MAINT: use Github based GPU instance (#181)

* MAINT: use Github based GPU instance

* TMP: disable build cache

* update status page

* TST: remove docker layer and test locally

* try building html only and add numpyro

* try cuda_pip installer

* Revert "try cuda_pip installer"

This reverts commit 6b3a167.

* remove numpyro

* revert to docker container to check nvidia driver versions

* TST: fetch github actions hosted container

* try install updated nvidia drivers (need reboot?)

* update apt sources

* use latest cuda=12.5 container

* enable latex and download notebook builds

* switch cache and publish to github actions

Author: mmcky

.github/workflows/cache.yml

@@ -4,30 +4,10 @@ on:
     branches:
       - main
 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
   cache:
-    needs: deploy-runner
-    runs-on: [self-hosted, cml-gpu]
+    runs-on: ubuntu-latest-gpu
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.5.0-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v4

.github/workflows/ci.yml

@@ -1,30 +1,10 @@
-name: Build Project [using jupyter-book]
+name: Build Preview [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]
+    runs-on: ubuntu-latest-gpu
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.5.0-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v4

.github/workflows/publish.yml

@@ -4,31 +4,11 @@ on:
     tags:
       - 'publish*'
 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
   publish:
     if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
-    needs: deploy-runner
-    runs-on: [self-hosted, cml-gpu]
+    runs-on: ubuntu-latest-gpu
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
+      image: ghcr.io/quantecon/lecture-python-container:cuda-12.5.0-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - name: Checkout
@@ -46,7 +26,7 @@ jobs:
         run: pip list
       # Download Build Cache from cache.yml
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v2
+        uses: dawidd6/action-download-artifact@v3
         with:
           workflow: cache.yml
           branch: main

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: