23040531

diffusion.py

@@ -271,7 +271,7 @@ class TrainConfig:
     stride = (2,4) if dim == 2 else (2,2,2)
     num_image = 3000#480#1200#120#3000#300#3000#6000#30#60#6000#1000#2000#20000#15000#7000#25600#3000#10000#1000#10000#5000#2560#800#2560
     batch_size = 1#1#10#50#10#50#20#50#1#2#50#20#2#100 # 10
-    n_epoch = 2#0#1#50#10#1#50#1#50#5#50#5#50#100#50#100#30#120#5#4# 10#50#20#20#2#5#25 # 120
+    n_epoch = 50#20#1#50#10#1#50#1#50#5#50#5#50#100#50#100#30#120#5#4# 10#50#20#20#2#5#25 # 120
     HII_DIM = 64
     num_redshift = 64#256#512#256#512#256#512#256#512#64#512#64#512#64#256CUDAoom#128#64#512#128#64#512#256#256#64#512#128
     startat = 512-num_redshift
@@ -284,7 +284,7 @@ class TrainConfig:
             1: [10, 250], # HII_EFF_FACTOR
             },
         images = {
-            0: [0, 80], # brightness_temp
+            0: [-338, 54],#[0, 80], # brightness_temp
             }
         )
 

frontera_diffusion.sbatch

@@ -1,13 +1,13 @@
 #!/bin/bash
 #SBATCH -J diffusion # Job name
-#SBATCH -p rtx-dev
+#SBATCH -p rtx #-dev
 #SBATCH -N2            # Number of nodes and cores per node required
 #SBATCH --ntasks-per-node=1
-#SBATCH -t 02:00:00                                    # Duration of the job (Ex: 15 mins)
+#SBATCH -t 37:30:00                                    # Duration of the job (Ex: 15 mins)
 #SBATCH -oReport-%j                         # Combined output and error messages file
 #SBATCH --mail-type=BEGIN,END,FAIL              # Mail preferences
+#SBATCH --mail-user=xiabin@gatech.edu
 
-python -c "import torch; print('torch.cuda.is_available() =', torch.cuda.is_available()); print('torch.__version__ =', torch.__version__); print('torch.version.cuda =', torch.version.cuda)"
 pwd
 date
 #module load anaconda3/2022.05 # Load module dependencies
@@ -15,6 +15,7 @@ date
 #conda activate diffusers 
 conda env list
 module list
+python -c "import torch; print(torch.cuda.is_available(), torch.__version__, torch.__path__, torch.version.cuda)"
 cat $0
 
 MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
@@ -25,9 +26,9 @@ export MASTER_PORT=$MASTER_PORT
 
 srun python diffusion.py \
     --train "$SCRATCH/LEN128-DIM64-CUB16-Tvir[4, 6]-zeta[10, 250]-0809-123640.h5" \
-    --resume outputs/model-N2000-device_count1-node8-epoch19-19004529 \
     --num_new_img_per_gpu 50 \
     --max_num_img_per_gpu 2 \
-    --gradient_accumulation_steps 40 \
+    --gradient_accumulation_steps 30 \
+    #--resume outputs/model-N2000-device_count1-node8-epoch19-19004529 \
 ######################################################################################
 

