Metadata-Version: 2.4
Name: everpod
Version: 0.1.0
Summary: Python client for Everpod — GPU inference on Household AI Infrastructure
Project-URL: Homepage, https://everpod.io
Project-URL: Documentation, https://api.everpod.io/docs
Project-URL: Console, https://app.everpod.io
Author-email: Everpod <support@everpod.io>
License: Apache-2.0
Keywords: ai,everpod,gpu,image-generation,inference
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: httpx>=0.24
Description-Content-Type: text/markdown

# everpod

Python client for [Everpod](https://everpod.io) — GPU inference on Household AI Infrastructure.

```bash
pip install everpod
```

```python
from everpod import Everpod

client = Everpod(api_key="evp_...")  # or set EVERPOD_API_KEY

image = client.images.generate(prompt="a lighthouse at dusk, cinematic")
image.save("lighthouse.png")
```

Generation runs as a **job** on a real GPU node; `generate()` polls to
completion for you. For async control:

```python
job = client.images.submit(prompt="a lighthouse at dusk")
job = client.jobs.wait(job.id, timeout=120)
print(job.artifacts[0].url)
```

Check live capacity before submitting — models are only offered while a node
can serve them:

```python
for model in client.models.available():
    print(model.name, model.ready_workers)
```

Your metered usage (nothing is billed during the pilot):

```python
usage = client.usage.get(days=30)
print(usage.total_jobs, usage.total_gpu_seconds)
for row in usage.recent:
    print(row["job_id"], row["gpu_seconds"])
```

Get an API key at [app.everpod.io/keys](https://app.everpod.io/keys/) ·
API reference at [api.everpod.io/docs](https://api.everpod.io/docs).
