Open source framework for simulated distributed training methods. Instead of training with multiple ranks, we simulate the distributed training process by running multiple nodes on a single machine.

• CPU
• CUDA
• MPS (CPU-bound for copy operations, see here)

• AllReduce (Equivalent to PyTorch DDP)
• FedAvg
• DiLoCo
• SPARTA
• DeMo

from exogym import LocalTrainer
from exogym.strategy import DiLoCoStrategy

train_dataset, val_dataset = ...
model = ...

trainer = LocalTrainer(model, train_dataset, val_dataset)

strategy = DiLoCoStrategy(
  inner_optim='adam',
  H=100
)

trainer.fit(
  strategy=strategy,
  num_nodes=4,
  device='mps'
)

Install with core dependencies only:

pip install exogym

For experiment tracking with Weights & Biases:

pip install exogym[wandb]

For S3 dataset loading:

pip install exogym[s3]

For DeMo strategy support:

pip install exogym[demo]

For running examples:

pip install exogym[examples]

For all optional features:

pip install exogym[all]

For development:

pip install exogym[dev]

To install for development:

git clone https://github.com/MattyAB/DistributedSim.git
cd DistributedSim
pip install -e .[dev]

• Trainer: Builds simulation environment. Trainer will spawn multiple TrainNode instances, connect them together, and starts the training run.
• TrainNode: A single node (rank) running its own training loop. At each train step, instead of calling optim.step(), it calls strategy.step().
• Strategy: Abstract class for an optimization strategy, which both defines how the nodes communicate with each other and how model weights are updated. Typically, a gradient strategy will include an optimizer as well as a communication step. Sometimes (eg. DeMo), the optimizer step is comingled with the communication.

EXO Gym uses pytorch multiprocessing to a subprocess per-node, which are able to communicate with each other using regular operations such as all_reduce.

The model is expected in a form that takes a batch (the same format as dataset outputs), and returns a scalar loss over the entire batch. This ensures the model is agnostic to the format of the data (eg. masked LM training doesn't have a clear x/y split).

Recall that when we call trainer.fit(), $K$ subprocesses are spawned to handle each of the virtual workers. The dataset object is passed to every subprocess, and a DistributedSampler will be used to select indices per-node. If the dataset is entirely loaded into memory, this memory will be duplicated per-node - be careful not to run out of memory! If the dataset is larger, it should be lazily loaded.