Merge pull request #40 from DKreuter/main

fixing torch 2. and numpy 2. issues

.github/workflows/pytest.yml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@v3

.gitignore

@@ -16,9 +16,9 @@ __pycache__/*
 
 # Log folders
 **/lightning_logs/**
-**/fluid_logs/**
-**/Multiscale/**
 **/bosch/**
+**/experiments/**
+**/fluid_logs/**
 # Project files
 .ropeproject
 .project
@@ -57,3 +57,4 @@ MANIFEST
 .venv*/
 .conda*/
 _venv/
+.env/

2.0.0

@@ -0,0 +1,108 @@
+Collecting package metadata (current_repodata.json): ...working... done
+Solving environment: ...working... done
+
+## Package Plan ##
+
+  environment location: C:\python\torchphysics\_venv
+
+  added / updated specs:
+    - pytorch-lightning
+
+
+The following packages will be downloaded:
+
+    package                    |            build
+    ---------------------------|-----------------
+    comm-0.2.1                 |  py310haa95532_0          15 KB  defaults
+    debugpy-1.6.7              |  py310hd77b12b_0         2.9 MB  defaults
+    exceptiongroup-1.2.0       |  py310haa95532_0          31 KB  defaults
+    filelock-3.13.1            |  py310haa95532_0          22 KB  defaults
+    fsspec-2024.3.1            |  py310haa95532_0         278 KB  defaults
+    gmpy2-2.1.2                |  py310h7f96b67_0         160 KB  defaults
+    ipykernel-6.28.0           |  py310haa95532_0         199 KB  defaults
+    ipython-8.25.0             |  py310haa95532_0         1.2 MB  defaults
+    jupyter_client-7.1.2       |     pyhd3eb1b0_0          93 KB  defaults
+    jupyter_core-5.7.2         |  py310haa95532_0         110 KB  defaults
+    libuv-1.48.0               |       h827c3e9_0         322 KB  defaults
+    lightning-utilities-0.9.0  |  py310haa95532_0          38 KB  defaults
+    mpc-1.1.0                  |       h7edee0f_1         260 KB  defaults
+    mpfr-4.0.2                 |       h62dcd97_1         1.5 MB  defaults
+    mpir-3.0.0                 |       hec2e145_1         1.3 MB  defaults
+    mpmath-1.3.0               |  py310haa95532_0         834 KB  defaults
+    networkx-3.3               |  py310haa95532_0         2.5 MB  defaults
+    numpy-1.26.4               |  py310h055cbcc_0          11 KB  defaults
+    numpy-base-1.26.4          |  py310h65a83cf_0         8.6 MB  defaults
+    openssl-1.1.1w             |       h2bbff1b_0         5.5 MB  defaults
+    platformdirs-3.10.0        |  py310haa95532_0          36 KB  defaults
+    prompt-toolkit-3.0.43      |  py310haa95532_0         592 KB  defaults
+    prompt_toolkit-3.0.43      |       hd3eb1b0_0           5 KB  defaults
+    pytorch-2.3.0              |cpu_py310h9432977_0       111.3 MB  defaults
+    pytorch-lightning-2.3.0    |  py310haa95532_0         874 KB  defaults
+    pyyaml-6.0.1               |  py310h2bbff1b_0         155 KB  defaults
+    pyzmq-25.1.2               |  py310hd77b12b_0         412 KB  defaults
+    qtpy-2.4.1                 |  py310haa95532_0         127 KB  defaults
+    sympy-1.12                 |  py310haa95532_0        10.5 MB  defaults
+    torchmetrics-1.4.0.post0   |  py310haa95532_0         698 KB  defaults
+    tqdm-4.66.4                |  py310h9909e9c_0         162 KB  defaults
+    traitlets-5.14.3           |  py310haa95532_0         182 KB  defaults
+    typing-extensions-4.11.0   |  py310haa95532_0          10 KB  defaults
+    typing_extensions-4.11.0   |  py310haa95532_0          62 KB  defaults
+    ------------------------------------------------------------
+                                           Total:       150.9 MB
+
+The following NEW packages will be INSTALLED:
+
+  blas               artifactory/anaconda-pkgs-remote/main/win-64::blas-1.0-mkl
+  comm               artifactory/anaconda-pkgs-remote/main/win-64::comm-0.2.1-py310haa95532_0
+  exceptiongroup     artifactory/anaconda-pkgs-remote/main/win-64::exceptiongroup-1.2.0-py310haa95532_0
+  filelock           artifactory/anaconda-pkgs-remote/main/win-64::filelock-3.13.1-py310haa95532_0
+  fsspec             artifactory/anaconda-pkgs-remote/main/win-64::fsspec-2024.3.1-py310haa95532_0
+  gmpy2              artifactory/anaconda-pkgs-remote/main/win-64::gmpy2-2.1.2-py310h7f96b67_0
+  intel-openmp       artifactory/anaconda-pkgs-remote/main/win-64::intel-openmp-2023.1.0-h59b6b97_46320
