yury-lysogorskiy · Oct 31, 2026 · Oct 31, 2026
diff --git a/docs/pacemaker/releases.md → .github/RELEASING.md b/docs/pacemaker/releases.md → .github/RELEASING.md
diff --git a/docs/pacemaker/install.md b/docs/pacemaker/install.md
@@ -1,159 +1,33 @@
 # Installation
 
-`pacemaker` is installed by the `python-ace` package. TensorFlow fitting uses
-`tensorpotential.compat.pace` from the current
-[GRACE TensorPotential repository](https://github.com/ICAMS/grace-tensorpotential).
-The old `ICAMS/TensorPotential` repository requires Python <3.11 and should not
-be used for a new installation.
-
-## Create an environment
-
-Python 3.12 is a suitable starting point for a new environment:
+Use Python 3.12 in a separate environment:
 
 ```bash
 mamba create -n ace python=3.12 pip
 mamba activate ace
-python -m pip install --upgrade pip
-```
-
-`conda create` and `conda activate` can be used in place of the corresponding
-`mamba` commands. Use the same environment to build and run both packages.
-A C++ compiler is needed when building `python-ace` from source; its Python
-build dependencies, including CMake and Ninja, are installed automatically.
-
-## Install the fitting backend
-
-Install the GRACE backend from PyPI. Its distribution name is
-`tensorpotential`; `grace-tensorpotential` is the GitHub repository name.
-The following released-package combination works around the PACE metrics
-scalar-conversion bug in TensorPotential 0.6.2.1:
-
-```bash
-python -m pip install 'tensorpotential==0.6.2.1' 'numpy>=2,<2.4' 'tensorflow~=2.20.0' 'tf-keras~=2.20.0'
-```
-
-Keep NumPy below 2.4 when using that backend release. The NumPy fix and
-TensorFlow 2.21 support have been merged upstream but are not included in
-PyPI's 0.6.2.1 release. See the development installation below to use them.
-
-On Linux the backend dependencies include `tensorflow[and-cuda]`. GPU execution
-also requires a compatible NVIDIA driver. CPU fitting is available with
-`CUDA_VISIBLE_DEVICES=-1`. TensorFlow 2.21 publishes wheels for Python
-3.10–3.13; Python 3.14 is not covered by this installation recipe.
-
-## Install pacemaker
-
-Install the published pacemaker release candidate explicitly:
-
-```bash
-python -m pip install 'python-ace==0.4.0rc1'
 ```
 
-The version is explicit because an unqualified `pip install python-ace`
-selects the older stable 0.2.8 release. The modern package includes the maxvol
-extension used by the active-learning tools; a separate `maxvolpy`
-installation is unnecessary.
-
-### Development installation
+You can use `conda` instead of `mamba`.
 
-To use the merged TensorFlow 2.21 and NumPy fixes, install the backend from
-GitHub and pacemaker from the default `master` branch:
+## Install pacemaker and the GRACE backend
 
 ```bash
-python -m pip install --upgrade 'tensorpotential @ git+https://github.com/ICAMS/grace-tensorpotential.git' 'tensorflow~=2.21.0' 'tf-keras~=2.21.0'
-python -m pip install --upgrade 'python-ace @ git+https://github.com/ICAMS/python-ace.git'
+python -m pip install --upgrade pip
+python -m pip install 'python-ace==0.4.0rc1' 'tensorpotential==0.6.2.1' 'numpy>=2,<2.4' 'tensorflow~=2.20.0' 'tf-keras~=2.20.0'
 ```
 
-For a source checkout or to run the tests:
+The GRACE backend is published on PyPI as `tensorpotential`.
+Use the versions above together; this backend release requires NumPy below
+2.4 for pacemaker fitting.
 
