MAINT: Upgrade to anaconda=2024.02 and cuda=12.3.1 (#388)

* MAINT: update to anaconda=2024.02

* TMP: remove cache for full test build

* fix linkchecker

* update status page

* install interpolation from repo

* v2 of cml runner

* Update lectures/odu.md

reverts to pypi version which is currently `2.2.6`

* enable build cache

.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

.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

@@ -2522,3 +2522,11 @@ @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/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

@@ -271,6 +271,7 @@ Dynamic mode decomposition was introduced by {cite}`schmid2010`,
 
 You can read  about Dynamic Mode Decomposition {cite}`DMD_book` and {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`.