Tensorboard #138
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Tensorboard #138
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Tensorboard generates a ridiculous amount of event logs... more than 1.2GB of logs per epoch only for a basic transformer model. For instance after 4 epochs: Given that effect (like with a 10-fold cross-validation training), the tensorflow callbacks need to be defined at the application level, so that it is a user choice and not systematically used by the core library. |
lfoppiano
reviewed
May 10, 2022
delft/sequenceLabelling/models.py
Outdated
| @@ -870,10 +870,12 @@ def __init__(self, config, ntags=None, load_pretrained_weights=True, local_path: | |||
| x = Concatenate()([text_embedding_layer, features_embedding_out]) | |||
| x = Dropout(config.dropout)(x) | |||
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| ''' | |||
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Nope I was testing features with architecture having a transformer layer, because it's not working as expected.
I am still working on this branch, I should have put a draft flag !
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Looks good. Indeed, this tool can fill up the disk space faster than docker... 🎉 |
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FYI 1 run of 10-fold crossvalidation weight ... 82GB 😂 |
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This PR setup DeLFT to use tensorboard: logs, callbacks...
The idea is to cover the interesting metrics so that everything is visualized on Tensorboard.
How to use: basically nothing special, start a training and launch:
> tensorboard --logdir logs/summaries(tensorboard is already installed)
Then open http://localhost:6006/
TODO: