Remove outdated trainer argument videos

docs/source-pytorch/common/gradient_accumulation.rst

@@ -9,8 +9,6 @@ effective batch size is increased but there is no memory overhead.
     step, the effective batch size on each device will remain ``N*K`` but right before the ``optimizer.step()``, the gradient sync will make the effective
     batch size as ``P*N*K``. For DP, since the batch is split across devices, the final effective batch size will be ``N*K``.
 
-.. seealso:: :class:`~lightning.pytorch.trainer.trainer.Trainer`
-
 .. testcode::
 
     # DEFAULT (ie: no accumulated grads)

docs/source-pytorch/common/trainer.rst

@@ -239,14 +239,6 @@ Example::
 accumulate_grad_batches
 ^^^^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/accumulate_grad_batches.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/accumulate_grad_batches.mp4"></video>
-
-|
-
 Accumulates gradients over k batches before stepping the optimizer.
 
 .. testcode::
@@ -316,23 +308,18 @@ Example::
 callbacks
 ^^^^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/callbacks.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/callbacks.mp4"></video>
-
-|
-
-Add a list of :class:`~lightning.pytorch.callbacks.callback.Callback`. Callbacks run sequentially in the order defined here
+This argument can be used to add a :class:`~lightning.pytorch.callbacks.callback.Callback` or a list of them.
+Callbacks run sequentially in the order defined here
 with the exception of :class:`~lightning.pytorch.callbacks.model_checkpoint.ModelCheckpoint` callbacks which run
 after all others to ensure all states are saved to the checkpoints.
 
 .. code-block:: python
 
+    # single callback
+    trainer = Trainer(callbacks=PrintCallback())
+
     # a list of callbacks
-    callbacks = [PrintCallback()]
-    trainer = Trainer(callbacks=callbacks)
+    trainer = Trainer(callbacks=[PrintCallback()])
 
 Example::
 
@@ -389,7 +376,7 @@ Default path for logs and weights when no logger or
 :class:`lightning.pytorch.callbacks.ModelCheckpoint` callback passed.  On
 certain clusters you might want to separate where logs and checkpoints are
 stored. If you don't then use this argument for convenience. Paths can be local
-paths or remote paths such as `s3://bucket/path` or 'hdfs://path/'. Credentials
+paths or remote paths such as ``s3://bucket/path`` or ``hdfs://path/``. Credentials
 will need to be set up to use remote filepaths.
 
 .. testcode::
@@ -452,14 +439,6 @@ Number of devices to train on (``int``), which devices to train on (``list`` or
 enable_checkpointing
 ^^^^^^^^^^^^^^^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/checkpoint_callback.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/checkpoint_callback.mp4"></video>
-
-|
-
 By default Lightning saves a checkpoint for you in your current working directory, with the state of your last training epoch,
 Checkpoints capture the exact value of all parameters used by a model.
 To disable automatic checkpointing, set this to `False`.
@@ -545,12 +524,10 @@ gradient_clip_val
 
 Gradient clipping value
 
-- 0 means don't clip.
-
 .. testcode::
 
     # default used by the Trainer
-    trainer = Trainer(gradient_clip_val=0.0)
+    trainer = Trainer(gradient_clip_val=None)
 
 limit_train_batches
 ^^^^^^^^^^^^^^^^^^^
@@ -663,14 +640,6 @@ See Also:
 logger
 ^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/logger.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/logger.mp4"></video>
-
-|
-
 :doc:`Logger <../visualize/loggers>` (or iterable collection of loggers) for experiment tracking. A ``True`` value uses the default ``TensorBoardLogger`` shown below. ``False`` will disable logging.
 
 .. testcode::
@@ -869,14 +838,6 @@ Useful for quickly debugging or trying to overfit on purpose.
 plugins
 ^^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/cluster_environment.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/cluster_environment.mp4"></video>
-
-|
-
 :ref:`Plugins` allow you to connect arbitrary backends, precision libraries, clusters etc. For example:
 
 - :ref:`Checkpoint IO <checkpointing_expert>`
@@ -907,14 +868,6 @@ To define your own behavior, subclass the relevant class and pass it in. Here's
 precision
 ^^^^^^^^^
 
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/precision.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/precision.mp4"></video>
-
-|
-
 Lightning supports either double (64), float (32), bfloat16 (bf16), or half (16) precision training.
 
 Half precision, or mixed precision, is the combined use of 32 and 16 bit floating points to reduce memory footprint during model training. This can result in improved performance, achieving +3X speedups on modern GPUs.

docs/source-pytorch/extensions/callbacks.rst

@@ -7,24 +7,14 @@
 Callback
 ########
 
-.. raw:: html
-
-    <video width="100%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/callbacks.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/callbacks.mp4"></video>
-
-|
-
-A callback is a self-contained program that can be reused across projects.
+Callbacks allow you to add arbitrary self-contained programs to your training.
+At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality.
+It de-couples functionality that does not need to be in the :doc:`lightning module <../common/lightning_module>` and can be shared across projects.
 
 Lightning has a callback system to execute them when needed. Callbacks should capture NON-ESSENTIAL
 logic that is NOT required for your :doc:`lightning module <../common/lightning_module>` to run.
 
-Here's the flow of how the callback hooks are executed:
-
-.. raw:: html
-
-    <video width="100%" max-width="400px" controls autoplay muted playsinline src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/pt_callbacks_mov.m4v"></video>
+A complete list of Callback hooks can be found in :class:`~lightning.pytorch.callbacks.callback.Callback`.
 
 An overall Lightning system should have:
 
@@ -54,19 +44,8 @@ Example:
 We successfully extended functionality without polluting our super clean
 :doc:`lightning module <../common/lightning_module>` research code.
 
------------
-
-********
-Examples
-********
 You can do pretty much anything with callbacks.
 
-- `Add a MLP to fine-tune self-supervised networks <https://lightning-bolts.readthedocs.io/en/latest/callbacks/self_supervised.html#sslonlineevaluator>`_.
-- `Find how to modify an image input to trick the classification result <https://lightning-bolts.readthedocs.io/en/latest/callbacks/vision.html#confused-logit>`_.
-- `Interpolate the latent space of any variational model <https://lightning-bolts.readthedocs.io/en/latest/callbacks/variational.html#latent-dim-interpolator>`_.
-- `Log images to Tensorboard for any model <https://lightning-bolts.readthedocs.io/en/latest/callbacks/vision.html#tensorboard-image-generator>`_.
-
-
 --------------
 
 ******************