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CHANGELOG.md

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Changelog

Unreleased

  • Add kd.nn.WrapperModule to make a inner-module transparent with respect of .

1.0.0 - 2024-11-21

  • kd.kontext.Path now supports tensor slicing. So for example using keys like "interm.tensor[..., 0:10, :, -1]" will now work as expected.

  • kd.nn.interm_property now supports accessing any intermediates from within the model via self.interm.get_by_path('path.to.any.module.__call__[0]').

  • Deprecated: Remove --xp.sweep_info.names= flag. Instead, sweep are unified under --xp.sweep (see: https://kauldron.rtfd.io/en/latest/intro.html#sweeps)

  • Add kd.data.loader.TFData for arbitrary tf.data pipelines

  • Add kd.data.InMemoryPipeline for small datasets that fit in memory

  • Add kd.knn.convert to convert any Flax module to klinen.

  • Add kontext.path_builder_from to dynamically generate keys for the config with auto-complete and static type checking.

  • Add kd.data.BatchSize(XX) util

  • Breaking: Evaluator(run_every=XX) kwarg is removed. To migrate, use Evaluator(run=kd.evals.RunEvery(XX))

  • Added: Eval can now be launched in separate job:

    cfg.evals = {
    'eval_train': kd.evals.Evaluator(
        run=kd.evals.RunEvery(100),  # Run along `train`
    ),
    'eval_eval': kd.evals.Evaluator(
        run=kd.evals.RunXM(),  # Run in a separate `eval` job.
    ),
}

  • New XManager launcher

    xmanager launch third_party/py/kauldron/xm/launch.py -- \
      --cfg=third_party/py/kauldron/examples/mnist_autoencoder.py \
      --cfg.train_ds.batch_size=32 \
      --xp.sweep \
      --xp.platform=a100 \
      --xp.debug.catch_post_mortem

    This unlock many new features:

    • Based on konfig (so everything can be deeply configured).

    • Customize the work-unit directory name, default to {xid}/{wid}-{sweep_kwargs}, for better TensorBoard work-unit names.

    • Sweep on XManager architecture:

      def sweep():
  for platform in ['a100', 'v100']:
    yield {'cfg.xm_job': kxm.Job(platform=platform)}

    • Possibility to launch eval jobs in a separate job

    • ml_python & xreload support for much faster XM iteration cycles

    • New kd-xm colab to quickly launch experiments without even having to open a terminal

  • Changed: removed Checkpointer.partial_initializer and instead added cfg.init_transform which can be used to set multiple transformations for the params of the model (i.e. instances of AbstractPartialLoader).

  • Changed: konfig.imports() are not lazy by default anymore (config don't need to be resolved in with ecolab.adhoc() anymore!)

  • Added:

    • kd.optim: Optimizer / optax utils
    • kd.eval: Eval moved to their separate namespace

  • Changed: Resolved konfig can now use attribute access for dict:

    • Before (still supported): cfg.train_losses['my_loss']
    • After: cfg.train_losses.my_loss

  • Added: kd.nn.set_train_property to change the self.is_training property value inside a model:

    class MyModule(nn.Module):

  @nn.compact
  def __call__(self, x):
    with kd.nn.set_train_property(False):
      x = self.pretrained_encoder(x)

  • Added: kd.nn.ExternalModule(flax_module) to use any external flax modules inside Kauldron.

  • And many, many more changes...

0.1.0 - 2022-01-01

  • Initial release