Fix broken links (#2720)

Summary:
## Motivation

The recent website upgrade moved the location of tutorials and api reference breaking existing links to those. Here we fix those links and also configure Docusaurus to raise an error on broken links in the future.

Pull Request resolved: #2720

Test Plan:
Docusaurus checks for broken links when creating a production build. Running `./scripts/build_docs.sh -b` now results in a clean build with no broken links reported.

## Related PRs
- #2715

Reviewed By: saitcakmak, Balandat

Differential Revision: D69034874

Pulled By: CristianLara

fbshipit-source-id: 3a3c9488a6bb1c0a21d0cbb854972e84432eb467

Author: CristianLara

README.md

@@ -130,7 +130,7 @@ pip install -e ".[dev, tutorials]"
 
 Here's a quick run down of the main components of a Bayesian optimization loop.
 For more details see our [Documentation](https://botorch.org/docs/introduction) and the
-[Tutorials](https://botorch.org/tutorials).
+[Tutorials](https://botorch.org/docs/tutorials).
 
 1. Fit a Gaussian Process model to data
   ```python

botorch/models/cost.py

@@ -9,7 +9,7 @@
 
 Cost are useful for defining known cost functions when the cost of an evaluation
 is heterogeneous in fidelity. For a full worked example, see the
-`tutorial <https://botorch.org/tutorials/multi_fidelity_bo>`_ on continuous
+`tutorial <https://botorch.org/docs/tutorials/multi_fidelity_bo>`_ on continuous
 multi-fidelity Bayesian Optimization.
 """
 
@@ -29,7 +29,7 @@ class AffineFidelityCostModel(DeterministicModel):
         cost = fixed_cost + sum_j weights[j] * X[fidelity_dims[j]]
 
     For a full worked example, see the
-    `tutorial <https://botorch.org/tutorials/multi_fidelity_bo>`_ on continuous
+    `tutorial <https://botorch.org/docs/tutorials/multi_fidelity_bo>`_ on continuous
     multi-fidelity Bayesian Optimization.
 
     Example:

botorch/models/gp_regression_fidelity.py

@@ -8,7 +8,7 @@
 Multi-Fidelity Gaussian Process Regression models based on GPyTorch models.
 
 For more on Multi-Fidelity BO, see the
-`tutorial <https://botorch.org/tutorials/discrete_multi_fidelity_bo>`__.
+`tutorial <https://botorch.org/docs/tutorials/discrete_multi_fidelity_bo>`__.
 
 A common use case of multi-fidelity regression modeling is optimizing a
 "high-fidelity" function that is expensive to simulate when you have access to

botorch/models/transforms/input.py

@@ -1242,7 +1242,7 @@ class AppendFeatures(InputTransform):
     `RiskMeasureMCObjective` to optimize risk measures as described in
     [Cakmak2020risk]_. A tutorial notebook implementing the rhoKG acqusition
     function introduced in [Cakmak2020risk]_ can be found at
-    https://botorch.org/tutorials/risk_averse_bo_with_environmental_variables.
+    https://botorch.org/docs/tutorials/risk_averse_bo_with_environmental_variables.
 
     The steps for using this to obtain samples of a risk measure are as follows:
 
@@ -1505,7 +1505,7 @@ class InputPerturbation(InputTransform):
     on optimizing risk measures.
 
     A tutorial notebook using this with `qNoisyExpectedImprovement` can be found at
-    https://botorch.org/tutorials/risk_averse_bo_with_input_perturbations.
+    https://botorch.org/docs/tutorials/risk_averse_bo_with_input_perturbations.
     """
 
     is_one_to_many: bool = True

docs/acquisition.md

@@ -9,7 +9,7 @@ black box function.
 
 BoTorch supports both analytic as well as (quasi-) Monte-Carlo based acquisition
 functions. It provides a generic
-[`AcquisitionFunction`](../api/acquisition.html#acquisitionfunction) API that
+[`AcquisitionFunction`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.acquisition.AcquisitionFunction) API that
 abstracts away from the particular type, so that optimization can be performed
 on the same objects.
 
@@ -64,7 +64,7 @@ where $\mu(X)$ is the posterior mean of $f$ at $X$, and $L(X)L(X)^T = \Sigma(X)$
 is a root decomposition of the posterior covariance matrix.
 
 All MC-based acquisition functions in BoTorch are derived from
