candle.ckpt_pytorch_utils.CandleCkptPyTorch

class candle.ckpt_pytorch_utils.CandleCkptPyTorch(gParams, logger='DEFAULT', verbose=True)

PyTorch Callback for CANDLE-compliant Benchmarks to use for checkpointing Creates a JSON file alongside the weights and optimizer checkpoints that includes important metadata, particularly for restarting and tracking complex workflows.

__init__(gParams, logger='DEFAULT', verbose=True)

Parameters:

• logger (Logger) – The logger to use. May be None to disable or “DEFAULT” to use the default.

• verbose (boolean) – If True, more verbose logging Passed to helper_utils.set_up_logger(verbose) for this logger

Methods

__init__(gParams[, logger, verbose])	param Logger logger: The logger to use.
build_model(model_file)
checksum(dir_work)	Simple checksum dispatch dir_work: A pathlib.Path
checksum_file(filename)	Read file, compute checksum, return it as a string.
ckpt_epoch(epoch, metric_value)	The PyTorch training loop should call this each epoch
clean(epoch_now)	Clean old epoch directories
debug(message)
delete(epoch)
disabled(key)	Is this parameter set to False?
enabled(key)	Is this parameter set to True?
info(message)
keep(epoch, epoch_now, kept)	kept: Number of epochs already kept return True if we are keeping this epoch, else False
on_train_end([logs])
param(key, dflt[, type_, allowed])	Pull key from parameters with type checks and conversions
param_allowed(key, value, allowed)	Check that the value is in the list of allowed values If allowed is None, there is no check, simply success
param_type_check(key, value, type_)	Check that value is convertable to given type:
param_type_check_bool(key, value)
param_type_check_float(key, value, type_)
param_type_check_int(key, value, type_)
relpath(p)	If Path p is relative to CWD, relativize it and return it.
report_final()
report_initial()	Simply report that we are ready to run
restart(model[, verbose])	Possibly restarts model from CheckpointCallback according to given settings and the ckpt-info.json
restart_json(directory)
save_check(epoch, direction, metric_value)	Make sure we want to save this epoch based on the model metrics in given logs Also updates epoch_best if appropriate. epoch: The current epoch (just completed) direction: either "+" (metric_value should increase) or "-" (should decrease) metric_value: The current ckpt metric value.
save_check_best(epoch, direction, metric_value)
scan_params(gParams)	Simply translate gParameters into instance fields
set_model(model)	model: A dict with the model {'model':model, 'optimizer':optimizer}
symlink(src, dst)	Like os.symlink, but overwrites dst and logs
write_json(jsonfile, epoch)
write_model(dir_work, epoch)	Do the I/O, report stats dir_work: A pathlib.Path
write_model_backend(model, epoch)