ci, docs: add linkcheck job, fix links

.github/workflows/docs.yml

@@ -3,11 +3,13 @@ name: Build docs
 on:
   push:
     branches: [master]
+  pull_request:
   release:
     types: [published]
 
 jobs:
   build-deploy:
+    if: github.event_name == 'push'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
@@ -34,3 +36,23 @@ jobs:
           publish_dir: docs/build/html
           publish_branch: gh-pages
           commit_message: Deploy pytorch/ignite docs
+
+  linkcheck:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+
+      - uses: actions/cache@v2
+        with:
+          path: ~/.cache/pip
+          key: pip-${{ hashFiles('requirements-dev.txt') }}-${{ hashFiles('docs/requirements.txt') }}
+
+      - name: Install docs deps
+        run: bash .github/workflows/install_docs_deps.sh
+
+      - name: make linkcheck
+        working-directory: ./docs/
+        run: make linkcheck --jobs 2

docs/source/about.rst

@@ -14,12 +14,12 @@ been leading the development.
 
 Governance
 ----------
-PyTorch-Ignite is a [NumFOCUS Affiliated Project](https://www.numfocus.org/) and open-source community effort. 
-Project's affiliation to `PyTorch organization <https://github.com/pytorch>`_ is due to historical reasons and it 
+PyTorch-Ignite is a `NumFOCUS Affiliated Project <https://numfocus.org/>`_ and open-source community effort.
+Project's affiliation to `PyTorch organization <https://github.com/pytorch>`_ is due to historical reasons and it
 has the same implication to the project's governance as for other community projects from
 `PyTorch Ecosystem <https://pytorch.org/ecosystem/>`_.
 
-The decision making process and governance structure of the project is described in the `governance document <governance.html>`_.
+The decision making process and governance structure of the project is described in the :ref:`governance document<PyTorch-Ignite governance>`.
 
 Authors
 -------

docs/source/faq.rst

@@ -82,7 +82,7 @@ flexibility to the user to allow for this:
 
     Events defined by user should inherit from :class:`~ignite.engine.events.EventEnum`
 
-More detailed implementation can be found in `TBPTT Trainer <_modules/ignite/contrib/engines/tbptt.html#create_supervised_tbptt_trainer>`_.
+More detailed implementation can be found in `TBPTT Trainer <./_modules/ignite/contrib/engines/tbptt.html#create_supervised_tbptt_trainer>`_.
 
 
 Gradients accumulation
@@ -503,4 +503,4 @@ Other questions
 
 Other questions and answers can be also found on the github among the issues labeled by
 `question <https://github.com/pytorch/ignite/issues?utf8=%E2%9C%93&q=is%3Aissue+label%3Aquestion+>`_ and on the forum
-`Discuss.PyTorch <https://discuss.pytorch.org/c/ignite>`_, category "Ignite".
+`Discuss.PyTorch <https://discuss.pytorch.org/c/ignite/15>`_, category "Ignite".

ignite/contrib/handlers/lr_finder.py

@@ -270,7 +270,7 @@ def attach(
 
             to_save = {"model": model, "optimizer": optimizer}
             with lr_finder.attach(trainer, to_save=to_save) as trainer_with_lr_finder:
-                trainer_with_lr_finder.run(dataloader)`
+                trainer_with_lr_finder.run(dataloader)
 
         Args:
             trainer: lr_finder is attached to this trainer. Please, keep in mind that all attached handlers

ignite/contrib/handlers/neptune_logger.py

@@ -42,7 +42,7 @@ class NeptuneLogger(BaseLogger):
     Args:
         api_token: Required in online mode. Neputne API token, found on https://neptune.ai.
             Read how to get your API key
-            https://docs.neptune.ai/python-api/tutorials/get-started.html#copy-api-token.
+            https://docs.neptune.ai/security-and-privacy/api-tokens/how-to-find-and-set-neptune-api-token.html.
         project_name: Required in online mode. Qualified name of a project in a form of
            "namespace/project_name" for example "tom/minst-classification".
            If None, the value of NEPTUNE_PROJECT environment variable will be taken.

ignite/contrib/handlers/visdom_logger.py

@@ -30,12 +30,12 @@ class VisdomLogger(BaseLogger):
     """
     VisdomLogger handler to log metrics, model/optimizer parameters, gradients during the training and validation.
 
-    This class requires `visdom <https://github.com/facebookresearch/visdom/>`_ package to be installed:
+    This class requires `visdom <https://github.com/fossasia/visdom/>`_ package to be installed:
 
     .. code-block:: bash
 
 
-        pip install git+https://github.com/facebookresearch/visdom.git
+        pip install git+https://github.com/fossasia/visdom.git
 
     Args:
         server: visdom server URL. It can be also specified by environment variable `VISDOM_SERVER_URL`
@@ -44,7 +44,7 @@ class VisdomLogger(BaseLogger):
             visdom server. Default, `num_workers=1`. If `num_workers=0` and logger uses the main thread. If using
             Python 2.7 and `num_workers>0` the package `futures` should be installed: `pip install futures`
         kwargs: kwargs to pass into
-            `visdom.Visdom <https://github.com/facebookresearch/visdom#visdom-arguments-python-only>`_.
+            `visdom.Visdom <https://github.com/fossasia/visdom#visdom-arguments-python-only>`_.
 
