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set auto dp if no backend

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William Falcon
2020-01-16 10:25:51 -05:00
parent 0b0f2c01e1
commit cebcd3039c
1 changed files with 25 additions and 3 deletions
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pytorch_lightning/trainer/trainer.py
+25 -3
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@@ -359,15 +359,37 @@ class Trainer(TrainerIOMixin,
checkpoint_callback=checkpoint_callback,
weights_save_path='my/path'
)
amp_level (str): The optimization level to use (O1, O2, etc...).
Check nvidia docs for level (https://nvidia.github.io/apex/amp.html#opt-levels)
Example::
# default used by the Trainer
trainer = Trainer(amp_level='O1')
num_sanity_val_steps (int): Sanity check runs n batches of val before starting the training routine.
This catches any bugs in your validation without having to wait for the first validation check.
The Trainer uses 5 steps by default. Turn it off or modify it here.
Example::
# default used by the Trainer
trainer = Trainer(num_sanity_val_steps=5)
# turn it off
trainer = Trainer(num_sanity_val_steps=0)
truncated_bptt_steps (int): Truncated back prop breaks performs backprop every k steps of a much longer sequence
If this is enabled, your batches will automatically get truncated
and the trainer will apply Truncated Backprop to it. Make sure your batches have a sequence dimension.
`Williams, Ronald J., and Jing Peng. "An efficient gradient-based algorithm for on-line training of recurrent network trajectories."
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7941&rep=rep1&type=pdf>`_
Example::
# default used by the Trainer (ie: disabled)
trainer = Trainer(truncated_bptt_steps=None)
# backprop every 5 steps in a batch
trainer = Trainer(truncated_bptt_steps=5)
"""
# :param int num_sanity_val_steps: How many val steps before a full train loop.
# :param int truncated_bptt_steps: Enables multiple backward passes for each batch.
#
# .. warning:: Following arguments become deprecated and they will be removed in v0.8.0:
# - `gradient_clip`,