diff --git a/example/chronos.ipynb b/example/chronos.ipynb index 8af182f..9ed04c1 100644 --- a/example/chronos.ipynb +++ b/example/chronos.ipynb @@ -11,15 +11,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "Loading Chronos model from Huggingface repository\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n" + ] } ], "source": [ @@ -55,9 +71,9 @@ "metadata": {}, "outputs": [], "source": [ - "train_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv', \n", + "train_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv', \n", " mode='train', batch_size=8)\n", - "val_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv', \n", + "val_dataset = ChronosDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv', \n", " mode='test', batch_size=8)" ] }, @@ -77,15 +93,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "/nethome/sli999/TSFMProject/src/tsfmproject/model.py:251: 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", + "/nethome/sli999/Samay/src/samay/model.py:349: 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", " input_seq = torch.tensor(input_seq)\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(465, 7, 512)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, diff --git a/example/chronosbolt.ipynb b/example/chronosbolt.ipynb index b6b15f3..98d827f 100644 --- a/example/chronosbolt.ipynb +++ b/example/chronosbolt.ipynb @@ -18,7 +18,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/nethome/hkamarthi3/scratch/Samay/.venv/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + + "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n" ] }, @@ -30,32 +32,13 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fccf7e0bf23c46f09da01257413da52d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "config.json: 0%| | 0.00/1.12k [00:00" + "
" + ] }, "metadata": {}, diff --git a/example/moment_forecasting.ipynb b/example/moment_forecasting.ipynb index 2118385..2836def 100644 --- a/example/moment_forecasting.ipynb +++ b/example/moment_forecasting.ipynb @@ -11,15 +11,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n", + "INFO:p-2597098:t-140082893653824:moment.py:_validate_inputs:Setting d_model to 1024\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "Loading MOMENT model from AutonLab/MOMENT-1-large\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:p-2597098:t-140082893653824:moment.py:_get_transformer_backbone:Initializing pre-trained transformer from google/flan-t5-large.\n", + "INFO:p-2597098:t-140082893653824:moment.py:_get_transformer_backbone:Enabling gradient checkpointing.\n" + ] } ], "source": [ @@ -57,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -72,38 +89,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0: Train loss: 0.572\n", - "Epoch 1: Train loss: 0.502\n", - "Epoch 2: Train loss: 0.473\n", - "Epoch 3: Train loss: 0.457\n", - "Epoch 4: Train loss: 0.452\n" + "Epoch 0: Train loss: 0.068\n", + "Epoch 1: Train loss: 0.064\n", + "Epoch 2: Train loss: 0.060\n", + "Epoch 3: Train loss: 0.056\n", + "Epoch 4: Train loss: 0.053\n" ] }, { "data": { "text/plain": [ - "{'mse': 0.5570129,\n", - " 'mae': 0.5200917,\n", - " 'mase': 0.82687783,\n", - " 'mape': -0.30029196,\n", - " 'rmse': 0.746333,\n", - " 'nrmse': 0.07844285181705611,\n", - " 'smape': 0.8625615,\n", - " 'msis': 0.081994146,\n", - " 'nd': 28.05683713775537}" + "{'mse': 0.06429593,\n", + " 'mae': 0.05884363,\n", + " 'mase': 1.8647041,\n", + " 'mape': 0.02874577,\n", + " 'rmse': 0.2535664,\n", + " 'nrmse': 0.02665093539652813,\n", + " 'smape': 0.2105672,\n", + " 'msis': 0.046476997,\n", + " 'nd': 26.39926135498086}" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_dataset = MomentDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv', \n", + "train_dataset = MomentDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv', \n", " mode='train', horizon_len=192)\n", "# dtl = train_dataset.get_data_loader()\n", "\n", - "val_dataset = MomentDataset(name=\"ett\", datetime_col='date', path='../src/tsfmproject/models/moment/data/ETTh1.csv',\n", + "val_dataset = MomentDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv',\n", " mode='test', horizon_len=192)\n", "# path = '../src/tsfmproject/models/moment/data/ETTh1.csv'\n", "\n", @@ -191,7 +208,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "torch", "language": "python", "name": "python3" }, @@ -205,7 +222,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/example/timemoe.ipynb b/example/timemoe.ipynb index 00229ff..3a3d475 100644 --- a/example/timemoe.ipynb +++ b/example/timemoe.ipynb @@ -147,47 +147,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "9846b368", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([32, 1, 1])) that is different to the input size (torch.Size([32, 512, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([32, 1, 8, 1])) that is different to the