+  ipykernel          artifactory/anaconda-pkgs-remote/main/win-64::ipykernel-6.28.0-py310haa95532_0
+  ipython            artifactory/anaconda-pkgs-remote/main/win-64::ipython-8.25.0-py310haa95532_0
+  jupyter_client     artifactory/anaconda-pkgs-remote/main/noarch::jupyter_client-7.1.2-pyhd3eb1b0_0
+  libsodium          artifactory/anaconda-pkgs-remote/main/win-64::libsodium-1.0.18-h62dcd97_0
+  libuv              artifactory/anaconda-pkgs-remote/main/win-64::libuv-1.48.0-h827c3e9_0
+  lightning-utiliti~ artifactory/anaconda-pkgs-remote/main/win-64::lightning-utilities-0.9.0-py310haa95532_0
+  mkl                artifactory/anaconda-pkgs-remote/main/win-64::mkl-2023.1.0-h6b88ed4_46358
+  mkl-service        artifactory/anaconda-pkgs-remote/main/win-64::mkl-service-2.4.0-py310h2bbff1b_1
+  mkl_fft            artifactory/anaconda-pkgs-remote/main/win-64::mkl_fft-1.3.8-py310h2bbff1b_0
+  mkl_random         artifactory/anaconda-pkgs-remote/main/win-64::mkl_random-1.2.4-py310h59b6b97_0
+  mpc                artifactory/anaconda-pkgs-remote/main/win-64::mpc-1.1.0-h7edee0f_1
+  mpfr               artifactory/anaconda-pkgs-remote/main/win-64::mpfr-4.0.2-h62dcd97_1
+  mpir               artifactory/anaconda-pkgs-remote/main/win-64::mpir-3.0.0-hec2e145_1
+  mpmath             artifactory/anaconda-pkgs-remote/main/win-64::mpmath-1.3.0-py310haa95532_0
+  networkx           artifactory/anaconda-pkgs-remote/main/win-64::networkx-3.3-py310haa95532_0
+  numpy              artifactory/anaconda-pkgs-remote/main/win-64::numpy-1.26.4-py310h055cbcc_0
+  numpy-base         artifactory/anaconda-pkgs-remote/main/win-64::numpy-base-1.26.4-py310h65a83cf_0
+  platformdirs       artifactory/anaconda-pkgs-remote/main/win-64::platformdirs-3.10.0-py310haa95532_0
+  psutil             artifactory/anaconda-pkgs-remote/main/win-64::psutil-5.9.0-py310h2bbff1b_0
+  pytorch            artifactory/anaconda-pkgs-remote/main/win-64::pytorch-2.3.0-cpu_py310h9432977_0
+  pytorch-lightning  artifactory/anaconda-pkgs-remote/main/win-64::pytorch-lightning-2.3.0-py310haa95532_0
+  pyyaml             artifactory/anaconda-pkgs-remote/main/win-64::pyyaml-6.0.1-py310h2bbff1b_0
+  pyzmq              artifactory/anaconda-pkgs-remote/main/win-64::pyzmq-25.1.2-py310hd77b12b_0
+  qtpy               artifactory/anaconda-pkgs-remote/main/win-64::qtpy-2.4.1-py310haa95532_0
+  sympy              artifactory/anaconda-pkgs-remote/main/win-64::sympy-1.12-py310haa95532_0
+  tbb                artifactory/anaconda-pkgs-remote/main/win-64::tbb-2021.8.0-h59b6b97_0
+  torchmetrics       artifactory/anaconda-pkgs-remote/main/win-64::torchmetrics-1.4.0.post0-py310haa95532_0
+  tqdm               artifactory/anaconda-pkgs-remote/main/win-64::tqdm-4.66.4-py310h9909e9c_0
+  yaml               artifactory/anaconda-pkgs-remote/main/win-64::yaml-0.2.5-he774522_0
+  zeromq             artifactory/anaconda-pkgs-remote/main/win-64::zeromq-4.3.5-hd77b12b_0
+
+The following packages will be UPDATED:
+
+  ca-certificates              2022.6.15-boschca_h5b45459_0 --> 2024.7.4-boschca_h56e8100_0
+  certifi            bosch-ca-injected/win-64::certifi-202~ --> bosch-ca-injected/noarch::certifi-2024.7.4-boschca_pyhd8ed1ab_0
+  debugpy                             1.5.1-py310hd77b12b_0 --> 1.6.7-py310hd77b12b_0
+  jupyter_core                       4.10.0-py310haa95532_0 --> 5.7.2-py310haa95532_0
+  openssl                                 1.1.1p-h2bbff1b_0 --> 1.1.1w-h2bbff1b_0
+  prompt-toolkit     artifactory/anaconda-pkgs-remote/main~ --> artifactory/anaconda-pkgs-remote/main/win-64::prompt-toolkit-3.0.43-py310haa95532_0
+  prompt_toolkit                          3.0.20-hd3eb1b0_0 --> 3.0.43-hd3eb1b0_0
+  traitlets          artifactory/anaconda-pkgs-remote/main~ --> artifactory/anaconda-pkgs-remote/main/win-64::traitlets-5.14.3-py310haa95532_0
+  typing-extensions  artifactory/anaconda-pkgs-remote/main~ --> artifactory/anaconda-pkgs-remote/main/win-64::typing-extensions-4.11.0-py310haa95532_0
+  typing_extensions  artifactory/anaconda-pkgs-remote/main~ --> artifactory/anaconda-pkgs-remote/main/win-64::typing_extensions-4.11.0-py310haa95532_0
+  vs2015_runtime                     14.27.29016-h5e58377_2 --> 14.29.30133-h43f2093_4
+
+
+Proceed ([y]/n)? 

