Test all examples in CI - integration testing

.github/workflows/build.yml

@@ -14,12 +14,12 @@ concurrency:
  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
 
 jobs:
- build:
+ build_with_tensorflow_v1:
  runs-on: ${{ matrix.os }}
  strategy:
  fail-fast: false
  matrix:
- python-version: ["3.8", "3.9", "3.10"]
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
  os: [ubuntu-latest, macos-latest, windows-latest]
 
  steps:
@@ -35,41 +35,134 @@ jobs:
  - name: List environment
  run: python -m pip freeze
 
- - name: Test build with TensorFlow v1
+ - name: Integration tests with TensorFlow v1
  env:
  DDE_BACKEND: tensorflow.compat.v1
  run: |
  python -m pip install tensorflow
- python -c "import deepxde"
+ cd examples/
+ make run_all_examples
+ make clean
 
- - name: Test build with TensorFlow
+ build_with_tensorflow:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
+ os: [ubuntu-latest, macos-latest, windows-latest]
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install DeepXDE with requirements
+ run: python -m pip install -e .
+
+ - name: List environment
+ run: python -m pip freeze
+
+ - name: Integration tests with TensorFlow
  env:
  DDE_BACKEND: tensorflow
  run: |
- python -m pip install tensorflow-probability
- python -c "import deepxde"
+ python -m pip install tensorflow tensorflow-probability
+ cd examples/
+ make run_all_examples
+ make clean
+
+ build_with_pytorch:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
+ os: [ubuntu-latest, macos-latest, windows-latest]
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install DeepXDE with requirements
+ run: python -m pip install -e .
+
+ - name: List environment
+ run: python -m pip freeze
 
- - name: Test build with PyTorch
+ - name: Integration tests with PyTorch
  env:
  DDE_BACKEND: pytorch
  run: |
  python -m pip install torch
- python -c "import deepxde"
+ cd examples/
+ make run_all_examples
+ make clean
 
- - name: Test build with Jax
- if: matrix.os != 'windows-latest' # jaxlib is not available on windows
+ build_with_jax:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
+ os: [ubuntu-latest, macos-latest]
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install DeepXDE with requirements
+ run: python -m pip install -e .
+
+ - name: List environment
+ run: python -m pip freeze
+
+ - name: Integration tests with Jax
  env:
  DDE_BACKEND: jax
  run: |
  python -m pip install jax flax optax
- python -c "import deepxde"
+ cd examples/
+ make run_all_examples
+ make clean
+
+ build_with_paddle:
+ runs-on: ${{ matrix.os }}
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.8", "3.9", "3.10", "3.11"]
+ os: [ubuntu-latest, macos-latest, windows-latest]
+
+ steps:
+ - uses: actions/checkout@v3
+
+ - uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install DeepXDE with requirements
+ run: python -m pip install -e .
+
+ - name: List environment
+ run: python -m pip freeze
 
- - name: Test build with Paddle
+ - name: Integration tests with Paddle
  env:
  DDE_BACKEND: paddle
  ubuntu-latest: "https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html"
  macos-latest: "https://www.paddlepaddle.org.cn/whl/mac/cpu/develop.html"
  windows-latest: "https://www.paddlepaddle.org.cn/whl/windows/cpu-mkl-avx/develop.html"
  run: |
  python -m pip install paddlepaddle==0.0.0 -f ${{ env[matrix.os] }}
- python -c "import deepxde"
+ cd examples/
+ make run_all_examples
+ make clean

deepxde/config.py

@@ -1,4 +1,5 @@
 import os
+import sys
 import random
 
 import numpy as np
@@ -52,7 +53,7 @@
 elif backend_name == "jax":
  xla_jit = True
 if xla_jit:
- print("Enable just-in-time compilation with XLA.\n")
+ print("Enable just-in-time compilation with XLA.\n", file=sys.stderr, flush=True)
 
 
 def default_float():
@@ -179,9 +180,9 @@ def enable_xla_jit(mode=True):
  global xla_jit
  xla_jit = mode
  if xla_jit:
- print("Enable just-in-time compilation with XLA.\n")
+ print("Enable just-in-time compilation with XLA.\n", file=sys.stderr, flush=True)
  else:
- print("Disable just-in-time compilation with XLA.\n")
+ print("Disable just-in-time compilation with XLA.\n", file=sys.stderr, flush=True)
 
 
 def disable_xla_jit():

docs/demos/pinn_forward/heat.resample.rst

@@ -110,7 +110,7 @@ We then train the model for 20000 iterations:
 
 .. code-block:: python
 
- losshistory, train_state = model.train(epochs=200000, callbacks=[pde_resampler])
+ losshistory, train_state = model.train(iterations=200000, callbacks=[pde_resampler])
  
 After we train the network using Adam, we continue to train the network using L-BFGS to achieve a smaller loss:
 

docs/demos/pinn_forward/helmholtz.2d.sound.hard.abc.rst

@@ -67,7 +67,7 @@ Then, we begin by defining the general parameters for the problem. The PINN will
 .. code-block:: python
 
  weights = 1
- epochs = 10000
+ iterations = 10000
  learning_rate = 1e-3
  num_dense_layers = 3
  num_dense_nodes = 350
@@ -216,7 +216,7 @@ We first train the model for 5000 iterations with Adam optimizer:
 
