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8 changes: 8 additions & 0 deletions CHANGELOG.md
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# Changelog

## [0.25.2](https://github.com/runpod/tetra-rp/compare/v0.25.1...v0.25.2) (2026-02-03)

### ⚠️ DEPRECATED

This is the final release of `tetra-rp`. All future development and releases will be under the new package name `runpod-flash`. See [runpod/flash](https://github.com/runpod/flash) for the new repository.

**Migration guide:** Uninstall `tetra-rp`, install `runpod-flash`, and update imports from `from tetra_rp import ...` to `from runpod_flash import ...`

## [0.25.1](https://github.com/runpod/tetra-rp/compare/v0.25.0...v0.25.1) (2026-02-03)


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19 changes: 19 additions & 0 deletions README.md
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# ⚠️ DEPRECATED: tetra-rp

> **This package is deprecated.** The final release of `tetra-rp` is version 0.25.2. All future development and releases will be under the new package name `runpod-flash`.
>
> **Migration Required:** To upgrade, install the new package and update your imports:
> ```bash
> pip uninstall tetra-rp
> pip install runpod-flash
> ```
> Then update your imports from `from tetra_rp import ...` to `from runpod_flash import ...`
>
> See [runpod/flash](https://github.com/runpod/flash) for the new repository.

# Flash: Serverless computing for AI workloads

Runpod Flash is a Python SDK that streamlines the development and deployment of AI workflows on Runpod's [Serverless infrastructure](http://docs.runpod.io/serverless/overview). Write Python functions locally, and Flash handles the infrastructure, provisioning GPUs and CPUs, managing dependencies, and transferring data, allowing you to focus on building AI applications.
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### Step 1: Install Flash

> **Note:** This documentation describes the deprecated `tetra-rp` package. For new projects, use `runpod-flash`:
> ```bash
> pip install runpod-flash
> ```

For the legacy package:
```bash
pip install tetra_rp
```
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2 changes: 1 addition & 1 deletion pyproject.toml
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[project]
name = "tetra_rp"
version = "0.25.1"
version = "0.25.2"
description = "A Python library for distributed inference and serving of machine learning models"
authors = [
{ name = "Runpod", email = "engineer@runpod.io" },
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2 changes: 1 addition & 1 deletion uv.lock

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