diff --git a/CITATION.cff b/CITATION.cff
index c406f3db1e..c51c8d0ea6 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -14,7 +14,7 @@ identifiers:
value: "10.5281/zenodo.4323058"
license: "Apache-2.0"
repository-code: "https://github.com/Project-MONAI/MONAI"
-url: "https://monai.io"
+url: "https://project-monai.github.io/"
cff-version: "1.2.0"
message: "If you use this software, please cite it using these metadata."
preferred-citation:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e87804a3e3..df7f5e336c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -198,7 +198,7 @@ The first line of the comment must be `/black` so that it will be interpreted by
#### Adding new optional dependencies
In addition to the minimal requirements of PyTorch and Numpy, MONAI's core modules are built optionally based on 3rd-party packages.
-The current set of dependencies is listed in [installing dependencies](https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies).
+The current set of dependencies is listed in [installing dependencies](https://monai.readthedocs.io/en/stable/installation.html#installing-the-recommended-dependencies).
To allow for flexible integration of MONAI with other systems and environments,
the optional dependency APIs are always invoked lazily. For example,
diff --git a/README.md b/README.md
index e4bcdbb815..c327846d8e 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@
[](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)
[](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)
-[](https://docs.monai.io/en/latest/)
+[](https://monai.readthedocs.io/en/latest/)
[](https://codecov.io/gh/Project-MONAI/MONAI)
[](https://piptrends.com/package/monai)
@@ -26,7 +26,7 @@ Its ambitions are as follows:
## Features
-> _Please see [the technical highlights](https://docs.monai.io/en/latest/highlights.html) and [What's New](https://docs.monai.io/en/latest/whatsnew.html) of the milestone releases._
+> _Please see [the technical highlights](https://monai.readthedocs.io/en/latest/highlights.html) and [What's New](https://monai.readthedocs.io/en/latest/whatsnew.html) of the milestone releases._
- flexible pre-processing for multi-dimensional medical imaging data;
- compositional & portable APIs for ease of integration in existing workflows;
@@ -51,7 +51,7 @@ To install [the current release](https://pypi.org/project/monai/), you can simpl
pip install monai
```
-Please refer to [the installation guide](https://docs.monai.io/en/latest/installation.html) for other installation options.
+Please refer to [the installation guide](https://monai.readthedocs.io/en/latest/installation.html) for other installation options.
## Getting Started
@@ -68,7 +68,7 @@ If you have used MONAI in your research, please cite us! The citation can be exp
## Model Zoo
[The MONAI Model Zoo](https://github.com/Project-MONAI/model-zoo) is a place for researchers and data scientists to share the latest and great models from the community.
-Utilizing [the MONAI Bundle format](https://docs.monai.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.
+Utilizing [the MONAI Bundle format](https://monai.readthedocs.io/en/latest/bundle_intro.html) makes it easy to [get started](https://github.com/Project-MONAI/tutorials/tree/main/model_zoo) building workflows with MONAI.