 .. code-block:: python
 
- losshistory, train_state = model.train(epochs=epochs)
+ losshistory, train_state = model.train(iterations=iterations)
 
 
 Complete code

docs/demos/pinn_inverse/lorenz.inverse.rst

@@ -124,7 +124,7 @@ We then train the model for 60000 iterations:
 
 .. code-block:: python
 
- losshistory, train_state = model.train(epochs=60000, callbacks=[variable])
+ losshistory, train_state = model.train(iterations=60000, callbacks=[variable])
 
 Complete code
 --------------

examples/Makefile

@@ -1,5 +1,5 @@
 # Directories where to look for *.py example scripts
-EXAMPLE_DIRECTORIES := function pinn_forward pinn_inverse
+EXAMPLE_DIRECTORIES := function operator pinn_forward pinn_inverse
 
 # Environment variables for the scripts
 # Note that the PYTHONPATH is relative to each example subdirectory, not the
@@ -24,12 +24,12 @@ run_all_examples: $(example_files)
  @echo -----------------------------------------------------
  @echo $@
  @echo -----------------------------------------------------
- @cd $(dir $@) && $(DDE_TEST_ENV) python ../sample_to_test.py $(notdir $@) | PYTHONPATH=../.. python -
+ @cd $(dir $@) && $(DDE_TEST_ENV) python3 ../sample_to_test.py $(notdir $@) | PYTHONPATH=../.. python3 -
  @echo
  @echo
  @cd ..
 
 
 # Clean all data files in example directories
-clean: $(foreach dir,$(EXAMPLE_DIRECTORIES),$(wildcard $(dir)/*.dat)) $(foreach dir,$(EXAMPLE_DIRECTORIES),$(wildcard $(dir)/*.npz))
+clean: $(foreach dir,$(EXAMPLE_DIRECTORIES),$(wildcard $(dir)/*.dat)) $(foreach dir,$(EXAMPLE_DIRECTORIES),$(wildcard $(dir)/*.npz)) $(foreach dir,$(EXAMPLE_DIRECTORIES),$(wildcard $(dir)/*.pdf))
  rm $^

examples/sample_to_test.py

@@ -9,9 +9,10 @@
 
 import re
 import sys
+import deepxde as dde
 
 
-EPOCHS_RE = re.compile(r"^(.*)epochs\s*=\s*(\d+)(.*)$", re.DOTALL)
+ITERATIONS_RE = re.compile(r"^(.*)iterations\s*=\s*(\d+)(.*)$", re.DOTALL)
 
 PROLOG = """
 from deepxde.optimizers import set_LBFGS_options
@@ -24,9 +25,9 @@
 
 def transform(line, file_name):
  """Apply transformations to line."""
- m = re.match(EPOCHS_RE, line)
+ m = re.match(ITERATIONS_RE, line)
  if m is not None:
- line = m.expand(r"\1epochs=1\3")
+ line = m.expand(r"\1iterations=1\3")
 
  # Burgers_RAR.py has an additional convergence loop: force 1 single pass
  if file_name == "Burgers_RAR.py" and line.startswith("while"):
@@ -45,5 +46,42 @@ def transform(line, file_name):
 
  print(PROLOG)
  with open(file_name, "r") as input:
- for line in input:
- sys.stdout.write(transform(line, file_name))
+ if file_name not in (
+ # this example uses skopt which is not maintained
+ # and uses deprecated numpy API
+ "Allen_Cahn.py",
+ # the below examples have different code for different
+ # backends
+ "Beltrami_flow.py",
+ "Helmholtz_Dirichlet_2d_HPO.py",
+ "Laplace_disk.py",
+ "Lotka_Volterra.py",
+ "Poisson_Dirichlet_1d.py",
+ "Poisson_periodic_1d.py",
+ "diffusion_1d_exactBC.py",
+ "diffusion_1d_resample.py",
+ "diffusion_1d.py",
+ "diffusion_reaction.py",
+ "fractional_diffusion_1d.py",
+ "fractional_Poisson_1d.py",
+ "fractional_Poisson_2d.py",
+ "fractional_Poisson_3d.py",
+ "ide.py",
+ "diffusion_1d_inverse.py",
+ "fractional_Poisson_1d_inverse.py",
+ "fractional_Poisson_2d_inverse.py",
+ "Poisson_Lshape.py",
+ "ode_system.py",
+ "Lorenz_inverse.py",
+ # gives error with tensorflow.compat.v1
+ "Volterra_IDE.py",
+ # the dataset is large and not included in repo
+ "antiderivative_unaligned.py",
+ "antiderivative_aligned.py",
+ ):
+ lines = input.readlines()
+ if dde.backend.get_preferred_backend() in lines[0].replace(",", "").replace(
+ '"""', ""
+ ).replace("\n", "").split(" "):
+ for line in lines:
+ sys.stdout.write(transform(line, file_name))