testbed/huggingface__accelerate/examples/by_feature/README.md

What are these scripts?

All scripts in this folder originate from the nlp_example.py file, as it is a very simplistic NLP training example using Accelerate with zero extra features.

From there, each further script adds in just one feature of Accelerate, showing how you can quickly modify your own scripts to implement these capabilities.

A full example with all of these parts integrated together can be found in the complete_nlp_example.py script and complete_cv_example.py script.

Adjustments to each script from the base nlp_example.py file can be found quickly by searching for "# New Code #"

Example Scripts by Feature and their Arguments

Base Example (../nlp_example.py)

• Shows how to use Accelerator in an extremely simplistic PyTorch training loop
• Arguments available:
  • mixed_precision, whether to use mixed precision. ("no", "fp16", or "bf16")
  • cpu, whether to train using only the CPU. (yes/no/1/0)

All following scripts also accept these arguments in addition to their added ones.

These arguments should be added at the end of any method for starting the python script (such as python, accelerate launch, python -m torch.distributed.launch), such as:

accelerate launch ../nlp_example.py --mixed_precision fp16 --cpu 0

Checkpointing and Resuming Training (checkpointing.py)

• Shows how to use Accelerator.save_state and Accelerator.load_state to save or continue training
• It is assumed you are continuing off the same training script
• Arguments available:
  • checkpointing_steps, after how many steps the various states should be saved. ("epoch", 1, 2, ...)
  • output_dir, where saved state folders should be saved to, default is current working directory
  • resume_from_checkpoint, what checkpoint folder to resume from. ("epoch_0", "step_22", ...)

These arguments should be added at the end of any method for starting the python script (such as python, accelerate launch, python -m torch.distributed.launch), such as:

(Note, resume_from_checkpoint assumes that we've ran the script for one epoch with the --checkpointing_steps epoch flag)

accelerate launch ./checkpointing.py --checkpointing_steps epoch output_dir "checkpointing_tutorial" --resume_from_checkpoint "checkpointing_tutorial/epoch_0"

Cross Validation (cross_validation.py)

• Shows how to use Accelerator.free_memory and run cross validation efficiently with datasets.
• Arguments available:
  • num_folds, the number of folds the training dataset should be split into.

These arguments should be added at the end of any method for starting the python script (such as python, accelerate launch, python -m torch.distributed.launch), such as:

accelerate launch ./cross_validation.py --num_folds 2

Experiment Tracking (tracking.py)

• Shows how to use Accelerate.init_trackers and Accelerator.log
• Can be used with Weights and Biases, TensorBoard, or CometML.
• Arguments available:
  • with_tracking, whether to load in all available experiment trackers from the environment.

These arguments should be added at the end of any method for starting the python script (such as python, accelerate launch, python -m torch.distributed.launch), such as:

accelerate launch ./tracking.py --with_tracking

Gradient Accumulation (gradient_accumulation.py)

• Shows how to use Accelerator.no_sync to prevent gradient averaging in a distributed setup.
• Arguments available:
  • gradient_accumulation_steps, the number of steps to perform before the gradients are accumulated and the optimizer and scheduler are stepped + zero_grad

These arguments should be added at the end of any method for starting the python script (such as python, accelerate launch, python -m torch.distributed.launch), such as:

accelerate launch ./gradient_accumulation.py --gradient_accumulation_steps 5