Remove ax dependency from tutorials (#2839)

Summary:
Updates BoTorch tutorials to remove dependency on Ax.
- Custom BoTorch model w/ Ax tutorial is removed. We have another custom model tutorial that covers the non-Ax subject.
- Custom acquisition tutorial is updated to remove Ax parts and moved to bite-sized tutorials.
- Tutorials index page is updated to point to the Ax tutorial that is being added in facebook/Ax#3722. The link will not work until Ax 1.0 is released.
- Ax dependency is removed from `setup.py` and the GHA workflows are updated to remove Ax related blocks.

New tutorial landing page:
<img width="1918" alt="Screenshot 2025-05-05 at 1 46 58 PM" src="https://github.com/user-attachments/assets/472d6a09-99e1-464c-9b45-07f4464283b2" />

Custom acquisition tutorial:
<img width="1916" alt="Screenshot 2025-05-05 at 1 49 12 PM" src="https://github.com/user-attachments/assets/bb813158-e711-4ce0-b399-2530f2e9b8f6" />

Pull Request resolved: #2839

Reviewed By: mpolson64

Differential Revision: D74189651

Pulled By: saitcakmak

fbshipit-source-id: b8cffa21a337605e922886bb9f92fd33f9765d0f

Author: saitcakmak

.github/workflows/nightly.yml

@@ -75,9 +75,8 @@ jobs:
       contents: write
 
   run_tutorials:
-    name: Run tutorials without smoke test on latest PyTorch / GPyTorch / Ax
+    name: Run tutorials without smoke test on latest PyTorch / GPyTorch
     uses: ./.github/workflows/reusable_tutorials.yml
     with:
       smoke_test: false
       use_stable_pytorch_gpytorch: false
-      use_stable_ax: false

.github/workflows/publish_website.yml

@@ -47,11 +47,8 @@ jobs:
     - name: Install dependencies
       env:
         ALLOW_LATEST_GPYTORCH_LINOP: true
-        ALLOW_BOTORCH_LATEST: true  # Allow Ax to install w/ new BoTorch release.
       run: |
         uv pip install ."[dev, tutorials]"
-        # There may not be a compatible Ax uv pip version, so we use the development version.
-        uv pip install git+https://github.com/facebook/Ax.git
     - if: ${{ inputs.new_version }}
       name: Create new docusaurus version
       run: |

.github/workflows/reusable_tutorials.yml

@@ -9,9 +9,6 @@ on:
       use_stable_pytorch_gpytorch:
         required: true
         type: boolean
-      use_stable_ax:
-        required: true
-        type: boolean
   workflow_call:
     inputs:
       smoke_test:
@@ -22,10 +19,6 @@ on:
         required: false
         type: boolean
         default: false
-      use_stable_ax:
-        required: false
-        type: boolean
-        default: false
 
 jobs:
   tutorials:
@@ -66,19 +59,6 @@ jobs:
         ALLOW_LATEST_GPYTORCH_LINOP: true
       run: |
         uv pip install .[tutorials]
-    - if: ${{ !inputs.use_stable_ax }}
-      name: Install latest Ax
-      env:
-        # This is so Ax's setup doesn't install a pinned BoTorch version.
-        ALLOW_BOTORCH_LATEST: true
-      run: |
-        uv pip install git+https://github.com/facebook/Ax.git
-    - if: ${{ inputs.use_stable_ax }}
-      name: Install stable Ax
-      env:
-        ALLOW_BOTORCH_LATEST: true
-      run: |
-        uv pip install ax-platform --no-binary ax-platform
     - if: ${{ inputs.smoke_test }}
       name: Run tutorials with smoke test
       run: |

.github/workflows/test_stable.yml

@@ -52,18 +52,16 @@ jobs:
       run: |
         pytest -ra test_community/ --cov botorch_community/ --cov-report term-missing --cov-report xml:botorch_community_cov.xml
 
