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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.chdir('../')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'c:\\\\mlops projects\\\\text-summarization'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"from pathlib import Path\n",
"@dataclass(frozen=True)\n",
"class ModelTrainerConfig:\n",
" root_dir : Path\n",
" data_path : Path\n",
" model_ckpt : Path\n",
" num_train_epochs : int\n",
" warmup_steps : int\n",
" per_device_train_batch_size : int\n",
" weight_decay : float\n",
" logging_steps : int\n",
" evaluation_strategy: str\n",
" eval_steps: int\n",
" save_steps: float\n",
" gradient_accumulation_steps: int"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from textsummarizer.constants import *\n",
"from textsummarizer.utils.common import read_yaml,create_directories\n",
"\n",
"class ConfigurationManager:\n",
" def __init__(\n",
" self,\n",
" config_filepath = CONFIG_FILE_PATH,\n",
" params_filepath = PARAMS_FILE_PATH):\n",
"\n",
" self.config = read_yaml(config_filepath)\n",
" self.params = read_yaml(params_filepath)\n",
"\n",
" create_directories([self.config.artifacts_root])\n",
" \n",
" \n",
" def get_model_trainer_config(self) -> ModelTrainerConfig:\n",
" config = self.config.model_trainer\n",
" params = self.params.TrainingArguments\n",
"\n",
" create_directories([config.root_dir])\n",
" \n",
" \n",
" model_trainer_config = ModelTrainerConfig(\n",
" root_dir = config.root_dir,\n",
" data_path = config.data_path,\n",
" model_ckpt = config.model_ckpt,\n",
" num_train_epochs =params.num_train_epochs,\n",
" warmup_steps =params.warmup_steps,\n",
" per_device_train_batch_size = params.per_device_train_batch_size,\n",
" weight_decay = params.weight_decay,\n",
" logging_steps = params.logging_steps,\n",
" evaluation_strategy =params.evaluation_strategy,\n",
" eval_steps =params.eval_steps,\n",
" save_steps = params.save_steps,\n",
" gradient_accumulation_steps = params.gradient_accumulation_steps\n",
" )\n",
" \n",
" return model_trainer_config\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from transformers import TrainingArguments, Trainer\n",
"from transformers import DataCollatorForSeq2Seq\n",
"from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
"from datasets import load_dataset, load_from_disk\n",
"import torch\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class ModelTrainer:\n",
" def __init__(self, config : ModelTrainerConfig):\n",
" self.config = config\n",
" os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
" \n",
" \n",
" def train(self):\n",
" device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
" tokenizer = AutoTokenizer.from_pretrained(self.config.model_ckpt)\n",
" model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(self.config.model_ckpt).to(device)\n",
" seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model_pegasus)\n",
" \n",
" #loading data \n",
" dataset_samsum_pt = load_from_disk(self.config.data_path)\n",
" \n",
" \n",
" trainer_args = TrainingArguments(\n",
" output_dir=self.config.root_dir, num_train_epochs=self.config.num_train_epochs, warmup_steps=self.config.warmup_steps,\n",
" per_device_train_batch_size=self.config.per_device_train_batch_size, per_device_eval_batch_size=self.config.per_device_train_batch_size,\n",
" weight_decay=self.config.weight_decay, logging_steps=self.config.logging_steps,\n",
" evaluation_strategy=self.config.evaluation_strategy, eval_steps=self.config.eval_steps, save_steps=1e6,\n",
" gradient_accumulation_steps=self.config.gradient_accumulation_steps,\n",
" report_to=\"none\"\n",
" \n",
" ) \n",
" \n",
" \n",
" trainer = Trainer(model=model_pegasus, args=trainer_args,\n",
" tokenizer=tokenizer, data_collator=seq2seq_data_collator,\n",
" train_dataset=dataset_samsum_pt[\"test\"], \n",
" eval_dataset=dataset_samsum_pt[\"validation\"])\n",
" \n",
" \n",
" trainer.train()\n",
"\n",
" ## Save model\n",
" model_pegasus.save_pretrained(os.path.join(self.config.root_dir,\"pegasus-samsum-model\"))\n",
" ## Save tokenizer\n",
" tokenizer.save_pretrained(os.path.join(self.config.root_dir,\"tokenizer\"))\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" config = ConfigurationManager()\n",
" model_trainer_config = config.get_model_trainer_config()\n",
" model_trainer_config = ModelTrainer(config=model_trainer_config)\n",
" model_trainer_config.train()\n",
"except Exception as e:\n",
" raise e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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