Fixed small summary with model architectures until now
This commit is contained in:
Victor Mylle
@@ -42,15 +42,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"#### Data Processor ####\n",
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"data_config = DataConfig()\n",
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"data_config.NRV_HISTORY = True\n",
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"data_config.LOAD_HISTORY = False\n",
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"data_config.LOAD_FORECAST = False\n",
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"data_config.LOAD_HISTORY = True\n",
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"data_config.LOAD_FORECAST = True\n",
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"\n",
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"data_config.WIND_FORECAST = False\n",
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"data_config.WIND_HISTORY = False\n",
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@@ -72,16 +72,32 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "Interrupted by user",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/workspaces/Thesis/src/notebooks/training.ipynb Cell 5\u001b[0m line \u001b[0;36m7\n\u001b[1;32m <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f546865736973222c226c6f63616c446f636b6572223a66616c73652c22636f6e66696746696c65223a7b22246d6964223a312c2270617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f5468657369732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a227673636f64652d66696c65486f7374227d7d@ssh-remote%2Bvictormylle.be/workspaces/Thesis/src/notebooks/training.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m trainer \u001b[39m=\u001b[39m ProbabilisticBaselineTrainer(\n\u001b[1;32m <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f546865736973222c226c6f63616c446f636b6572223a66616c73652c22636f6e66696746696c65223a7b22246d6964223a312c2270617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f5468657369732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a227673636f64652d66696c65486f7374227d7d@ssh-remote%2Bvictormylle.be/workspaces/Thesis/src/notebooks/training.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m quantiles\u001b[39m=\u001b[39mquantiles, data_processor\u001b[39m=\u001b[39mdata_processor, clearml_helper\u001b[39m=\u001b[39mclearml_helper\n\u001b[1;32m <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f546865736973222c226c6f63616c446f636b6572223a66616c73652c22636f6e66696746696c65223a7b22246d6964223a312c2270617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f5468657369732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a227673636f64652d66696c65486f7374227d7d@ssh-remote%2Bvictormylle.be/workspaces/Thesis/src/notebooks/training.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m )\n\u001b[1;32m <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f546865736973222c226c6f63616c446f636b6572223a66616c73652c22636f6e66696746696c65223a7b22246d6964223a312c2270617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f5468657369732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a227673636f64652d66696c65486f7374227d7d@ssh-remote%2Bvictormylle.be/workspaces/Thesis/src/notebooks/training.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m trainer\u001b[39m.\u001b[39madd_metrics_to_track([CRPSLoss()])\n\u001b[0;32m----> <a href='vscode-notebook-cell://dev-container%2B7b22686f737450617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f546865736973222c226c6f63616c446f636b6572223a66616c73652c22636f6e66696746696c65223a7b22246d6964223a312c2270617468223a222f686f6d652f766963746f726d796c6c652f53656144726976652f4d79204c69627261726965732f4750552d7365727665722f5468657369732f2e646576636f6e7461696e65722f646576636f6e7461696e65722e6a736f6e222c22736368656d65223a227673636f64652d66696c65486f7374227d7d@ssh-remote%2Bvictormylle.be/workspaces/Thesis/src/notebooks/training.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m trainer\u001b[39m.\u001b[39;49mtrain()\n",
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"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/probabilistic_baseline.py:43\u001b[0m, in \u001b[0;36mProbabilisticBaselineTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtrain\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m---> 43\u001b[0m task \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minit_clearml_task()\n\u001b[1;32m 44\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 45\u001b[0m time_steps \u001b[39m=\u001b[39m [[] \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m96\u001b[39m)]\n",
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"File \u001b[0;32m/workspaces/Thesis/src/notebooks/../../src/trainers/probabilistic_baseline.py:28\u001b[0m, in \u001b[0;36mProbabilisticBaselineTrainer.init_clearml_task\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclearml_helper:\n\u001b[1;32m 26\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m task_name \u001b[39m=\u001b[39m \u001b[39minput\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39mEnter a task name: \u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 29\u001b[0m \u001b[39mif\u001b[39;00m task_name \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 30\u001b[0m task_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mUntitled Task\u001b[39m\u001b[39m\"\u001b[39m\n",
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"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py:1251\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m 1249\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1250\u001b[0m \u001b[39mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1251\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_input_request(\n\u001b[1;32m 1252\u001b[0m \u001b[39mstr\u001b[39;49m(prompt),\n\u001b[1;32m 1253\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parent_ident[\u001b[39m\"\u001b[39;49m\u001b[39mshell\u001b[39;49m\u001b[39m\"\u001b[39;49m],\n\u001b[1;32m 1254\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_parent(\u001b[39m\"\u001b[39;49m\u001b[39mshell\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[1;32m 1255\u001b[0m password\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 1256\u001b[0m )\n",
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"File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py:1295\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m 1292\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 1293\u001b[0m \u001b[39m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m 1294\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mInterrupted by user\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1295\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m(msg) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[1;32m 1297\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlog\u001b[39m.\u001b[39mwarning(\u001b[39m\"\u001b[39m\u001b[39mInvalid Message:\u001b[39m\u001b[39m\"\u001b[39m, exc_info\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