quantify_results.ipynb

@@ -2,154 +2,157 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import h5py\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "os.environ['TENSORBOARD_BINARY'] = '/home1/09986/binxia/miniconda3/envs/diffusers/bin/tensorboard'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mFailed to start the Kernel. \n",
-      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
-      "\u001b[1;31mError code from Jupyter: 1\n",
-      "\u001b[1;31mTraceback (most recent call last):\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/bin/jupyter-notebook\", line 7, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from notebook.notebookapp import main\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/notebook/notebookapp.py\", line 44, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from jinja2 import Environment, FileSystemLoader\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/__init__.py\", line 12, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .environment import Environment\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/environment.py\", line 25, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .defaults import BLOCK_END_STRING\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/defaults.py\", line 3, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/filters.py\", line 13, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from markupsafe import soft_unicode\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31mImportError: cannot import name 'soft_unicode' from 'markupsafe' (/storage/home/hcoda1/3/bxia34/.local/lib/python3.9/site-packages/markupsafe/__init__.py). \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<KeysViewHDF5 ['brightness_temp', 'density', 'kwargs', 'params', 'redshifts_distances', 'seeds', 'xH_box']>\n"
      ]
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import h5py\n",
-    "import matplotlib.pyplot as plt"
+    "with h5py.File(\n",
+    "    os.path.join(\n",
+    "        os.getenv('SCRATCH'),\n",
+    "        \"LEN128-DIM64-CUB16-Tvir4.4-zeta131.341-0812-104709.h5\"\n",
+    "    )\n",
+    ") as f:\n",
+    "    print(f.keys())\n",
+    "    Tb = np.array(f[\"brightness_temp\"][:100,0,...,512-64:512])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mFailed to start the Kernel. \n",
-      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
-      "\u001b[1;31mError code from Jupyter: 1\n",
-      "\u001b[1;31mTraceback (most recent call last):\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/bin/jupyter-notebook\", line 7, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from notebook.notebookapp import main\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/notebook/notebookapp.py\", line 44, in <module>\n",
-      "\u001b[1;31m    from jinja2 import Environment, FileSystemLoader\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/__init__.py\", line 12, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .environment import Environment\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/environment.py\", line 25, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .defaults import BLOCK_END_STRING\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/defaults.py\", line 3, in <module>\n",
-      "\u001b[1;31m    from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/filters.py\", line 13, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from markupsafe import soft_unicode\n",
-      "\u001b[1;31mImportError: cannot import name 'soft_unicode' from 'markupsafe' (/storage/home/hcoda1/3/bxia34/.local/lib/python3.9/site-packages/markupsafe/__init__.py). \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The tensorboard extension is already loaded. To reload it, use:\n",
+      "  %reload_ext tensorboard\n"
      ]
     }
    ],
    "source": [
-    "with h5py.File(\"/storage/home/hcoda1/3/bxia34/scratch/LEN128-DIM64-CUB8-4.4-131.341.h5\") as f:\n",
-    "    print(f.keys())\n",
-    "    Tb = np.array(f[\"brightness_temp\"][:10,0])"
+    "%load_ext tensorboard"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "      <iframe id=\"tensorboard-frame-5f491a36d9c000da\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
+       "      </iframe>\n",
+       "      <script>\n",
+       "        (function() {\n",
+       "          const frame = document.getElementById(\"tensorboard-frame-5f491a36d9c000da\");\n",
+       "          const url = new URL(\"/\", window.location);\n",
+       "          const port = 6007;\n",
+       "          if (port) {\n",
+       "            url.port = port;\n",
+       "          }\n",
+       "          frame.src = url;\n",
+       "        })();\n",
+       "      </script>\n",
+       "    "
+      ],
       "text/plain": [
-       "(10, 64, 514)"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "Tb.shape"
+    "%tensorboard --logdir=/home1/09986/binxia/work/ml21cm/outputs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mFailed to start the Kernel. \n",
-      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
-      "\u001b[1;31mError code from Jupyter: 1\n",
-      "\u001b[1;31mTraceback (most recent call last):\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/bin/jupyter-notebook\", line 7, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from notebook.notebookapp import main\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/notebook/notebookapp.py\", line 44, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from jinja2 import Environment, FileSystemLoader\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/__init__.py\", line 12, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .environment import Environment\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/environment.py\", line 25, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .defaults import BLOCK_END_STRING\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/defaults.py\", line 3, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/filters.py\", line 13, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from markupsafe import soft_unicode\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31mImportError: cannot import name 'soft_unicode' from 'markupsafe' (/storage/home/hcoda1/3/bxia34/.local/lib/python3.9/site-packages/markupsafe/__init__.py). \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 64, 64)\n",
+      "-0.5610267\n",
+      "28.256622\n"
      ]
     }
    ],
    "source": [
-    "plt.figure(dpi=200)\n",
+    "print(Tb.shape)\n",
+    "print(Tb.min())\n",
+    "print(Tb.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x2b2faa369670>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(dpi=100)\n",
     "plt.imshow(Tb[0])\n",
     "# plt.close()\n",
-    "plt.figure(dpi=200)\n",
+    "plt.figure(dpi=100)\n",
     "plt.imshow(Tb[1])\n",
     "# plt.close()\n",
     "# plt.figure(dpi=200)\n",
@@ -159,37 +162,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mFailed to start the Kernel. \n",
-      "\u001b[1;31mJupyter server crashed. Unable to connect. \n",
-      "\u001b[1;31mError code from Jupyter: 1\n",
-      "\u001b[1;31mTraceback (most recent call last):\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/bin/jupyter-notebook\", line 7, in <module>\n",
-      "\u001b[1;31m    from notebook.notebookapp import main\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/notebook/notebookapp.py\", line 44, in <module>\n",
-      "\u001b[1;31m    from jinja2 import Environment, FileSystemLoader\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/__init__.py\", line 12, in <module>\n",
-      "\u001b[1;31m    from .environment import Environment\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/environment.py\", line 25, in <module>\n",
-      "\u001b[1;31m    from .defaults import BLOCK_END_STRING\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/defaults.py\", line 3, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from .filters import FILTERS as DEFAULT_FILTERS  # noqa: F401\n",
-      "\u001b[1;31m  File \"/usr/local/pace-apps/manual/packages/anaconda3/2022.05/lib/python3.9/site-packages/jinja2/filters.py\", line 13, in <module>\n",
-      "\u001b[1;31m\n",
-      "\u001b[1;31m    from markupsafe import soft_unicode\n",
-      "\u001b[1;31mImportError: cannot import name 'soft_unicode' from 'markupsafe' (/storage/home/hcoda1/3/bxia34/.local/lib/python3.9/site-packages/markupsafe/__init__.py). \n",
-      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "Tb0 = Tb[0,:,-128:]\n",
     "Tb1 = Tb[1,:,-128:]"
@@ -2307,9 +2282,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python (diffusers)",
    "language": "python",
-   "name": "python3"
+   "name": "diffusers"
   },
   "language_info": {
    "codemirror_mode": {
@@ -2321,7 +2296,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,