TEST: use Github based GPU instance for CI

.github/workflows/ci.yml

@@ -1,35 +1,38 @@
 name: Build Project [using jupyter-book]
 on: [pull_request]
 jobs:
-  deploy-runner:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: iterative/setup-cml@v2
-      - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.pull_request.head.sha }}
-      - name: Deploy runner on EC2
-        env:
-          REPO_TOKEN: ${{ secrets.QUANTECON_SERVICES_PAT }}
-          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-        run: |
-          cml runner launch \
-              --cloud=aws \
-              --cloud-region=us-west-2 \
-              --cloud-type=p3.2xlarge \
-              --labels=cml-gpu \
-              --cloud-hdd-size=40
   preview:
-    needs: deploy-runner
-    runs-on: [self-hosted, cml-gpu]
-    container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
-      options: --gpus all
+    runs-on: ubuntu-latest-gpu
+    env:
+      DOCKER_IMG_NAME: mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
     steps:
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
+      - name: Restore Docker Image Cache if it exists
+        id: cache-docker-mmcky
+        uses: actions/cache@v4
+        with:
+          path: ci/cache/docker/cuda
+          key: cache-docker-${{ env.DOCKER_IMG_NAME }}
+
+      - name: Update Docker Image Cache if cache miss
+        if: steps.cache-docker-mmcky.outputs.cache-hit != 'true'
+        run: |
+          docker pull $DOCKER_IMG_NAME
+          mkdir -p ci/cache/docker/cuda
+          docker image save $DOCKER_IMG_NAME --output ./ci/cache/docker/cuda/docker-img.tar
+
+      - name: Use Docker Image Cache if cache hit
+        if: steps.cache-docker-mmcky.outputs.cache-hit == 'true'
+        run: docker image load --input ./ci/cache/docker/cuda/docker-img.tar
+
+      # - name: Install JAX[CUDA] and Numpyro[CUDA]
+      #   shell: bash -l {0}
+      #   run: |
+      #     pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+      #     pip install --upgrade "numpyro[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+
       - name: Check nvidia drivers
         shell: bash -l {0}
         run: |
@@ -38,9 +41,9 @@ jobs:
         shell: bash -l {0}
         run: |
           python --version
-      - name: Display Conda Environment Versions
-        shell: bash -l {0}
-        run: conda list
+      # - name: Display Conda Environment Versions
+      #   shell: bash -l {0}
+      #   run: conda list
       - name: Display Pip Versions
         shell: bash -l {0}
         run: pip list
@@ -52,18 +55,18 @@ jobs:
           name: build-cache
           path: _build
       # Build Assets (Download Notebooks and PDF via LaTeX)
-      - name: Build Download Notebooks (sphinx-tojupyter)
-        shell: bash -l {0}
-        run: |
-          jb build lectures --path-output ./ --builder=custom --custom-builder=jupyter -n -W --keep-going
-          mkdir -p _build/html/_notebooks
-          cp -u _build/jupyter/*.ipynb _build/html/_notebooks
-      - name: Build PDF from LaTeX
-        shell: bash -l {0}
-        run: |
-          jb build lectures --builder pdflatex --path-output ./ -n -W --keep-going
-          mkdir _build/html/_pdf
-          cp -u _build/latex/*.pdf _build/html/_pdf
+      # - name: Build Download Notebooks (sphinx-tojupyter)
+      #   shell: bash -l {0}
+      #   run: |
+      #     jb build lectures --path-output ./ --builder=custom --custom-builder=jupyter -n -W --keep-going
+      #     mkdir -p _build/html/_notebooks
+      #     cp -u _build/jupyter/*.ipynb _build/html/_notebooks
+      # - name: Build PDF from LaTeX
+      #   shell: bash -l {0}
+      #   run: |
+      #     jb build lectures --builder pdflatex --path-output ./ -n -W --keep-going
+      #     mkdir _build/html/_pdf
+      #     cp -u _build/latex/*.pdf _build/html/_pdf
       # Final Build of HTML
       - name: Build HTML
         shell: bash -l {0}
@@ -90,4 +93,4 @@ jobs:
           deploy-message: "Preview Deploy from GitHub Actions"
         env:
           NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
-          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
+          NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}

