xtrain 0.3.2

A Flax trainer

XTRAIN: a tiny library for training Flax models.

Design goals:

• Help avoiding boiler-plate code
• Minimal functionality and dependency
• Agnostic to hardware configuration (e.g. GPU->TPU)

General workflow

Step 1: define your model

class MyFlaxModule(nn.Module):
  @nn.compact
  def __call__(self, x):
    ...

Step 2: define loss function

def my_loss_func(batch, prediction):
    x, y_true = batch
    loss = ....
    return loss

Step 3: create an iterator that supplies training data

my_data = zip(sequence_of_inputs, sequence_of_labels)

Step 4: train

# create and initialize a Trainer object
trainer = xtrain.Trainer(
  model = MyFlaxModule(),
  losses = my_loss_func,
  optimizer = optax.adam(1e-4),
)

train_iter = trainer.train(my_data) # returns a iterable object

# iterate the train_iter trains the model
for epoch in range(3):
  for model_out in train_iter:
    pass
  print(train_iter.loss_logs)
  train_iter.reset_loss_logs()

Training data format

• tensowflow Dataset
• torch dataloader
• generator function
• other python iterable that produce numpy data

Checkpointing

train_iter is orbax compatible.

import orbax.checkpoint as ocp
ocp.StandardCheckpointer().save(cp_path, args=ocp.args.StandardSave(train_iter))

Freeze submodule

train_iter.freeze("submodule/Dense_0/kernel")

Simple batch parallelism on multiple device

# Add a new batch dim to you dataset
ds = ds.batch(8)
# create trainer with the Distributed strategy
trainer_iter = xtrain.Trainer(model, losses, optimizer, strategy=xtrain.Distributed).train(ds)

API documentation

https://jiyuuchc.github.io/xtrain/