Comments (2)
Hi,
Docs updates are in WIP mode for now, nevertheless the API is quite strainforward.
If you are using catalyst through notebook-interface, like here, all you need to specify are:
main_metric
and minimize_metric
. For example, in "Setup 4 - training with additional metrics" you can run
# model training
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
callbacks=[
PrecisionCallback(),
EarlyStoppingCallback(patience=2, min_delta=0.01)
],
logdir=logdir,
n_epochs=n_epochs,
main_metric="precision01", # "precision03" or "precision05"
minimize_metric=False,
verbose=True
)
and checkpointer will save model based on "precision01" (aka accuracy) metric.
By default, SupervisedRunner
(and SupervisedExperiment
) have checkpointer turned on during train stage.
If you are using config-interface, like here, all you need to do - specify state_params
here: main_metric
and minimize_metric
again.
All the experiment checkpoints are located at {your_logdir}/checkpoints/*
. By default, catalyst additionally saves best checkpoint (based on chosen metric) - best.pth
and last one - last.pth
.
FYI, source code for train
method is here and checkpointer logic through CheckpointCallback
is here.
from catalyst.
Awesome, thank you!
from catalyst.
Related Issues (20)
- Dependency `packaging` not specified — ModuleNotFoundError HOT 2
- os.environ["CUDA_VISIBLE_DEVICES"] = "" does not use CPU HOT 5
- comprehensive classification example HOT 3
- `runner.evaluate_loader` does not work with DataParallelEngine HOT 4
- How to enforce WandbLogger::log_artifact to be invoked? HOT 2
- Idea: offload all launch-related code Accelerate HOT 2
- Custom loader stages HOT 1
- Crashes on 2xT4 GPUs HOT 3
- utils.process_model_params HOT 2
- Multi Criterion Training HOT 4
- No utils.initialization file HOT 3
- DynamicBalanceClassSampler example does not use sampler HOT 1
- KWArg fp16 does not exist HOT 6
- Replace getters with properties HOT 1
- Bug in catalyst/callbacks/backward.py if the grad_clip_fn value is set. HOT 2
- Bug in catalyst/callbacks/backward.py if the grad_clip_fn value is set. HOT 1
- Bug in catalyst/callbacks/backward.py if the grad_clip_fn value is set. HOT 1
- `DataParallelEngine.prepare_model` missing `device_placement` kwarg. HOT 2
- Columns and DataType Not Explicitly Set on line 96 of report.py HOT 2
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from catalyst.