diff --git "a/working_training.ipynb" "b/working_training.ipynb" --- "a/working_training.ipynb" +++ "b/working_training.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "215a1aae", "metadata": { "id": "215a1aae" @@ -59,19 +59,147 @@ ], "metadata": { "id": "J5Tlgp4tNd0U", - "outputId": "5d45330f-ec42-4766-8bf6-85ba08af7c3b", + "outputId": "99a653e0-5291-4bdf-96c4-b8f846da0dd8", "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 428, + "referenced_widgets": [ + "9dfca3689eeb4b64b797175fedfdc316", + "e9486d69d9b242428b72394220a18d94", + "c91237d00ad4422785b445c968ff8103", + "0abc6b94087242709420535219362e6a", + "1ad3561f342b49c4b093ca0251880040", + "4db17ff0e3254ea082a242397c2c8376", + "584cb067264f43088f525c488a9e9d7b", + "ca009d244f524878a938062f9592073e", + "04105cc523ac476681add09b0eb12e18", + "1de5a96aebf1437fbdba810d9dc28df8", + "b45f13b9a8404c2eb1817b032326f46f", + "87237002780e417f8af42146060e6325", + "c415db0b99824641969a474c0ef1a940", + "28bbab5da6744625993fc974365d03a0", + "ab3d3249d3b04ad389dd48c6ac8c9420", + "93661e86d04e4da8a714647df51ef3a4", + "b3a06f1d88084261970e09822427c002", + "e84a137de419449398d83cfcf7d16a66", + "8b961a54a86a4fd7851dc67204f91574", + "996635c71c38465ab4d4a650c38fd172", + "fbb14d7712c1429d88c614614efa5dca", + "c6121c67057c4a8498dd19c1b848ac36", + "ef3f5da34579496ab82adfc3f3cf5efd", + "de560eeff9604df58b92b6fe4af6b9e4", + "96a5df6642a848af9add887d0f8c217d", + "64b3b60085744934aea5ab39b6fb7392", + "51acffb0660e42d4be3309337280abb8", + "1af2f909788d44b4afae08bff00d9a46", + "92d7308f662640a9a0259dc31c98aabd", + "a6292c9dc82a4e748df2e38d5b8adf2e", + "e8a9f7b401924200b960270f64a3dea3", + "f7a9bb144741443a9e5d30d8cb4e6a60", + "7147441029ea45eb91e6605c022bda3b", + "9cd6c2321b0246cdabbaf5efb7872aed", + "7377e94f8d344e2caf059347daac2a3b", + "be0474860639484c8d704cf82fd49657", + "235a89dcc1454d528fbd2a41f05c8003", + "a5c25ac340db42ee8f909f3a04042904", + "c464a2ec7ccb4f1289f899fa9f563948", + "aed130bf1932468aafb07d02e1b10d86", + "de698e46a6f543f2adb7e7ed79707ed7", + "59cab1b0a6f04d5fb0d0e65e90505705", + "840f1e85a7bb4b23a94da533da3eb5ca", + "968ea639f39d4193b298b87765e77f1c", + "ab9d46685952413c85ab363c3c64c047", + "46af83c0a34540608650458e139a22c2", + "c3aad08e667c460f8adc5cfb777ab4d5", + "4c4473bb5c0b43799a868e4588ae8413", + "789b06802ed843ad9e7b544f42b2a3f9", + "3f84dd2dbfe144088de8513df631eb08", + "1d0ea60737bd4da485ba12789206e8f7", + "ffb6bc9d3252487b908085a7ed067616", + "5cf53e2960394c77a06d78f56cc870fb", + "5cfa96d071e84b19983f1fb48a0c8260", + "76be7bb04797457798b3e01994c2fbbb" + ] } }, "id": "J5Tlgp4tNd0U", - 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" - ] - }, - "metadata": {} - }, - { - "output_type": "error", - "ename": "KeyboardInterrupt", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1660\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inner_training_loop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_find_batch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1661\u001b[0m )\n\u001b[0;32m-> 1662\u001b[0;31m return inner_training_loop(\n\u001b[0m\u001b[1;32m 1663\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1664\u001b[0m \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1927\u001b[0m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1928\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1929\u001b[0;31m \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1931\u001b[0m if (\n", - "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2715\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepspeed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2716\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2717\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2719\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 485\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 486\u001b[0m )\n\u001b[0;32m--> 487\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 488\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m )\n", - "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 201\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 202\u001b[0m allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!nvidia-smi" 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