-[`MCAcquisitionFunction`](../api/acquisition.html#mcacquisitionfunction).
+[`MCAcquisitionFunction`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.monte_carlo.MCAcquisitionFunction).
 
 Acquisition functions expect input tensors $X$ of shape
 $\textit{batch\_shape} \times q \times d$, where $d$ is the dimension of the
@@ -122,15 +122,15 @@ above.
 
 BoTorch also provides implementations of analytic acquisition functions that
 do not depend on MC sampling. These acquisition functions are subclasses of
-[`AnalyticAcquisitionFunction`](../api/acquisition.html#analyticacquisitionfunction)
+[`AnalyticAcquisitionFunction`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.analytic.AnalyticAcquisitionFunction)
 and only exist for the case of a single candidate point ($q = 1$). These
 include classical acquisition functions such as Expected Improvement (EI),
 Upper Confidence Bound (UCB), and Probability of Improvement (PI). An example
-comparing [`ExpectedImprovement`](../api/acquisition.html#expectedimprovement),
+comparing [`ExpectedImprovement`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.analytic.ExpectedImprovement),
 the analytic version of EI, to it's MC counterpart
-[`qExpectedImprovement`](../api/acquisition.html#qexpectedimprovement)
+[`qExpectedImprovement`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.monte_carlo.qExpectedImprovement)
 can be found in
-[this tutorial](../tutorials/compare_mc_analytic_acquisition).
+[this tutorial](tutorials/compare_mc_analytic_acquisition).
 
 Analytic acquisition functions allow for an explicit expression in terms of the
 summary statistics of the posterior distribution at the evaluated point(s).

docs/batching.md

@@ -19,7 +19,7 @@ referred to as q-Acquisition Functions. For instance, BoTorch ships with support
 for q-EI, q-UCB, and a few others.
 
 As discussed in the
-[design philosophy](design_philosophy#batching-batching-batching),
+[design philosophy](/docs/design_philosophy#parallelism-through-batched-computations),
 BoTorch has adopted the convention of referring to batches in the
 batch-acquisition sense as "q-batches", and to batches in the torch
 batch-evaluation sense as "t-batches".
@@ -35,9 +35,9 @@ with samples from the posterior in a consistent fashion.
 
 #### Batch-Mode Decorator
 
-In order to simplify the user-facing API for evaluating acquisition functions,  
+In order to simplify the user-facing API for evaluating acquisition functions,
 BoTorch implements the
-[`@t_batch_mode_transform`](../api/utils.html#botorch.utils.transforms.t_batch_mode_transform)
+[`@t_batch_mode_transform`](https://botorch.readthedocs.io/en/latest/utils.html#botorch.utils.transforms.t_batch_mode_transform)
 decorator, which allows the use of non-batch mode inputs. If applied to an
 instance method with a single `Tensor` argument, an input tensor to that method
 without a t-batch dimension (i.e. tensors of shape $q \times d$) will automatically
@@ -66,7 +66,7 @@ distribution:
   of $b_1 \times \cdots \times b_k$, with $n$ data points of $d$-dimensions each in every batch)
   yields a posterior with `event_shape` being $b_1 \times \cdots \times b_k \times n \times 1$.
   In most cases, the t-batch-shape will be single-dimensional (i.e., $k=1$).
-- Evaluating a multi-output model with $o$ outputs at a $b_1 \times \cdots \times b_k   
+- Evaluating a multi-output model with $o$ outputs at a $b_1 \times \cdots \times b_k
   \times n \times d$ tensor yields a posterior with `event_shape` equal to
   $b_1 \times \cdots \times b_k \times n \times o$.
 - Recall from the previous section that internally, with the help of the
@@ -123,7 +123,7 @@ The shape of the test points must support broadcasting to the $\textit{batch_sha
   necessary over $\textit{batch_shape}$)
 
 #### Batched Multi-Output Models
-The [`BatchedMultiOutputGPyTorchModel`](../api/models.html#batchedmultioutputgpytorchmodel)