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]
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}
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],
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"source": [
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"data_processor.set_full_day_skip(True)\n",
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"quantiles = [0.01, 0.05, 0.1, 0.15, 0.4, 0.5, 0.6, 0.85, 0.9, 0.95, 0.99]\n",
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"quantiles = [0.01, 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.99]\n",
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"trainer = ProbabilisticBaselineTrainer(\n",
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" quantiles=quantiles, data_processor=data_processor, clearml_helper=clearml_helper\n",
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")\n",
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"trainer.add_metrics_to_track([CRPSLoss(quantiles=quantiles)])\n",
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"trainer.add_metrics_to_track([CRPSLoss()])\n",
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"trainer.train()"
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]
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},
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@@ -100,7 +116,7 @@
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"source": [
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"#### Hyperparameters ####\n",
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"inputDim = data_processor.get_input_size()\n",
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"learningRate = 0.00005\n",
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"learningRate = 0.0001\n",
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"epochs = 150\n",
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"\n",
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"# model = LinearRegression(inputDim, 96)\n",
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@@ -133,8 +149,8 @@
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"learningRate = 0.0003\n",
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"epochs = 50\n",
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"\n",
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"# model = LinearRegression(inputDim, 1)\n",
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"model = NonLinearRegression(inputDim, 1, hiddenSize=1024, numLayers=5)\n",
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"model = LinearRegression(inputDim, 1)\n",
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"# model = NonLinearRegression(inputDim, 1, hiddenSize=1024, numLayers=5)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)\n",
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"\n",
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"#### Data Processor ####\n",
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@@ -167,13 +183,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ClearML Task: created new task id=1182d39a984b478c9301aafb4a81ff1b\n",
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"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/1182d39a984b478c9301aafb4a81ff1b/output/log\n",
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"96\n"
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]
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},
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@@ -181,72 +199,66 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:68: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" quantiles_tensor = torch.tensor(quantiles)\n",
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"/workspaces/Thesis/src/notebooks/../../src/losses/pinball_loss.py:8: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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" self.quantiles_tensor = torch.tensor(quantiles, dtype=torch.float32)\n",
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"InsecureRequestWarning: Certificate verification is disabled! Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n"
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"Connecting multiple input models with the same name: `checkpoint`. This might result in the wrong model being used when executing remotely\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ClearML Task: created new task id=4652507a84f5435fb6bd98c645d15f24\n",
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"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/4652507a84f5435fb6bd98c645d15f24/output/log\n",
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"2023-11-26 22:15:47,860 - clearml.Task - INFO - Storing jupyter notebook directly as code\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Switching to remote execution, output log page http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/4652507a84f5435fb6bd98c645d15f24/output/log\n"
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]
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},
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{
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"ename": "",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
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"Early stopping triggered\n"
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]
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}
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],
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"source": [
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"task = clearml_helper.get_task(task_name=\"Autoregressive Quantile Regression + Quarter + Load Forecast + Load History\")\n",
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"data_config = task.connect(data_config, name=\"data_features\")\n",
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"\n",
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"#### Hyperparameters ####\n",
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"data_processor.set_output_size(1)\n",
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"inputDim = data_processor.get_input_size()\n",
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"learningRate = 0.0001\n",
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"epochs = 100\n",
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"epochs = 300\n",
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"\n",
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"# quantiles = torch.tensor([0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99]).to(\"cuda\")\n",
