Update project_urls. (#2274)

.github/ISSUE_TEMPLATE/config.yml

@@ -1,5 +1,5 @@
 blank_issues_enabled: false
 contact_links:
   - name: Questions and Discussions
-    url: https://github.com/awslabs/gluon-ts/discussions
+    url: https://github.com/awslabs/gluonts/discussions
     about: Use GitHub Discussions to ask and answer questions, exchange ideas, and share learning.

.github/workflows/test_release_unix_nightly.yml

@@ -21,7 +21,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Clone and install dependencies
       run: |
-        git clone https://github.com/awslabs/gluon-ts --branch $(curl https://api.github.com/repos/awslabs/gluon-ts/releases/latest | grep tag_name | cut -d : -f 2,3 | tr -d \"\ | tr -d \,\ )
+        git clone https://github.com/awslabs/gluonts --branch $(curl https://api.github.com/repos/awslabs/gluonts/releases/latest | grep tag_name | cut -d : -f 2,3 | tr -d \"\ | tr -d \,\ )
         cd gluon-ts
         python -m pip install pip==20.2
         pip install mxnet~=1.8.0

.github/workflows/test_release_win32_nightly.yml

@@ -21,9 +21,9 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Clone and install dependencies
       run: |
-        $tmp=(Invoke-WebRequest -Uri https://api.github.com/repos/awslabs/gluon-ts/releases/latest).Content | ConvertFrom-Json | Select-Object tag_name
+        $tmp=(Invoke-WebRequest -Uri https://api.github.com/repos/awslabs/gluonts/releases/latest).Content | ConvertFrom-Json | Select-Object tag_name
         $tmp=$tmp.psobject.properties.value.trim() 
-        git clone https://github.com/awslabs/gluon-ts --branch $tmp
+        git clone https://github.com/awslabs/gluonts --branch $tmp
         cd gluon-ts
         python -m pip install -U pip
         pip install mxnet~=1.7.0

CONTRIBUTING.md

@@ -8,13 +8,13 @@ information to effectively respond to your bug report or contribution.
 
 ## Questions and discussion topics
 
-Questions and discussion topics can be proposed using [discussions](https://github.com/awslabs/gluon-ts/discussions).
+Questions and discussion topics can be proposed using [discussions](https://github.com/awslabs/gluonts/discussions).
 
 ## Reporting Bugs/Feature Requests
 
-We welcome you to use the GitHub [issue tracker](https://github.com/awslabs/gluon-ts/issues/new/choose) to report bugs or suggest features.
+We welcome you to use the GitHub [issue tracker](https://github.com/awslabs/gluonts/issues/new/choose) to report bugs or suggest features.
 
-When filing an issue, please check [existing open](https://github.com/awslabs/gluon-ts/issues), or [recently closed](https://github.com/awslabs/gluon-ts/issues?utf8=%E2%9C%93&q=is%3Aissue%20is%3Aclosed%20), issues to make sure somebody else hasn't already
+When filing an issue, please check [existing open](https://github.com/awslabs/gluonts/issues), or [recently closed](https://github.com/awslabs/gluonts/issues?utf8=%E2%9C%93&q=is%3Aissue%20is%3Aclosed%20), issues to make sure somebody else hasn't already
 reported the issue. Please try to include as much information as you can. Details like these are incredibly useful:
 
 * A reproducible test case or series of steps
@@ -49,8 +49,8 @@ Please refer to the [documentation](https://ts.gluon.ai/dev/community/devsetup.h
 ## Finding contributions to work on
 
 Looking at the existing issues is a great way to find something to contribute on: issues labeled with
-['good first issue'](https://github.com/awslabs/gluon-ts/labels/good%20first%20issue) or
-['help wanted'](https://github.com/awslabs/gluon-ts/labels/help%20wanted)
+['good first issue'](https://github.com/awslabs/gluonts/labels/good%20first%20issue) or
+['help wanted'](https://github.com/awslabs/gluonts/labels/help%20wanted)
 are a great place to start.
 
 ## Code of Conduct
@@ -65,6 +65,6 @@ If you discover a potential security issue in this project we ask that you notif
 
 ## Licensing
 
-See the [LICENSE](https://github.com/awslabs/gluon-ts/blob/dev/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
+See the [LICENSE](https://github.com/awslabs/gluonts/blob/dev/LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.
 
