train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from accelerate import notebook_launcher\n",
    "import torch\n",
    "\n",
    "from model.trainer import ChatTrainer\n",
    "from config import TrainConfig, T5ModelConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_config = TrainConfig()\n",
    "model_config = T5ModelConfig()\n",
    "\n",
    "print(train_config)\n",
    "print(model_config)\n",
    "\n",
    "gpu_count = torch.cuda.device_count()\n",
    "print('gpu device count: {}'.format(gpu_count))\n",
    "\n",
    "chat_trainer = ChatTrainer(train_config=train_config, model_config=model_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = chat_trainer.train\n",
    "\n",
    "# chat_trainer.train() args:  is_keep_training: bool, is_finetune: bool\n",
    "train_args = (False, False)\n",
    "\n",
    "# 使用notebook_launcher函数启动多卡训练\n",
    "notebook_launcher(train, num_processes=gpu_count, args=train_args, mixed_precision=train_config.mixed_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = chat_trainer.test\n",
    "notebook_launcher(test, num_processes=gpu_count, mixed_precision=train_config.mixed_precision)"
   ]
  }
 ],
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