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

Merged
merged 12 commits into from
Apr 29, 2024
2 changes: 1 addition & 1 deletion .github/workflows/cache.yml
Original file line number Diff line number Diff line change
Expand Up @@ -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
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4 changes: 2 additions & 2 deletions .github/workflows/ci.yml
Original file line number Diff line number Diff line change
Expand Up @@ -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 }}
Expand All @@ -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
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2 changes: 1 addition & 1 deletion .github/workflows/publish.yml
Original file line number Diff line number Diff line change
Expand Up @@ -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
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2 changes: 1 addition & 1 deletion environment.yml
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ channels:
- default
dependencies:
- python=3.11
- anaconda=2023.09
- anaconda=2024.02
- pip
- pip:
- jupyter-book==0.15.1
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8 changes: 8 additions & 0 deletions lectures/_static/quant-econ.bib
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Expand Up @@ -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}
}
17 changes: 14 additions & 3 deletions lectures/status.md
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Expand Up @@ -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
```
1 change: 1 addition & 0 deletions lectures/var_dmd.md
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Expand Up @@ -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`.


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