Merge branch 'main' into fix-deprecation+future

mmcky · web-flow · commit a529e01056a7 · 2025-05-14T13:24:56.000+10:00
diff --git a/.github/runs-on.yml b/.github/runs-on.yml
@@ -0,0 +1,5 @@
+images:
+  quantecon_ubuntu2404:
+    platform: "linux"
+    arch: "x64"
+    ami: "ami-09baf66e396fa7cfd"
diff --git a/.github/workflows/cache.yml b/.github/workflows/cache.yml
@@ -6,14 +6,27 @@ on:
   workflow_dispatch:
 jobs:
   cache:
-    runs-on: quantecon-gpu
-    container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.8.1-anaconda-2024-10-py312
-      options: --gpus all
+    runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
     steps:
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: "3.12"
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Install JAX, Numpyro, PyTorch
+        shell: bash -l {0}
+        run: |
+          pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
+          pip install --upgrade "jax[cuda12-local]"
+          pip install numpyro  
+          python scripts/test-jax-install.py
       - name: Check nvidia drivers
         shell: bash -l {0}
         run: |
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -1,17 +1,32 @@
 name: Build Project [using jupyter-book]
-on: [pull_request]
+on: 
+  pull_request:
+  workflow_dispatch:
 jobs:
   preview:
-    runs-on: quantecon-gpu
-    container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.8.1-anaconda-2024-10-py312
-      options: --gpus all
+    runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
     steps:
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
-      # Check nvidia drivers
-      - name: nvidia Drivers
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: "3.12"
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Install JAX, Numpyro, PyTorch
+        shell: bash -l {0}
+        run: |
+          pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
+          pip install pyro-ppl
+          pip install --upgrade "jax[cuda12-local]"
+          pip install numpyro pyro-ppl
+          python scripts/test-jax-install.py
+      - name: Check nvidia Drivers
         shell: bash -l {0}
         run: nvidia-smi
       - name: Display Conda Environment Versions
@@ -21,7 +36,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main
diff --git a/.github/workflows/collab.yml b/.github/workflows/collab.yml
@@ -2,9 +2,9 @@ name: Build Project on Google Collab (Execution)
 on: [pull_request]
 jobs:
   execution-checks:
-    runs-on: quantecon-gpu
+    runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=ubuntu24-gpu-x64/disk=large"
     container:
-      image: docker://us-docker.pkg.dev/colab-images/public/runtime
+      image: docker://us-docker.pkg.dev/colab-images/public/runtime:latest
       options: --gpus all
     steps:
       - uses: actions/checkout@v4
@@ -22,7 +22,7 @@ jobs:
         shell: bash -l {0}
         run: pip list
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main
@@ -32,7 +32,7 @@ jobs:
       - name: Install Build Software
         shell: bash -l {0}
         run: |
-          pip install jupyter-book==0.15.1 docutils==0.17.1 quantecon-book-theme==0.7.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinx-reredirects sphinx-exercise==0.4.1 sphinxcontrib-youtube==1.1.0 sphinx-togglebutton==0.3.1 arviz==0.13.0 sphinx-proof
+          pip install jupyter-book==1.0.3 quantecon-book-theme==0.8.2 sphinx-tojupyter==0.3.0 sphinxext-rediraffe==0.2.7 sphinxcontrib-youtube==1.3.0 sphinx-togglebutton==0.3.2 arviz sphinx-proof sphinx-exercise sphinx-reredirects
       # Build of HTML (Execution Testing)
       - name: Build HTML
         shell: bash -l {0}
diff --git a/.github/workflows/linkcheck.yml b/.github/workflows/linkcheck.yml
@@ -27,7 +27,7 @@ jobs:
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
@@ -6,16 +6,26 @@ on:
 jobs:
   publish:
     if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
-    runs-on: quantecon-gpu
-    container:
-      image: ghcr.io/quantecon/lecture-python-container:cuda-12.8.1-anaconda-2024-10-py312
-      options: --gpus all
+    runs-on: "runs-on=${{ github.run_id }}/family=g4dn.2xlarge/image=quantecon_ubuntu2404/disk=large"
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      - name: Install Git (required to commit notebooks)
+      - name: Setup Anaconda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          auto-activate-base: true
+          miniconda-version: 'latest'
+          python-version: "3.12"
+          environment-file: environment.yml
+          activate-environment: quantecon
+      - name: Install JAX, Numpyro, PyTorch
         shell: bash -l {0}
-        run: apt-get install -y git
+        run: |
+          pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
+          pip install --upgrade "jax[cuda12-local]"
+          pip install numpyro  
+          python scripts/test-jax-install.py
       - name: Check nvidia drivers
         shell: bash -l {0}
         run: |
@@ -28,7 +38,7 @@ jobs:
         run: pip list
       # Download Build Cache from cache.yml
       - name: Download "build" folder (cache)
-        uses: dawidd6/action-download-artifact@v8
+        uses: dawidd6/action-download-artifact@v9
         with:
           workflow: cache.yml
           branch: main
diff --git a/environment.yml b/environment.yml
@@ -16,6 +16,3 @@ dependencies:
     - ghp-import==1.1.0
     - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
     - sphinx-togglebutton==0.3.2
-    # Docker Requirements
-    - pytz
-
diff --git a/lectures/_admonition/gpu.md b/lectures/_admonition/gpu.md
@@ -0,0 +1,9 @@
+```{admonition} GPU
+:class: warning
+
+This lecture was built using a machine with the latest CUDA and CUDANN frameworks installed with access to a GPU.
+
+To run this lecture on [Google Colab](https://colab.research.google.com/), click on the "play" icon top right, select Colab, and set the runtime environment to include a GPU.
+
+To run this lecture on your own machine, you need to install the software listed following this notice.
+```
diff --git a/lectures/ar1_bayes.md b/lectures/ar1_bayes.md
@@ -13,15 +13,25 @@ kernelspec:
 
