Skorch

You can use Weights & Biases with Skorch to automatically log the model with the best performance – along with all model performance metrics, the model topology and compute resources after each epoch. Every file saved in wandb_run.dir is automatically logged to W&B servers.
See example run.
Parameters
Parameter
Description
wandb_run: 
wandb.wandb_run.Run
wandb run used to log data.
save_model
bool (default=True)
Whether to save a checkpoint of the best model and upload it to your Run on W&B servers.
keys_ignored
str or list of str (default=None)
Key or list of keys that should not be logged to tensorboard. Note that in addition to the keys provided by the user, keys such as those starting with ‘event_’ or ending on ‘_best’ are ignored by default.
Example Code
We've created a few examples for you to see how the integration works:
​Colab: A simple demo to try the integration
​A step by step guide: to tracking your Skorch model performance
# Install wandb
... pip install wandb
​
import wandb
from skorch.callbacks import WandbLogger
​
# Create a wandb Run
wandb_run = wandb.init()
# Alternative: Create a wandb Run without a W&B account
wandb_run = wandb.init(anonymous="allow")
​
# Log hyper-parameters (optional)
wandb_run.config.update({"learning rate": 1e-3, "batch size": 32})
​
net = NeuralNet(..., callbacks=[WandbLogger(wandb_run)])
net.fit(X, y)
Methods
Method
Description
initialize()
(Re-)Set the initial state of the callback.
on_batch_begin(net[, X, y, training])
Called at the beginning of each batch.
on_batch_end(net[, X, y, training])
Called at the end of each batch.
on_epoch_begin(net[, dataset_train, …])
Called at the beginning of each epoch.
on_epoch_end(net, **kwargs)
Log values from the last history step and save best model
on_grad_computed(net, named_parameters[, X, …])
Called once per batch after gradients have been computed but before an update step was performed.
on_train_begin(net, **kwargs)
Log model topology and add a hook for gradients
on_train_end(net[, X, y])
Called at the end of training.

Catalyst

SimpleTransformers

Last updated 7 months ago

Parameter	Description
wandb_run: wandb.wandb_run.Run	wandb run used to log data.
save_model bool (default=True)	Whether to save a checkpoint of the best model and upload it to your Run on W&B servers.
keys_ignored str or list of str (default=None)	Key or list of keys that should not be logged to tensorboard. Note that in addition to the keys provided by the user, keys such as those starting with ‘event_’ or ending on ‘_best’ are ignored by default.

Method	Description
`initialize`()	(Re-)Set the initial state of the callback.
`on_batch_begin`(net[, X, y, training])	Called at the beginning of each batch.
`on_batch_end`(net[, X, y, training])	Called at the end of each batch.
`on_epoch_begin`(net[, dataset_train, …])	Called at the beginning of each epoch.
`on_epoch_end`(net, **kwargs)	Log values from the last history step and save best model
`on_grad_computed`(net, named_parameters[, X, …])	Called once per batch after gradients have been computed but before an update step was performed.
`on_train_begin`(net, **kwargs)	Log model topology and add a hook for gradients
`on_train_end`(net[, X, y])	Called at the end of training.