 We may ask you to sign a [Contributor License Agreement (CLA)](http://en.wikipedia.org/wiki/Contributor_License_Agreement) for larger changes.

README.md

@@ -3,7 +3,7 @@
 # GluonTS - Probabilistic Time Series Modeling in Python
 
 [![PyPI](https://img.shields.io/pypi/v/gluonts.svg?style=flat-square&color=b75347)](https://pypi.org/project/gluonts/)
-[![GitHub](https://img.shields.io/github/license/awslabs/gluon-ts.svg?style=flat-square&color=df7e66)](./LICENSE)
+[![GitHub](https://img.shields.io/github/license/awslabs/gluonts.svg?style=flat-square&color=df7e66)](./LICENSE)
 [![Static](https://img.shields.io/static/v1?label=docs&message=stable&color=edc775&style=flat-square)](https://ts.gluon.ai/)
 [![Static](https://img.shields.io/static/v1?label=docs&message=dev&color=edc775&style=flat-square)](https://ts.gluon.ai/dev/)
 [![PyPI Downloads](https://img.shields.io/pypi/dm/gluonts?style=flat-square&color=94b594)](https://pypi.org/project/gluonts/)
@@ -69,7 +69,7 @@ centered around the median.
 ## Contributing
 
 If you wish to contribute to the project, please refer to our
-[contribution guidelines](https://github.com/awslabs/gluon-ts/tree/dev/CONTRIBUTING.md).
+[contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md).
 
 ## Citing
 
@@ -115,7 +115,7 @@ in addition to any model-specific references that are relevant for your work:
 
 * [JMLR MLOSS Paper](http://www.jmlr.org/papers/v21/19-820.html)
 * [ArXiv Paper](https://arxiv.org/abs/1906.05264)
-* [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluon-ts/tree/dev/REFERENCES.md): a bibliography.
+* [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluonts/tree/dev/REFERENCES.md): a bibliography.
 
 ### Tutorials and Workshops
 

docs/getting_started/install.md

@@ -13,7 +13,7 @@ GluonTS uses [Semantic Versioning](https://semver.org) for managing versions.
 Since the library is actively developed we use `v0` as the major version.
 
 We plan to release a new minor version at the end of each quarter. The current
-planned releases can be found on [GitHub](https://github.com/awslabs/gluon-ts/milestones).
+planned releases can be found on [GitHub](https://github.com/awslabs/gluonts/milestones).
 
 **Version Guarantees**
 

docs/getting_started/models.md

@@ -51,28 +51,28 @@ NPTS                                                         | Local        | Un
 
 <!-- Links to code -->
 
-[DeepAR_mx]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/deepar/_estimator.py
-[DeepAR_torch]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/torch/model/deepar/estimator.py
-[DeepState]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/deepstate/_estimator.py
-[DeepFactor]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/deep_factor/_estimator.py
-[DeepRenewal]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/renewal/_estimator.py
-[GP]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/gp_forecaster/_estimator.py
-[MQDNN]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/seq2seq/_mq_dnn_estimator.py
-[NBeats]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/n_beats/_estimator.py
-[Rotbaum]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/rotbaum/_estimator.py
-[SAN]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/san/_estimator.py
-[TFT]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/tft/_estimator.py
-[Transformer]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/transformer/_estimator.py
-[WaveNet]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/wavenet/_estimator.py
-[SFF_mx]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/simple_feedforward/_estimator.py
-[SFF_torch]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/torch/model/simple_feedforward/estimator.py
-[DeepVAR]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/deepvar/_estimator.py
-[DeepVARHierarchical]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/deepvar_hierarchical/_estimator.py
-[GPVAR]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/gpvar/_estimator.py
-[LSTNet]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/lstnet/_estimator.py
-[DeepTPP]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/mx/model/tpp/deeptpp/_estimator.py
-[RForecast]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/r_forecast/_predictor.py
-[Prophet]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/prophet/_predictor.py
-[NaiveSeasonal]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/seasonal_naive/_predictor.py
-[Naive2]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/naive_2/_predictor.py
-[NPTS]: https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/npts/_predictor.py
+[DeepAR_mx]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepar/_estimator.py
+[DeepAR_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/deepar/estimator.py
+[DeepState]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepstate/_estimator.py
+[DeepFactor]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deep_factor/_estimator.py
+[DeepRenewal]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/renewal/_estimator.py
+[GP]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/gp_forecaster/_estimator.py
+[MQDNN]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/seq2seq/_mq_dnn_estimator.py
+[NBeats]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/n_beats/_estimator.py
+[Rotbaum]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/rotbaum/_estimator.py
+[SAN]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/san/_estimator.py
+[TFT]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/tft/_estimator.py
+[Transformer]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/transformer/_estimator.py
+[WaveNet]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/wavenet/_estimator.py
+[SFF_mx]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/simple_feedforward/_estimator.py
+[SFF_torch]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/torch/model/simple_feedforward/estimator.py
+[DeepVAR]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepvar/_estimator.py
+[DeepVARHierarchical]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/deepvar_hierarchical/_estimator.py
+[GPVAR]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/gpvar/_estimator.py
+[LSTNet]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/lstnet/_estimator.py
+[DeepTPP]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/mx/model/tpp/deeptpp/_estimator.py
+[RForecast]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/r_forecast/_predictor.py
+[Prophet]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/prophet/_predictor.py
+[NaiveSeasonal]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/seasonal_naive/_predictor.py
+[Naive2]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/naive_2/_predictor.py
+[NPTS]: https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/npts/_predictor.py

