# Release 0.8.396

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The headline of this release is the move to **Chemprop 2.2.4**, which also brings a handful of new model-tuning knobs to the Chemprop template. The rest of the release is a concise batch of endpoint, pipeline, and tooling improvements.

## Chemprop 2.2.4

Both the PyTorch/Chem **training and inference images** are rebuilt on Chemprop 2.2.4 (training + inference parity), and the model template now exposes new tuning options that come with it:

* **Learning-rate schedule** — `warmup_epochs`, `init_lr`, `max_lr`, and `final_lr` are now settable hyperparameters. Defaults match Chemprop's, so existing models are unchanged.
* **Per-layer FFN dimensions** — `ffn_hidden_dim` now accepts a list (e.g. `[1024, 256, 64]`) for a tapered prediction head, in addition to a single fixed width.
* **CheMeleon foundation-model fine-tuning** — initialize the message-passing layers from the CheMeleon foundation model via `from_foundation`, with `freeze_mpnn_epochs` to stabilize the FFN head before unfreezing the encoder.

## Endpoints

* **New 2D (salt-keeping) + 3D feature endpoint**, plus clearer endpoint naming.
* **Dynamic batch sizing** for meta endpoints, and async child endpoints now inherit `max_instances`.

## Pipelines & Plugins

* The **ml_pipeline launcher** can now run a single script by its full filename.
* Refined **plugin resolution**.

## Tooling

* **Secret-leak detection** — a pre-commit config plus a CI workflow (PR-scoped, local binary) to catch accidental credential commits.
* Improved chunk-progress display for long-running operations.

## Questions?
<img align="right" src="../../images/scp.png" width="180">

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