Record experiment data easily
pip install record_keeper
Easily add loggable information when you write a new function. First, create a list that contains the names of the attributes you want to record ("self.record_these" in the example below).
class YourNewLossFunction: def __init__(self, **kwargs): self.avg_embedding_norm = 0 self.some_other_useful_stat = 0 self.record_these = ["avg_embedding_norm", "some_other_useful_stat"] super().__init__(**kwargs) def compute_loss(self, embeddings, labels): self.avg_embedding_norm = torch.mean(torch.norm(embeddings, p=2, dim=1)) self.some_other_useful_stat = some_cool_function(embeddings)
Then tell RecordKeeper the name of the list to read. RecordKeeper will then log and save all the attributes described in the list.
from torch.utils.tensorboard import SummaryWriter import record_keeper as record_keeper_package pickler_and_csver = record_keeper_package.PicklerAndCSVer(your_folder_for_logs) tensorboard_writer = SummaryWriter(log_dir=your_tensorboard_folder) record_keeper = record_keeper_package.RecordKeeper(tensorboard_writer, pickler_and_csver, ["record_these"]) # Then during training: record_keeper.update_records(your_dict_of_objects, current_iteration)
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