merge from main

Junang-Wang · Mar 21, 2024 · cd06c96 · cd06c96
1 parent 4dc26b2
commit cd06c96
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Showing 4 changed files with 735 additions and 13 deletions.
diff --git a/.vscode/launch.json b/.vscode/launch.json
@@ -9,7 +9,8 @@
             "type": "python",
             "request": "launch",
             "module": "enter-your-module-name",
-            "justMyCode": true
+            "justMyCode": true,
+            "env": {"PL_TORCH_DISTRIBUTED_BACKEND":"gloo"}
         }
     ]
 }
diff --git a/Modeling eMNS/Generative_model_v2.ipynb b/Modeling eMNS/Generative_model_v2.ipynb
@@ -18,9 +18,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Good to go\n"
+     ]
+    }
+   ],
    "source": [
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -38,9 +46,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([146, 6, 21, 21, 21])\n",
+      "current shape torch.Size([146, 12])\n",
+      "Bfield shape torch.Size([146, 3, 16, 16, 16])\n"
+     ]
+    }
+   ],
    "source": [
     "from ReadData import ReadCurrentAndField_CNN\n",
     "import glob\n",
@@ -108,9 +126,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "2024-03-21 15:20:55,592\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
+      "2024-03-21 15:20:58,015\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
+      "2024-03-21 15:20:58,119\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
+      "2024-03-21 15:21:07,788\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n",
+      "2024-03-21 15:21:10,410\tINFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Trainer(...)`.\n",
+      "2024-03-21 15:21:10,412\tINFO tune.py:583 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see /~https://github.com/ray-project/ray/issues/36949\n"
+     ]
+    },
+    {
+     "ename": "ArrowInvalid",
+     "evalue": "URI has empty scheme: '~/Trained_model'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mArrowInvalid\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 122\u001b[0m\n\u001b[0;32m    114\u001b[0m trainer \u001b[38;5;241m=\u001b[39m TorchTrainer(\n\u001b[0;32m    115\u001b[0m     train_loop_per_worker \u001b[38;5;241m=\u001b[39m train_GM,\n\u001b[0;32m    116\u001b[0m     train_loop_config \u001b[38;5;241m=\u001b[39m train_loop_config,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    119\u001b[0m \n\u001b[0;32m    120\u001b[0m )\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# train the model\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;66;03m#----------------------------------------------\u001b[39;00m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;66;03m# tuner = tune.Tuner(\u001b[39;00m\n\u001b[0;32m    125\u001b[0m \u001b[38;5;66;03m#     trainer,\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    134\u001b[0m \u001b[38;5;66;03m# # tune the model    \u001b[39;00m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;66;03m# results = tuner.fit()\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\base_trainer.py:625\u001b[0m, in \u001b[0;36mBaseTrainer.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    619\u001b[0m restore_msg \u001b[38;5;241m=\u001b[39m TrainingFailedError\u001b[38;5;241m.\u001b[39m_RESTORE_MSG\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    620\u001b[0m     trainer_cls_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[0;32m    621\u001b[0m     path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(experiment_local_path),\n\u001b[0;32m    622\u001b[0m )\n\u001b[0;32m    624\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 625\u001b[0m     result_grid \u001b[38;5;241m=\u001b[39m \u001b[43mtuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    626\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TuneError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    627\u001b[0m     \u001b[38;5;66;03m# Catch any `TuneError`s raised by the `Tuner.fit` call.\u001b[39;00m\n\u001b[0;32m    628\u001b[0m     \u001b[38;5;66;03m# Unwrap the `TuneError` if needed.\u001b[39;00m\n\u001b[0;32m    629\u001b[0m     parent_error \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;129;01mor\u001b[39;00m e\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\tuner.py:381\u001b[0m, in \u001b[0;36mTuner.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_ray_client:\n\u001b[0;32m    380\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 381\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_local_tuner\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    382\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m TuneError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    383\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m TuneError(\n\u001b[0;32m    384\u001b[0m             _TUNER_FAILED_MSG\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    385\u001b[0m                 path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local_tuner\u001b[38;5;241m.\u001b[39mget_experiment_checkpoint_dir()\n\u001b[0;32m    386\u001b[0m             )\n\u001b[0;32m    387\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\impl\\tuner_internal.py:509\u001b[0m, in \u001b[0;36mTunerInternal.fit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    507\u001b[0m param_space \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparam_space)\n\u001b[0;32m    508\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_restored:\n\u001b[1;32m--> 509\u001b[0m     analysis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam_space\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    511\u001b[0m     analysis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_resume(trainable, param_space)\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\impl\\tuner_internal.py:628\u001b[0m, in \u001b[0;36mTunerInternal._fit_internal\u001b[1;34m(self, trainable, param_space)\u001b[0m\n\u001b[0;32m    615\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fitting for a fresh Tuner.\"\"\"\u001b[39;00m\n\u001b[0;32m    616\u001b[0m args \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    617\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tune_run_arguments(trainable),\n\u001b[0;32m    618\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    626\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tuner_kwargs,\n\u001b[0;32m    627\u001b[0m }\n\u001b[1;32m--> 628\u001b[0m analysis \u001b[38;5;241m=\u001b[39m run(\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39margs,\n\u001b[0;32m    630\u001b[0m )\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclear_remote_string_queue()\n\u001b[0;32m    632\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m analysis\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\tune.py:772\u001b[0m, in \u001b[0;36mrun\u001b[1;34m(run_or_experiment, name, metric, mode, stop, time_budget_s, config, resources_per_trial, num_samples, storage_path, storage_filesystem, search_alg, scheduler, checkpoint_config, verbose, progress_reporter, log_to_file, trial_name_creator, trial_dirname_creator, sync_config, export_formats, max_failures, fail_fast, restore, resume, reuse_actors, raise_on_failed_trial, callbacks, max_concurrent_trials, keep_checkpoints_num, checkpoint_score_attr, checkpoint_freq, checkpoint_at_end, chdir_to_trial_dir, local_dir, _remote, _remote_string_queue, _entrypoint)\u001b[0m\n\u001b[0;32m    770\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, exp \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(experiments):\n\u001b[0;32m    771\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exp, Experiment):\n\u001b[1;32m--> 772\u001b[0m         