diff --git a/examples/tutorial/auto_parallel/auto_ckpt_demo.ipynb b/examples/tutorial/auto_parallel/auto_ckpt_demo.ipynb new file mode 100644 index 000000000..cacf5d5f3 --- /dev/null +++ b/examples/tutorial/auto_parallel/auto_ckpt_demo.ipynb @@ -0,0 +1,878 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/lcsjy/.conda/envs/autoparallel/lib/python3.10/site-packages/tqdm/auto.py:22: 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" + ] + }, + { + "data": { + "text/html": [ + "
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                    INFO     colossalai - colossalai - INFO: initialized seed on rank 0, numpy: 1024, python       \n",
+       "                             random: 1024, ParallelMode.DATA: 1024, ParallelMode.TENSOR: 1024,the default parallel \n",
+       "                             seed is ParallelMode.DATA.                                                            \n",
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+       "                             launch                                                                                \n",
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                    INFO     colossalai - colossalai - INFO: Distributed environment is initialized, data parallel \n",
+       "                             size: 1, pipeline parallel size: 1, tensor parallel size: 1                           \n",
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"cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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