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Commit ca57701

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mmckymmcky
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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
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‎.github/workflows/cache.yml‎

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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-12.1.0-anaconda-2023-09-py311-c
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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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steps:
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- uses: actions/checkout@v3
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‎.github/workflows/ci.yml‎

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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@v1
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- uses: iterative/setup-cml@v2
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- uses: actions/checkout@v3
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with:
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ref: ${{ github.event.pull_request.head.sha }}
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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-12.1.0-anaconda-2023-09-py311-c
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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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steps:
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- uses: actions/checkout@v3
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‎.github/workflows/publish.yml‎

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image: docker://mmcky/quantecon-lecture-python:cuda-12.1.0-anaconda-2023-09-py311-c
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image: docker://mmcky/quantecon-lecture-python:cuda-12.3.1-anaconda-2024-02-py311
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- name: Checkout
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‎environment.yml‎

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- default
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dependencies:
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- python=3.11
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- anaconda=2023.09
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- anaconda=2024.02
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- pip
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- pip:
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- jupyter-book==0.15.1
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‎lectures/_static/quant-econ.bib‎

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year = {2019},
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publisher = {Elsevier}
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}
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@book{Brunton_Kutz_2019,
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place = {Cambridge},
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title = {Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control},
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publisher = {Cambridge University Press},
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author = {Brunton, Steven L. and Kutz, J. Nathan},
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year = {2019}
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}
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‎lectures/status.md‎

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(status:machine-details)=
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These lectures are built on `linux` instances through `github actions` and `amazon web services (aws)` to
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enable access to a `gpu`. These lectures are built on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)
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that has access to `8 vcpu's`, a `V100 NVIDIA Tesla GPU`, and `61 Gb` of memory.
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These lectures are built on `linux` instances through `github actions`.
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These lectures are using the following python version
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```{code-cell} ipython
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!python --version
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```
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and the following package versions
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```{code-cell} ipython
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:tags: [hide-output]
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!conda list
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```
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‎lectures/var_dmd.md‎

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You can read about Dynamic Mode Decomposition {cite}`DMD_book` and {cite}`Brunton_Kutz_2019` (section 7.2).
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**Dynamic Mode Decomposition** (DMD) computes a rank $ r < p $ approximation to the least squares regression coefficients $ \hat A $ described by formula {eq}`eq:AhatSVDformula`.
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