## Contributing
@@ -82,9 +82,9 @@ Ask and answer questions over on [MONAI's GitHub Discussions tab](https://github
## Links
-- Website:
-- API documentation (milestone):
-- API documentation (latest dev):
+- Website:
+- API documentation (milestone):
+- API documentation (latest dev):
- Code:
- Project tracker:
- Issue tracker:
diff --git a/docs/source/applications.md b/docs/source/applications.md
index c77cb4065c..44fb9bbf14 100644
--- a/docs/source/applications.md
+++ b/docs/source/applications.md
@@ -1,20 +1,20 @@
# Research and Application Highlights
### COPLE-Net for COVID-19 Pneumonia Lesion Segmentation
-[A reimplementation](https://monai.io/research/coplenet-pneumonia-lesion-segmentation) of the COPLE-Net originally proposed by:
+[A reimplementation](https://project-monai.github.io/research/coplenet-pneumonia-lesion-segmentation.html) of the COPLE-Net originally proposed by:
G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan, H. Zhu, T. Meng, K. Li, N. Huang, S. Zhang. (2020) "A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images." IEEE Transactions on Medical Imaging. 2020. [DOI: 10.1109/TMI.2020.3000314](https://doi.org/10.1109/TMI.2020.3000314)
### LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
-[A reimplementation](https://monai.io/research/lamp-automated-model-parallelism) of the LAMP system originally proposed by:
+[A reimplementation](https://project-monai.github.io/research/lamp-automated-model-parallelism.html) of the LAMP system originally proposed by:
Wentao Zhu, Can Zhao, Wenqi Li, Holger Roth, Ziyue Xu, and Daguang Xu (2020) "LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation." MICCAI 2020 (Early Accept, paper link: https://arxiv.org/abs/2006.12575)
### DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
-MONAI integrated the `DiNTS` module to support more flexible topologies and joint two-level search. It provides a topology guaranteed discretization algorithm and a discretization aware topology loss for the search stage to minimize the discretization gap, and a cost usage aware search method which can search 3D networks with different GPU memory requirements. For more details, please check the [DiNTS tutorial](https://monai.io/research/dints.html).
+MONAI integrated the `DiNTS` module to support more flexible topologies and joint two-level search. It provides a topology guaranteed discretization algorithm and a discretization aware topology loss for the search stage to minimize the discretization gap, and a cost usage aware search method which can search 3D networks with different GPU memory requirements. For more details, please check the [DiNTS tutorial](https://project-monai.github.io/research/dints.html).
diff --git a/docs/source/config_syntax.md b/docs/source/config_syntax.md
index 742841acca..4b24415d2f 100644
--- a/docs/source/config_syntax.md
+++ b/docs/source/config_syntax.md
@@ -68,7 +68,7 @@ or additionally, tune the input parameters then instantiate the component:
BasicUNet features: (32, 32, 32, 64, 64, 64).
```
-For more details on the `ConfigParser` API, please see [`monai.bundle.ConfigParser`](https://docs.monai.io/en/latest/bundle.html#config-parser).
+For more details on the `ConfigParser` API, please see [`monai.bundle.ConfigParser`](https://monai.readthedocs.io/en/latest/bundle.html#config-parser).
## Syntax examples explained
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 85adee7e44..4279e522d3 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -85,15 +85,15 @@ Model Zoo
---------
`The MONAI Model Zoo `_ is a place for researchers and data scientists to share the latest and great models from the community.
-Utilizing `the MONAI Bundle format `_ makes it easy to `get started `_ building workflows with MONAI.
+Utilizing `the MONAI Bundle format `_ makes it easy to `get started `_ building workflows with MONAI.
Links
-----
-- Website: https://monai.io/
-- API documentation (milestone): https://docs.monai.io/
-- API documentation (latest dev): https://docs.monai.io/en/latest/
+- Website: https://project-monai.github.io/
+- API documentation (milestone): https://monai.readthedocs.io/
+- API documentation (latest dev): https://monai.readthedocs.io/en/latest/
- Code: https://github.com/Project-MONAI/MONAI
- Project tracker: https://github.com/Project-MONAI/MONAI/projects
- Issue tracker: https://github.com/Project-MONAI/MONAI/issues
diff --git a/docs/source/modules.md b/docs/source/modules.md
index ea8362083c..b2e95658bf 100644
--- a/docs/source/modules.md
+++ b/docs/source/modules.md
@@ -240,7 +240,7 @@ users and programs to understand how the model is used and for what purpose. A b
single network as a pickled state dictionary plus optionally a Torchscript object and/or an ONNX object. Additional JSON
files are included to store metadata about the model, information for constructing training, inference, and
post-processing transform sequences, plain-text description, legal information, and other data the model creator wishes
-to include. More details are available at [bundle specification](https://docs.monai.io/en/latest/mb_specification.html).
+to include. More details are available at [bundle specification](https://monai.readthedocs.io/en/latest/mb_specification.html).
The key benefits of bundle are to define the model package and support building Python-based workflows via structured configurations:
- Self-contained model package include all the necessary information.