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"quantiles = torch.tensor(\n",
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" [0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]\n",
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").to(\"cuda\")\n",
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"quantiles = task.get_parameter(\"general/quantiles\", cast=True)\n",
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"if quantiles is None:\n",
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" quantiles = [0.01, 0.05, 0.1, 0.15, 0.3, 0.4, 0.5, 0.6, 0.7, 0.85, 0.9, 0.95, 0.99]\n",
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" task.set_parameter(\"general/quantiles\", quantiles)\n",
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"else:\n",
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" if isinstance(quantiles, str):\n",
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" quantiles = eval(quantiles)\n",
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"\n",
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"# model = LinearRegression(inputDim, len(quantiles))\n",
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"time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), 4)\n",
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"non_linear_regression_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=1024, numLayers=5)\n",
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"model_parameters = {\n",
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" \"learning_rate\": 0.0001,\n",
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" \"hidden_size\": 512,\n",
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" \"num_layers\": 3,\n",
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" \"dropout\": 0.2,\n",
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" \"time_feature_embedding\": 2,\n",
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"}\n",
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"\n",
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"model_parameters = task.connect(model_parameters, name=\"model_parameters\")\n",
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"\n",
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"time_embedding = TimeEmbedding(data_processor.get_time_feature_size(), model_parameters[\"time_feature_embedding\"])\n",
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"# linear_regression = LinearRegression(time_embedding.output_dim(inputDim), len(quantiles))\n",
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"non_linear_regression_model = NonLinearRegression(time_embedding.output_dim(inputDim), len(quantiles), hiddenSize=model_parameters[\"hidden_size\"], numLayers=model_parameters[\"num_layers\"], dropout=model_parameters[\"dropout\"])\n",
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"model = nn.Sequential(time_embedding, non_linear_regression_model)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=learningRate)\n",
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"optimizer = torch.optim.Adam(model.parameters(), lr=model_parameters[\"learning_rate\"])\n",
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"\n",
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"#### Trainer ####\n",
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"trainer = AutoRegressiveQuantileTrainer(\n",
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" model,\n",
|
||||
" inputDim,\n",
|
||||
" optimizer,\n",
|
||||
" data_processor,\n",
|
||||
" quantiles,\n",
|
||||
" \"cuda\",\n",
|
||||
" debug=True,\n",
|
||||
" clearml_helper=clearml_helper,\n",
|
||||
" debug=False,\n",
|
||||
")\n",
|
||||
"trainer.add_metrics_to_track(\n",
|
||||
" [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss(quantiles)]\n",
|
||||
" [PinballLoss(quantiles), MSELoss(), L1Loss(), CRPSLoss()]\n",
|
||||
")\n",
|
||||
"trainer.early_stopping(patience=10)\n",
|
||||
"trainer.plot_every(5)\n",
|
||||
"trainer.train(epochs=epochs, remotely=True)"
|
||||
"trainer.train(task=task, epochs=epochs, remotely=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -258,49 +270,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:335: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
" quantiles_tensor = torch.tensor(quantiles)\n",
|
||||
"/workspaces/Thesis/src/notebooks/../../src/losses/pinball_loss.py:22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
|
||||
" self.quantiles_tensor = torch.tensor(quantiles, dtype=torch.float32)\n",
|
||||
"InsecureRequestWarning: Certificate verification is disabled! Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"ClearML Task: created new task id=0c748cf6ec0f4c748cc35be78ae4c6c1\n",
|
||||
"ClearML results page: http://192.168.1.182:8080/projects/2e46d4af6f1e4c399cf9f5aa30bc8795/experiments/0c748cf6ec0f4c748cc35be78ae4c6c1/output/log\n",
|
||||
"2023-11-26 16:15:07,490 - clearml.Task - INFO - Storing jupyter notebook directly as code\n",
|
||||
"2023-11-26 16:15:09,255 - clearml.model - WARNING - 500 model found when searching for `file:///workspaces/Thesis/src/notebooks/checkpoint.pt`\n",
|
||||
"2023-11-26 16:15:09,256 - clearml.model - WARNING - Selected model `Autoregressive Quantile Regression (quarter + day of week)` (id=bc0cb0d7fc614e2e8b0edf5b85348646)\n",
|
||||
"2023-11-26 16:15:09,265 - clearml.frameworks - INFO - Found existing registered model id=bc0cb0d7fc614e2e8b0edf5b85348646 [/workspaces/Thesis/src/notebooks/checkpoint.pt] reusing it.\n",
|
||||
"2023-11-26 16:15:09,958 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Untitled%20Task.0c748cf6ec0f4c748cc35be78ae4c6c1/models/checkpoint.pt\n",
|
||||
"2023-11-26 16:15:10,998 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Untitled%20Task.0c748cf6ec0f4c748cc35be78ae4c6c1/models/checkpoint.pt\n",
|
||||
"2023-11-26 16:15:12,118 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Untitled%20Task.0c748cf6ec0f4c748cc35be78ae4c6c1/models/checkpoint.pt\n",
|
||||
"2023-11-26 16:15:13,152 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Untitled%20Task.0c748cf6ec0f4c748cc35be78ae4c6c1/models/checkpoint.pt\n",
|
||||
"2023-11-26 16:15:14,540 - clearml.Task - INFO - Completed model upload to http://192.168.1.182:8081/Thesis/NrvForecast/Untitled%20Task.0c748cf6ec0f4c748cc35be78ae4c6c1/models/checkpoint.pt\n",
|
||||
"Early stopping triggered\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/workspaces/Thesis/src/notebooks/../../src/trainers/quantile_trainer.py:366: UserWarning:\n",
|
||||
"\n",
|
||||
"Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343967769/work/torch/csrc/utils/tensor_new.cpp:245.)\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"### Data Processor ###\n",
|
||||
"data_processor.set_full_day_skip(True)\n",
|
||||
|
||||
Reference in New Issue
Block a user