examples/GluonTS_SageMaker_SDK_Tutorial.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install --upgrade mxnet==1.6  git+https://github.com/awslabs/gluon-ts.git#egg=gluonts[dev]"
+    "!pip install --upgrade mxnet==1.6  git+https://github.com/awslabs/gluonts.git#egg=gluonts[dev]"
    ]
   },
   {
@@ -364,7 +364,7 @@
    "outputs": [],
    "source": [
     "requirements_dot_txt_file_name = \"requirements.txt\"\n",
-    "requirements_dot_txt_file_content = \"git+https://github.com/awslabs/gluon-ts.git\""
+    "requirements_dot_txt_file_content = \"git+https://github.com/awslabs/gluonts.git\""
    ]
   },
   {

examples/dockerfiles/README.md

@@ -13,7 +13,7 @@ docker build ../.. -f <dockerfile>
 ```
 
 The built images are compatible with sagemaker.
-For more information about the shell and the available params, see the [shell documentation](https://github.com/awslabs/gluon-ts/tree/dev/src/gluonts/shell).
+For more information about the shell and the available params, see the [shell documentation](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/shell).
 
 
 ## How to choose between the dockerfiles

setup.py

@@ -222,7 +222,11 @@ def run(self):
     ),
     long_description=read("README.md"),
     long_description_content_type="text/markdown",
-    url="https://github.com/awslabs/gluon-ts",
+    url="https://github.com/awslabs/gluonts/",
+    project_urls={
+        "Documentation": "https://ts.gluon.ai/stable/",
+        "Source Code": "https://github.com/awslabs/gluonts/",
+    },
     author="Amazon",
     author_email="[email protected]",
     maintainer_email="[email protected]",

src/gluonts/dataset/repository/_gp_copula_2019.py

@@ -89,7 +89,7 @@ class GPCopulaDataset(NamedTuple):
         name="wiki-rolling_nips",
         # That file lives on GitHub Large file storage (lfs). We need to use
         # the exact link, otherwise it will only open the lfs pointer file.
-        url="https://github.com/awslabs/gluon-ts/raw/1553651ca1fca63a16e012b8927bd9ce72b8e79e/datasets/wiki-rolling_nips.tar.gz",
+        url="https://github.com/awslabs/gluonts/raw/1553651ca1fca63a16e012b8927bd9ce72b8e79e/datasets/wiki-rolling_nips.tar.gz",
         num_series=9535,
         prediction_length=30,
         freq="D",

src/gluonts/mx/batchify.py

@@ -75,7 +75,7 @@ def stack(
         data = _pad_arrays(data, axis=0, is_right_pad=is_right_pad)
     if isinstance(data[0], mx.nd.NDArray):
         # TODO: think about using shared context NDArrays
-        #  https://github.com/awslabs/gluon-ts/blob/42bee73409f801e7bca73245ca21cd877891437c/src/gluonts/dataset/parallelized_loader.py#L157
+        #  https://github.com/awslabs/gluonts/blob/42bee73409f801e7bca73245ca21cd877891437c/src/gluonts/dataset/parallelized_loader.py#L157
         return mx.nd.stack(*data)
     if isinstance(data[0], np.ndarray):
         data = mx.nd.array(data, dtype=dtype, ctx=ctx)

src/gluonts/nursery/QRX-Wrapped-M5-Accuracy-Solution/0. README.md

@@ -1,9 +1,9 @@
 # Introduction
 / Hilaf Hasson
 
