ophelian 1.0.1

Declarative ML framework: write a Pipeline once, run it anywhere — local Docker, AWS, GCP, Azure, or routed to the cheapest cloud.

Ophelian

Write your ML pipeline once. Run it anywhere. Route to the cheapest available GPU across AWS, GCP, and Azure.

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Ophelian is a small, opinionated Python framework for taking ML / AI prototypes to production without rewriting them every time the runtime changes. Declare a Pipeline, pick an env, and Ophelian compiles + runs it — locally in Docker, on AWS, GCP, or Azure, or routed automatically to the cheapest cloud for the GPU you need.

Install

pip install ophelian                  # core, runs locally
pip install 'ophelian[aws]'           # + EC2 / S3
pip install 'ophelian[gcp]'           # + GCE / GCS
pip install 'ophelian[azure]'         # + Azure VM / Blob
pip install 'ophelian[huggingface]'   # + Transformers + PyTorch
pip install 'ophelian[all]'           # every adapter and provider

Python 3.11+. Full extras list (pytorch, sklearn, xgboost, otel, ...) in pyproject.toml.

Hello, pipeline

from ophelian import Pipeline, Train, Auto

pipe = Pipeline([
    Train(
        model="meta-llama/Llama-3.2-1B",
        data="s3://my-bucket/dataset.jsonl",
        epochs=3,
    ),
])

# Picks the cheapest A100 across AWS / GCP / Azure right now.
pipe.run(env=Auto(cheapest_gpu="A100"))

The same source runs on your laptop, on EC2 spot, on a GCE preemptible VM, or on an Azure Spot VM — Ophelian handles checkpointing, artifact persistence (S3 / GCS / Azure Blob), structured logs, and a rich summary at the end.

Envs

from ophelian import Standalone, AWS, GCP, Azure, Auto

Standalone(local=True)                                     # local Docker / in-process
AWS(region="us-east-1", instance="g5.xlarge", spot=True)   # EC2 / S3
GCP(project="p", region="us-central1",
    machine_type="n1-standard-4", gpu_type="nvidia-tesla-t4",
    preemptible=True)                                      # GCE / GCS
Azure(subscription_id=..., resource_group="ml",
      region="eastus", vm_size="Standard_NC6s_v3",
      spot=True)                                           # Azure VM / Blob
Auto(cheapest_gpu="A100",
     regions=["us-east-1", "us-central1", "eastus"])       # cost router

The same Pipeline(...) runs on every one of them. Each cloud env has a Local*Driver mirror so the test suite (and any contributor without cloud creds) exercises the full framework offline.

Why Ophelian

	Ophelian	SageMaker	Vertex AI	Azure ML	Bare cloud SDKs
Single API across AWS + GCP + Azure	✅	❌	❌	❌	❌
Write pipeline once, run anywhere	✅	❌	❌	❌	❌
Auto-router that picks the cheapest GPU	✅	❌	❌	❌	❌
Spot / preemptible resume	✅	partial	partial	partial	DIY
Local-first dev (no cloud auth)	✅	❌	❌	❌	❌
Apache-2.0, no vendor lock-in	✅	❌	❌	❌	mixed

Documentation

Full docs live at https://ophelianio.github.io/ophelian/: Quickstart · Concepts · Envs · Cookbook · Troubleshooting.

Runnable examples in examples/. Roadmap and release history in CHANGELOG.md.

Community

• Questions → GitHub Discussions
• Bugs / features → Issues
• Security → see SECURITY.md — please do not file a public issue
• Contributing → CONTRIBUTING.md (we follow the Contributor Covenant 2.1)

Citation

@software{ophelian_2026,
  author    = {Falva, Luis and the Ophelian contributors},
  title     = {{Ophelian: a declarative, multi-cloud ML pipeline framework}},
  year      = {2026},
  version   = {1.0.1},
  license   = {Apache-2.0},
  url       = {https://github.com/ophelianio/ophelian},
}

License

Copyright © 2024–2026 Luis Falva and the Ophelian contributors. Licensed under the Apache License, Version 2.0 — see LICENSE and NOTICE for third-party attributions.