v1.3.0

• Various bug fixes and improvements

v1.2.2

• --xp.debug.catch_post_mortem flag now works externally as well
• Fixed a problem with init_transforms that affected optim.decay_to_init
• Further removed deprecated summaries protocol
• Fixes regarding grain workers and non-thread-safe imports such as einops
• Lifted jit restriction for merging auto_state.sum_field
• Added support for ImmutableDict inside konfig
• Added ShowTexts summary
• Make grain an optional dependency on Windows
• Reduced logging noise
• Several other minor bugfixes

v1.2.1

• Minor bug fixes.

v1.2.0

• Fix kd.sharding.FSDPSharding() to supports jax.ShapeDtypeStruct
• kd.data:
  • kd.data.py.PyGrainPipeline supports direct indexing (ds[0]).
  • kd.data.py.HuggingFace supports
• Typeguard / typechecking
• +various changes and improvements

v1.1.1

• Restore numpy 1.26 compatibility

v1.1.0

• Add kd.nn.WrapperModule to make a inner-module transparent with
  respect of Flax modules.
• Many other changes...

v1.0.0

• 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_transforms 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...