If you’re diving into the world of deep learning with PyTorch, look no further than Trainer, a powerful and opinionated framework designed to streamline the model training process. This article will guide you through installation, implementation, and advanced usage to optimize your model training experience.

Installation

To get started with Trainer, you can install it in two convenient ways:

• From GitHub:
  Open your terminal and run the following commands:

git clone https://github.com/coqui-ai/Trainer
cd Trainer
make install

• From PyPI:
  Alternatively, you can install the trainer using pip:

pip install trainer

However, installing from GitHub is preferred as it provides more stable updates.

Implementing and Training a Model

Once installed, you can start implementing your own models by subclassing and overriding functions in the TrainerModel().

Training with Auto-Optimization

Try out the MNIST example to see how to train your models using auto-optimization.

Training with Advanced Optimization

For those craving more control, advanced optimization allows you to define your training loop exactly how you want. Imagine trying to build a sandwich—advanced optimization gives you the ingredients and the freedom to layer them as desired, whereas auto-optimization hands you a premade sandwich. Here’s how you can implement it:

def optimize(self, batch, trainer):
    imgs, _ = batch
    # Sample noise
    z = torch.randn(imgs.shape[0], 100)
    z = z.type_as(imgs)
    
    # Train discriminator
    imgs_gen = self.generator(z)
    logits = self.discriminator(imgs_gen.detach())
    fake = torch.zeros(imgs.size(0), 1)
    fake = fake.type_as(imgs)
    loss_fake = trainer.criterion(logits, fake)
    
    valid = torch.ones(imgs.size(0), 1)
    valid = valid.type_as(imgs)
    logits = self.discriminator(imgs)
    loss_real = trainer.criterion(logits, valid)
    
    loss_disc = (loss_real + loss_fake) / 2
    
    # Step discriminator
    _, _ = self.scaled_backward(loss_disc, None, trainer, trainer.optimizer[0])
    if trainer.total_steps_done % trainer.grad_accum_steps == 0:
        trainer.optimizer[0].step()
        trainer.optimizer[0].zero_grad()
    
    # Train generator
    imgs_gen = self.generator(z)
    valid = torch.ones(imgs.size(0), 1)
    valid = valid.type_as(imgs)
    logits = self.discriminator(imgs_gen)
    loss_gen = trainer.criterion(logits, valid)
    
    # Step generator
    _, _ = self.scaled_backward(loss_gen, None, trainer, trainer.optimizer[1])
    if trainer.total_steps_done % trainer.grad_accum_steps == 0:
        trainer.optimizer[1].step()
        trainer.optimizer[1].zero_grad()
    
    return model_outputs: logits, loss_gen: loss_gen, loss_disc: loss_disc

This code is a bit like a competitive cooking challenge where the judge (discriminator) assesses your dishes (images). By training both the chef (generator) and the judge intelligently, you enhance the quality of your final dish!

Training with Batch Size Finder

The batch size finder can effectively utilize your hardware by searching for the largest batch size that fits. To use this, call:

trainer.fit_with_largest_batch_size(starting_batch_size=2048)

This is a handy tool if maximizing your GPU memory is a priority!

Training with Distributed Data Parallel (DDP)

To engage multi-GPU training effectively, run the following command:

$ python -m trainer.distribute --script pathtoyourtrain.py --gpus 0,1

The choice not to use .spawn() addresses several limitations, ensuring smooth multi-GPU training.

Using Accelerate

For those leveraging the Accelerate library, simply set use_accelerate=True in your TrainingArgs to enable advanced training features.

CUDA_VISIBLE_DEVICES=0,1,2 accelerate launch --multi_gpu --num_processes 3 train_recipe_autoregressive_prompt.py

Adding Callbacks

You can customize your training runs by adding callbacks. Here’s an example of how to provide a callback explicitly for weight reinitialization:

def my_callback(trainer):
    print("My callback was called.")

trainer = Trainer(..., callbacks={'on_init_end': my_callback})
trainer.fit()

Profiling and Experiment Loggers

Create custom profilers by utilizing the Torch profiler for monitoring your training:

import torch

profiler = torch.profiler.profile(
    activities=[
        torch.profiler.ProfilerActivity.CPU,
        torch.profiler.ProfilerActivity.CUDA,
    ],
    schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
    on_trace_ready=torch.profiler.tensorboard_trace_handler('.profiler'),
    record_shapes=True,
    profile_memory=True,
    with_stack=True,
)
prof = trainer.profile_fit(profiler, epochs=1, small_run=64)

Don’t forget to run Tensorboard with the command:

tensorboard --logdir=.profiler

Trainer supports various experiment loggers, enhancing your log-keeping capabilities to suit your specific project needs.

Troubleshooting Tips

If you encounter issues or have questions during your training journey, consider the following:

• Check installation steps for any missed configurations.
• Review the implementation of callbacks to ensure they are correctly set up.
• For multi-GPU issues, ensure the environment variables are configured accurately.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.