MAINT: Upgrade to anaconda=2024.02 and cuda=12.3.1

.github/workflows/cache.yml

@@ -27,7 +27,7 @@ jobs:
     needs: deploy-runner
     runs-on: [self-hosted, cml-gpu]
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.1.0-anaconda-2023-09-py311-c
+      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v3

.github/workflows/ci.yml

@@ -4,7 +4,7 @@ jobs:
   deploy-runner:
     runs-on: ubuntu-latest
     steps:
-      - uses: iterative/setup-cml@v1
+      - uses: iterative/setup-cml@v2
       - uses: actions/checkout@v3
         with:
           ref: ${{ github.event.pull_request.head.sha }}
@@ -24,7 +24,7 @@ jobs:
     needs: deploy-runner
     runs-on: [self-hosted, cml-gpu]
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.1.0-anaconda-2023-09-py311-c
+      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v3
@@ -40,13 +40,13 @@ jobs:
       - name: Display Pip Versions
         shell: bash -l {0}
         run: pip list
-      - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v2
-        with:
-          workflow: cache.yml
-          branch: main
-          name: build-cache
-          path: _build
+      # - name: Download "build" folder (cache)
+      #   uses: dawidd6/action-download-artifact@v2
+      #   with:
+      #     workflow: cache.yml
+      #     branch: main
+      #     name: build-cache
+      #     path: _build
       # Build Assets (Download Notebooks and PDF via LaTeX)
       - name: Build Download Notebooks (sphinx-tojupyter)
         shell: bash -l {0}

.github/workflows/publish.yml

@@ -28,7 +28,7 @@ jobs:
     needs: deploy-runner
     runs-on: [self-hosted, cml-gpu]
     container:
-      image: docker://mmcky/quantecon-lecture-python:cuda-12.1.0-anaconda-2023-09-py311-c
+      image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
       options: --gpus all
     steps:
       - name: Checkout

environment.yml

@@ -3,7 +3,7 @@ channels:
   - default
 dependencies:
   - python=3.11
-  - anaconda=2023.09
+  - anaconda=2024.02
   - pip
   - pip:
     - jupyter-book==0.15.1

lectures/_static/quant-econ.bib

@@ -2514,3 +2514,11 @@ @article{stachurski2019impossibility
   year      = {2019},
   publisher = {Elsevier}
 }
+
+@book{Brunton_Kutz_2019, 
+  place     = {Cambridge}, 
+  title     = {Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control}, 
+  publisher = {Cambridge University Press}, 
+  author    = {Brunton, Steven L. and Kutz, J. Nathan}, 
+  year      = {2019}
+}

lectures/odu.md

@@ -30,7 +30,7 @@ In addition to what’s in Anaconda, this lecture deploys the libraries:
 ---
 tags: [hide-output]
 ---
-!pip install interpolation
+!pip install git+https://github.com/EconForge/interpolation.py
 ```
 
 ## Overview

lectures/status.md

@@ -18,6 +18,17 @@ 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`. 
+
+These lectures are using the following python version
+
+```{code-cell} ipython
+!python --version
+```
+
+and the following package versions
+
+```{code-cell} ipython
+:tags: [hide-output]
+!conda list
+```

lectures/var_dmd.md

@@ -269,7 +269,7 @@ Here an $ m \times n+1 $  data matrix $ \tilde X $ contains many more attributes
 
 Dynamic mode decomposition was introduced by {cite}`schmid2010`,
 
-You can read  about Dynamic Mode Decomposition here {cite}`DMD_book` and here [[BK19](https://python.quantecon.org/zreferences.html#id25)] (section 7.2).
+You can read  about Dynamic Mode Decomposition here {cite}`DMD_book` and here {cite}`Brunton_Kutz_2019` (section 7.2).
 
 **Dynamic Mode Decomposition** (DMD) computes a rank $ r < p  $ approximation to the least squares regression coefficients $ \hat A $  described by formula {eq}`eq:AhatSVDformula`.