input size (torch.Size([32, 512, 8, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([32, 1, 32, 1])) that is different to the input size (torch.Size([32, 512, 32, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([32, 1, 64, 1])) that is different to the input size (torch.Size([32, 512, 64, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n" - ] - }, - { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([13, 1, 1])) that is different to the input size (torch.Size([13, 512, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([13, 1, 8, 1])) that is different to the input size (torch.Size([13, 512, 8, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([13, 1, 32, 1])) that is different to the input size (torch.Size([13, 512, 32, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n", - "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/loss.py:1100: UserWarning: Using a target size (torch.Size([13, 1, 64, 1])) that is different to the input size (torch.Size([13, 512, 64, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " return F.huber_loss(input, target, reduction=self.reduction, delta=self.delta)\n" + "predictions shape: torch.Size([32, 512, 1])\n" ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0, Loss: 0.1356\n", - "Epoch 1, Loss: 0.1170\n", - "Epoch 2, Loss: 0.1028\n", - "Epoch 3, Loss: 0.0947\n", - "Epoch 4, Loss: 0.0839\n" + "ename": "RuntimeError", + "evalue": "shape '[32, 512, 96, -1]' is invalid for input of size 16384", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtme\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinetune\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Samay/src/samay/model.py:2077\u001b[0m, in \u001b[0;36mTimeMoEModel.finetune\u001b[0;34m(self, dataset, **kwargs)\u001b[0m\n\u001b[1;32m 2075\u001b[0m loss_mask \u001b[38;5;241m=\u001b[39m loss_mask\u001b[38;5;241m.\u001b[39mfloat()\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m 2076\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m-> 2077\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforecast_seq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss_masks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mloss_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2078\u001b[0m loss \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mloss\n\u001b[1;32m 2079\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n", + "File \u001b[0;32m~/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\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_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/anaconda3/envs/torch/lib/python3.11/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\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_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n", + "File \u001b[0;32m~/Samay/src/samay/models/Time_MoE/time_moe/models/modeling_time_moe.py:1019\u001b[0m, in \u001b[0;36mTimeMoeForPrediction.forward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, loss_masks, use_cache, output_attentions, output_hidden_states, return_dict, max_horizon_length)\u001b[0m\n\u001b[1;32m 1017\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m lm_head, horizon_length \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlm_heads, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mhorizon_lengths):\n\u001b[1;32m 1018\u001b[0m one_predictions \u001b[38;5;241m=\u001b[39m lm_head(hidden_states)\n\u001b[0;32m-> 1019\u001b[0m one_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalc_ar_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_predictions\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss_masks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhorizon_length\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1020\u001b[0m ar_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m one_loss\n\u001b[1;32m 1021\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m predictions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Samay/src/samay/models/Time_MoE/time_moe/models/modeling_time_moe.py:1077\u001b[0m, in \u001b[0;36mTimeMoeForPrediction.calc_ar_loss\u001b[0;34m(self, predictions, labels, loss_masks, horizon_length)\u001b[0m\n\u001b[1;32m 1075\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredictions shape: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpredictions\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1076\u001b[0m batch_size, seq_len, output_size \u001b[38;5;241m=\u001b[39m predictions\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m-> 1077\u001b[0m shift_predictions \u001b[38;5;241m=\u001b[39m \u001b[43mpredictions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseq_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhorizon_length\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;66;03m# pad to the same length with