+The [`BatchedMultiOutputGPyTorchModel`](https://botorch.readthedocs.io/en/latest/models.html#botorch.models.gpytorch.BatchedMultiOutputGPyTorchModel)
 class implements a fast multi-output model (assuming conditional independence of
 the outputs given the input) by batching over the outputs.
 
@@ -157,5 +157,5 @@ back-propagating.
 
 #### Batched Cross Validation
 See the
-[Using batch evaluation for fast cross validation](../tutorials/batch_mode_cross_validation)
+[Using batch evaluation for fast cross validation](tutorials/batch_mode_cross_validation)
 tutorial for details on using batching for fast cross validation.

docs/botorch_and_ax.md

@@ -18,7 +18,7 @@ it easy to drive the car.
 
 
 Ax provides a
-[`BotorchModel`](https://ax.dev/api/models.html#ax.models.torch.botorch.BotorchModel)
+[`BotorchModel`](https://https://ax.readthedocs.io/en/latest/models.html#ax.models.torch.botorch.BotorchModel)
 that is a sensible default for modeling and optimization which can be customized
 by specifying and passing in bespoke model constructors, acquisition functions,
 and optimization strategies.
@@ -43,7 +43,7 @@ the the Bayesian Optimization loop untouched. It is then straightforward to plug
 your custom BoTorch model or acquisition function into Ax to take advantage of
 Ax's various loop control APIs, as well as its powerful automated metadata
 management, data storage, etc. See the
-[Using a custom BoTorch model in Ax](../tutorials/custom_botorch_model_in_ax)
+[Using a custom BoTorch model in Ax](tutorials/custom_botorch_model_in_ax)
 tutorial for more on how to do this.
 
 
@@ -53,8 +53,8 @@ If you're working in a non-standard setting, such as structured feature or
 design spaces, or where the model fitting process requires interactive work,
 then using Ax may not be the best solution for you. In such a situation, you
 might be better off writing your own full Bayesian Optimization loop in BoTorch.
-The [q-Noisy Constrained EI](../tutorials/closed_loop_botorch_only) tutorial and
-[variational auto-encoder](../tutorials/vae_mnist) tutorial give examples of how
+The [q-Noisy Constrained EI](tutorials/closed_loop_botorch_only) tutorial and
+[variational auto-encoder](tutorials/vae_mnist) tutorial give examples of how
 this can be done.
 
 You may also consider working purely in BoTorch if you want to be able to

docs/constraints.md

@@ -41,7 +41,7 @@ the constrained expected improvement variant is mathematically equivalent to the
 unconstrained expected improvement of the objective, multiplied by the probability of
 feasibility under the modeled outcome constraint.
 
-See the [Closed-Loop Optimization](../tutorials/closed_loop_botorch_only)
+See the [Closed-Loop Optimization](tutorials/closed_loop_botorch_only)
 tutorial for an example of using outcome constraints in BoTorch.
 
 

docs/design_philosophy.md

@@ -69,7 +69,7 @@ all data available. In typical machine learning model training, a stochastic
 version of the empirical loss, obtained by "mini-batching" the data, is
 optimized using stochastic optimization algorithms.
 
-In BoTorch, [`AcquisitionFunction`](../api/acquisition.html#acquisitionfunction)
+In BoTorch, [`AcquisitionFunction`](https://botorch.readthedocs.io/en/latest/acquisition.html#botorch.acquisition.acquisition.AcquisitionFunction)
 modules map an input design $X$ to the acquisition function value. Optimizing
 the acquisition function means optimizing the output over the possible values of
 $X$. If the acquisition function is deterministic, then so is the optimization

docs/getting_started.mdx

@@ -89,13 +89,13 @@ Here's a quick run down of the main components of a Bayesian Optimization loop.
 ## Tutorials
 
 Our Jupyter notebook tutorials help you get off the ground with BoTorch.
-View and download them [here](../tutorials).
+View and download them [here](tutorials).
 
 
 ## API Reference
 
 For an in-depth reference of the various BoTorch internals, see our
-[API Reference](../api).
+[API Reference](https://botorch.readthedocs.io/).
 
 
 ## Contributing