experiments[i] \u001b[38;5;241m=\u001b[39m \u001b[43mExperiment\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    773\u001b[0m \u001b[43m            \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    774\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrun\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    775\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    776\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtime_budget_s\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_budget_s\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    777\u001b[0m \u001b[43m            \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    778\u001b[0m \u001b[43m            \u001b[49m\u001b[43mresources_per_trial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresources_per_trial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    779\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnum_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    780\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    781\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstorage_filesystem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_filesystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    782\u001b[0m \u001b[43m            \u001b[49m\u001b[43msync_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msync_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    783\u001b[0m \u001b[43m            \u001b[49m\u001b[43mcheckpoint_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheckpoint_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    784\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtrial_name_creator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial_name_creator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    785\u001b[0m \u001b[43m            \u001b[49m\u001b[43mtrial_dirname_creator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial_dirname_creator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    786\u001b[0m \u001b[43m            \u001b[49m\u001b[43mlog_to_file\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_to_file\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    787\u001b[0m \u001b[43m            \u001b[49m\u001b[43mexport_formats\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexport_formats\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    788\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmax_failures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_failures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    789\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrestore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrestore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    790\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    792\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fail_fast \u001b[38;5;129;01mand\u001b[39;00m max_failures \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    793\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_failures must be 0 if fail_fast=True.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\tune\\experiment\\experiment.py:166\u001b[0m, in \u001b[0;36mExperiment.__init__\u001b[1;34m(self, name, run, stop, time_budget_s, config, resources_per_trial, num_samples, storage_path, storage_filesystem, sync_config, checkpoint_config, trial_name_creator, trial_dirname_creator, log_to_file, export_formats, max_failures, restore, local_dir)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m name:\n\u001b[0;32m    164\u001b[0m     name \u001b[38;5;241m=\u001b[39m StorageContext\u001b[38;5;241m.\u001b[39mget_experiment_dir_name(run)\n\u001b[1;32m--> 166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_storage_context_cls\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    167\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    168\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_filesystem\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_filesystem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    169\u001b[0m \u001b[43m    \u001b[49m\u001b[43msync_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msync_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    170\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexperiment_dir_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    171\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    172\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStorageContext on the DRIVER:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    174\u001b[0m config \u001b[38;5;241m=\u001b[39m config \u001b[38;5;129;01mor\u001b[39;00m {}\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\_internal\\storage.py:452\u001b[0m, in \u001b[0;36mStorageContext.__init__\u001b[1;34m(self, storage_path, experiment_dir_name, sync_config, storage_filesystem, trial_dir_name, current_checkpoint_index)\u001b[0m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcurrent_checkpoint_index \u001b[38;5;241m=\u001b[39m current_checkpoint_index\n\u001b[0;32m    448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msync_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    449\u001b[0m     dataclasses\u001b[38;5;241m.\u001b[39mreplace(sync_config) \u001b[38;5;28;01mif\u001b[39;00m sync_config \u001b[38;5;28;01melse\u001b[39;00m SyncConfig()\n\u001b[0;32m    450\u001b[0m )\n\u001b[1;32m--> 452\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_filesystem, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_fs_path \u001b[38;5;241m=\u001b[39m \u001b[43mget_fs_and_path\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    453\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstorage_filesystem\u001b[49m\n\u001b[0;32m    454\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    455\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_fs_path \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstorage_fs_path)\u001b[38;5;241m.\u001b[39mas_posix()\n\u001b[0;32m    457\u001b[0m \u001b[38;5;66;03m# Syncing is always needed if a custom `storage_filesystem` is provided.\u001b[39;00m\n\u001b[0;32m    458\u001b[0m \u001b[38;5;66;03m# Otherwise, syncing is only needed if storage_local_path\u001b[39;00m\n\u001b[0;32m    459\u001b[0m \u001b[38;5;66;03m# and storage_fs_path point to different locations.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\ray\\train\\_internal\\storage.py:306\u001b[0m, in \u001b[0;36mget_fs_and_path\u001b[1;34m(storage_path, storage_filesystem)\u001b[0m\n\u001b[0;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m storage_filesystem:\n\u001b[0;32m    304\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m storage_filesystem, storage_path\n\u001b[1;32m--> 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpyarrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFileSystem\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_uri\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstorage_path\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\_fs.pyx:348\u001b[0m, in \u001b[0;36mpyarrow._fs.FileSystem.from_uri\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\error.pxi:143\u001b[0m, in \u001b[0;36mpyarrow.lib.pyarrow_internal_check_status\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\14032\\.conda\\envs\\myenv\\lib\\site-packages\\pyarrow\\error.pxi:99\u001b[0m, in \u001b[0;36mpyarrow.lib.check_status\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mArrowInvalid\u001b[0m: URI has empty scheme: '~/Trained_model'"
+     ]
+    }
+   ],
    "source": [
     "from Neural_network import eMNS_Dataset\n",
     "from Training_loop_v2 import train_GM\n",