Update references to meta-pytorch github org. (#3023)

Balandat · facebook-github-bot · commit e3d05e9daa4c · 2025-09-25T09:08:33.000-07:00
Summary: The repo was moved from the pytorch to the meta-pytorch GitHub org. This updates the references in the code accordingly. Pull Request resolved: #3023 Reviewed By: saitcakmak, esantorella Differential Revision: D83248792 Pulled By: Balandat fbshipit-source-id: 39df200cf88a3139c4d426078af9df1833081581
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -56,15 +56,15 @@ body:
     id: pull-request
     attributes:
       label: Pull Request
-      description: Are you willing to open a pull request fixing the bug outlined in this issue? (See [Contributing to BoTorch](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md))
+      description: Are you willing to open a pull request fixing the bug outlined in this issue? (See [Contributing to BoTorch](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md))
       options:
         - "Yes"
         - "No"
   - type: checkboxes
     id: terms
     attributes:
       label: Code of Conduct
-      description: By submitting this issue, you agree to follow BoTorch's [Code of Conduct](https://github.com/pytorch/botorch/blob/main/CODE_OF_CONDUCT.md).
+      description: By submitting this issue, you agree to follow BoTorch's [Code of Conduct](https://github.com/meta-pytorch/botorch/blob/main/CODE_OF_CONDUCT.md).
       options:
         - label: I agree to follow BoTorch's Code of Conduct
           required: true
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,7 +1,7 @@
 blank_issues_enabled: false
 contact_links:
   - name: General Support & Discussions
-    url: https://github.com/pytorch/botorch/discussions
+    url: https://github.com/meta-pytorch/botorch/discussions
     about: Please use discussions for general support requests and to ask broader questions related to BoTorch. This includes any support request that does not require changes to BoTorch source code.
   - name: Bug Bounty Program
     url: https://bugbounty.meta.com/
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -32,15 +32,15 @@ body:
     id: pull-request
     attributes:
       label: Pull Request
-      description: Are you willing to open a pull request implementing this feature? (See [Contributing to BoTorch](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md))
+      description: Are you willing to open a pull request implementing this feature? (See [Contributing to BoTorch](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md))
       options:
         - "Yes"
         - "No"
   - type: checkboxes
     id: terms
     attributes:
       label: Code of Conduct
-      description: By submitting this issue, you agree to follow BoTorch's [Code of Conduct](https://github.com/pytorch/botorch/blob/main/CODE_OF_CONDUCT.md).
+      description: By submitting this issue, you agree to follow BoTorch's [Code of Conduct](https://github.com/meta-pytorch/botorch/blob/main/CODE_OF_CONDUCT.md).
       options:
         - label: I agree to follow BoTorch's Code of Conduct
           required: true
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
@@ -3,14 +3,14 @@ Thank you for sending the PR! We appreciate you spending the time to make BoTorc
 
 Help us understand your motivation by explaining why you decided to make this change.
 
-You can learn more about contributing to BoTorch here: https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md
+You can learn more about contributing to BoTorch here: https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md
 -->
 
 ## Motivation
 
 (Write your motivation here.)
 
-### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?
+### Have you read the [Contributing Guidelines on pull requests](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?
 
 (Write your answer here.)
 
@@ -20,4 +20,4 @@ You can learn more about contributing to BoTorch here: https://github.com/pytorc
 
 ## Related PRs
 
-(If this PR adds or changes functionality, please take some time to update the docs at https://github.com/pytorch/botorch, and link to your PR here.)
+(If this PR adds or changes functionality, please take some time to update the docs at https://github.com/meta-pytorch/botorch, and link to your PR here.)
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -8,7 +8,7 @@ To get the development installation with all the necessary dependencies for
 linting, testing, and building the documentation, run the following:
 
 ```bash
-git clone https://github.com/pytorch/botorch.git
+git clone https://github.com/meta-pytorch/botorch.git
 cd botorch
 pip install -e ".[dev]"
 ```
diff --git a/README.md b/README.md
@@ -5,10 +5,10 @@
 <hr/>
 