     Note:
         We can also specify username/password using environment variables: VISDOM_USERNAME, VISDOM_PASSWORD
@@ -153,7 +153,7 @@ def __init__(
             raise RuntimeError(
                 "This contrib module requires visdom package. "
                 "Please install it with command:\n"
-                "pip install git+https://github.com/facebookresearch/visdom.git"
+                "pip install git+https://github.com/fossasia/visdom.git"
             )
 
         if num_workers > 0:

ignite/contrib/handlers/wandb_logger.py

@@ -10,7 +10,7 @@
 
 
 class WandBLogger(BaseLogger):
-    """`Weights & Biases <https://app.wandb.ai/>`_ handler to log metrics, model/optimizer parameters, gradients
+    """`Weights & Biases <https://wandb.ai/site>`_ handler to log metrics, model/optimizer parameters, gradients
     during training and validation. It can also be used to log model checkpoints to the Weights & Biases cloud.
 
     .. code-block:: bash
@@ -23,7 +23,7 @@ class WandBLogger(BaseLogger):
     Args:
         args: Positional arguments accepted by `wandb.init`.
         kwargs: Keyword arguments accepted by `wandb.init`.
-            Please see `wandb.init <https://docs.wandb.com/library/init>`_ for documentation of possible parameters.
+            Please see `wandb.init <https://docs.wandb.ai/library/init>`_ for documentation of possible parameters.
 
     Examples:
 

ignite/contrib/metrics/average_precision.py

@@ -18,7 +18,7 @@ def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor)
 
 class AveragePrecision(EpochMetric):
     """Computes Average Precision accumulating predictions and the ground-truth during an epoch
-    and applying `sklearn.metrics.average_precision_score <http://scikit-learn.org/stable/modules/generated/
+    and applying `sklearn.metrics.average_precision_score <https://scikit-learn.org/stable/modules/generated/
     sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score>`_ .
 
     Args:
@@ -27,7 +27,7 @@ class AveragePrecision(EpochMetric):
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
         check_compute_fn (bool): Default False. If True, `average_precision_score
-            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
+            <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
             #sklearn.metrics.average_precision_score>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.
 

ignite/contrib/metrics/precision_recall_curve.py

@@ -19,7 +19,7 @@ def precision_recall_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Te
 class PrecisionRecallCurve(EpochMetric):
     """Compute precision-recall pairs for different probability thresholds for binary classification task
     by accumulating predictions and the ground-truth during an epoch and applying
-    `sklearn.metrics.precision_recall_curve <http://scikit-learn.org/stable/modules/generated/
+    `sklearn.metrics.precision_recall_curve <https://scikit-learn.org/stable/modules/generated/
     sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>`_ .
 
     Args:
@@ -28,7 +28,7 @@ class PrecisionRecallCurve(EpochMetric):
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
         check_compute_fn (bool): Default False. If True, `precision_recall_curve
-            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
+            <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
             #sklearn.metrics.precision_recall_curve>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.
 

ignite/contrib/metrics/roc_auc.py

@@ -30,7 +30,7 @@ def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) ->
 class ROC_AUC(EpochMetric):
     """Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC)
     accumulating predictions and the ground-truth during an epoch and applying
-    `sklearn.metrics.roc_auc_score <http://scikit-learn.org/stable/modules/generated/
+    `sklearn.metrics.roc_auc_score <https://scikit-learn.org/stable/modules/generated/
     sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_ .
 
     Args:
@@ -39,7 +39,7 @@ class ROC_AUC(EpochMetric):
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
         check_compute_fn (bool): Default False. If True, `roc_curve
-            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#
+            <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#
             sklearn.metrics.roc_auc_score>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.
 
@@ -66,7 +66,7 @@ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: b
 class RocCurve(EpochMetric):
     """Compute Receiver operating characteristic (ROC) for binary classification task
     by accumulating predictions and the ground-truth during an epoch and applying
-    `sklearn.metrics.roc_curve <http://scikit-learn.org/stable/modules/generated/
+    `sklearn.metrics.roc_curve <https://scikit-learn.org/stable/modules/generated/
     sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>`_ .
 
     Args:
@@ -75,7 +75,7 @@ class RocCurve(EpochMetric):
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
         check_compute_fn (bool): Default False. If True, `sklearn.metrics.roc_curve
-            <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
+            <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
             sklearn.metrics.roc_curve>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.