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Getting Started

Zeus automatically tunes the batch size and GPU power limit of a recurring DNN training job.

Important

Zeus can optimize the batch size of recurring jobs, i.e. training jobs that re-run multiple times over time. However, Zeus can still optimize the GPU power limit even if your jobs does not recur.

Info

Zeus currently supports single GPU training and single node data parallel training. Support for distributed data parallel training will be added soon.

How it works

Zeus in action, integrated with Stable Diffusion fine-tuning:

Just measuring GPU time and energy

Prerequisites

If your NVIDIA GPU's architecture is Volta or newer, simply do the following in your Python environment 

pip install zeus-ml
and get going with ZeusMonitor.

Otherwise, we recommend using our Docker container:

  1. Set up your environment.
  2. Install and build Zeus components.

ZeusMonitor

ZeusMonitor makes it very simple to measure the GPU time and energy consumption of arbitrary Python code blocks.

from zeus.monitor import ZeusMonitor

# All GPUs are measured simultaneously if `gpu_indices` is not given.
monitor = ZeusMonitor(gpu_indices=[torch.cuda.current_device()])

for epoch in range(100):
    monitor.begin_window("epoch")

    measurements = []
    for x, y in train_loader:
        monitor.begin_window("step")
        train_one_step(x, y)
        result = monitor.end_window("step")
        measurements.append(result)

    eres = monitor.end_window("epoch")
    print(f"Epoch {epoch} consumed {eres.time} s and {eres.total_energy} J.")

    avg_time = sum(map(lambda m: m.time, measurements)) / len(measurements)
    avg_energy = sum(map(lambda m: m.total_energy, measurements)) / len(measurements)
    print(f"One step took {avg_time} s and {avg_energy} J on average.")

Optimizing a single training job's energy consumption

All GPU power limits can be profiled quickly during training and used to optimize the energy consumption of the training job.

Prerequisites

In order to change the GPU's power limit, the process requires the Linux SYS_ADMIN security capability, and the easiest way to do this is to spin up a container and give it --cap-add SYS_ADMIN. We provide ready-to-go Docker images.

GlobalPowerLimitOptimizer

After going through the prerequisites, GlobalPowerLimitOptimizer into your training script.

Refer to our integration example with ImageNet for complete running examples for single-GPU and multi-GPU data parallel training.

from zeus.monitor import ZeusMonitor
from zeus.optimizer import GlobalPowerLimitOptimizer

# Data parallel training with four GPUs.
# Omitting `gpu_indices` will use all GPUs, while respecting
# `CUDA_VISIBLE_DEVICES`.
monitor = ZeusMonitor(gpu_indices=[0,1,2,3])
# The power limit optimizer profiles power limits during training
# using the `ZeusMonitor` instance.
plo = GlobalPowerLimitOptimizer(monitor)

for epoch in range(100):
    plo.on_epoch_begin()

    for x, y in train_dataloader:
        plo.on_step_begin()
        # Learn from x and y!
        plo.on_step_end()

    plo.on_epoch_end()

    # Validate the model if needed, but `plo` won't care.

Important

What is the optimal power limit? The GlobalPowerLimitOptimizer supports multiple OptimumSelectors that chooses one power limit among all the profiled power limits. Selectors that are current implemented are Energy, Time, ZeusCost and MaxSlowdownConstraint.

Recurring jobs

Info

We plan to integrate ZeusMaster with an MLOps platform like KubeFlow. Let us know about your preferences, use cases, and expectations by posting an issue!

The cost-optimal batch size is located across multiple job runs using a Multi-Armed Bandit algorithm. First, go through the steps for non-recurring jobs. ZeusDataLoader will transparently optimize the GPU power limit for any given batch size. Then, you can use ZeusMaster to drive recurring jobs and batch size optimization.

This example will come in handy: