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44 changes: 44 additions & 0 deletions .github/workflows/linkcheck.yml
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@@ -0,0 +1,44 @@
name: Link Checker [Anaconda, Linux]
on:
pull_request:
types: [opened, reopened]
schedule:
# UTC 12:00 is early morning in Australia
- cron: '0 12 * * *'
jobs:
execution-tests-linux:
name: Execution Tests (${{ matrix.python-version }}, ${{ matrix.os }})
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: ["ubuntu-latest"]
python-version: ["3.9"]
steps:
- name: Checkout
uses: actions/checkout@v2
- name: Setup Anaconda
uses: conda-incubator/setup-miniconda@v2
with:
auto-update-conda: true
auto-activate-base: true
miniconda-version: 'latest'
python-version: 3.9
environment-file: environment.yml
activate-environment: quantecon
- name: Download "build" folder (cache)
uses: dawidd6/action-download-artifact@v2
with:
workflow: cache.yml
branch: main
name: build-cache
path: _build
- name: Link Checker
shell: bash -l {0}
run: jb build lectures --path-output=./ --builder=custom --custom-builder=linkcheck
- name: Upload Link Checker Reports
uses: actions/upload-artifact@v2
if: failure()
with:
name: linkcheck-reports
path: _build/linkcheck
2 changes: 1 addition & 1 deletion lectures/about_py.md
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Expand Up @@ -474,7 +474,7 @@ Apart from the cloud computing options listed above, you might like to consider
```{index} single: parallel computing; Dask
```

* [Dask](https://www.dask.org/) parallelises PyData and Machine Learning in Python.
* [Dask](https://docs.dask.org/en/stable/) parallelises PyData and Machine Learning in Python.

```{index} single: parallel computing; pycuda
```
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2 changes: 1 addition & 1 deletion lectures/parallelization.md
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Expand Up @@ -342,7 +342,7 @@ wealth of the group at the end of a long simulation.
Moreover, provided the simulation period is long enough, initial conditions
don't matter.

* This is due to something called ergodicity, which we will discuss [later on](https://python-intro.quantecon.org/finite_markov.html#Ergodicity).
* This is due to something called ergodicity, which we will discuss [later on](https://python.quantecon.org/finite_markov.html#id15).

So, in summary, we are going to simulate 50,000 households by

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