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 #"

• 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.run), such as:

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

• 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 torchrun), 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"

• 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 torchrun), such as:

accelerate launch ./cross_validation.py --num_folds 2

• 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 torchrun), such as:

accelerate launch ./tracking.py --with_tracking

• 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 torchrun), such as:

accelerate launch ./gradient_accumulation.py --gradient_accumulation_steps 5

• Shows how to use Accelerator.no_sync to prevent gradient averaging in a distributed setup. However, unlike gradient accumulation, this method does not change the effective batch size. Local SGD can be combined with gradient accumulation.

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

accelerate launch ./local_sgd.py --local_sgd_steps 4

• Shows how to use DDP Communication Hooks to control and optimize gradient communication across workers in a DistributedDataParallel setup.
• Arguments available:
  • ddp_comm_hook, the type of DDP communication hook to use. Choose between no, fp16, bf16, power_sgd, and batched_power_sgd.

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

accelerate launch ./ddp_comm_hook.py --mixed_precision fp16 --ddp_comm_hook power_sgd

• Shows how to use the profiling capabilities of Accelerate to profile PyTorch models during training.
• Uses the ProfileKwargs handler to customize profiling options, including activities, scheduling, and additional profiling options.
• Can generate and save profiling traces in JSON format for visualization in Chrome's tracing tool.

Arguments available:

• --record_shapes: If passed, records shapes for profiling.
• --profile_memory: If passed, profiles memory usage.
• --with_stack: If passed, profiles stack traces.
• --with_flops: If passed, profiles floating point operations (FLOPS).
• --output_trace_dir: If specified, saves the profiling trace to the given dir in JSON format.
• --cpu: If passed, trains on the CPU instead of GPU.

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

accelerate launch ./profiler.py --record_shapes --profile_memory --with_flops --output_trace_dir "profiler"