Merge pull request #1126 from jdb78/maintenance/poetry-update

Update new poetry install method

.github/workflows/pypi_release.yml

@@ -24,11 +24,11 @@ jobs:
       - name: Install poetry
         shell: bash
         run: |
-          curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python
-          python -m pip install poetry-dynamic-versioning
+          curl -sSL https://install.python-poetry.org | python3 -
+          python -m pip install poetry-dynamic-versioning[plugin]
 
       - name: Set poetry path variable
-        run: echo "$HOME/.poetry/bin" >> $GITHUB_PATH
+        run: echo "/Users/runner/.local/bin" >> $GITHUB_PATH
 
       - name: Build
         run: |

.github/workflows/test.yml

@@ -43,10 +43,10 @@ jobs:
       - name: Install poetry
         shell: bash
         run: |
-          curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python
+          curl -sSL https://install.python-poetry.org | python3 -
 
       - name: Set poetry path variable
-        run: echo "$HOME/.poetry/bin" >> $GITHUB_PATH
+        run: echo "/Users/runner/.local/bin" >> $GITHUB_PATH
 
       - name: Configure poetry
         shell: bash
@@ -70,15 +70,11 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: poetry install -E "github-actions graph"
+        run: poetry install -E "github-actions graph mqf2"
 
       - name: Install pytorch geometric dependencies
         shell: bash
-        run: poetry run pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.11.0+cpu.html
-
-      - name: Install CP flow dependencies
-        shell: bash
-        run: poetry run pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps
+        run: poetry run pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.1+cpu.html
 
       - name: Run pytest
         shell: bash

CHANGELOG.md

@@ -1,5 +1,16 @@
 # Release Notes
 
+## v0.10.3 Poetry update (07/09/2022)
+
+### Fixed
+
+- Removed pandoc from dependencies as issue with poetry install (#1126)
+- Added metric attributes for torchmetric resulting in better multi-GPU performance (#1126)
+
+### Added
+
+- "robust" encoder method can be customized by setting "center", "lower" and "upper" quantiles (#1126)
+
 ## v0.10.2 Multivariate networks (23/05/2022)
 
 ### Added
@@ -489,7 +500,7 @@ Fix bug where predictions were not correctly logged in case of `decoder_length =
 
 ## v0.2.3 Make pip installable from master branch (23/08/2020)
 
-Update build system requirements to be parsed correctly when installing with `pip install https://github.com/jdb78/pytorch-forecasting/`
+Update build system requirements to be parsed correctly when installing with `pip install git+https://github.com/jdb78/pytorch-forecasting`
 
 ---
 

README.md

@@ -56,7 +56,7 @@ Alternatively, you can install the package via conda
 PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is install from the pytorch channel.
 
 To use the MQF2 loss (multivariate quantile loss), also install
-`pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps`
+`pip install pytorch-forecasting[mqf2]`
 
 # Documentation
 

docs/source/_static/custom.css

@@ -1,22 +1,24 @@
 .container-xl {
   max-width: 4000px;
 }
+
+.bd-content {
+  flex-grow: 1;
+  max-width: 100%;
+}
+
 a.reference.internal.nav-link {
   color: #727991 !important;
 }
 
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-.active > a,
-.active .nav-link,
-.nav-link,
-.navbar-nav > .active > a.nav-link {
-  color: #ee4c2c !important;
+html[data-theme="light"] {
+  --pst-color-primary: #ee4c2c;
 }
+a.nav-link
+{
+  color: #647db6 !important;
+}
+
 a.nav-link[href="https://github.com/jdb78/pytorch-forecasting"]
 {
   color: #ee4c2c !important;

docs/source/conf.py

@@ -144,6 +144,8 @@ def setup(app: Sphinx):
     "github_url": "https://github.com/jdb78/pytorch-forecasting",
     "navbar_end": ["navbar-icon-links.html", "search-field.html"],
     "show_nav_level": 2,
+    "header_links_before_dropdown": 10,
+    "external_links": [{"name": "GitHub", "url": "https://github.com/jdb78/pytorch-forecasting"}],
 }
 
 html_sidebars = {

docs/source/getting-started.rst

@@ -25,7 +25,7 @@ Alternatively, to installl the package via conda:
 PyTorch Forecasting is now installed from the conda-forge channel while PyTorch is install from the pytorch channel.
 
 To use the MQF2 loss (multivariate quantile loss), also install
-`pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps`
+`pip install pytorch-forecasting[mqf2]`
 
 
 Usage

docs/source/index.rst

@@ -51,7 +51,7 @@ To use the MQF2 loss (multivariate quantile loss), also execute
 
 .. code-block::
 
-   pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps
+   pip install pytorch-forecasting[mqf2]
 
 Vist :ref:`Getting started <getting-started>` to learn more about the package and detailled installation instruction.
 The :ref:`Tutorials <tutorials>` section provides guidance on how to use models and implement new ones.
@@ -70,7 +70,6 @@ The :ref:`Tutorials <tutorials>` section provides guidance on how to use models
    contribute
    api
    CHANGELOG
-   GitHub <https://github.com/jdb78/pytorch-forecasting>
 
 
 Indices and tables

docs/source/tutorials/nhits.ipynb

@@ -251,7 +251,7 @@
         "\n",
         "PyTorch Forecasting is flexible enough to use NHiTS with different loss functions, enabling not only point forecasts but also probabilistic ones. Here, we will demonstrate not only a typical quantile regression but a multivariate quantile regression with :py:class:`~pytorch_forecasting.metrics.distributions.MQF2DistributionLoss` which allows calculation sampling consistent paths along a prediction horizon. This allows calculation of quantiles, e.g. of the sum over the prediction horizon while still avoiding the accumulating error problem from auto-regressive methods such as DeepAR. One needs to install an additional package for this quantile function:\n",
         "\n",
-        "``pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps``"
+        "``pip install pytorch-forecasting[mqf2]``"
       ]
     },
     {