notebooks are running, tests still in test

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 <nick7@uni-bremen.de>
 * Tom Freudenberg, TomF98 <tomfre@uni-bremen.de>
+* Daniel Kreuter, dkreuter <danielChristopher.Kreuter@de.bosch.com>

examples/tutorial/Introduction_Tutorial_PINNs.ipynb

@@ -623,13 +623,21 @@
    "source": [
     "# Start the training\n",
     "trainer = pl.Trainer(\n",
-    "    gpus=device, # or None if CPU is used\n",
+    "    # gpus=device, # or None if CPU is used\n",
     "    max_steps=5000, # number of training steps\n",
     "    logger=False,\n",
     "    benchmark=True,\n",
     "    # checkpoint_callback=False  # Uncomment this for more verbose\n",
-    ")\n",
-    "\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72bfec0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "trainer.fit(solver) # start training"
    ]
   },
@@ -852,14 +860,6 @@
     "plt.imshow(np.rot90(output[:, :]), 'gray', vmin=vmin, vmax=vmax)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9840aad9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -878,7 +878,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

examples/tutorial/Tutorial_PINNs_Parameter_Dependency.ipynb

@@ -354,8 +354,6 @@
     "import pytorch_lightning as pl\n",
     "import os\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" if torch.cuda.is_available() else \"0\"\n",
-    "device = 1 if torch.cuda.is_available() else None\n",
-    "print('Training on', device)\n",
     "print (\"GPU available: \" + str(torch.cuda.is_available()))"
    ]
   },
@@ -389,7 +387,8 @@
    "outputs": [],
    "source": [
     "# Start the training\n",
-    "trainer = pl.Trainer(devices=device, accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
+    "trainer = pl.Trainer(devices=1, \n",
+    "                    #  accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
     "                     max_steps=2000, # number of training steps\n",
@@ -412,7 +411,8 @@
     "pde_condition.sampler = pde_condition.sampler.make_static() # LBFGS can not work with varying points!\n",
     "solver = tp.solver.Solver(train_conditions=training_conditions, optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(devices=device, accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
+    "trainer = pl.Trainer(devices=1,\n",
+    "                    #  accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
     "                     max_steps=3000, # number of training steps\n",

examples/tutorial/domain_creation.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.10.4"
   },
   "orig_nbformat": 4
  },

examples/tutorial/polygons_external_objects.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +218,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.10.4"
   },
   "orig_nbformat": 4
  },

examples/tutorial/solve_pde.ipynb

@@ -212,7 +212,8 @@
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" if torch.cuda.is_available() else \"0\"\n",
     "device = 1 if torch.cuda.is_available() else None\n",
     "\n",
-    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
+    "trainer = pl.Trainer(devices=1, \n",
+    "                    #  accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
     "                     max_steps=4000, # number of training steps\n",
@@ -242,7 +243,8 @@
     "pde_cond.sampler = pde_cond.sampler.make_static() # LBFGS can not work with varying points!\n",
     "solver = tp.solver.Solver(train_conditions=[bound_cond, pde_cond], optimizer_setting=optim)\n",
     "\n",
-    "trainer = pl.Trainer(devices=1, accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
+    "trainer = pl.Trainer(devices=1, \n",
+    "                    #  accelerator=\"gpu\", # what to use to solve problem and how many devices\n",
     "                     num_sanity_val_steps=0,\n",
     "                     benchmark=True,\n",
     "                     max_steps=3000, # number of training steps\n",
@@ -300,12 +302,6 @@
     "\n",
     "More in-depth information can be found in the [tutorial](https://torchphysics.readthedocs.io/en/latest/tutorial/tutorial_start.html)."
    ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -327,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,