Merge pull request #11 from QuantEcon/fix-cuda

FIX: Update CUDA and improve status page with diagnostics

.github/workflows/cache.yml

@@ -7,7 +7,7 @@ jobs:
   deploy-runner:
     runs-on: ubuntu-latest
     steps:
-      - uses: iterative/setup-cml@v1
+      - uses: iterative/setup-cml@v2
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
@@ -22,12 +22,12 @@ jobs:
               --cloud-region=us-west-2 \
               --cloud-type=p3.2xlarge \
               --labels=cml-gpu \
-              --cloud-hdd-size=40
+              --cloud-hdd-size=100
   cache:
     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-11.8.0-anaconda-2023-09-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v4

.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@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
@@ -19,12 +19,12 @@ jobs:
               --cloud-region=us-west-2 \
               --cloud-type=p3.2xlarge \
               --labels=cml-gpu \
-              --cloud-hdd-size=40
+              --cloud-hdd-size=100
   preview:
     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-11.8.0-anaconda-2023-09-py311
       options: --gpus all
     steps:
       - uses: actions/checkout@v4

.github/workflows/publish.yml

@@ -7,7 +7,7 @@ jobs:
   deploy-runner:
     runs-on: ubuntu-latest
     steps:
-      - uses: iterative/setup-cml@v1
+      - uses: iterative/setup-cml@v2
       - uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.sha }}
@@ -22,13 +22,13 @@ jobs:
               --cloud-region=us-west-2 \
               --cloud-type=p3.2xlarge \
               --labels=cml-gpu \
-              --cloud-hdd-size=40
+              --cloud-hdd-size=100
   publish:
     if: github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags')
     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-11.8.0-anaconda-2023-09-py311
       options: --gpus all
     steps:
       - name: Checkout

lectures/ar1_bayes.md

@@ -13,15 +13,12 @@ kernelspec:
 
 # Posterior Distributions for  AR(1) Parameters
 
-We'll begin with some Python imports.
-
-
 ```{code-cell} ipython3
-:tags: [hide-output]
-
-!pip install arviz pymc numpyro jax
+!pip install pymc
 ```
 
+We'll begin with some Python imports.
+
 ```{code-cell} ipython3
 
 import arviz as az

lectures/ar1_turningpts.md

@@ -13,12 +13,6 @@ kernelspec:
 
 # Forecasting  an AR(1) Process
 
-```{code-cell} ipython3
-:tags: [hide-output]
-
-!pip install arviz pymc
-```
-
 This lecture describes methods for forecasting statistics that are functions of future values of a univariate autogressive process.  
 
 The methods are designed to take into account two possible sources of uncertainty about these statistics:

lectures/back_prop.md

@@ -16,7 +16,6 @@ kernelspec:
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install --upgrade jax jaxlib
 !conda install -y -c plotly plotly plotly-orca retrying
 ```
 

lectures/bayes_nonconj.md

@@ -41,14 +41,6 @@ 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

lectures/mix_model.md

@@ -14,14 +14,6 @@ kernelspec:
 (likelihood-ratio-process)=
 # Incorrect Models
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-```{code-cell} ipython
----
-tags: [hide-output]
----
-!pip install numpyro jax
-```
-
 ## Overview
 
 This  is a sequel to {doc}`this  quantecon lecture <likelihood_bayes>`.

lectures/status.md

@@ -20,4 +20,18 @@ This table contains the latest execution statistics.
 
 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.
+that has access to `8 vcpu's`, a `V100 NVIDIA Tesla GPU`, and `61 Gb` of memory.
+
+You can check the backend used by JAX using:
+
+```{code-cell} ipython3
+import jax
+# Check if JAX is using GPU
+print(f"JAX backend: {jax.devices()[0].platform}")
+```
+
+and the hardware we are running on:
+
+```{code-cell} ipython3
+!nvidia-smi
+```