 [![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)
-[![Lint](https://github.com/pytorch/botorch/workflows/Lint/badge.svg)](https://github.com/pytorch/botorch/actions?query=workflow%3ALint)
-[![Test](https://github.com/pytorch/botorch/workflows/Test/badge.svg)](https://github.com/pytorch/botorch/actions?query=workflow%3ATest)
-[![Docs](https://github.com/pytorch/botorch/workflows/Docs/badge.svg)](https://github.com/pytorch/botorch/actions?query=workflow%3ADocs)
-[![Nightly](https://github.com/pytorch/botorch/actions/workflows/nightly.yml/badge.svg)](https://github.com/pytorch/botorch/actions?query=workflow%3ANightly)
+[![Lint](https://github.com/meta-pytorch/botorch/workflows/Lint/badge.svg)](https://github.com/meta-pytorch/botorch/actions?query=workflow%3ALint)
+[![Test](https://github.com/meta-pytorch/botorch/workflows/Test/badge.svg)](https://github.com/meta-pytorch/botorch/actions?query=workflow%3ATest)
+[![Docs](https://github.com/meta-pytorch/botorch/workflows/Docs/badge.svg)](https://github.com/meta-pytorch/botorch/actions?query=workflow%3ADocs)
+[![Nightly](https://github.com/meta-pytorch/botorch/actions/workflows/nightly.yml/badge.svg)](https://github.com/meta-pytorch/botorch/actions?query=workflow%3ANightly)
 [![Codecov](https://img.shields.io/codecov/c/github/pytorch/botorch.svg)](https://codecov.io/github/pytorch/botorch)
 
 [![PyPI](https://img.shields.io/pypi/v/botorch.svg)](https://pypi.org/project/botorch)
@@ -74,7 +74,7 @@ pip install botorch
 _Note_: Make sure the `pip` being used is actually the one from the newly created
 Conda environment. If you're using a Unix-based OS, you can use `which pip` to check.
 
-BoTorch [stopped publishing](https://github.com/pytorch/botorch/discussions/2613#discussion-7431533)
+BoTorch [stopped publishing](https://github.com/meta-pytorch/botorch/discussions/2613#discussion-7431533)
 an official Anaconda package to the `pytorch` channel after the 0.12 release. However,
 users can still use the package published to the `conda-forge` channel and install botorch via
 
@@ -91,7 +91,7 @@ current `gpytorch` and `linear_operator` development versions:
 ```bash
 pip install --upgrade git+https://github.com/cornellius-gp/linear_operator.git
 pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
-pip install --upgrade git+https://github.com/pytorch/botorch.git
+pip install --upgrade git+https://github.com/meta-pytorch/botorch.git
 ```
 
 ### Option 3: Editable/dev install
@@ -105,15 +105,15 @@ before proceeding.
 #### Option 3a: Bare-bones editable install
 
 ```bash
-git clone https://github.com/pytorch/botorch.git
+git clone https://github.com/meta-pytorch/botorch.git
 cd botorch
 pip install -e .
 ```
 
 #### Option 3b: Editable install with development and tutorials dependencies
 
 ```bash
-git clone https://github.com/pytorch/botorch.git
+git clone https://github.com/meta-pytorch/botorch.git
 cd botorch
 pip install -e ".[dev, tutorials]"
 ```
@@ -138,7 +138,7 @@ For more details see our [Documentation](https://botorch.org/docs/introduction)
   from gpytorch.mlls import ExactMarginalLogLikelihood
 
   # Double precision is highly recommended for GPs.
-  # See https://github.com/pytorch/botorch/discussions/1444
+  # See https://github.com/meta-pytorch/botorch/discussions/1444
   train_X = torch.rand(10, 2, dtype=torch.double) * 2
   Y = 1 - (train_X - 0.5).norm(dim=-1, keepdim=True)  # explicit output dimension
   Y += 0.1 * torch.rand_like(Y)
diff --git a/botorch/acquisition/joint_entropy_search.py b/botorch/acquisition/joint_entropy_search.py
@@ -53,7 +53,7 @@
 FULLY_BAYESIAN_ERROR_MSG = (
     "JES is not yet available with Fully Bayesian GPs. Track the issue, "
     "which regards conditioning on a number of optima on a collection "
-    "of models, in detail at https://github.com/pytorch/botorch/issues/1680"
+    "of models, in detail at https://github.com/meta-pytorch/botorch/issues/1680"
 )
 
 
diff --git a/botorch/exceptions/warnings.py b/botorch/exceptions/warnings.py
@@ -105,6 +105,6 @@ def _get_single_precision_warning(dtype_str: str) -> str:
         f"The model inputs are of type {dtype_str}. It is strongly recommended "
         "to use double precision in BoTorch, as this improves both "
         "precision and stability and can help avoid numerical errors. "
-        "See https://github.com/pytorch/botorch/discussions/1444"
+        "See https://github.com/meta-pytorch/botorch/discussions/1444"
     )
     return msg
diff --git a/botorch/models/gp_regression.py b/botorch/models/gp_regression.py
@@ -63,7 +63,7 @@ class SingleTaskGP(BatchedMultiOutputGPyTorchModel, ExactGP, FantasizeMixin):
     noise. Importantly, The model works best when covariates are normalized to the unit
     cube and outcomes are standardized (zero mean, unit variance). For a detailed
     discussion on the hyperparameter priors, see
-    https://github.com/pytorch/botorch/discussions/2451.
+    https://github.com/meta-pytorch/botorch/discussions/2451.
 