 # Posterior Distributions for  AR(1) Parameters
 
-We'll begin with some Python imports.
+```{include} _admonition/gpu.md
+```
 
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install numpyro jax
+```
+
+In addition to what's included in base Anaconda, we need to install the following packages
 
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install arviz pymc numpyro jax
+!pip install arviz pymc
 ```
 
+We'll begin with some Python imports.
+
 ```{code-cell} ipython3
 
 import arviz as az
diff --git a/lectures/back_prop.md b/lectures/back_prop.md
@@ -13,10 +13,21 @@ kernelspec:
 
 # Introduction to Artificial Neural Networks
 
+```{include} _admonition/gpu.md
+```
+
+```{code-cell} ipython3
+:tags: [skip-execution]
+
+!pip install --upgrade jax
+```
+
+In addition to what's included in base Anaconda, we need to install the following packages
+
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install --upgrade jax jaxlib kaleido
+!pip install kaleido
 !conda install -y -c plotly plotly plotly-orca retrying
 ```
 
diff --git a/lectures/bayes_nonconj.md b/lectures/bayes_nonconj.md
@@ -13,6 +13,15 @@ kernelspec:
 
 # Non-Conjugate Priors
 
+```{include} _admonition/gpu.md
+```
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install numpyro pyro-ppl torch jax
+```
+
 This lecture is a sequel to the {doc}`quantecon lecture <prob_meaning>`.
 
 That lecture offers a Bayesian interpretation of probability in a setting in which the likelihood function and the prior distribution
@@ -43,13 +52,6 @@ The two Python modules are
 
 As usual, we begin by importing some Python code.
 
-```{code-cell} ipython3
-:tags: [hide-output]
-
-# install dependencies
-!pip install numpyro pyro-ppl torch jax
-```
-
 ```{code-cell} ipython3
 import numpy as np
 import seaborn as sns
diff --git a/lectures/imp_sample.md b/lectures/imp_sample.md
@@ -11,15 +11,15 @@ kernelspec:
   name: python3
 ---
 
