Rewrite PyTorch Ignite integration page (#622)

docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md

@@ -1,6 +1,5 @@
 ---
 title: PyTorch Ignite TensorboardLogger
-displayed_sidebar: mainSidebar
 ---
 
 The [cifar_ignite.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/ignite/cifar_ignite.py) example 

docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md

@@ -1,6 +1,5 @@
 ---
 title: PyTorch Ignite ClearMLLogger
-displayed_sidebar: mainSidebar
 ---
 
 The `ignite` repository contains the [mnist_with_clearml_logger.py](https://github.com/pytorch/ignite/blob/master/examples/contrib/mnist/mnist_with_clearml_logger.py)

docs/integrations/ignite.md

@@ -0,0 +1,146 @@
+---
+title: PyTorch Ignite
+---
+
+[PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in 
+PyTorch. You can integrate ClearML into your code using Ignite’s built-in loggers: [TensorboardLogger](#tensorboardlogger) 
+and [ClearMLLogger](#clearmllogger). 
+
+## TensorboardLogger
+
+ClearML integrates seamlessly with TensorboardLogger, and automatically captures all information logged through the 
+handler: metrics, parameters, images, and gradients.
+
+All you have to do is add two lines of code to your script:
+
+```python
+from clearml import Task
+task = Task.init(task_name="<task_name>", project_name="<project_name>")
+```
+
+This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details, 
+uncommitted code, python environment, all information logged through `TensorboardLogger`, and more. 
+
+Visualize all the captured information in the experiment's page in ClearML's [WebApp](#webapp)
+
+See a code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/ignite/cifar_ignite.py).
+
+## ClearMLLogger
+PyTorch Ignite supports a ClearML Logger to log metrics, text, model/optimizer parameters, plots, and model checkpoints 
+during training and validation. 
+
+Integrate ClearML with the following steps:
+1. Create a `ClearMLLogger` object:
+
+  ```python
+  from ignite.contrib.handlers.clearml_logger import *
+
+  clearml_logger = ClearMLLogger(task_name="ignite", project_name="examples")
+  ```
+
+  This creates a [ClearML Task](../fundamentals/task.md) called `ignite` in the `examples` project, which captures your 
+  script's information, including Git details, uncommitted code, python environment. 
+  
+  You can also pass the following parameters to the `ClearMLLogger` object:
+  * `task_type` – The type of experiment (see [task types](../fundamentals/task.md#task-types)).
+  * `report_freq` – The histogram processing frequency (handles histogram values every X calls to the handler). Affects 
+    `GradsHistHandler` and `WeightsHistHandler` (default: 100).
+  * `histogram_update_freq_multiplier` – The histogram report frequency (report first X histograms and once every X 
+    reports afterwards) (default: 10).
+  * `histogram_granularity` - Histogram sampling granularity (default: 50).
+
+1. Attach the C`learMLLogger` to output handlers to log metrics: 
+
+   ```python 
+   # Attach the logger to the trainer to log training loss 
+   clearml_logger.attach_output_handler(
+       trainer,
+       event_name=Events.ITERATION_COMPLETED(every=100),
+       tag="training",
+       output_transform=lambda loss: {"batchloss": loss},
+   )
+  
+   # Attach the logger to log loss and accuracy for both training and validation
+   for tag, evaluator in [("training metrics", train_evaluator), ("validation metrics", validation_evaluator)]:
+       clearml_logger.attach_output_handler(
+           evaluator,
+           event_name=Events.EPOCH_COMPLETED,
+           tag=tag,
+           metric_names=["loss", "accuracy"],
+           global_step_transform=global_step_from_engine(trainer),
+       )
+   ```
+
+1. Attach the ClearMLLogger object to helper handlers to log experiment outputs. Ignite supports the following helper handlers for ClearML:
+
+   * **ClearMLSaver** - Saves input snapshots as ClearML artifacts.
+   * **GradsHistHandler** and **WeightsHistHandler** - Logs the model's gradients and weights respectively as histograms.
+   * **GradsScalarHandler** and **WeightsScalarHandler** - Logs gradients and weights respectively as scalars.
+   * **OptimizerParamsHandler** - Logs optimizer parameters
+
+  ```python
+  # Attach the logger to the trainer to log model's weights norm
+  clearml_logger.attach(
+      trainer, log_handler=WeightsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)
+  )
+
+  # Attach the logger to the trainer to log model's weights as a histogram 
+  clearml_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=100))
+
+  # Attach the logger to the trainer to log model’s gradients as scalars
+  clearml_logger.attach(
+      trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)
+  )
+
+  #Attach the logger to the trainer to log model's gradients as a histogram    
+  clearml_logger.attach(trainer, log_handler=GradsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=100))
+
+  handler = Checkpoint(
+      {"model": model},
+      ClearMLSaver(),
+      n_saved=1,
+      score_function=lambda e: e.state.metrics["accuracy"],
+      score_name="val_acc",
+      filename_prefix="best",
+      global_step_transform=global_step_from_engine(trainer),
+  )
+  validation_evaluator.add_event_handler(Events.EPOCH_COMPLETED, handler)
+   
+  # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration
+  clearml_logger.attach(
+     trainer,
+     log_handler=OptimizerParamsHandler(optimizer),
+     event_name=Events.ITERATION_STARTED
+  )
+  ```
+   
+Visualize all the captured information in the experiment's page in ClearML's [WebApp](#webapp).
+
+For more information, see the [ignite documentation](https://pytorch.org/ignite/generated/ignite.contrib.handlers.clearml_logger.html). 
+
+See code example [here](https://github.com/pytorch/ignite/blob/master/examples/contrib/mnist/mnist_with_clearml_logger.py)
+
+## WebApp
+
+All the experiment information that ClearML captures can be viewed in the [WebApp](../webapp/webapp_overview.md): 
+
+### Models
+
+View saved model snapshots in the **ARTIFACTS** tab.
+
+![Model snapshots](../img/ignite_artifact.png)
+
+### Scalars 
+
+View the scalars in the experiment's **SCALARS** tab.
+
+![Scalars](../img/examples_cifar_scalars.png)
+
+
+### Debug Samples
+
+ClearML automatically tracks images logged to `TensorboardLogger`. They appear in the experiment's **DEBUG SAMPLES**.
+
+
+![Debug Samples](../img/examples_integration_pytorch_ignite_debug.png)
+

sidebars.js

@@ -66,7 +66,7 @@ module.exports = {
                 'guides/frameworks/lightgbm/lightgbm_example', 'guides/frameworks/matplotlib/matplotlib_example',
                 'guides/frameworks/megengine/megengine_mnist', 'integrations/openmmv', 'integrations/optuna',
                 'integrations/python_fire', 'guides/frameworks/pytorch/pytorch_mnist',
-                {'PyTorch Ignite':['guides/frameworks/pytorch_ignite/integration_pytorch_ignite', 'guides/frameworks/pytorch_ignite/pytorch_ignite_mnist']},
+                'integrations/ignite',
                 'guides/frameworks/pytorch_lightning/pytorch_lightning_example', 'guides/frameworks/scikit-learn/sklearn_joblib_example',
                 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/tensorboardx/tensorboardx', 'guides/frameworks/tensorflow/tensorflow_mnist',
                 'integrations/seaborn', 'integrations/xgboost', 'integrations/yolov5', 'integrations/yolov8'