     This model works in batch mode (each batch having its own hyperparameters).
     When the training observations include multiple outputs, this model will use
diff --git a/botorch/models/likelihoods/sparse_outlier_noise.py b/botorch/models/likelihoods/sparse_outlier_noise.py
@@ -365,7 +365,7 @@ def forward(
         # and GPyTorch generally pass the training inputs to the likelihood, e.g.:
         # 1) in fit_gpytorch_mll:
         # (
-        #   github.com/pytorch/botorch/blob/3ca48d0ac5865a017ac6b2294807b432d6472bcf/
+        #   github.com/meta-pytorch/botorch/blob/3ca48d0ac5865a017ac6b2294807b432d6472bcf/
         #   botorch/optim/closures/model_closures.py#L185
         # )
         # 2) in the exact prediction strategy:
@@ -377,7 +377,7 @@ def forward(
         # 3) In the model's `posterior` method, if `observation_noise` is True, in which
         # case the test inputs will be passed to the likelihood:
         # (
-        #    https://github.com/pytorch/botorch/blob/
+        #    https://github.com/meta-pytorch/botorch/blob/
         #    4190f74363757ad97bfb0b437402b749ae50ba4c/
         #    botorch/models/gpytorch.py#L198
         # )
diff --git a/botorch/models/transforms/input.py b/botorch/models/transforms/input.py
@@ -145,7 +145,7 @@ def preprocess_transform(self, X: Tensor) -> Tensor:
         """
         if self.transform_on_train:
             # We need to disable learning of bounds / affine coefficients here.
-            # See why: https://github.com/pytorch/botorch/issues/1078.
+            # See why: https://github.com/meta-pytorch/botorch/issues/1078.
             if hasattr(self, "learn_coefficients"):
                 learn_coefficients = self.learn_coefficients
                 self.learn_coefficients = False
diff --git a/botorch/models/utils/assorted.py b/botorch/models/utils/assorted.py
@@ -166,7 +166,7 @@ def check_min_max_scaling(
             msg = "contained"
         if msg is not None:
             # NOTE: If you update this message, update the warning filters as well.
-            # See https://github.com/pytorch/botorch/pull/2508.
+            # See https://github.com/meta-pytorch/botorch/pull/2508.
             msg = (
                 f"Data (input features) is not {msg} to the unit cube. "
                 "Please consider min-max scaling the input data."
@@ -198,7 +198,7 @@ def check_standardization(
         if Y.shape[-2] <= 1:
             if mean_not_zero:
                 # NOTE: If you update this message, update the warning filters as well.
-                # See https://github.com/pytorch/botorch/pull/2508.
+                # See https://github.com/meta-pytorch/botorch/pull/2508.
                 msg = (
                     f"Data (outcome observations) is not standardized (mean = {Ymean})."
                     " Please consider scaling the input to zero mean and unit variance."
@@ -211,7 +211,7 @@ def check_standardization(
             std_not_one = torch.abs(Ystd - 1).max() > atol_std
             if mean_not_zero or std_not_one:
                 # NOTE: If you update this message, update the warning filters as well.
-                # See https://github.com/pytorch/botorch/pull/2508.
+                # See https://github.com/meta-pytorch/botorch/pull/2508.
                 msg = (
                     "Data (outcome observations) is not standardized "
                     f"(std = {Ystd}, mean = {Ymean})."
diff --git a/botorch/optim/optimize.py b/botorch/optim/optimize.py
@@ -238,7 +238,7 @@ def _validate_sequential_inputs(opt_inputs: OptimizeAcqfInputs) -> None:
                 )
 
     # TODO: Validate constraints if provided:
-    # https://github.com/pytorch/botorch/pull/1231
+    # https://github.com/meta-pytorch/botorch/pull/1231
     if opt_inputs.batch_initial_conditions is not None:
         raise UnsupportedError(
             "`batch_initial_conditions` is not supported for sequential "
diff --git a/botorch/sampling/get_sampler.py b/botorch/sampling/get_sampler.py
@@ -111,7 +111,7 @@ def _get_sampler_list(
 
