantecon-book-theme==0.8.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinxcontrib-youtube==1.3.0 sphinx-togglebutton==0.3.2 arviz sphinx-proof sphinx-exercise sphinx-reredirects
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# Build of HTML (Execution Testing)
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- name: Build HTML
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shell: bash -l {0}
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run: |
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jb build lectures --path-output ./ -n -W --keep-going
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- name: Upload Execution Reports
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uses: actions/upload-artifact@v4
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if: failure()
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with:
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name: execution-reports
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path: _build/html/reports
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- name: Preview Deploy to Netlify
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uses: nwtgck/actions-netlify@v2
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with:
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publish-dir: '_build/html/'
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production-branch: main
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github-token: ${{ secrets.GITHUB_TOKEN }}
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deploy-message: "Preview Deploy from GitHub Actions"
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env:
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NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
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NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}

‎.github/workflows/linkcheck.yml‎

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fail-fast: false
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matrix:
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os: ["ubuntu-latest"]
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python-version: ["3.11"]
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python-version: ["3.12"]
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: '3.11'
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python-version: '3.12'
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Download "build" folder (cache)

‎.github/workflows/publish.yml‎

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tags:
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- 'publish*'
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jobs:
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deploy-runner:
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runs-on: ubuntu-latest
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steps:
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- uses: iterative/setup-cml@v3
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- uses: actions/checkout@v4
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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- name: Deploy runner on EC2
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env:
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REPO_TOKEN: ${{ secrets.QUANTECON_SERVICES_PAT }}
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AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
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run: |
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cml runner launch \
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--cloud=aws \
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--cloud-region=us-west-2 \
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--cloud-type=p3.2xlarge \
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--labels=cml-gpu \
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--cloud-hdd-size=50
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publish:
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if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
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needs: deploy-runner
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runs-on: [self-hosted, cml-gpu]
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container:
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image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
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options: --gpus all
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runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Install Git (required to commit notebooks)
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- name: Setup Anaconda
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uses: conda-incubator/setup-miniconda@v3
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with:
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auto-update-conda: true
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auto-activate-base: true
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miniconda-version: 'latest'
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python-version: "3.12"
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environment-file: environment.yml
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activate-environment: quantecon
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- name: Install JAX, Numpyro
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shell: bash -l {0}
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run: apt-get install -y git
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run: |
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pip install --upgrade "jax[cuda12-local]"
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pip install numpyro
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python scripts/test-jax-install.py
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- name: Check nvidia drivers
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shell: bash -l {0}
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run: |

‎environment.yml‎

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channels:
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- default
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dependencies:
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- python=3.11
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- anaconda=2024.02
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- python=3.12
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- anaconda=2024.10
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- pip
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- pip:
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- jupyter-book==0.15.1
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- docutils==0.17.1
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- quantecon-book-theme==0.7.1
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- sphinx-reredirects==0.1.3
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- jupyter-book==1.0.3
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- quantecon-book-theme==0.7.6
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- sphinx-tojupyter==0.3.0
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- sphinxext-rediraffe==0.2.7
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- sphinx-exercise==0.4.1
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- ghp-import==2.1.0
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- sphinxcontrib-youtube==1.2.0
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- sphinx-reredirects==0.1.4
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- sphinx-exercise==1.0.1
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- sphinx-proof==0.2.0
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- ghp-import==1.1.0
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- sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
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- sphinx-togglebutton==0.3.2
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- arviz==0.13.0
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- kaleido
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# Docker Requirements
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- pytz

‎lectures/_admonition/gpu.md‎

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```{admonition} GPU
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:class: warning
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This lecture was built using a machine with the latest CUDA and CUDANN frameworks installed with access to a GPU.
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To run this lecture on [Google Colab](https://colab.research.google.com/), click on the "play" icon top right, select Colab, and set the runtime environment to include a GPU.
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To run this lecture on your own machine, you need to install the software listed following this notice.
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```

‎lectures/ar1_bayes.md‎

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# Posterior Distributions for AR(1) Parameters
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We'll begin with some Python imports.
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```{include} _admonition/gpu.md
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```
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```{code-cell} ipython3
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:tags: [hide-output]
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!pip install numpyro jax
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```
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In addition to what's included in base Anaconda, we need to install the following packages
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```{code-cell} ipython3
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:tags: [hide-output]
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!pip install arviz pymc numpyro jax
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!pip install arviz pymc
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```
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We'll begin with some Python imports.
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```{code-cell} ipython3
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import arviz as az

‎lectures/back_prop.md‎

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extension: .md
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format_name: myst
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format_version: 0.13
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jupytext_version: 1.11.5
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jupytext_version: 1.16.7
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kernelspec:
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display_name: Python 3
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display_name: Python 3 (ipykernel)
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language: python
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name: python3
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---
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# Introduction to Artificial Neural Networks
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```{include} _admonition/gpu.md
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```
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```{code-cell} ipython3
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:tags: [skip-execution]
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!pip install --upgrade jax
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```
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```{code-cell} ipython3
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import jax
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## to check that gpu is activated in environment
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print(f"JAX backend: {jax.devices()[0].platform}")
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```
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In addition to what's included in base Anaconda, we need to install the following packages
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```{code-cell} ipython3
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:tags: [hide-output]
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!pip install --upgrade jax jaxlib kaleido
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!pip install kaleido
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!conda install -y -c plotly plotly plotly-orca retrying
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```
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# notebook locally
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```
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```{code-cell} ipython3
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## to check that gpu is activated in environment
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from jax.lib import xla_bridge
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print(xla_bridge.get_backend().platform)
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```
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```{note}
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**Cloud Environment:** This lecture site is built in a server environment that doesn't have access to a `gpu`
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If you run this lecture locally this lets you know where your code is being executed, either
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via the `cpu` or the `gpu`
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```

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