-```bash
-git clone https://github.com/ICAMS/python-ace.git
-cd python-ace
-python -m pip install .
-```
-
-Check the installed versions and command-line tools:
+## Check the installation
 
 ```bash
 python -m pip check
-python -c 'import pyace, tensorpotential, tensorflow as tf; print(pyace.__version__, tensorpotential.__version__, tf.__version__)'
 pacemaker --help
-pace_info --help
-```
-
-To run the test suite from the `python-ace` checkout:
-
-```bash
-python -m pip install pytest
-CUDA_VISIBLE_DEVICES=-1 python -m pytest tests/ --runtensorpot
-```
-
-The test workflow is configured for Python 3.10–3.13 with TensorFlow 2.20 and
-2.21 on Linux CPUs, plus Python 3.9 with TensorFlow 2.20. GPU execution needs
-separate validation on the target hardware.
-
-### Tested development environment
-
-The source refresh was checked on 29 October 2026 with Python 3.12.15,
-TensorFlow 2.21.0, tf-keras 2.21.0, NumPy 2.5.4, and pandas 2.3.3:
-
-| Execution target | Validation |
-| --- | --- |
-| Linux CPU | 294 tests and both Cu–I and Ethanol CLI workflows passed |
-| NVIDIA H200 NVL, driver 595.58.03 | All 9 fitting tests passed; 185 fitting calls verified on GPU |
-| NVIDIA A100 80 GB PCIe, driver 575.57.08 | All 9 fitting tests passed; 185 fitting calls verified on GPU |
-
-The GPU checks verify device placement of energies, forces, and coefficient
-gradients as well as the fitting-test results. They cover short fits, rather
-than long-running memory stability or performance. These results require the
-source fixes described above and do not describe an unmodified TensorPotential
-0.6.2.1 installation.
-
-## Troubleshooting
-
-### GPU validation
-
-To check fitting on a selected GPU from the source checkout:
-
-```bash
-CUDA_VISIBLE_DEVICES=0 TF_FORCE_GPU_ALLOW_GROWTH=true python tools/validate_gpu.py
 ```
 
-This runs the TensorFlow fitting tests and checks that energies, forces, and
-coefficient gradients reside on the GPU. It fails if no GPU is detected or if
-those results fall back to the CPU. Select one GPU; pacemaker does not fit on
-multiple GPUs in parallel. Memory growth keeps the test from reserving all
-available device memory.
-
-If TensorFlow reports `Cannot dlopen some GPU libraries`, an existing system
-CUDA installation may take precedence over the libraries installed by pip.
-For the current shell, put the environment's CUDA library directories first:
-
-```bash
-pace_cuda_libraries=$(python - <<'PY'
-from pathlib import Path
-import sysconfig
-print(':'.join(str(path) for path in sorted(
-    (Path(sysconfig.get_paths()['purelib']) / 'nvidia').glob('*/lib')
-)))
-PY
-)
-export LD_LIBRARY_PATH="$pace_cuda_libraries${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
-```
-
-Then rerun the GPU check. This changes the shell environment and leaves the
-system CUDA installation untouched.
-
-### Python and dependency errors
-
-If fitting raises `TypeError: only 0-dimensional arrays can be converted to
-Python scalars`, check that both `python-ace` and the backend include the
-NumPy scalar-conversion fix, then rebuild the installed packages.
-
-For an import error or segmentation fault, check `which python` and rebuild
-the extensions using the interpreter from the active environment. Avoid mixing
-extensions built for different Python versions.
+GPU fitting requires a compatible NVIDIA driver. To run on the CPU, set
+`CUDA_VISIBLE_DEVICES=-1` before starting pacemaker.
 
-For a protobuf descriptor error, check `python -m pip check` and reinstall a
-consistent TensorFlow environment. The old TensorFlow 2.5/2.8 and protobuf
-3.20 instructions do not describe the current dependency set.
+Continue with the [quick start](quickstart.md) to prepare data and run a fit.
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -27,7 +27,6 @@ extra_javascript:
 nav:
     - Home: index.md
     - Installation: pacemaker/install.md
-    - Building and publishing: pacemaker/releases.md
     - Quick start: pacemaker/quickstart.md
     - Pacemaker workflow: pacemaker/workflow.md
     - Extrapolation grade and active learning: pacemaker/active_learning.md