Fix ffnet build for Python 3.13+ and mamba 2.x compatibility

.gitignore

@@ -11,3 +11,5 @@ Miniconda2/
 xor*
 tmp-for-ffnet/
 *.png
+
+data/

CHANGELOG.md

@@ -7,15 +7,50 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+
+### Changed
+
+### Deprecated
+
+### Removed
+
+### Fixed
+
+## [26.1.1-3] - 2026-04-03
+
 ### Fixed
 
+- Fix `ffnet` installation for Python 3.13+ and modern pip (26.x):
+  - Remove `--no-use-pep517` flag, which was dropped in pip 23.1
+  - Add `--no-build-isolation` for Python 3.13+ so that `numpy` (required at
+    Fortran compile time) is visible to pip's build backend instead of being
+    hidden inside an isolated sandbox
+  - Simplify misleading echo message in the ffnet install block
+- Remove `--show-channel-urls` flag from `mamba list` calls; the flag is not
+  supported by mamba 2.x and caused the end-of-install package listing to be
+  skipped with a spurious warning
+
 ### Changed
 
+- Update PyTorch examples to support Apple Metal Performance Shaders (MPS)
+- Update example Python version to 3.14
+- Update example Miniforge version to 26.1.1-3
+- Disable TensorFlow installation for Python 3.14 (see https://github.com/tensorflow/tensorflow/issues/102890)
+
 ### Added
 
+- Add `torchvision` to the `pip install` list in `install_miniforge.bash`
+- Add CPU vs. Accelerated (CUDA/MPS) comparisons to PyTorch tests (`tests/torch_example.py` and `tests/torch_example_like_tflow.py`)
+- Explicit Conda Packages
+  - basemap (latest version for Python 3.14 support only on conda-forge)
+- Explicit Pip Packages
+  - python-docx
+
 ### Removed
 
-### Deprecated
+- Explicit Pip Packages
+  - basemap (latest version for Python 3.14 support only on conda-forge)
 
 ## [25.3.1] - 2025-10-02
 

README.GMAO

@@ -1,5 +1,5 @@
 The command for installing on ford1 is:
 
 ```
-$ ./install_miniconda.bash --python_version 3.12 --miniconda_version 24.5.0-0 --prefix /ford1/share/gmao_SIteam/GEOSpyD
+$ ./install_miniforge.bash --python_version 3.14 --miniforge_version 26.1.1-3 --prefix /ford1/share/gmao_SIteam/GEOSpyD
 ```

README.NAS

@@ -1,5 +1,5 @@
 The command for installing on NAS systems is:
 
 ```
-$ ./install_miniconda.bash --python_version 3.12 --miniconda_version 24.5.0-0 --prefix /nobackup/gmao_SIteam/GEOSpyD
+$ ./install_miniforge.bash --python_version 3.14 --miniforge_version 26.1.1-3 --prefix /nobackup/gmao_SIteam/GEOSpyD
 ```

README.NCCS

@@ -1,5 +1,5 @@
 The command for installing on NCCS system is:
 
 ```
-$ ./install_miniconda.bash --python_version 3.12 --miniconda_version 24.5.0-0 --prefix /usr/local/other/python/GEOSpyD/
+$ ./install_miniforge.bash --python_version 3.14 --miniforge_version 26.1.1-3 --prefix /usr/local/other/python/GEOSpyD/
 ```

README.md

@@ -14,12 +14,12 @@ to prevent infection from the Anaconda `defaults` channel, the script at the end
 In order to use the install script, you can run:
 
 ```
-./install_miniforge.bash --python_version 3.12 --miniforge_version 24.5.0-0 --prefix /opt/GEOSpyD
+./install_miniforge.bash --python_version 3.14 --miniforge_version 26.1.1-3 --prefix /opt/GEOSpyD
 ```
 
 will create an install at:
 ```
-/opt/GEOSpyD/24.5.0-0_py3.12/YYYY-MM-DD
+/opt/GEOSpyD/26.1.1-3/YYYY-MM-DD
 ```
 
