Remove references to Binder notebooks in Ray Tune tutorials (#50621)

## Why are these changes needed?

* deletes the binder/ folder
* deletes all references to launching Binder notebooks
* unhides some important cells while hiding the output of others
* clarifies the pip requirements for Tune tutorials
* adds `.conda` to `.gitignore`

## Related issue number

N/A

## Checks

- [x] I've signed off every commit(by using the -s flag, i.e., `git
commit -s`) in this PR.
- [ ] I've run `scripts/format.sh` to lint the changes in this PR.
- [ ] I've included any doc changes needed for
https://docs.ray.io/en/master/.
- [ ] I've added any new APIs to the API Reference. For example, if I
added a
method in Tune, I've added it in `doc/source/tune/api/` under the
           corresponding `.rst` file.
- [ ] I've made sure the tests are passing. Note that there might be a
few flaky tests, see the recent failures at https://flakey-tests.ray.io/
- Testing Strategy
   - [ ] Unit tests
   - [ ] Release tests
   - [ ] This PR is not tested :(

---------

Signed-off-by: Ricardo Decal <rdecal@anyscale.com>

.gitignore

@@ -180,6 +180,7 @@ java/**/pom.xml
 # python virtual env
 venv
 .venv
+.conda
 
 # pyenv version file
 .python-version

doc/source/_templates/template.ipynb

@@ -72,7 +72,7 @@
     "You can hide cells, so that they will toggle when you click on the cell header.\n",
     "You can use different `:tags:` like `hide-cell`, `hide-input`, or `hide-output` to hide cell content,\n",
     "and you can use `remove-cell`, `remove-input`, or `remove-output` to remove the cell completely when rendered.\n",
-    "Those cells will still show up in the notebook itself, e.g. when you launch it in binder."
+    "Those cells will still show up in the notebook itself."
    ]
   },
   {
@@ -151,4 +151,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

