Modernize the Ray Tune tutorial (#3694)

This is the first of several PRs against the Pytorch Tutorials. This PR
unstales the Ray Tune tutorial:

- bumps Ray version from 2.7.2 -> 2.52.1
- update the tutorial with the latest code from the Pytorch CIFAR10
tutorial which it is based off of
- update to use the latest Ray Tune APIs
- fixed the norm values for CIFAR10
- rewrote most of the prose
- added new sections for sampling, observability, conclusion, additional
links
- updated tags in the doc indices. Will need approval on the new
`Ray-Distributed` tag.

Opening this as a draft PR so that I can get feedback from the Ray Tune
core devs first

cc @albanD @jbschlosser @pcmoritz @robertnishihara
@matthewdeng @richardliaw

---------

Co-authored-by: Ricardo Decal <[email protected]>
Co-authored-by: Svetlana Karslioglu <[email protected]>

Author: 3 people

.ci/docker/requirements.txt

@@ -32,7 +32,7 @@ bs4
 awscliv2==2.1.1
 flask
 spacy==3.4.1
-ray[tune]==2.7.2
+ray[tune]==2.52.1
 tensorboard
 jinja2==3.1.3
 pytorch-lightning

.gitignore

@@ -21,6 +21,8 @@ beginner_source/hymenoptera_data/
 intermediate_source/data/
 *.zip
 MNIST/
+data/cifar-10-batches-py/*
+*.tar.gz
 
 #builds
 _build/
@@ -105,6 +107,7 @@ target/
 celerybeat-schedule
 # dotenv
 .env
+.envrc
 
 # virtualenv
 venv/
@@ -124,8 +127,10 @@ cleanup.sh
 # PyTorch things
 *.pt
 
-# VSCode
+# IDEs
 *.vscode
+.cfg/
+.cursor
 
 # pyspelling
 dictionary.dic

.jenkins/metadata.json

@@ -10,7 +10,8 @@
     "duration": 1200
   },
   "beginner_source/hyperparameter_tuning_tutorial.py": {
-    "duration": 0
+    "needs": "linux.g5.4xlarge.nvidia.gpu",
+    "duration": 1800
   },
   "advanced_source/dynamic_quantization_tutorial.py": {
     "duration": 380