@@ -262,26 +262,26 @@ A typical bundle example can include:
┣━ *README.md
┗━ *license.txt
```
-Details about the bundle config definition and syntax & examples are at [config syntax](https://docs.monai.io/en/latest/config_syntax.html).
+Details about the bundle config definition and syntax & examples are at [config syntax](https://monai.readthedocs.io/en/latest/config_syntax.html).
A step-by-step [get started](https://github.com/Project-MONAI/tutorials/blob/main/bundle/README.md) tutorial notebook can help users quickly set up a bundle. [[bundle examples](https://github.com/Project-MONAI/tutorials/tree/main/bundle), [model-zoo](https://github.com/Project-MONAI/model-zoo)]
## Federated Learning
-Using the MONAI bundle configurations, we can use MONAI's [`MonaiAlgo`](https://docs.monai.io/en/latest/fl.html#monai.fl.client.MonaiAlgo)
-class, an implementation of the abstract [`ClientAlgo`](https://docs.monai.io/en/latest/fl.html#clientalgo) class for federated learning (FL),
+Using the MONAI bundle configurations, we can use MONAI's [`MonaiAlgo`](https://monai.readthedocs.io/en/latest/fl.html#monai.fl.client.MonaiAlgo)
+class, an implementation of the abstract [`ClientAlgo`](https://monai.readthedocs.io/en/latest/fl.html#clientalgo) class for federated learning (FL),
to execute bundles from the [MONAI model zoo](https://github.com/Project-MONAI/model-zoo).
-Note that [`ClientAlgo`](https://docs.monai.io/en/latest/fl.html#clientalgo) is provided as an abstract base class for
+Note that [`ClientAlgo`](https://monai.readthedocs.io/en/latest/fl.html#clientalgo) is provided as an abstract base class for
defining an algorithm to be run on any federated learning platform.
-[`MonaiAlgo`](https://docs.monai.io/en/latest/fl.html#monai.fl.client.MonaiAlgo) implements the main functionalities needed
+[`MonaiAlgo`](https://monai.readthedocs.io/en/latest/fl.html#monai.fl.client.MonaiAlgo) implements the main functionalities needed
to run federated learning experiments, namely `train()`, `get_weights()`, and `evaluate()`, that can be run using single- or multi-GPU training.
On top, it provides implementations for life-cycle management of the component such as `initialize()`, `abort()`, and `finalize()`.
The MONAI FL client also allows computing summary data statistics (e.g., intensity histograms) on the datasets defined in the bundle configs
-using the [`MonaiAlgoStats`](https://docs.monai.io/en/latest/fl.html#monai.fl.client.MonaiAlgoStats) class.
+using the [`MonaiAlgoStats`](https://monai.readthedocs.io/en/latest/fl.html#monai.fl.client.MonaiAlgoStats) class.
These statistics can be shared and visualized on the FL server.
[NVIDIA FLARE](https://github.com/NVIDIA/NVFlare), the federated learning platform developed by NVIDIA, has already built [the integration piece](https://github.com/NVIDIA/NVFlare/tree/2.2/integration/monai)
-with [`ClientAlgo`](https://docs.monai.io/en/latest/fl.html#clientalgo) to allow easy experimentation with MONAI bundles within their federated environment.
+with [`ClientAlgo`](https://monai.readthedocs.io/en/latest/fl.html#clientalgo) to allow easy experimentation with MONAI bundles within their federated environment.
Our [[federated learning tutorials]](https://github.com/Project-MONAI/tutorials/tree/main/federated_learning/nvflare) shows
examples of single- & multi-GPU training and federated statistics workflows.
@@ -289,7 +289,7 @@ examples of single- & multi-GPU training and federated statistics workflows.
-[Auto3DSeg](https://monai.io/apps/auto3dseg.html) is a comprehensive solution for large-scale 3D medical image segmentation.
+[Auto3DSeg](https://project-monai.github.io/apps/auto3dseg.html) is a comprehensive solution for large-scale 3D medical image segmentation.
