| Name | groundhog-hpc JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | Iterative HPC function development. As many 'first tries' as you need. |
| upload_time | 2025-10-27 14:29:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
faas
function serving
globus compute
hpc
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Home of `hog` ☀️🦫🕳️
## Quickstart
Groundhog makes it easy to run, tweak, and re-run python functions on HPC clusters via [Globus Compute](https://www.globus.org/compute) using simple decorators.
Groundhog automatically manages remote environments (powered by [uv](https://docs.astral.sh/uv/))—just update Python versions or dependencies in your script, no SSH needed.
**Key concepts:**
- `@hog.function()` - Configures a function to run on a Globus Compute endpoint. Decorator kwargs (like `endpoint`, `account`) become the default `user_endpoint_config`.
- `@hog.harness()` - Marks a local entry point that orchestrates remote calls via `.remote()` or `.submit()`.
- The desired remote Python environment (version and dependencies) is specified alongside your code via [PEP 723](https://peps.python.org/pep-0723/) metadata.
```python
# /// script
# requires-python = ">=3.10"
# dependencies = ["numpy"]
# ///
import groundhog_hpc as hog
@hog.function(endpoint="your-endpoint-id", account="your-account")
def compute(x: int) -> int:
import numpy as np
return int(np.sum(range(x)))
@hog.harness()
def main():
result = compute.remote(100)
print(result)
```
Run with: `hog run myscript.py main`
---
see also: [examples/README.md](./examples/README.md)
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