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ANSIBLE
Simple, agentless IT automation for ML infrastructure
GPL-3.0
ABOUT
Managing ML infrastructure — GPU servers, model serving clusters, data pipelines, and experiment tracking environments — requires consistent, repeatable configuration across potentially hundreds of machines. Ansible provides agentless automation using SSH and YAML playbooks, making it easy to provision GPU drivers, configure CUDA libraries, deploy ML serving containers, and manage experiment tracking infrastructure without installing agents on target hosts. Its push-based model and idempotent operations make it ideal for teams that need infrastructure reproducibility but aren't ready for full Kubernetes adoption.
INSTALL
pip install ansibleINTEGRATION GUIDE
1. Provision GPU servers with CUDA, cuDNN, NCCL, and container runtimes for ML training
2. Deploy and configure ML serving infrastructure (Triton, MLflow, BentoML) across clusters
3. Automate experiment tracking environment setup with W&B, MLflow, or Neptune agents
4. Manage multi-node Ray clusters for distributed training and hyperparameter tuning
TAGS
automationconfiguration-managementdevopsinfrastructureorchestration