-  run_tutorials_stable_w_latest_ax:
-    name: Run tutorials without smoke test on min req. versions of PyTorch & GPyTorch and latest Ax
+  run_tutorials_stable:
+    name: Run tutorials without smoke test on min req. versions of PyTorch & GPyTorch
     uses: ./.github/workflows/reusable_tutorials.yml
     with:
       smoke_test: false
       use_stable_pytorch_gpytorch: true
-      use_stable_ax: false
 
   run_tutorials_stable_smoke_test:
-    name: Run tutorials with smoke test on min req. versions of PyTorch & GPyTorch and latest Ax
+    name: Run tutorials with smoke test on min req. versions of PyTorch & GPyTorch
     uses: ./.github/workflows/reusable_tutorials.yml
     with:
       smoke_test: true
       use_stable_pytorch_gpytorch: true
-      use_stable_ax: false

.github/workflows/tutorials_smoke_test.yml

@@ -10,9 +10,8 @@ on:
 
 jobs:
   run_tutorials_with_smoke_test:
-    name: Run tutorials with smoke test on latest PyTorch / GPyTorch / Ax
+    name: Run tutorials with smoke test on latest PyTorch / GPyTorch
     uses: ./.github/workflows/reusable_tutorials.yml
     with:
       smoke_test: true
       use_stable_pytorch_gpytorch: false
-      use_stable_ax: false

docs/tutorials/index.mdx

@@ -4,27 +4,32 @@ title: BoTorch Tutorials
 The tutorials here will help you understand and use BoTorch in
 your own work. They assume that you are familiar with both
 Bayesian optimization (BO) and PyTorch.
-
-If you are new to BO, we recommend you start with the
+* If you are new to BO, we recommend you start with the
 [Ax docs](https://ax.dev/docs/bayesopt) and the
 following
 [tutorial paper](https://arxiv.org/abs/1807.02811).
-
-If you are new to PyTorch, the easiest way to get started is
+* If you are new to PyTorch, the easiest way to get started is
 with the [What is PyTorch?](https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py)
 tutorial.
 
-The BoTorch tutorials are grouped into the following four areas.
-
 
 <h4>Using BoTorch with Ax</h4>
-These tutorials give you an overview of how to leverage
-[Ax](https://ax.dev), a platform for sequential
-experimentation, in order to simplify the management of your BO
-loop. Doing so can help you focus on the main aspects of BO
-(models, acquisition functions, optimization of acquisition
-functions), rather than tedious loop control. See our
-[Documentation](/docs/botorch_and_ax)
+_For practitioners_ who are interested in running experiments
+to optimize various objectives using Bayesian optimization,
+we recommend using [Ax](https://ax.dev) rather than BoTorch.
+[Ax](https://ax.dev) provides a user-friendly interface for
+experiment configuration and orchestration, while choosing an
+appropriate Bayesian optimization algorithm to optimize the
+given objective, following BoTorch best practices.
+
+_For researchers_ who are interested in running experiments with
+their custom BoTorch models and acquisition functions,
+[Ax](https://ax.dev)'s Modular BoTorch Interface offers a convenient
+way to leverage custom BoTorch objects while utilizing
+[Ax](https://ax.dev) experiment configuration and orchestration. Check out
+[Modular BoTorch tutorial](https://ax.dev/docs/tutorials/modular_botorch/)
+to learn how to use custom BoTorch objects in Ax!
+See [this documentation](/docs/botorch_and_ax)
 for additional information.
 
 <h4>Full Optimization Loops</h4>

setup.py

@@ -17,10 +17,8 @@
 TEST_REQUIRES = ["pytest", "pytest-cov", "requests"]
 FMT_REQUIRES = ["flake8", "ufmt", "flake8-docstrings"]
 TUTORIALS_REQUIRES = [
-    "ax-platform",
     "cma",
     "jupyter",
-    "kaleido",
     "matplotlib",
     "memory_profiler",
     "papermill",