AUTHORS.rst

@@ -4,3 +4,4 @@ Contributors
 
 * Nick Heilenkötter, nheilenkoetter <[email protected]>
 * Tom Freudenberg, TomF98 <[email protected]>
+* Daniel Kreuter, dkreuter <[email protected]>

README.rst

@@ -80,35 +80,29 @@ Installation
 ============
 TorchPhysics reqiueres the follwing dependencies to be installed: 
 
-- PyTorch_ >= 1.7.1, < 2.0.0
-- `PyTorch Lightning`_ >= 1.3.4, < 2.0.0
-- Numpy_ >= 1.20.2
+- Python >= 3.8
+- PyTorch_ >= 2.0.0
+- `PyTorch Lightning`_ >= 2.0.0
+- Numpy_ >= 1.20.2, < 2.0
 - Matplotlib_ >= 3.0.0
 - Scipy_ >= 1.6.3
 
-Installing TorchPhysics with ``pip``, automatically downloads everything that is needed:
+To install TorchPhysics you can run the following code in any Python environment where ``pip`` is installed
 
 .. code-block:: python
 
   pip install torchphysics
 
-Additionally, to use the ``Shapely`` and ``Trimesh`` functionalities, install the library 
-with the option ``all``:
-
-.. code-block:: python
-
-  pip install torchphysics[all]
-
-
-If you want to add functionalities or modify the code. We recommend copying the 
-repository and installing it locally:
+Or by
 
 .. code-block:: python
 
   git clone https://github.com/boschresearch/torchphysics 
   cd path_to_torchphysics_folder
   pip install .[all]
 
+if you want to modify the code.
+
 .. _Numpy: https://numpy.org/
 .. _Matplotlib: https://matplotlib.org/
 .. _Scipy: https://scipy.org/

docs/requirements.txt

@@ -5,8 +5,8 @@ protobuf~=3.19.0 # fix github test and docu creation
 sphinx>=3.2.1
 sphinx_rtd_theme
 
-torch>=1.7.1
-pytorch-lightning>=1.3.4
+torch>=2.0.0
+pytorch-lightning>=2.0.0
 numpy>=1.20.2
 matplotlib>=3.4.2
 trimesh>=3.9.19

docs/tutorial/solve_pde.rst

@@ -169,11 +169,12 @@ Afterwards we switch to LBFGS:
 
     solver = tp.solver.Solver(train_conditions=[bound_cond, pde_cond], optimizer_setting=optim)
 
