Redco

Latest version: v0.4.23

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0.4.23

* An argument updated in `Trainer.fit()`: `save_every_ckpt` -> `save_ckpt_every_k_epochs`
* Added `params_sharded` and `opt_state_sharded` in `Trainer.__init__()`, for memory saving.

0.4.22

* Simplified argument names for the random key in `loss_fn()` and `pred_fn()`:
* `train_rng`/`pred_rng` -> `rng`

0.4.21

* Accelerated Inference for multi-host, purely data parallel case
* Added optional argument `train_step_fn` in `Trainer` for fully customizing every training step, e.g., per-sample gradient noising for data-private training.
* Slight argument name change in `Deployer.get_lr_schedule_fn()`: `warmup_rate` -> `warmup_ratio`

0.4.20

* Updated data example type support -- can be a `list` of whatever types now, e.g., `examples=[str, str, str, ...]` or `examples=[dict, dict, dict, ...]`
* Updated mixed-precision training -- by setting `compute_dtype`, e.g., `Trainer(compute_dtype=jnp.bfloat16)`.

0.4.19

* Accelerated multi-host running
* Updated WandB login, e.g., `redco.Deployer(wandb_init_kwargs={'project': '...', 'name': '...'})`
* Updated customization of `params_sharding_rules`

0.4.18

Simplified checkpoint loading.

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