It leverages the latest advances in MONAI
and GPUs to efficiently develop and deploy algorithms with state-of-the-art performance.
It first analyzes the global information such as intensity, dimensionality, and resolution of the dataset,
diff --git a/docs/source/whatsnew_0_6.md b/docs/source/whatsnew_0_6.md
index 8df0503142..0efe9847cb 100644
--- a/docs/source/whatsnew_0_6.md
+++ b/docs/source/whatsnew_0_6.md
@@ -42,7 +42,7 @@ The following illustrates target body organs that are segmentation in this tutor
Please visit UNETR repository for more details:
-https://monai.io/research/unetr-btcv-multi-organ-segmentation
+https://project-monai.github.io/research/unetr-btcv-multi-organ-segmentation
## Pythonic APIs to load the pretrained models from Clara Train MMARs
[The MMAR (Medical Model ARchive)](https://docs.nvidia.com/clara/clara-train-sdk/pt/mmar.html)
@@ -93,4 +93,4 @@ MONAI Label enables application developers to build labeling apps in a serverles
where custom labeling apps are exposed as a service through the MONAI Label Server.
Please visit MONAILabel documentation website for details:
-https://docs.monai.io/projects/label/en/latest/
+https://monai.readthedocs.io/projects/label/en/latest/
diff --git a/docs/source/whatsnew_0_8.md b/docs/source/whatsnew_0_8.md
index 3eb4bea167..ac63c8dbd0 100644
--- a/docs/source/whatsnew_0_8.md
+++ b/docs/source/whatsnew_0_8.md
@@ -15,7 +15,7 @@ It provides a topology guaranteed discretization algorithm and a
discretization-aware topology loss for the search stage to minimize the
discretization gap. The module is memory usage aware and is able to search 3D
networks with different GPU memory requirements. For more details, please check out the
-[DiNTS tutorial](https://monai.io/research/dints.html).
+[DiNTS tutorial](https://project-monai.github.io/research/dints.html).
diff --git a/docs/source/whatsnew_0_9.md b/docs/source/whatsnew_0_9.md
index 357dc01b35..4b884ebb78 100644
--- a/docs/source/whatsnew_0_9.md
+++ b/docs/source/whatsnew_0_9.md
@@ -7,7 +7,7 @@
- MetaTensor API preview
## MONAI Bundle
-MONAI Bundle format defines portable described of deep learning models ([docs](https://docs.monai.io/en/latest/bundle_intro.html)).
+MONAI Bundle format defines portable described of deep learning models ([docs](https://monai.readthedocs.io/en/latest/bundle_intro.html)).
A bundle includes the critical information necessary during a model development life cycle,
and allows users and programs to understand the purpose and usage of the models.
The key benefits of Bundle and the `monai.bundle` APIs are:
diff --git a/docs/source/whatsnew_1_0.md b/docs/source/whatsnew_1_0.md
index 7e347780bf..91ce13351c 100644
--- a/docs/source/whatsnew_1_0.md
+++ b/docs/source/whatsnew_1_0.md
@@ -17,7 +17,7 @@ For more details about how to use the models, please see [the tutorials](https:/
## Auto3DSeg
-[Auto3DSeg](https://monai.io/apps/auto3dseg.html) is a comprehensive solution for large-scale 3D medical image segmentation.
+[Auto3DSeg](https://project-monai.github.io/apps/auto3dseg.html) is a comprehensive solution for large-scale 3D medical image segmentation.
It leverages the latest advances in MONAI
and GPUs to efficiently develop and deploy algorithms with state-of-the-art performance.
It first analyzes the global information such as intensity, dimensionality, and resolution of the dataset,
@@ -35,7 +35,7 @@ MONAI now includes the federated learning (FL) client algorithm APIs that are ex
for defining an algorithm to be run on any federated learning platform.
[NVIDIA FLARE](https://github.com/NVIDIA/NVFlare), the federated learning platform developed by [NVIDIA](https://www.nvidia.com/en-us/),
has already built [the integration piece](https://github.com/NVIDIA/NVFlare/tree/dev/integration/monai) with these new APIs.