-This work wraps the [winning solution in the M5 Accuracy competition](https://github.com/Mcompetitions/M5-methods/tree/master/Code%20of%20Winning%20Methods/A1) (by YeonJun Im) with [QRX](https://github.com/awslabs/gluon-ts/blob/dev/src/gluonts/model/rotbaum/_model.py).
+This work wraps the [winning solution in the M5 Accuracy competition](https://github.com/Mcompetitions/M5-methods/tree/master/Code%20of%20Winning%20Methods/A1) (by YeonJun Im) with [QRX](https://github.com/awslabs/gluonts/blob/dev/src/gluonts/model/rotbaum/_model.py).
 
-QRX is an algorithm that takes a point forecaster as input and outputs a probabilistic forecaster. I designed it originally for the purpose of being the engine for [Rotbaum](https://github.com/awslabs/gluon-ts/tree/dev/src/gluonts/model/rotbaum), the flagship tree-based algorithm in [gluonts](https://github.com/awslabs/gluon-ts).
+QRX is an algorithm that takes a point forecaster as input and outputs a probabilistic forecaster. I designed it originally for the purpose of being the engine for [Rotbaum](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/model/rotbaum), the flagship tree-based algorithm in [gluonts](https://github.com/awslabs/gluonts).
 
 The rough idea behind the logic of QRX is that it uses the point forecaster's predictions to create bins of true values that are being sampled at inference. We will outline the exact algorithm in a future paper.
 

src/gluonts/nursery/sagemaker_sdk/estimator.py

@@ -113,7 +113,7 @@ class GluonTSFramework(Framework):
     defined by the "entry_point" argument of the :meth:`GluonTSFramework.run`
     method. Technical documentation on preparing GluonTSFramework scripts for
     SageMaker training and using the GluonTsFramework Estimator is available on
-    the project home-page: https://github.com/awslabs/gluon-ts. See
+    the project home-page: https://github.com/awslabs/gluonts. See
     how_to_notebooks for examples of how to use this SDK.
 
     Parameters

src/gluonts/nursery/tsbench/README.md

@@ -35,8 +35,8 @@ In order to use the code in this repository, you should first clone the GluonTS
 go into the directory of this project:
 
 ```bash
-git clone [email protected]:awslabs/gluon-ts.git
-cd gluon-ts/src/gluonts/nursery/tsbench
+git clone [email protected]:awslabs/gluonts.git
+cd gluonts/src/gluonts/nursery/tsbench
 ```
 
 Then, in the root of the repository, you can install all dependencies via

src/gluonts/nursery/tsbench/pyproject.toml

@@ -15,7 +15,7 @@ catch22 = "^0.2.0"
 click = "^7.1.2"
 fastparquet = "^0.6.1"
 fbprophet = "^0.7.1"
-gluonts = {git = "https://github.com/awslabs/gluon-ts.git", rev = "7c94c1149875f6ad2e0d7b0a6bcee952f14d3fb1"}
+gluonts = {git = "https://github.com/awslabs/gluonts.git", rev = "7c94c1149875f6ad2e0d7b0a6bcee952f14d3fb1"}
 holidays = "^0.11.1"
 lightkit = "^0.3.6"
 mxnet = "1.8.0.post0"

test/mx/test_mx_rolling.py

@@ -68,7 +68,7 @@ def test_dynamic_integration(
 ):
     """
     Trains an estimator on a rolled dataset with dynamic features.
-    Tests https://github.com/awslabs/gluon-ts/issues/1390
+    Tests https://github.com/awslabs/gluonts/issues/1390
     """
     train_ds = create_dynamic_dataset(
         target_start, train_length, num_dynamic_feat