From b890e552ed1df9a763c47da7699d0d2404dc840e Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Mon, 31 Jul 2023 15:34:56 +0300 Subject: [PATCH] Rewrite PyTorch Ignite integration page (#622) --- .../integration_pytorch_ignite.md | 1 - .../pytorch_ignite/pytorch_ignite_mnist.md | 1 - docs/integrations/ignite.md | 146 ++++++++++++++++++ sidebars.js | 2 +- 4 files changed, 147 insertions(+), 3 deletions(-) create mode 100644 docs/integrations/ignite.md diff --git a/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md b/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md index 69d54a94..1726a97e 100644 --- a/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md +++ b/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 diff --git a/docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md b/docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md index adc39cad..ea6aab2f 100644 --- a/docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md +++ b/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) diff --git a/docs/integrations/ignite.md b/docs/integrations/ignite.md new file mode 100644 index 00000000..eb2bbaa3 --- /dev/null +++ b/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="", 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) + diff --git a/sidebars.js b/sidebars.js index 2e832a2c..da52e959 100644 --- a/sidebars.js +++ b/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'