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 |
'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 |
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
|