-    trainer = pl.Trainer(gpus=1,
-                         max_steps=3000, # number of training steps
-                         logger=False,
-                         benchmark=True,
-                         checkpoint_callback=False)
+    trainer = pl.Trainer(devices=1, accelerator="gpu",
+                        num_sanity_val_steps=0,
+                        benchmark=True,
+                        max_steps=3000, 
+                        logger=False, 
+                        enable_checkpointing=False)
                         
     trainer.fit(solver)
 

docs/tutorial/solver_info.rst

@@ -22,15 +22,20 @@ the basic trainer is defined as follows:
   
   import pytorch_lightning as pl
 
-  trainer = pl.Trainer(gpus=1, max_steps=4000, check_val_every_n_epoch=10)
+  trainer = pl.Trainer(devices=1, accelerator="gpu", 
+                        max_steps=4000, check_val_every_n_epoch=10)
   trainer.fit(solver) # start training
 
 Some important keywords are:
 
-- **gpus**: The number of GPUs that should be used. If only one or more CPUs are available
-  set ```gpus=None``. Depending on the operating system, the GPUs may have to be further
-  specified beforehand via ``os.environ`` or other ways. There are more different possiblities to 
-  specify the used device, see the above mentionde documentation.
+- **devices**: The number of devices that should be used. 
+- **accelerator**: On what kind of device the network will be trained.
+  Usually, one needs at least one GPU to train a neural network sufficiently well. If only 
+  CPUs are available set ``"cpu"``. 
+  Depending on the operating system, the GPUs may have to be further
+  specified beforehand via ``os.environ`` or other ways. 
+  There are more different possibilities to specify the used device, 
+  see the above-mentioned documentation.
 - **max_steps**: The maximum number of training iterations. In each iteration
   all defined training conditions will be evaluated 
   (e.g. points sampled, model output computed, residuals evaluated, etc.) and a 

examples/deeponet/inverse_ode.ipynb

@@ -258,13 +258,12 @@
     "\n",
     "solver = tp.solver.Solver([data_condition], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(gpus='-1' if torch.cuda.is_available() else None,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     max_steps=10000,\n",
-    "                     logger=False,\n",
-    "                     checkpoint_callback=False\n",
-    "                     )\n",
+    "                     max_steps=10000, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "\n",
     "trainer.fit(solver)"
    ]
@@ -327,13 +326,12 @@
     "\n",
     "solver = tp.solver.Solver([data_condition], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(gpus='-1' if torch.cuda.is_available() else None,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     max_steps=1000,\n",
-    "                     logger=False,\n",
-    "                     checkpoint_callback=False\n",
-    "                     )\n",
+    "                     max_steps=1000, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "\n",
     "trainer.fit(solver)"
    ]

examples/deeponet/ode.ipynb

@@ -191,13 +191,12 @@
     "\n",
     "solver = tp.solver.Solver([ode_cond, initial_cond], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(gpus='-1' if torch.cuda.is_available() else None,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     max_steps=5000,\n",
-    "                     logger=False,\n",
-    "                     checkpoint_callback=False\n",
-    "                     )\n",
+    "                     max_steps=5000, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "\n",
     "trainer.fit(solver)"
    ]
@@ -284,11 +283,12 @@
     "\n",
     "solver = tp.solver.Solver(train_conditions=[ode_cond, initial_cond], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(gpus=1,\n",
-    "                     max_steps=3000, \n",
-    "                     logger=False,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
+    "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     checkpoint_callback=False)\n",
+    "                     max_steps=3000, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "                     \n",
     "trainer.fit(solver)"
    ]

examples/deeponet/oscillator.ipynb

@@ -8,8 +8,8 @@
    "source": [
     "import os\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n",
-    "import torch\n",
     "import torchphysics as tp\n",
+    "import torch\n",
     "import pytorch_lightning as pl"
    ]
   },
@@ -219,13 +219,12 @@
    "source": [
     "solver = tp.solver.Solver([ode_cond])\n",
     "\n",
-    "trainer = pl.Trainer(gpus='-1' if torch.cuda.is_available() else None,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     max_steps=2000,\n",
-    "                     logger=False,\n",
-    "                     checkpoint_callback=False\n",
-    "                     )\n",
+    "                     max_steps=30000, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "\n",
     "trainer.fit(solver)"
    ]
@@ -311,12 +310,12 @@
     "\n",
     "solver = tp.solver.Solver(train_conditions=[ode_cond], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(gpus=1,\n",
-    "                     max_steps=2500, \n",
-    "                     logger=False,\n",
+    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\",\n",
+    "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
-    "                     checkpoint_callback=False)\n",
-    "                     \n",
+    "                     max_steps=2500, \n",
+    "                     logger=False, \n",
+    "                     enable_checkpointing=False)\n",
     "trainer.fit(solver)"
    ]
   },
@@ -454,10 +453,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.11.7"
   },
   "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}