 where YYYY-MM-DD is the date of the install. We use a date so that if
@@ -28,17 +28,19 @@ the stack is re-installed, the previous install is not overwritten.
 ## Usage
 
 ```
-Usage: ./install_miniforge.bash --python_version <python version> --miniforge_version <miniforge> --prefix <prefix> [--micromamba | --mamba] [--blas <blas>]
+Usage: ./install_miniforge.bash --python_version <python version> --miniforge_version <miniforge> --prefix <prefix>
+                   [--micromamba | --mamba] [--blas <blas>] [--ffnet-hack]
 
    Required arguments:
-      --python_version <python version> (e.g., 3.12)
-      --miniforge_version <miniforge_version version> (e.g., 24.5.0-0)
+      --python_version <python version> (e.g., 3.14)
+      --miniforge_version <miniforge_version version> (e.g., 26.1.1-3)
       --prefix <full path to installation directory> (e.g, /opt/GEOSpyD)
 
    Optional arguments:
       --blas <blas> (default: accelerate, options: mkl, openblas, accelerate, blis)
       --micromamba: Use micromamba installer (default)
       --mamba: Use mamba installer
+      --ffnet-hack: Install ffnet from fork (used on Bucy due to odd issue not finding gfortran)
       --help: Print this message
 
    By default we use the micromamba installer on both Linux and macOS
@@ -47,17 +49,17 @@ Usage: ./install_miniforge.bash --python_version <python version> --miniforge_ve
    NOTE 1: This script installs within /opt/GEOSpyD with a path based on:
 
         1. The Miniforge version
-        2. The Python version
-        3. The date of the installation
+        2. The date of the installation
 
-   For example: ./install_miniforge.bash --python_version 3.12 --miniforge_version 24.5.0-0 --prefix /opt/GEOSpyD
+   For example: ./install_miniforge.bash --python_version 3.14 --miniforge_version 26.1.1-3 --prefix /opt/GEOSpyD
 
    will create an install at:
-       /opt/GEOSpyD/24.5.0-0_py3.12/2024-08-29
+       /opt/GEOSpyD/26.1.1-3/2026-03-11
 
   NOTE 2: This script will create or substitute a .mambarc
   and .condarc file in the user's home directory.  If you
   have an existing .mambarc and/or .condarc file, it will be
   restored after installation.  We do this to ensure that the
   installation uses conda-forge as the default channel.
 ```
+

install_miniforge.bash

@@ -72,8 +72,8 @@ fi
 # Usage
 # -----
 
-EXAMPLE_PY_VERSION="3.13"
-EXAMPLE_MINI_VERSION="25.3.1-0"
+EXAMPLE_PY_VERSION="3.14"
+EXAMPLE_MINI_VERSION="26.1.1-3"
 EXAMPLE_INSTALLDIR="/opt/GEOSpyD"
 EXAMPLE_DATE=$(date +%F)
 usage() {
@@ -605,6 +605,8 @@ $PACKAGE_INSTALL uxarray
 
 $PACKAGE_INSTALL rasterio contextily
 
+$PACKAGE_INSTALL basemap
+
 # Only install pythran on linux. On mac it brings in an old clang
 if [[ $MINIFORGE_ARCH == Linux ]]
 then
@@ -651,26 +653,34 @@ $PIP_INSTALL PyRTF3 pipenv pymp-pypi h5py
 $PIP_INSTALL pycircleci metpy siphon questionary xgrads
 $PIP_INSTALL ruamel.yaml
 $PIP_INSTALL xgboost
-$PIP_INSTALL tensorflow evidential-deep-learning silence_tensorflow
-$PIP_INSTALL torch
+
+# Tensorflow does not support Python 3.14 yet
+# https://github.com/tensorflow/tensorflow/issues/102890
+if [[ $PYTHON_VER_WITHOUT_DOT -ge 314 ]]
+then
+   echo "Skipping tensorflow installation as Python $PYTHON_VER is 3.14 or higher"
+else
+   $PIP_INSTALL tensorflow evidential-deep-learning silence_tensorflow
+fi
+$PIP_INSTALL torch torchvision
 $PIP_INSTALL yaplon
 $PIP_INSTALL lxml
 $PIP_INSTALL juliandate
 $PIP_INSTALL pybufrkit
 $PIP_INSTALL pyephem
-$PIP_INSTALL basemap
 $PIP_INSTALL redis
 $PIP_INSTALL Flask
 $PIP_INSTALL goes2go
 $PIP_INSTALL nco
 $PIP_INSTALL cdo
 $PIP_INSTALL ecmwf-opendata
+$PIP_INSTALL python-docx
 
 # some packages require a Fortran compiler. This sometimes isn't available
 # on macs (though usually is)
 if [[ $FORTRAN_AVAILABLE == TRUE ]]
 then
-   echo "We have a Fortran compiler and are Python 3.12 or older. Installing ffnet"
+   echo "We have a Fortran compiler. Installing ffnet"
    # we need to install ffnet from https://github.com/mrkwjc/ffnet.git
    # This is because the version in PyPI is not compatible with Python 3
    # and latest scipy
@@ -682,8 +692,14 @@ then
    if [[ $PYTHON_VER_WITHOUT_DOT -ge 313 ]]
    then
       $PIP_INSTALL setuptools wheel
-      # We also need a new flag for Python 3.13
-      EXTRA_PIP_FLAGS='--no-use-pep517'
+   fi
+   # For Python 3.13+, pip's isolated build environment does not inherit the
+   # conda env packages (e.g. numpy), which ffnet needs at build time. Passing
+   # --no-build-isolation tells pip to use the already-installed packages from
+   # the conda env instead of creating a fresh isolated sandbox.
+   if [[ $PYTHON_VER_WITHOUT_DOT -ge 313 ]]
+   then
+      EXTRA_PIP_FLAGS='--no-build-isolation'
    else
       EXTRA_PIP_FLAGS=''
    fi
@@ -755,8 +771,8 @@ $PIP_INSTALL prompt_toolkit
 # Use mamba to output list of packages installed
 # ----------------------------------------------
 cd $MINIFORGE_ENVDIR
-$MINIFORGE_BINDIR/mamba list -n $MINIFORGE_ENVNAME --show-channel-urls --explicit > distribution_spec_file.txt
-$MINIFORGE_BINDIR/mamba list -n $MINIFORGE_ENVNAME --show-channel-urls > mamba_list_packages.txt
+$MINIFORGE_BINDIR/mamba list -n $MINIFORGE_ENVNAME --explicit > distribution_spec_file.txt
+$MINIFORGE_BINDIR/mamba list -n $MINIFORGE_ENVNAME > mamba_list_packages.txt
 ./bin/pip freeze > pip_freeze_packages.txt
 