     NOTE: Does not dispatch to Sobol sampling for normal posteriors due to
     correlations between samplers. Instead uses `IIDNormalSampler`. See the following
-    issue for details: https://github.com/pytorch/botorch/issues/2658
+    issue for details: https://github.com/meta-pytorch/botorch/issues/2658
     """
     samplers = []
     for p in posterior.posteriors:
diff --git a/botorch/utils/sampling.py b/botorch/utils/sampling.py
@@ -631,14 +631,15 @@ def __init__(
                 "or bounds."
             )
         # Normalize constraints to avoid the following issue:
-        # https://github.com/pytorch/botorch/issues/1225
+        # https://github.com/meta-pytorch/botorch/issues/1225
         offset, scale = None, None
         if inequality_constraints or equality_constraints:
             if bounds is None:
                 warnings.warn(
                     "HitAndRunPolytopeSampler did not receive `bounds`, which can "
                     "lead to non-uniform sampling if the parameter ranges are very "
-                    "different (see https://github.com/pytorch/botorch/issues/1225).",
+                    "different (see "
+                    "https://github.com/meta-pytorch/botorch/issues/1225).",
                     UserInputWarning,
                     stacklevel=3,
                 )
@@ -857,7 +858,7 @@ def normalize_sparse_linear_constraints(
                 "support the kind of 'inter-point constraints' that are supported by "
                 "`optimize_acqf()`. To achieve this behavior, you need define the "
                 "problem on the joint space over `q` points and impose use constraints,"
-                "see https://github.com/pytorch/botorch/issues/2468#issuecomment-2287706461"  # noqa: E501
+                "see https://github.com/meta-pytorch/botorch/issues/2468#issuecomment-2287706461"  # noqa: E501
             )
         lower, upper = bounds[:, index]
         s = upper - lower
@@ -916,7 +917,7 @@ def get_polytope_samples(
     NOTE: This method does not support the kind of "inter-point constraints" that
     are supported by `optimize_acqf()`. To achieve this behavior, you need define the
     problem on the joint space over `q` points and impose use constraints, see:
-    https://github.com/pytorch/botorch/issues/2468#issuecomment-2287706461
+    https://github.com/meta-pytorch/botorch/issues/2468#issuecomment-2287706461
 
     Args:
         n: The number of samples.
diff --git a/docs/getting_started.mdx b/docs/getting_started.mdx
@@ -38,7 +38,7 @@ conda install botorch -c gpytorch -c conda-forge
 </Tabs>
 
 For more installation options and detailed instructions, please see the
-[Project Readme](https://github.com/pytorch/botorch/blob/main/README.md)
+[Project Readme](https://github.com/meta-pytorch/botorch/blob/main/README.md)
 on GitHub.
 
 ## Basic Components
@@ -100,5 +100,5 @@ For an in-depth reference of the various BoTorch internals, see our
 ## Contributing
 
 You'd like to contribute to BoTorch? Great! Please see
-[here](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md)
+[here](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md)
 for how to help out.
diff --git a/docs/models.md b/docs/models.md
@@ -123,7 +123,7 @@ settings where the **input features are normalized to the unit cube** and the
 **observations are standardized** (zero mean, unit variance). The lengthscale
 priors scale with the input dimension, which makes them adaptable to both low
 and high dimensional problems. See
-[this discussion](https://github.com/pytorch/botorch/discussions/2451) for
+[this discussion](https://github.com/meta-pytorch/botorch/discussions/2451) for
 additional context on the default hyperparameters.
 
 ## Other useful models
diff --git a/docs/notebooks_community/index.mdx b/docs/notebooks_community/index.mdx
@@ -5,4 +5,4 @@ This section of the website displays a list of notebooks contributed and maintai
 by the BoTorch community. These notebooks often showcase recent methods from the
 literature that are implemented using BoTorch.
 