doc/source/_templates/template.md

@@ -68,7 +68,7 @@ checkpoint_path = train_ppo_model()
 You can hide cells, so that they will toggle when you click on the cell header.
 You can use different `:tags:` like `hide-cell`, `hide-input`, or `hide-output` to hide cell content,
 and you can use `remove-cell`, `remove-input`, or `remove-output` to remove the cell completely when rendered.
-Those cells will still show up in the notebook itself, e.g. when you launch it in binder.
+Those cells will still show up in the notebook itself.
 
 ```{code-cell} python3
 :tags: [hide-cell]

doc/source/ray-contribute/docs.md

@@ -161,8 +161,7 @@ comes down to _two types_ of documents:
   [key concepts](https://github.com/ray-project/ray/blob/master/doc/source/tune/key-concepts.rst) or
   [API documentation](https://github.com/ray-project/ray/blob/master/doc/source/tune/api/api.rst).
 - Notebooks, written in `.ipynb` format. All Tune examples are written as notebooks. These notebooks render in
-  the browser like `.md` or `.rst` files, but have the added benefit of adding launch buttons to the top of the
-  document, so that users can run the code themselves in either Binder or Google Colab.
+  the browser like `.md` or `.rst` files, but have the added benefit that users can easily run the code themselves.
 
 ## Fixing typos and improving explanations
 

doc/source/tune/examples/ax_example.ipynb

@@ -64,9 +64,7 @@
    "id": "59b1e0d1",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {

doc/source/tune/examples/bayesopt_example.ipynb

@@ -10,37 +10,19 @@
     "\n",
     "In this tutorial we introduce BayesOpt, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with BayesOpt and, as a result, allow you to seamlessly scale up a BayesOpt optimization process - without sacrificing performance.\n",
     "\n",
-    "BayesOpt is a constrained global optimization package utilizing Bayesian inference on gaussian processes, where the emphasis is on finding the maximum value of an unknown function in as few iterations as possible. BayesOpt's techniques are particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. Therefore BayesOpt falls in the domain of \"derivative-free\" and \"black-box\" optimization. In this example we minimize a simple objective to briefly demonstrate the usage of BayesOpt with Ray Tune via `BayesOptSearch`, including conditional search spaces. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `bayesian-optimization==1.2.0` library is installed. To learn more, please refer to [BayesOpt website](https://github.com/fmfn/BayesianOptimization)."
+    "BayesOpt is a constrained global optimization package utilizing Bayesian inference on gaussian processes, where the emphasis is on finding the maximum value of an unknown function in as few iterations as possible. BayesOpt's techniques are particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. Therefore BayesOpt falls in the domain of \"derivative-free\" and \"black-box\" optimization. In this example we minimize a simple objective to briefly demonstrate the usage of BayesOpt with Ray Tune via `BayesOptSearch`, including conditional search spaces. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `bayesian-optimization==1.2.0` library is installed. To learn more, please refer to [BayesOpt website](https://github.com/fmfn/BayesianOptimization).\n",
+    "\n",
+    "First, install the pre-requisites for this example."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "id": "7ed16354",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: bayesian-optimization==1.2.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (1.2.0)\n",
-      "Requirement already satisfied: scikit-learn>=0.18.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (0.24.2)\n",
-      "Requirement already satisfied: numpy>=1.9.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (1.21.6)\n",
-      "Requirement already satisfied: scipy>=0.14.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from bayesian-optimization==1.2.0) (1.4.1)\n",
-      "Requirement already satisfied: joblib>=0.11 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from scikit-learn>=0.18.0->bayesian-optimization==1.2.0) (1.1.0)\n",
-      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages (from scikit-learn>=0.18.0->bayesian-optimization==1.2.0) (3.0.0)\n",
-      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# !pip install ray[tune]\n",
-    "!pip install bayesian-optimization==1.2.0"
+    "!pip install -q bayesian-optimization==1.2.0 \"ray[tune]\""
    ]
   },
   {
@@ -49,9 +31,7 @@
    "id": "2236f834",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {
@@ -186,8 +166,7 @@
    },
    "outputs": [],
    "source": [
-    "# If 1000 samples take too long, you can reduce this number.\n",
-    "# We override this number here for our smoke tests.\n",
+    "# We reduce the num samples in this hidden cell for our smoke tests.\n",
     "num_samples = 10"
    ]
   },

doc/source/tune/examples/bohb_example.ipynb

@@ -78,7 +78,7 @@
     }
    ],
    "source": [
-    "# !pip install ray[tune]\n",
+    "!pip install ray[tune]\n",
     "!pip install ConfigSpace==0.4.18\n",
     "!pip install hpbandster==0.7.4"
    ]
@@ -89,9 +89,7 @@
    "id": "96641e94",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {

doc/source/tune/examples/hyperopt_example.ipynb

@@ -27,8 +27,7 @@
     "- [HyperOpt website](http://hyperopt.github.io/hyperopt)\n",
     "\n",
     "Necessary requirements:\n",
-    "- `pip install ray[tune]`\n",
-    "- `pip install hyperopt==0.2.5`"
+    "- `pip install ray[tune] hyperopt==0.2.5`"
    ]
   },
   {
@@ -76,9 +75,7 @@
    "id": "b8e9e0cd",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {
@@ -152,11 +149,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "33eddcb9",
-   "metadata": {
-    "tags": [
-     "remove-cell"
-    ]
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ray.init(configure_logging=False)"

doc/source/tune/examples/nevergrad_example.ipynb

@@ -12,7 +12,10 @@
     "\n",
     "Nevergrad provides gradient/derivative-free optimization able to handle noise over the objective landscape, including evolutionary, bandit, and Bayesian optimization algorithms. Nevergrad internally supports search spaces which are continuous, discrete or a mixture of thereof. It also provides a library of functions on which to test the optimization algorithms and compare with other benchmarks.\n",
     "\n",
-    "In this example we minimize a simple objective to briefly demonstrate the usage of Nevergrad with Ray Tune via `NevergradSearch`. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `nevergrad==0.4.3.post7` library is installed. To learn more, please refer to [Nevergrad website](https://github.com/facebookresearch/nevergrad)."
+    "In this example we minimize a simple objective to briefly demonstrate the usage of Nevergrad with Ray Tune via `NevergradSearch`. It's useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume `nevergrad==0.4.3.post7` library is installed. To learn more, please refer to [Nevergrad website](https://github.com/facebookresearch/nevergrad).\n",
+    "\n",
+    "Necessary requirements:\n",
+    "- `pip install ray[tune] nevergrad==0.4.3.post7`\n"
    ]
   },
   {
@@ -59,9 +62,7 @@
    "id": "66cb8206",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {

doc/source/tune/examples/optuna_example.ipynb

@@ -74,9 +74,7 @@
    "id": "467466a3",
    "metadata": {},
    "source": [
-    "Click below to see all the imports we need for this example.\n",
-    "You can also launch directly into a Binder instance to run this notebook yourself.\n",
-    "Just click on the rocket symbol at the top of the navigation."
+    "Click below to see all the imports we need for this example."
    ]
   },
   {
@@ -4124,7 +4122,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ray",
    "language": "python",
    "name": "python3"
   },
@@ -4138,7 +4136,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.10.16"
   },
   "orphan": true
  },