environment.yml

@@ -1,27 +1,26 @@
-name: lecture-jax
+name: quantecon
 channels:
   - default
 dependencies:
   - python=3.11
-  - anaconda=2023.09
+  - anaconda=2024.02
   - pip
   - pip:
     - jupyter-book==0.15.1
-    - docutils==0.17.1
     - quantecon-book-theme==0.7.1
     - sphinx-tojupyter==0.3.0
     - sphinxext-rediraffe==0.2.7
+    - sphinx-reredirects==0.1.3
     - sphinx-exercise==0.4.1
     - ghp-import==1.1.0
     - sphinxcontrib-youtube==1.1.0
     - sphinx-togglebutton==0.3.1
-    - arviz==0.13.0
+    - array-to-latex
+    - prettytable
     - kaleido
-    # Sandpit Requirements
-    # - quantecon
-    # - array-to-latex
-    # - PuLP
-    # - cvxpy
-    # - cvxopt
-    # - cylp
-    # - prettytable
+    - arviz
+    # Docker Requirements
+    - pytz
+    # Docutils Issue (https://github.com/mcmtroffaes/sphinxcontrib-bibtex/issues/322)
+    - docutils==0.17.1
+

lectures/status.md

@@ -18,9 +18,8 @@ This table contains the latest execution statistics.
 
 (status:machine-details)=
 
-These lectures are built on `linux` instances through `github actions`  and `amazon web services (aws)` to
-enable access to a `gpu`. These lectures are built on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)
-that has access to `8 vcpu's`, a `V100 NVIDIA Tesla GPU`, and `61 Gb` of memory.
+These lectures are built on `linux` instances through `github actions` that has 
+access to a `gpu`. These lectures make use of the nvidia `T4` card.
 
 You can check the backend used by JAX using:
 

setup_cuda.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+
+# Exit script on any error
+set -e
+
+# Define variables
+NV_CUDA_LIB_VERSION="12.4.1-1"
+NV_CUDA_CUDART_DEV_VERSION="12.4.127-1"
+NV_NVML_DEV_VERSION="12.4.127-1"
+NV_LIBCUSPARSE_DEV_VERSION="12.3.1.170-1"
+NV_LIBNPP_DEV_VERSION="12.2.5.30-1"
+NV_LIBNPP_DEV_PACKAGE="libnpp-dev-12-4=${NV_LIBNPP_DEV_VERSION}"
+NV_LIBCUBLAS_DEV_PACKAGE_NAME="libcublas-dev-12-4"
+NV_LIBCUBLAS_DEV_VERSION="12.4.5.8-1"
+NV_LIBCUBLAS_DEV_PACKAGE="${NV_LIBCUBLAS_DEV_PACKAGE_NAME}=${NV_LIBCUBLAS_DEV_VERSION}"
+NV_CUDA_NSIGHT_COMPUTE_VERSION="12.4.1-1"
+NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE="cuda-nsight-compute-12-4=${NV_CUDA_NSIGHT_COMPUTE_VERSION}"
+NV_NVPROF_VERSION="12.4.127-1"
+NV_NVPROF_DEV_PACKAGE="cuda-nvprof-12-4=${NV_NVPROF_VERSION}"
+NV_LIBNCCL_DEV_PACKAGE_NAME="libnccl-dev"
+NV_LIBNCCL_DEV_PACKAGE_VERSION="2.21.5-1"
+NCCL_VERSION="2.21.5-1"
+NV_LIBNCCL_DEV_PACKAGE="${NV_LIBNCCL_DEV_PACKAGE_NAME}=${NV_LIBNCCL_DEV_PACKAGE_VERSION}+cuda12.4"
+
+# Update package lists
+sudo apt-get update
+
+# Install CUDA development packages
+sudo apt-get install -y --no-install-recommends \
+    cuda-cudart-dev-12-4=${NV_CUDA_CUDART_DEV_VERSION} \
+    cuda-command-line-tools-12-4=${NV_CUDA_LIB_VERSION} \
+    cuda-minimal-build-12-4=${NV_CUDA_LIB_VERSION} \
+    cuda-libraries-dev-12-4=${NV_CUDA_LIB_VERSION} \
+    cuda-nvml-dev-12-4=${NV_NVML_DEV_VERSION} \
+    ${NV_NVPROF_DEV_PACKAGE} \
+    ${NV_LIBNPP_DEV_PACKAGE} \
+    libcusparse-dev-12-4=${NV_LIBCUSPARSE_DEV_VERSION} \
+    ${NV_LIBCUBLAS_DEV_PACKAGE} \
+    ${NV_LIBNCCL_DEV_PACKAGE} \
+    ${NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE}
+
+# Clean up
+sudo rm -rf /var/lib/apt/lists/*
+
+# Prevent auto-upgrade of specific packages
+sudo apt-mark hold ${NV_LIBCUBLAS_DEV_PACKAGE_NAME} ${NV_LIBNCCL_DEV_PACKAGE_NAME}
+
+# Set environment variable
+export LIBRARY_PATH=/usr/local/cuda/lib64/stubs
+
+echo "CUDA development environment setup is complete."