Merge pull request #12 from zhaiwenxi/docs/update-dpa-adapt-example

Docs/update dpa adapt example

Author: zhaiwenxi

.github/workflows/property_tools_tests.yml

@@ -3,13 +3,13 @@ name: DeePMD Property Tools Tests
 on:
   push:
     paths:
-      - "deepmd/dpa_tools/**"
-      - "source/tests/dpa_tools/**"
+      - "deepmd/dpa_adapt/**"
+      - "source/tests/dpa_adapt/**"
       - ".github/workflows/property_tools_tests.yml"
   pull_request:
     paths:
-      - "deepmd/dpa_tools/**"
-      - "source/tests/dpa_tools/**"
+      - "deepmd/dpa_adapt/**"
+      - "source/tests/dpa_adapt/**"
       - ".github/workflows/property_tools_tests.yml"
 
 jobs:
@@ -36,4 +36,4 @@ jobs:
 
       - name: Run unit tests
         run: |
-          python -m pytest source/tests/dpa_tools/ -v --ignore=source/tests/dpa_tools/test_trainer_dim_case_embd.py
+          python -m pytest source/tests/dpa_adapt/ -v --ignore=source/tests/dpa_adapt/test_trainer_dim_case_embd.py

README.md

@@ -24,7 +24,7 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/).
 - **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc.
 - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing.
 - **highly modularized**, easy to adapt to different descriptors for deep learning-based potential energy models.
-- **fine-tunes pre-trained DPA models through a scikit-learn-style Python API**, via [`dpa_tools`](deepmd/dpa_tools/README.md) — construct a `DPAFineTuner`, then `fit` and `predict` to adapt a large pre-trained model to your own property dataset, with no input files to write.
+- **fine-tunes pre-trained DPA models through a scikit-learn-style Python API**, via [`dpa_adapt`](dpa_adapt/README.md) — construct a `DPAFineTuner`, then `fit` and `predict` to adapt a large pre-trained model to your own property dataset, with no input files to write.
 
 ### License and credits
 
@@ -98,27 +98,13 @@ Then, read on for a brief overview of the usage of DeePMD-kit. You may start wit
 dp
 ```
 
-## Fine-tune pre-trained DPA models with `dpa_tools`
-
-`dpa_tools` is a scikit-learn-style **Python API for fine-tuning pre-trained DPA atomic models** on your own dataset: you construct a `DPAFineTuner`, call `fit(...)` then `predict(...)`, and pick a transfer-learning strategy — a frozen descriptor with a scikit-learn head, linear probing, full fine-tuning, or multi-task fine-tuning — without writing any DeePMD-kit JSON config or training pipeline. Use it to adapt a large pre-trained model to a downstream materials or molecular property (energy, band gap, HOMO–LUMO gap, …) from a modest labeled dataset. It ships with DeePMD-kit (`pip install deepmd-kit[dpa-tools]`); the full guide lives in [`deepmd/dpa_tools/README.md`](deepmd/dpa_tools/README.md).
-
-```python
-from deepmd.dpa_tools import DPAFineTuner
-
-model = DPAFineTuner(pretrained="DPA-3.1-3M", strategy="frozen_sklearn", predictor="rf")
-model.fit(train_data="data/train", target_key="bandgap")  # fine-tune on your labeled structures
-preds = model.predict("data/new_structures").predictions   # predict for new structures
-```
-
-The same workflow is also available from the command line as `dp dpa fit` / `dp dpa predict`.
-
 ## Code structure
 
 The code is organized as follows:
 
 - `examples`: examples.
 - `deepmd`: DeePMD-kit python modules.
-- `deepmd/dpa_tools`: scikit-learn-style Python API for fine-tuning pre-trained DPA models ([README](deepmd/dpa_tools/README.md)).
+- `dpa_adapt`: scikit-learn-style package for fine-tuning pre-trained DPA models.
 - `source/lib`: source code of the core library.
 - `source/op`: Operator (OP) implementation.
 - `source/api_cc`: source code of DeePMD-kit C++ API.

backend/find_pytorch.py

@@ -137,6 +137,11 @@ def get_pt_requirement(pt_version: str = "") -> dict:
             # https://github.com/pytorch/pytorch/commit/7e0c26d4d80d6602aed95cb680dfc09c9ce533bc
             else "torch>=2.1.0",
             "e3nn>=0.5.9",
+            # O(N) cell-list neighbor list for fast Python/ASE inference; the
+            # torch bindings (vesin-torch) ship only as a PyPI extra, so keep it
+            # under the torch extra rather than the core deps (conda-forge has
+            # vesin but not vesin-torch).
+            "vesin[torch]",
             *mpi_requirement,
             *cibw_requirement,
         ],

deepmd/__about__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+__version__ = "0.0.0"