 # Restore User's .mambarc and .condarc using cleanup function

tests/torch_example.py

@@ -2,44 +2,67 @@
 
 import torch
 import math
+import time
 
 
-dtype = torch.float
-device = torch.device("cpu")
-# device = torch.device("cuda:0") # Uncomment this to run on GPU
+def run_polynomial_regression(device_name):
+    device = torch.device(device_name)
+    print(f"\n========== Running on {device} ==========")
+    dtype = torch.float
 
-# Create random input and output data
-x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
-y = torch.sin(x)
+    # Create random input and output data
+    x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
+    y = torch.sin(x)
 
-# Randomly initialize weights
-a = torch.randn((), device=device, dtype=dtype)
-b = torch.randn((), device=device, dtype=dtype)
-c = torch.randn((), device=device, dtype=dtype)
-d = torch.randn((), device=device, dtype=dtype)
+    # Randomly initialize weights
+    a = torch.randn((), device=device, dtype=dtype)
+    b = torch.randn((), device=device, dtype=dtype)
+    c = torch.randn((), device=device, dtype=dtype)
+    d = torch.randn((), device=device, dtype=dtype)
 
-learning_rate = 1e-6
-for t in range(2000):
-    # Forward pass: compute predicted y
-    y_pred = a + b * x + c * x ** 2 + d * x ** 3
+    learning_rate = 1e-6
 
-    # Compute and print loss
-    loss = (y_pred - y).pow(2).sum().item()
-    if t % 100 == 99:
-        print(t, loss)
+    start_time = time.time()
+    for t in range(2000):
+        # Forward pass: compute predicted y
+        y_pred = a + b * x + c * x**2 + d * x**3
 
-    # Backprop to compute gradients of a, b, c, d with respect to loss
-    grad_y_pred = 2.0 * (y_pred - y)
-    grad_a = grad_y_pred.sum()
-    grad_b = (grad_y_pred * x).sum()
-    grad_c = (grad_y_pred * x ** 2).sum()
-    grad_d = (grad_y_pred * x ** 3).sum()
+        # Compute and print loss
+        loss = (y_pred - y).pow(2).sum().item()
 
-    # Update weights using gradient descent
-    a -= learning_rate * grad_a
-    b -= learning_rate * grad_b
-    c -= learning_rate * grad_c
-    d -= learning_rate * grad_d
+        # Backprop to compute gradients of a, b, c, d with respect to loss
+        grad_y_pred = 2.0 * (y_pred - y)
+        grad_a = grad_y_pred.sum()
+        grad_b = (grad_y_pred * x).sum()
+        grad_c = (grad_y_pred * x**2).sum()
+        grad_d = (grad_y_pred * x**3).sum()
 
+        # Update weights using gradient descent
+        a -= learning_rate * grad_a
+        b -= learning_rate * grad_b
+        c -= learning_rate * grad_c
+        d -= learning_rate * grad_d
 
-print(f'Result: y = {a.item()} + {b.item()} x + {c.item()} x^2 + {d.item()} x^3')
+    end_time = time.time()
+
+    print(f"Time taken:  {end_time - start_time:.4f} seconds")
+    print(f"Final loss:  {loss:.4f}")
+    print(
+        f"Result eq:   y = {a.item():.4f} + {b.item():.4f} x + {c.item():.4f} x^2 + {d.item():.4f} x^3"
+    )
+
+
+print(f"PyTorch version: {torch.__version__}")
+
+# Always run CPU first
+run_polynomial_regression("cpu")
+
+# Determine and run accelerated device
+if torch.cuda.is_available():
+    print(f"\nFound CUDA: {torch.cuda.get_device_name(0)}")
+    run_polynomial_regression("cuda:0")
+elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+    print("\nFound Apple Metal Performance Shaders (MPS)")
+    run_polynomial_regression("mps")
+else:
+    print("\nNo accelerated device (CUDA/MPS) found. Skipping accelerated run.")