Callbacks

Bases: Callback

Weights & Biases Logging callback. To use this callback pip install wandb. Any metric that contains epoch will be plotted with epoch and all the other metrics will be plotted against global_step which is total training steps. You can change the default axis by providing default_step_metric.

Parameters:

Name	Type	Description	Default
`log_model`	`bool`	Whether to upload model artifact to Wandb	`False`
`code_file`	`Optional[str]`	path of the code you want to upload as artifact to Wandb	`None`
`default_step_metric`		Metrics will be plotted against the `default_step_metric`. Default value is `global_step`.	`'global_step'`

from gradsflow.callbacks import WandbCallback
from timm import create_model

cnn = create_model("resnet18", pretrained=False, num_classes=1)
model = Model(cnn)
model.compile()
cb = WandbCallback()
autodataset = None  # create your dataset
model.fit(autodataset, callbacks=cb)

Bases: Callback

Tracks the carbon emissions produced by deep neural networks using CodeCarbon. To use this callback first install codecarbon using pip install codecarbon. For offline use, you must have to specify the country code.

Parameters:

Name	Type	Description	Default
`offline`	`bool`	whether to use internet connection or not. You will have to provide the country code `country_iso_code` for offline use.	`False`
`**kwargs`		passed directly to codecarbon class.	`{}`

Bases: Callback

Comet Logging callback. This callback requires comet-ml to be pre-installed (pip install comet-ml). Automatically log your Experiment to Comet logging platform. You need to provide API key either by setting environment variable COMET_API_KEY or directly pass as an argument to the callback. Checkout the documentation for more examples.

Parameters:

Name	Type	Description	Default
`project_name`	`str`	Name of the Project	`'awesome-project'`
`api_key`	`Optional[str]`	project API key	`os.environ.get('COMET_API_KEY')`
`offline`	`bool`	log experiment offline	`False`

Last update: January 14, 2022