PyTorch Ignite TensorboardLogger
The cifar_ignite.py example script integrates ClearML into code that uses PyTorch Ignite.
The example script does the following:
- Trains a neural network on the CIFAR10 dataset for image classification.
- Creates a ClearML Task named
image classification CIFAR10
in theexamples
project. - Calls the
Task.connect
method to track experiment configuration. - Uses
ignite
'sTensorboardLogger
and attaches handlers to it. SeeTensorboardLogger
.
ClearML's automatic logging captures information and outputs logged with TensorboardLogger
.
Hyperparameters
Parameters are explicitly reported to ClearML using the Task.connect
method.
params = {'number_of_epochs': 20, 'batch_size': 64, 'dropout': 0.25, 'base_lr': 0.001, 'momentum': 0.9, 'loss_report': 100}
params = task.connect(params) # enabling configuration override by clearml
The hyperparameter configurations can be viewed in the WebApp in the experiment's CONFIGURATION tab.
Ignite TensorboardLogger
TensorboardLogger
is a handler to log metrics, parameters, and gradients when training a model. When ClearML is integrated
into a script which uses TensorboardLogger
, all information logged through the handler is automatically captured by ClearML.
Scalars
ClearML automatically captures scalars logged through TensorboardLogger
.
View the scalars in the experiment's page in the ClearML Web UI, in SCALARS.
Model Snapshots
ClearML automatically captures the model logged with Torch, and saves it as an artifact.
View saved snapshots in the experiment's ARTIFACTS tab.
To view the model, in the ARTIFACTS tab, click the model name (or download it).
Debug Samples
ClearML automatically tracks images logged to TensorboardLogger. They appear in DEBUG SAMPLES.
Ignite ClearMLLogger
PyTorch Ignite also supports a dedicated ClearMLLogger
handler to log metrics, text, model / optimizer parameters, plots, and model
checkpoints during training and validation.
For more information, see the PyTorch Ignite ClearMLLogger example.