Update documentation links

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:

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,

README.md

@@ -13,7 +13,7 @@
 
 [![premerge](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml/badge.svg?branch=dev)](https://github.com/Project-MONAI/MONAI/actions/workflows/pythonapp.yml)
 [![postmerge](https://img.shields.io/github/checks-status/project-monai/monai/dev?label=postmerge)](https://github.com/Project-MONAI/MONAI/actions?query=branch%3Adev)
-[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://docs.monai.io/en/latest/)
+[![Documentation Status](https://readthedocs.org/projects/monai/badge/?version=latest)](https://monai.readthedocs.io/en/latest/)
 [![codecov](https://codecov.io/gh/Project-MONAI/MONAI/branch/dev/graph/badge.svg?token=6FTC7U1JJ4)](https://codecov.io/gh/Project-MONAI/MONAI)
 [![monai Downloads Last Month](https://assets.piptrends.com/get-last-month-downloads-badge/monai.svg 'monai Downloads Last Month by pip Trends')](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: <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>

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)
 ![coplenet](../images/coplenet.png)
 
 ### 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)
 
 ![LAMP UNet](../images/unet-pipe.png)
 
 ### 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).
 
 ![DiNTS](../images/dints-overview.png)
 

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
 

docs/source/index.rst

@@ -85,15 +85,15 @@ 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.
 
 
 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

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.
@@ -262,34 +262,34 @@ 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
 
 ![federated-learning](../images/federated.svg)
 
-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.
 
 ## Auto3dseg
 
 ![auto3dseg](../images/auto3dseg.png)
 
-[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,

docs/source/whatsnew_0_6.md

@@ -42,7 +42,7 @@ The following illustrates target body organs that are segmentation in this tutor
 ![BTCV_organs](../images/BTCV_organs.png)
 
 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/

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).
 
 ![DiNTS](../images/dints-overview.png)
 

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:

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](../images/auto3dseg.png)
 
-[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).
 ![MRI-reconstruction](../images/mri_recon.png)
 
 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.

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

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.