如何使用 Cloud Run 工作微調大型語言模型
程式碼研究室簡介
subject上次更新時間：6月 3, 2025
1. 簡介
總覽
在本程式碼研究室中，您將使用 Cloud Run 工作來微調 Gemma 模型，然後使用 vLLM 在 Cloud Run 上提供結果。
為了完成本程式碼研究室，您將使用文字轉 SQL 資料集，讓 LLM 在收到自然語言問題時，以 SQL 查詢回覆。
課程內容
- 如何使用 Cloud Run 工作 GPU 進行精細調整
- 如何搭配 vLLM 使用 Cloud Run 提供模型
- 如何為 GPU 工作使用直接虛擬私有雲設定，以便加快模型上傳及服務的速度
2. 事前準備
啟用 API
開始使用本程式碼研究室前，請先執行以下 API 啟用作業：
gcloud services enable run.googleapis.com \
compute.googleapis.com \
run.googleapis.com \
cloudbuild.googleapis.com \
secretmanager.googleapis.com \
artifactregistry.googleapis.com
GPU 配額
申請提高支援地區的配額。在 Cloud Run Admin API 下，配額為 nvidia_l4_gpu_allocation_no_zonal_redundancy。
注意：如果您使用的是新專案，啟用 API 後，可能需要幾分鐘，這個頁面才會顯示配額。
Hugging Face
本程式碼研究室使用 Hugging Face 託管的模型。如要取得這個模型，請使用「Read」權限要求 Hugging Face 使用者存取權杖。您稍後會以 YOUR_HF_TOKEN 的形式參照這個值區。
您也必須同意使用條款才能使用模型：https://huggingface.co/google/gemma-2b
3. 設定和需求
設定下列資源：
- IAM 服務帳戶和相關 IAM 權限
- Secret Manager 密鑰 (用於儲存 Hugging Face 權杖)
- Cloud Storage 值區，用於儲存經過微調的模型，
- Artifact Registry 存放區，用於儲存您用來微調模型的建構映像檔。
- 為本程式碼實驗室設定環境變數。我們已為您預先填入多個變數。指定專案 ID、區域和 Hugging Face 權杖。
export PROJECT_ID=<YOUR_PROJECT_ID>
export REGION=<YOUR_REGION>
export HF_TOKEN=<YOUR_HF_TOKEN>
export AR_REPO=codelab-finetuning-jobs
export IMAGE_NAME=finetune-to-gcs
export JOB_NAME=finetuning-to-gcs-job
export BUCKET_NAME=$PROJECT_ID-codelab-finetuning-jobs
export SECRET_ID=HF_TOKEN
export SERVICE_ACCOUNT="finetune-job-sa"
export SERVICE_ACCOUNT_ADDRESS=$SERVICE_ACCOUNT@$PROJECT_ID.iam.gserviceaccount.com - 執行下列指令，建立服務帳戶：
gcloud iam service-accounts create $SERVICE_ACCOUNT \
--display-name="Service account for fine-tuning codelab" - 使用 Secret Manager 儲存 Hugging Face 存取權杖：
gcloud secrets create $SECRET_ID \
--replication-policy="automatic"
printf $HF_TOKEN | gcloud secrets versions add $SECRET_ID --data-file=- - 將 Secret Manager 密鑰存取者角色授予服務帳戶：
gcloud secrets add-iam-policy-binding $SECRET_ID \
--member serviceAccount:$SERVICE_ACCOUNT_ADDRESS \
--role='roles/secretmanager.secretAccessor' - 建立值區來代管微調後的模型：
gcloud storage buckets create -l $REGION gs://$BUCKET_NAME - 授予服務帳戶對 bucket 的存取權：
gcloud storage buckets add-iam-policy-binding gs://$BUCKET_NAME \
--member=serviceAccount:$SERVICE_ACCOUNT_ADDRESS \
--role=roles/storage.objectAdmin - 建立 Artifact Registry 存放區來儲存容器映像檔：
gcloud artifacts repositories create $AR_REPO \
--repository-format=docker \
--location=$REGION \
--description="codelab for finetuning using CR jobs" \
--project=$PROJECT_ID
4. 建立 Cloud Run 工作映像檔
在下一個步驟中，您將建立可執行以下操作的程式碼：
- 從 Hugging Face 匯入 Gemma 模型
- 使用 Hugging Face 的資料集對模型進行微調。該工作會使用單一 L4 GPU 進行精細調整。
- 將名為
new_model的精修模型上傳至 Cloud Storage 值區
- 建立目錄來放置微調工作程式碼。
mkdir codelab-finetuning-job
cd codelab-finetuning-job - 建立名為
finetune.py的檔案# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer
# Cloud Storage bucket to upload the model
bucket_name = os.getenv("BUCKET_NAME", "YOUR_BUCKET_NAME")
# The model that you want to train from the Hugging Face hub
model_name = os.getenv("MODEL_NAME", "google/gemma-2b")
# The instruction dataset to use
dataset_name = "b-mc2/sql-create-context"
# Fine-tuned model name
new_model = os.getenv("NEW_MODEL", "gemma-2b-sql")
################################################################################
# QLoRA parameters
################################################################################
# LoRA attention dimension
lora_r = int(os.getenv("LORA_R", "4"))
# Alpha parameter for LoRA scaling
lora_alpha = int(os.getenv("LORA_ALPHA", "8"))
# Dropout probability for LoRA layers
lora_dropout = 0.1
################################################################################
# bitsandbytes parameters
################################################################################
# Activate 4-bit precision base model loading
use_4bit = True
# Compute dtype for 4-bit base models
bnb_4bit_compute_dtype = "float16"
# Quantization type (fp4 or nf4)
bnb_4bit_quant_type = "nf4"
# Activate nested quantization for 4-bit base models (double quantization)
use_nested_quant = False
################################################################################
# TrainingArguments parameters
################################################################################
# Output directory where the model predictions and checkpoints will be stored
output_dir = "./results"
# Number of training epochs
num_train_epochs = 1
# Enable fp16/bf16 training (set bf16 to True with an A100)
fp16 = True