-Interested in contributing? Check out our [contributing guide](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#community-contributions-beta)!
+Interested in contributing? Check out our [contributing guide](https://github.com/meta-pytorch/botorch/blob/main/CONTRIBUTING.md#community-contributions-beta)!
diff --git a/pyproject.toml b/pyproject.toml
@@ -71,7 +71,7 @@ tutorials = [
 [project.urls]
 Homepage = "https://botorch.org"
 Documentation = "https://botorch.org"
-Repository = "https://github.com/pytorch/botorch"
+Repository = "https://github.com/meta-pytorch/botorch"
 
 [tool.setuptools.packages]
 find = {exclude = ["test", "test.*"]}
diff --git a/sphinx/source/index.rst b/sphinx/source/index.rst
@@ -1,4 +1,4 @@
-:github_url: https://github.com/pytorch/botorch
+:github_url: https://github.com/meta-pytorch/botorch
 
 BoTorch API Reference
 ===================================
diff --git a/test/models/test_gp_regression.py b/test/models/test_gp_regression.py
@@ -429,7 +429,7 @@ def test_construct_inputs(self):
             self.assertTrue(Y.equal(data_dict["train_Y"]))
 
     def test_set_transformed_inputs(self):
-        # This intended to catch https://github.com/pytorch/botorch/issues/1078.
+        # This intended to catch https://github.com/meta-pytorch/botorch/issues/1078.
         # More general testing of _set_transformed_inputs is done under ModelListGP.
         X = torch.rand(5, 2)
         Y = X**2
diff --git a/test/models/test_gpytorch.py b/test/models/test_gpytorch.py
@@ -695,7 +695,7 @@ def test_condition_on_observations_input_transform_consistency(self):
         """Test that input transforms are applied consistently in
          condition_on_observations.
 
-        This addresses https://github.com/pytorch/botorch/issues/2533:
+        This addresses https://github.com/meta-pytorch/botorch/issues/2533:
         inputs should be transformed when conditioning.
         """
         for dtype in (torch.float, torch.double):
diff --git a/test/optim/utils/test_model_utils.py b/test/optim/utils/test_model_utils.py
@@ -283,7 +283,7 @@ def test_univariate_prior(self) -> None:
             self.assertEqual(len(torch.unique(new_lengthscales)), 3 * batch.numel())
 
     def test_with_multivariate_prior(self) -> None:
-        # This is modified from https://github.com/pytorch/botorch/issues/780.
+        # This is modified from https://github.com/meta-pytorch/botorch/issues/780.
         tkwargs: dict[str, Any] = {"device": self.device, "dtype": torch.double}
         for batch in (torch.Size([]), torch.Size([3])):
             model = SingleTaskGP(
diff --git a/test/utils/test_sampling.py b/test/utils/test_sampling.py
@@ -364,7 +364,7 @@ def test_sample_polytope_infeasible(self) -> None:
 
     def test_sample_polytope_boundary(self) -> None:
         # Check that sample_polytope does not get stuck at the boundary.
-        # This replicates https://github.com/pytorch/botorch/issues/2351.
+        # This replicates https://github.com/meta-pytorch/botorch/issues/2351.
         samples = sample_polytope(
             A=torch.tensor(
                 [
diff --git a/tutorials/composite_mtbo/composite_mtbo.ipynb b/tutorials/composite_mtbo/composite_mtbo.ipynb
diff --git a/tutorials/custom_model/custom_model.ipynb b/tutorials/custom_model/custom_model.ipynb
diff --git a/tutorials/relevance_pursuit_robust_regression/relevance_pursuit_robust_regression.ipynb b/tutorials/relevance_pursuit_robust_regression/relevance_pursuit_robust_regression.ipynb
diff --git a/website/README.md b/website/README.md
diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js

Original file line number	Diff line number	Diff line change
`@@ -53,7 +53,7 @@`
`53`	`53`	`FULLY_BAYESIAN_ERROR_MSG = (`
`54`	`54`	`"JES is not yet available with Fully Bayesian GPs. Track the issue, "`
`55`	`55`	`"which regards conditioning on a number of optima on a collection "`
`56`		`- "of models, in detail at https://github.com/pytorch/botorch/issues/1680"`
	`56`	`+ "of models, in detail at https://github.com/meta-pytorch/botorch/issues/1680"`
`57`	`57`	`)`
`58`	`58`
`59`	`59`
Original file line number	Diff line number	Diff line change
`@@ -105,6 +105,6 @@ def _get_single_precision_warning(dtype_str: str) -> str:`
`105`	`105`	`f"The model inputs are of type {dtype_str}. It is strongly recommended "`
`106`	`106`	`"to use double precision in BoTorch, as this improves both "`
`107`	`107`	`"precision and stability and can help avoid numerical errors. "`
`108`		`- "See https://github.com/pytorch/botorch/discussions/1444"`
	`108`	`+ "See https://github.com/meta-pytorch/botorch/discussions/1444"`
`109`	`109`	`)`
`110`	`110`	`return msg`