Train with GPUs

Train ZenML pipelines on GPUs and scale out with 🤗 Accelerate.

Need more compute than your laptop can offer? This tutorial shows how to:

1. Request GPU resources for individual steps.

2. Build a CUDA‑enabled container image so the GPU is actually visible.

3. Reset the CUDA cache between steps (optional but handy for memory‑heavy jobs).

4. Scale to multiple GPUs or nodes with the 🤗 Accelerate integration.

---

1 Request extra resources for a step

If your orchestrator supports it you can reserve CPU, GPU and RAM directly on a ZenML @step:

from zenml import step
from zenml.config import ResourceSettings

@step(settings={
    "resources": ResourceSettings(cpu_count=8, gpu_count=2, memory="16GB")
})
def training_step(...):
    ...  # heavy training logic

👉 Check your orchestrator's docs; some (e.g. SkyPilot) expose dedicated settings instead of ResourceSettings.

If your orchestrator can't satisfy these requirements, consider off‑loading the step to a dedicated step operator.

---

2 Build a CUDA‑enabled container image

Requesting a GPU is not enough—your Docker image needs the CUDA runtime, too.

from zenml import pipeline
from zenml.config import DockerSettings

docker = DockerSettings(
    parent_image="pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime",
    python_package_installer_args={"system": None},
    requirements=["zenml", "torchvision"]
)

@pipeline(settings={"docker": docker})
def my_gpu_pipeline(...):
    ...

Use the official CUDA images for TensorFlow/PyTorch or the pre‑built ones offered by AWS, GCP or Azure.

---

Optional – clear the CUDA cache

If you squeeze every last MB out of the GPU consider clearing the cache at the beginning of each step:

import gc, torch

def cleanup_memory():
    while gc.collect():
        torch.cuda.empty_cache()

Call cleanup_memory() at the start of your GPU steps.

---

3 Multi‑GPU / multi‑node training with 🤗 Accelerate

ZenML integrates with the Hugging Face Accelerate launcher. Wrap your training step with run_with_accelerate to fan it out over multiple GPUs or machines:

from zenml import step, pipeline
from zenml.integrations.huggingface.steps import run_with_accelerate

@run_with_accelerate(num_processes=4, multi_gpu=True)
@step
def training_step(...):
    ...  # your distributed training code

@pipeline
def dist_pipeline(...):
    training_step(...)

Common arguments:

• num_processes: total processes to launch (one per GPU)

• multi_gpu=True: enable multi‑GPU mode

• cpu=True: force CPU training

• mixed_precision : "fp16" / "bf16" / "no"

Accelerate‑decorated steps must be called with keyword arguments and cannot be wrapped a second time inside the pipeline definition.

Prepare the container

Use the same CUDA image as above plus add Accelerate to the requirements:

DockerSettings(
    parent_image="pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime",
    python_package_installer_args={"system": None},
    requirements=["zenml", "accelerate", "torchvision"]
)

---

4 Troubleshooting & Tips

Problem	Quick fix
GPU is unused	Verify CUDA toolkit inside container (nvcc --version), check driver compatibility
OOM even after cache reset	Reduce batch size, use gradient accumulation, or request more GPU memory
Accelerate hangs	Make sure ports are open between nodes; pass main_process_port explicitly

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