-With [the new federated learning APIs](https://docs.monai.io/en/latest/fl.html), MONAI bundles can seamlessly be extended to a federated paradigm
+With [the new federated learning APIs](https://monai.readthedocs.io/en/latest/fl.html), MONAI bundles can seamlessly be extended to a federated paradigm
and executed using single- or multi-GPU training.
The MONAI FL client also allows computing summary data statistics (e.g., intensity histograms) on the datasets defined in the bundle configs.
These can be shared and visualized on the FL server, for example, using NVIDIA FLARE's federated statistics operators,
@@ -60,8 +60,8 @@ examples](https://github.com/Project-MONAI/tutorials/tree/main/pathology).
This release includes initial components for various popular accelerated MRI reconstruction workflows.
-Many of them are general-purpose tools, for example the [`SSIMLoss`](https://docs.monai.io/en/latest/losses.html?highlight=ssimloss#ssimloss) function.
-Some new functionalities are task-specific, for example [`FastMRIReader`](https://docs.monai.io/en/latest/data.html?highlight=fastmri#monai.apps.reconstruction.fastmri_reader.FastMRIReader).
+Many of them are general-purpose tools, for example the [`SSIMLoss`](https://monai.readthedocs.io/en/latest/losses.html?highlight=ssimloss#ssimloss) function.
+Some new functionalities are task-specific, for example [`FastMRIReader`](https://monai.readthedocs.io/en/latest/data.html?highlight=fastmri#monai.apps.reconstruction.fastmri_reader.FastMRIReader).
For more details, please see [this tutorial](https://github.com/Project-MONAI/tutorials/tree/main/reconstruction/MRI_reconstruction/unet_demo) for using a baseline model for this task,
and [this tutorial](https://github.com/Project-MONAI/tutorials/tree/main/reconstruction/MRI_reconstruction/varnet_demo) for using a state-of-the-art model.
diff --git a/docs/source/whatsnew_1_1.md b/docs/source/whatsnew_1_1.md
index 71e1951d64..b4b2f4026e 100644
--- a/docs/source/whatsnew_1_1.md
+++ b/docs/source/whatsnew_1_1.md
@@ -52,7 +52,7 @@ data in sliding-window inference. For more details about how to enable it, pleas
## New models in MONAI Model Zoo
-New pretrained models are being created and released [in the Model Zoo](https://monai.io/model-zoo.html).
+New pretrained models are being created and released [in the Model Zoo](https://project-monai.github.io/model-zoo.html).
Notably,
- The `mednist_reg` model demonstrates how to build image registration workflows in MONAI bundle
diff --git a/docs/source/whatsnew_1_2.md b/docs/source/whatsnew_1_2.md
index 618eac95ec..a5ea7d3165 100644
--- a/docs/source/whatsnew_1_2.md
+++ b/docs/source/whatsnew_1_2.md
@@ -73,5 +73,5 @@ cropping transforms into a single operation. This allows MONAI to reduce the num
Lazy Resampling pipelines can use a mixture of MONAI and non-MONAI transforms, so
should work with almost all existing pipelines simply by setting `lazy=True`
on MONAI `Compose` instances. See the
-[Lazy Resampling topic](https://docs.monai.io/en/stable/lazy_resampling.html)
+[Lazy Resampling topic](https://monai.readthedocs.io/en/stable/lazy_resampling.html)
in the documentation for more details.
diff --git a/monai/apps/auto3dseg/auto_runner.py b/monai/apps/auto3dseg/auto_runner.py
index 28ba2a88f9..d06effcd1a 100644
--- a/monai/apps/auto3dseg/auto_runner.py
+++ b/monai/apps/auto3dseg/auto_runner.py
@@ -87,7 +87,7 @@ class AutoRunner:
tracking Server; MLflow runs will be recorded locally in algorithms' model folder if the value is None.
mlflow_experiment_name: the name of the experiment in MLflow server.
kwargs: image writing parameters for the ensemble inference. The kwargs format follows the SaveImage
- transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage.
+ transform. For more information, check https://monai.readthedocs.io/en/stable/transforms.html#saveimage.
Examples:
@@ -621,7 +621,7 @@ def set_image_save_transform(self, **kwargs: Any) -> AutoRunner:
Args:
kwargs: image writing parameters for the ensemble inference. The kwargs format follows SaveImage
- transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage.
+ transform. For more information, check https://monai.readthedocs.io/en/stable/transforms.html#saveimage.
"""
@@ -631,7 +631,7 @@ def set_image_save_transform(self, **kwargs: Any) -> AutoRunner:
else:
raise ValueError(
f"{extra_args} are not supported in monai.transforms.SaveImage,"
- "Check https://docs.monai.io/en/stable/transforms.html#saveimage for more information."
+ "Check https://monai.readthedocs.io/en/stable/transforms.html#saveimage for more information."
)
return self
diff --git a/monai/apps/auto3dseg/ensemble_builder.py b/monai/apps/auto3dseg/ensemble_builder.py
index b2bea806de..e574baf7c8 100644
--- a/monai/apps/auto3dseg/ensemble_builder.py
+++ b/monai/apps/auto3dseg/ensemble_builder.py
@@ -477,7 +477,7 @@ def _pop_kwargs_to_get_image_save_transform(self, **kwargs):
Args:
kwargs: image writing parameters for the ensemble inference. The kwargs format follows SaveImage
- transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage .
+ transform. For more information, check https://monai.readthedocs.io/en/stable/transforms.html#saveimage .
Returns:
save_image: a dictionary that can be used to instantiate a SaveImage class in ConfigParser.
@@ -525,7 +525,7 @@ def set_image_save_transform(self, **kwargs: Any) -> None:
Args:
kwargs: image writing parameters for the ensemble inference. The kwargs format follows SaveImage
- transform. For more information, check https://docs.monai.io/en/stable/transforms.html#saveimage .
+ transform. For more information, check https://monai.readthedocs.io/en/stable/transforms.html#saveimage .
"""
are_all_args_present, extra_args = check_kwargs_exist_in_class_init(SaveImage, kwargs)
@@ -534,7 +534,7 @@ def set_image_save_transform(self, **kwargs: Any) -> None:
else:
raise ValueError(
f"{extra_args} are not supported in monai.transforms.SaveImage,"
- "Check https://docs.monai.io/en/stable/transforms.html#saveimage for more information."
+ "Check https://monai.readthedocs.io/en/stable/transforms.html#saveimage for more information."
)
def set_num_fold(self, num_fold: int = 5) -> None:
diff --git a/monai/apps/generation/maisi/networks/autoencoderkl_maisi.py b/monai/apps/generation/maisi/networks/autoencoderkl_maisi.py
index 53985cc174..16697204c5 100644
--- a/monai/apps/generation/maisi/networks/autoencoderkl_maisi.py
+++ b/monai/apps/generation/maisi/networks/autoencoderkl_maisi.py
@@ -128,7 +128,7 @@ class MaisiConvolution(nn.Module):
print_info: Whether to print information.
save_mem: Whether to clean CUDA cache in order to save GPU memory, default to `True`.
Additional arguments for the convolution operation.
- https://docs.monai.io/en/stable/networks.html#convolution
+ https://monai.readthedocs.io/en/stable/networks.html#convolution
"""
def __init__(
diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py
index 9526eceddb..5b95441d51 100644
--- a/monai/bundle/workflows.py
+++ b/monai/bundle/workflows.py
@@ -78,7 +78,7 @@ def __init__(
if isinstance(meta_file, str) and not os.path.isfile(meta_file):
logger.error(
f"Cannot find the metadata config file: {meta_file}. "
- "Please see: https://docs.monai.io/en/stable/mb_specification.html"
+ "Please see: https://monai.readthedocs.io/en/stable/mb_specification.html"
)
meta_file = None
if isinstance(meta_file, list):
@@ -86,7 +86,7 @@ def __init__(
if not os.path.isfile(f):
logger.error(
f"Cannot find the metadata config file: {f}. "
- "Please see: https://docs.monai.io/en/stable/mb_specification.html"
+ "Please see: https://monai.readthedocs.io/en/stable/mb_specification.html"
)
meta_file = None
@@ -363,7 +363,7 @@ class ConfigWorkflow(BundleWorkflow):
Specification for the config-based bundle workflow.