predictions\u001b[39;00m\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# shape -> [B, input_size, seq_len + horizon_length -1]\u001b[39;00m\n\u001b[1;32m 1081\u001b[0m labels \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mpad(labels\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m), (\u001b[38;5;241m0\u001b[39m, horizon_length \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m), mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconstant\u001b[39m\u001b[38;5;124m'\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", + "\u001b[0;31mRuntimeError\u001b[0m: shape '[32, 512, 96, -1]' is invalid for input of size 16384" ] } ], diff --git a/example/timesfm.ipynb b/example/timesfm.ipynb index 54bed30..604c724 100644 --- a/example/timesfm.ipynb +++ b/example/timesfm.ipynb @@ -14,6 +14,21 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "src_path: /nethome/sli999/Samay/src\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nethome/sli999/anaconda3/envs/torch/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -24,7 +39,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b79c3a37421a40f3a8493976d7a9f2eb", + "model_id": "06de33f9c7cd45c8b5249574de9dab51", "version_major": 2, "version_minor": 0 }, @@ -39,13 +54,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:p-321350:t-140115981268800:timesfm_torch.py:load_from_checkpoint:Loading checkpoint from /nethome/hkamarthi3/.cache/huggingface/hub/models--google--timesfm-1.0-200m-pytorch/snapshots/0581e2c56cb06feb51cfd98fc2b4005b74f7187b/torch_model.ckpt\n", - "INFO:p-321350:t-140115981268800:timesfm_torch.py:load_from_checkpoint:Sending checkpoint to device cuda:1\n" + "INFO:p-2465993:t-139809485936448:timesfm_torch.py:load_from_checkpoint:Loading checkpoint from /nethome/sli999/.cache/huggingface/hub/models--google--timesfm-1.0-200m-pytorch/snapshots/0581e2c56cb06feb51cfd98fc2b4005b74f7187b/torch_model.ckpt\n", + "INFO:p-2465993:t-139809485936448:timesfm_torch.py:load_from_checkpoint:Sending checkpoint to device cuda:1\n" ] } ], "source": [ "import numpy as np\n", + "import os\n", + "import sys\n", + "\n", + "src_path = os.path.abspath(os.path.join(\"..\", \"src\"))\n", + "if src_path not in sys.path:\n", + " sys.path.insert(0, src_path)\n", + "print(f\"src_path: {src_path}\")\n", "\n", "from samay.model import TimesfmModel\n", "from samay.dataset import TimesfmDataset\n", @@ -79,17 +101,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:p-321350:t-140115981268800:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", - "INFO:p-321350:t-140115981268800:data_loader.py:train_gen:Hist len: 512\n", - "INFO:p-321350:t-140115981268800:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", - "INFO:p-321350:t-140115981268800:data_loader.py:test_val_gen:Hist len: 512\n" + "INFO:p-2465993:t-139809485936448:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", + "INFO:p-2465993:t-139809485936448:data_loader.py:train_gen:Hist len: 512\n", + "INFO:p-2465993:t-139809485936448:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", + "INFO:p-2465993:t-139809485936448:data_loader.py:test_val_gen:Hist len: 512\n" ] } ], @@ -175,14 +197,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'mse': np.float32(17.94159), 'mae': np.float32(2.2708952), 'mase': np.float32(0.55817515), 'mape': np.float32(1283.0344), 'rmse': np.float32(4.235751), 'nrmse': np.float32(0.092243955), 'smape': np.float32(0.5231085), 'msis': np.float32(0.07495749), 'nd': np.float32(0.60295093), 'mwsq': np.float32(2.145765), 'crps': np.float64(80.78891892065549)}\n" + "{'mse': 17.941584, 'mae': 2.2708952, 'mase': 2.3713315, 'mape': 1283.0352, 'rmse': 4.2357507, 'nrmse': 0.09224394849614606, 'smape': 0.52310854, 'msis': 0.07495748, 'nd': 0.6029509394239408, 'mwsq': 2.1457655, 'crps': 80.7888972655018}\n" ] } ], @@ -316,7 +338,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "torch", "language": "python", "name": "python3" }, @@ -330,7 +352,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.11" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/example/tinytimemixer.ipynb b/example/tinytimemixer.ipynb index 8696623..7e5e229 100644 --- a/example/tinytimemixer.ipynb +++ b/example/tinytimemixer.ipynb @@ -6,7 +6,7 @@ "source": [ "# Timesfm Usage Example\n", "\n", - "## Loading Timesfm Model" + "## Loading TinyTimeMixer Model" ] }, { @@ -15,63 +15,32 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "INFO:p-324173:t-140070873831232:config.py::PyTorch version 2.5.1 available.\n", - "/nethome/hkamarthi3/scratch2/Samay/.venv/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", - " warnings.warn(\n" + "src_path: /nethome/sli999/Samay/src\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8cdefc66af534b12b7c9611963ad2566", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "config.json: 0%| | 0.00/1.56k [00:00