---
title: PyTorch Ignite
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[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 `ClearMLLogger` 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/v0.5.0.post2/generated/ignite.handlers.clearml_logger.html).
See code example [here](https://github.com/pytorch/ignite/blob/master/examples/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.
### Scalars
View the scalars in the experiment's **SCALARS** tab.
### Debug Samples
ClearML automatically tracks images logged to `TensorboardLogger`. They appear in the experiment's **DEBUG SAMPLES**.