Standardized the `initialize`, `run`, `finalize` behavior in a config-based training, evaluation, or inference.
Before `run`, we add bundle root directory to Python search directories automatically.
- For more information: https://docs.monai.io/en/latest/mb_specification.html.
+ For more information: https://monai.readthedocs.io/en/latest/mb_specification.html.
Args:
config_file: filepath of the config file, if this is a list of file paths, their contents will be merged in order.
diff --git a/monai/config/deviceconfig.py b/monai/config/deviceconfig.py
index 05842245ce..aa1f2a0b53 100644
--- a/monai/config/deviceconfig.py
+++ b/monai/config/deviceconfig.py
@@ -111,7 +111,7 @@ def print_config(file=sys.stdout):
print(f"{k} version: {v}", file=file, flush=True)
print("\nFor details about installing the optional dependencies, please visit:", file=file, flush=True)
print(
- " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+ " https://monai.readthedocs.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
file=file,
flush=True,
)
diff --git a/monai/networks/nets/segresnet_ds.py b/monai/networks/nets/segresnet_ds.py
index 8f575f4793..b234ab23ca 100644
--- a/monai/networks/nets/segresnet_ds.py
+++ b/monai/networks/nets/segresnet_ds.py
@@ -236,7 +236,7 @@ class SegResNetDS(nn.Module):
"""
SegResNetDS based on `3D MRI brain tumor segmentation using autoencoder regularization
`_.
- It is similar to https://docs.monai.io/en/stable/networks.html#segresnet, with several
+ It is similar to https://monai.readthedocs.io/en/stable/networks.html#segresnet, with several
improvements including deep supervision and non-isotropic kernel support.
Args:
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index cae2d3cd1a..0628a7fbc4 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -282,7 +282,7 @@ def __call__(self, filename: Sequence[PathLike] | PathLike, reader: ImageReader
raise RuntimeError(
f"{self.__class__.__name__} cannot find a suitable reader for file: {filename}.\n"
" Please install the reader libraries, see also the installation instructions:\n"
- " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies.\n"
+ " https://monai.readthedocs.io/en/latest/installation.html#installing-the-recommended-dependencies.\n"
f" The current registered: {self.readers}.\n{msg}"
)
img_array: NdarrayOrTensor
@@ -519,7 +519,7 @@ def __call__(
raise RuntimeError(
f"{self.__class__.__name__} cannot find a suitable writer for {filename}.\n"
" Please install the writer libraries, see also the installation instructions:\n"
- " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies.\n"
+ " https://monai.readthedocs.io/en/latest/installation.html#installing-the-recommended-dependencies.\n"
f" The current registered writers for {self.output_ext}: {self.writers}.\n{msg}"
)
diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py
index 0c1e484739..1208a339dc 100644
--- a/monai/transforms/spatial/array.py
+++ b/monai/transforms/spatial/array.py
@@ -2023,7 +2023,7 @@ def __init__(
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
When `USE_COMPILED` is `True`, this argument uses
``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
- See also: https://docs.monai.io/en/stable/networks.html#grid-pull (experimental).
+ See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
When it's an integer, the numpy (cpu tensor)/cupy (cuda tensor) backends will be used
and the value represents the order of the spline interpolation.
See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
@@ -2031,7 +2031,7 @@ def __init__(
Padding mode for outside grid values. Defaults to ``"border"``.
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
When `USE_COMPILED` is `True`, this argument uses an integer to represent the padding mode.
- See also: https://docs.monai.io/en/stable/networks.html#grid-pull (experimental).
