Deploying a PyTorch training task is divided into four main steps:
- environmental preparation: Install Python 3.8+ and create a virtual environment, execute the
pip install "skypilot[all]"Install the full dependency package. - Writing the YAML configuration: Create
train.yamlThe document defines resource requirements and execution logic:resources:
accelerators: A100:1
num_nodes: 1
setup: |
pip install torch torchvision
run: |
python main.py --epochs 10 - Initiate tasks: Run
sky launch -c my-cluster train.yamlThe system will automatically select the optimal cloud resources. - RMON: By
sky statusTo view the cluster status, use thesky logs my-clusterGet real-time logs.
Advanced Tips: Add--use-spotUse a low-cost Spot instance, or pass--cloud cheapestEnable fully automated cloud merchant selection.
This answer comes from the articleSkyPilot: an open-source framework for efficiently running AI and batch tasks in any cloudThe
May not be reproduced without permission:AI productivity tools " How to deploy a PyTorch model training task in the cloud using SkyPilot?