-# Computing Mean of a Likelihood Ratio Process
+# Mean of a Likelihood Ratio Process
 
 ```{contents} Contents
 :depth: 2
 ```
 
 ## Overview
 
-In  {doc}`this lecture <likelihood_ratio_process>` we described a peculiar property of a likelihood ratio process, namely, that it's mean equals one for all $t \geq 0$ despite it's converging to zero almost surely.
+In  {doc}`this lecture <likelihood_ratio_process>` we described a peculiar property of a likelihood ratio process, namely, that its mean equals one for all $t \geq 0$ despite it's converging to zero almost surely.
 
 While it is easy to verify that peculiar properly analytically (i.e., in population), it is challenging to use a computer simulation to verify it via an application of a law of large numbers that entails studying sample averages of repeated simulations.
 
@@ -86,7 +86,7 @@ g = jit(lambda w: p(w, G_a, G_b))
 ```
 
 ```{code-cell} ipython3
-w_range = np.linspace(1e-5, 1-1e-5, 1000)
+w_range = np.linspace(1e-2, 1-1e-5, 1000)
 
 plt.plot(w_range, g(w_range), label='g')
 plt.plot(w_range, f(w_range), label='f')
@@ -178,7 +178,7 @@ plt.ylim([0., 3.])
 plt.show()
 ```
 
-## Approximating a cumulative likelihood ratio
+## Approximating a Cumulative Likelihood Ratio
 
 We now study how to use importance sampling to approximate
 ${E} \left[L(\omega^t)\right] = \left[\prod_{i=1}^T \ell \left(\omega_i\right)\right]$.
@@ -319,12 +319,11 @@ for i, t in enumerate([1, 5, 10, 20]):
 plt.show()
 ```
 
-The simulation exercises above show that the importance sampling estimates are unbiased under all $T$
-while the standard Monte Carlo estimates are biased downwards.
+The simulation exercises above show that the importance sampling estimates are unbiased under all $T$ while the standard Monte Carlo estimates are biased downwards.
 
 Evidently, the bias increases with increases in $T$.
 
-## More Thoughts about Choice of Sampling Distribution
+## Choosing a  Sampling Distribution
 
 +++
 
@@ -375,7 +374,7 @@ plt.ylim([0., 3.])
 plt.show()
 ```
 
-We consider two additonal distributions.
+We consider two additional distributions.
 
 As a reminder $h_1$ is the original $Beta(0.5,0.5)$ distribution that we used above.
 
@@ -458,10 +457,9 @@ for i, t in enumerate([1, 20]):
 plt.show()
 ```
 
-However, $h_3$ is evidently a poor importance sampling distribution forpir problem,
+However, $h_3$ is evidently a poor importance sampling distribution for our problem,
 with a mean estimate far away from $1$ for $T = 20$.
 
-Notice that evan at $T = 1$, the mean estimate with importance sampling is more biased than just sampling with  $g$ itself.
+Notice that even at $T = 1$, the mean estimate with importance sampling is more biased than  sampling with  just $g$ itself.
 
-Thus, our simulations suggest that we would be better off simply using Monte Carlo
-approximations under $g$ than using $h_3$ as an importance sampling distribution for our problem.
+Thus, our simulations suggest that  for our problem we would be better off simply using Monte Carlo approximations under $g$ than using $h_3$ as an importance sampling distribution.
diff --git a/lectures/likelihood_ratio_process.md b/lectures/likelihood_ratio_process.md
diff --git a/lectures/mix_model.md b/lectures/mix_model.md
diff --git a/lectures/navy_captain.md b/lectures/navy_captain.md
diff --git a/lectures/troubleshooting.md b/lectures/troubleshooting.md
diff --git a/lectures/wald_friedman.md b/lectures/wald_friedman.md
diff --git a/scripts/test-jax-install.py b/scripts/test-jax-install.py