+ See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
When `mode` is an integer, using numpy/cupy backends, this argument accepts
{'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}.
See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
@@ -2075,7 +2075,7 @@ def __call__(
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
When `USE_COMPILED` is `True`, this argument uses
``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
- See also: https://docs.monai.io/en/stable/networks.html#grid-pull (experimental).
+ See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
When it's an integer, the numpy (cpu tensor)/cupy (cuda tensor) backends will be used
and the value represents the order of the spline interpolation.
See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
@@ -2083,7 +2083,7 @@ def __call__(
Padding mode for outside grid values. Defaults to ``self.padding_mode``.
See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
When `USE_COMPILED` is `True`, this argument uses an integer to represent the padding mode.
- See also: https://docs.monai.io/en/stable/networks.html#grid-pull (experimental).
+ See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
When `mode` is an integer, using numpy/cupy backends, this argument accepts
{'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}.
See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
diff --git a/monai/utils/module.py b/monai/utils/module.py
index 7bbbb4ab1e..a64f73cd6b 100644
--- a/monai/utils/module.py
+++ b/monai/utils/module.py
@@ -195,7 +195,7 @@ def load_submodules(
except ImportError as e:
msg = (
"\nMultiple versions of MONAI may have been installed?\n"
- "Please see the installation guide: https://docs.monai.io/en/stable/installation.html\n"
+ "Please see the installation guide: https://monai.readthedocs.io/en/stable/installation.html\n"
) # issue project-monai/monai#5193
raise type(e)(f"{e}\n{msg}").with_traceback(e.__traceback__) from e # raise with modified message
@@ -405,7 +405,7 @@ def __init__(self, *_args, **_kwargs):
_default_msg = (
f"{msg}."
+ "\n\nFor details about installing the optional dependencies, please visit:"
- + "\n https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies"
+ + "\n https://monai.readthedocs.io/en/latest/installation.html#installing-the-recommended-dependencies"
)
if tb is None:
self._exception = OptionalImportError(_default_msg)
diff --git a/monai/utils/tf32.py b/monai/utils/tf32.py
index 81f56477bb..ad5918a34a 100644
--- a/monai/utils/tf32.py
+++ b/monai/utils/tf32.py
@@ -66,7 +66,7 @@ def detect_default_tf32() -> bool:
warnings.warn(
f"Environment variable `{name} = {override_val}` is set.\n"
f" This environment variable may enable TF32 mode accidentally and affect precision.\n"
- f" See https://docs.monai.io/en/latest/precision_accelerating.html#precision-and-accelerating"
+ f" See https://monai.readthedocs.io/en/latest/precision_accelerating.html#precision-and-accelerating"
)
may_enable_tf32 = True
diff --git a/setup.cfg b/setup.cfg
index b3949213c2..ab03b906c1 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -2,7 +2,7 @@
name = monai
author = MONAI Consortium
author_email = monai.contact@gmail.com
-url = https://monai.io/
+url = https://project-monai.github.io/
description = AI Toolkit for Healthcare Imaging
long_description = file:README.md
long_description_content_type = text/markdown; charset=UTF-8
@@ -11,7 +11,7 @@ license = Apache License 2.0
license_files =
LICENSE
project_urls =
- Documentation=https://docs.monai.io/
+ Documentation=https://monai.readthedocs.io/
Bug Tracker=https://github.com/Project-MONAI/MONAI/issues
Source Code=https://github.com/Project-MONAI/MONAI
classifiers =
diff --git a/tests/profile_subclass/README.md b/tests/profile_subclass/README.md
index de16ef2d91..45d97d3f7e 100644
--- a/tests/profile_subclass/README.md
+++ b/tests/profile_subclass/README.md
@@ -12,7 +12,7 @@ pip install snakeviz # for viewing the cProfile results
```
./runtests.sh --build # from monai's root directory
```
-or follow the installation guide (https://docs.monai.io/en/latest/installation.html)
+or follow the installation guide (https://monai.readthedocs.io/en/latest/installation.html)
### Profiling the task of adding two MetaTensors
```bash