Auto backup/restore model snapshots of deep learning models:
- to/from local filesystem
- to/from remote FTP server
Current version supports only Keras >= 2.2 models. You're welcome to contribute.
pip3 install sizif
FTP Keras checkpoints backup/restore:
from sizif.keras import KerasModelWrapper
from sizif.storage import FTPFileCheckpointsMonitor
# your compiled Keras Model instance
model = build_model()
# Local filesystem snapshots monitor with FTP backup/restore
# Different model architectures should have different version parameter
# other parameters similar to Keras ModelCheckpoint
# See sizif.storage.FileCheckpointsMonitor for local file only backup/restore
cpm = FTPFileCheckpointsMonitor(1,
'weights.{epoch:03d}-vl{val_loss:.3f}-va{val_acc:.3f}.hdf5',
local_folder='/snapshots_local_dir',
remote_folder='/snapshots_ftp_dir',
host='ftp.your-host.com', login='your_ftp_login',
password='your_ftp_password',
die_on_ftperrors=True,
rotate_number=3,
monitor='val_loss',
verbose=1,
save_best_only=False,
save_weights_only=True,
mode='auto',
period=1)
# Keras wrapper, proxies all calls to the model
# except `fit` and `fit_generator` — which are surrounded
# by automated model state backup/recovery
km = KerasModelWrapper(model, cpm)
# all method parameters are proxied to Keras as is except callbacks
# callbacks are extended with `cpm` listener
km.fit_generator(training_set_generator,
epochs=25,
validation_data=test_set_generator,
callbacks=[tboard])See sources for detailed docstrings
- SSH/S3/Dropbox uploading monitors
- Tensorflow/Pytorch models support
python3 -m unittest
- numpy ~> 1.15
- Keras ~> 2.2
This project is released under the MIT license.