update package internal references

trainlib/datasets/disk.py

@@ -4,8 +4,8 @@ from typing import Any, NamedTuple
 from pathlib import Path
 from zipfile import ZipFile
 
-from mema.dataset import HomogenousDataset
-from mema.domains.disk import DiskDomain
+from trainlib.dataset import HomogenousDataset
+from trainlib.domains.disk import DiskDomain
 
 
 class DiskDataset[T: NamedTuple](HomogenousDataset[Path, bytes, T]):

trainlib/domains/disk.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 from collections.abc import Iterator
 
-from mema.domain import Domain
+from trainlib.domain import Domain
 
 
 class DiskDomain(Domain[Path, bytes]):

trainlib/estimators/rnn.py

@@ -8,10 +8,10 @@ from torch import nn, Tensor
 from torch.optim import Optimizer
 from torch.utils.tensorboard import SummaryWriter
 
-from mema.estimator import Estimator, EstimatorKwargs
-from mema.util.type import OptimizerKwargs
-from mema.util.module import get_grad_norm
-from mema.estimators.tdnn import TDNNLayer
+from trainlib.estimator import Estimator, EstimatorKwargs
+from trainlib.utils.type import OptimizerKwargs
+from trainlib.utils.module import get_grad_norm
+from trainlib.estimators.tdnn import TDNNLayer
 
 logger: logging.Logger = logging.getLogger(__name__)
 

trainlib/trainer.py

@@ -18,12 +18,12 @@ from torch.utils.tensorboard import SummaryWriter
 from trainlib.dataset import BatchedDataset
 from trainlib.estimator import Estimator, EstimatorKwargs
 from trainlib.transform import Transform
-from trainlib.util.type import (
+from trainlib.utils.type import (
     SplitKwargs,
     LoaderKwargs,
     BalanceKwargs,
 )
-from trainlib.util.module import ModelWrapper
+from trainlib.utils.module import ModelWrapper
 
 logger: logging.Logger = logging.getLogger(__name__)
 

trainlib/utils/job.py

@@ -5,7 +5,7 @@ from concurrent.futures import Future, as_completed
 from tqdm import tqdm
 from colorama import Fore, Style
 
-from mema.util.text import color_text
+from trainlib.utils.text import color_text
 
 logger: logging.Logger = logging.getLogger(__name__)
 
@@ -15,6 +15,15 @@ def process_futures(
     desc: str | None = None,
     unit: str | None = None,
 ) -> None:
+    """
+    Wait on futures results in a blocking loop, showing progress with TQDM.
+
+    Parameters:
+        futures: list of futures to wait on
+        desc: description for the TQDM bar
+        unit: unit to display for loops in the TQDM bar
+    """
+
     if desc is None:
         desc = "Awaiting futures"
 

trainlib/utils/type.py

@@ -4,7 +4,7 @@ from collections.abc import Callable, Iterable
 from torch import Tensor
 from torch.utils.data.sampler import Sampler
 
-from mema.dataset import BatchedDataset
+from trainlib.dataset import BatchedDataset
 
 
 class LoaderKwargs(TypedDict, total=False):