bf16 = False
# Batch size per GPU for training
per_device_train_batch_size = int(os.getenv("TRAIN_BATCH_SIZE", "1"))
# Batch size per GPU for evaluation
per_device_eval_batch_size = int(os.getenv("EVAL_BATCH_SIZE", "2"))
# Number of update steps to accumulate the gradients for
gradient_accumulation_steps = int(os.getenv("GRADIENT_ACCUMULATION_STEPS", "1"))
# Enable gradient checkpointing
gradient_checkpointing = True
# Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3
# Initial learning rate (AdamW optimizer)
learning_rate = 2e-4
# Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001
# Optimizer to use
optim = "paged_adamw_32bit"
# Learning rate schedule
lr_scheduler_type = "cosine"
# Number of training steps (overrides num_train_epochs)
max_steps = -1
# Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03
# Group sequences into batches with same length
# Saves memory and speeds up training considerably
group_by_length = True
# Save checkpoint every X updates steps
save_steps = 0
# Log every X updates steps
logging_steps = int(os.getenv("LOGGING_STEPS", "50"))
################################################################################
# SFT parameters
################################################################################
# Maximum sequence length to use
max_seq_length = int(os.getenv("MAX_SEQ_LENGTH", "512"))
# Pack multiple short examples in the same input sequence to increase efficiency
packing = False
# Load the entire model on the GPU 0
device_map = {'':torch.cuda.current_device()}
# Set limit to a positive number
limit = int(os.getenv("DATASET_LIMIT", "5000"))
dataset = load_dataset(dataset_name, split="train")
if limit != -1:
dataset = dataset.shuffle(seed=42).select(range(limit))
def transform(data):
question = data['question']
context = data['context']
answer = data['answer']
template = "Question: {question}\nContext: {context}\nAnswer: {answer}"
return {'text': template.format(question=question, context=context, answer=answer)}
transformed = dataset.map(transform)
# Load tokenizer and model with QLoRA configuration
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
# Check GPU compatibility with bfloat16
if compute_dtype == torch.float16 and use_4bit:
major, _ = torch.cuda.get_device_capability()
if major >= 8:
print("=" * 80)
print("Your GPU supports bfloat16")
print("=" * 80)
# Load base model
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map=device_map,
torch_dtype=torch.float16,
)
model.config.use_cache = False
model.config.pretraining_tp = 1
# Load LLaMA tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# Load LoRA configuration
peft_config = LoraConfig(
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
r=lora_r,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "v_proj"]
)
# Set training parameters
training_arguments = TrainingArguments(
output_dir=output_dir,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=per_device_train_batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
optim=optim,
save_steps=save_steps,
logging_steps=logging_steps,
learning_rate=learning_rate,
weight_decay=weight_decay,
fp16=fp16,
bf16=bf16,
max_grad_norm=max_grad_norm,
max_steps=max_steps,
warmup_ratio=warmup_ratio,
group_by_length=group_by_length,
lr_scheduler_type=lr_scheduler_type,
)
trainer = SFTTrainer(
model=model,
train_dataset=transformed,
peft_config=peft_config,
dataset_text_field="text",
max_seq_length=max_seq_length,
tokenizer=tokenizer,
args=training_arguments,
packing=packing,
)
trainer.train()
trainer.model.save_pretrained(new_model)
# Reload model in FP16 and merge it with LoRA weights
base_model = AutoModelForCausalLM.from_pretrained(
model_name,
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
device_map=device_map,
)
model = PeftModel.from_pretrained(base_model, new_model)
model = model.merge_and_unload()
# push to Cloud Storage
file_path_to_save_the_model = '/finetune/new_model'
model.save_pretrained(file_path_to_save_the_model)
tokenizer.save_pretrained(file_path_to_save_the_model) - 建立
requirements.txt檔案：accelerate==0.34.2
bitsandbytes==0.45.5
datasets==2.19.1
transformers==4.51.3
peft==0.11.1
trl==0.8.6
torch==2.3.0 - 建立
Dockerfile：FROM nvidia/cuda:12.6.2-runtime-ubuntu22.04
RUN apt-get update && \
apt-get -y --no-install-recommends install python3-dev gcc python3-pip git && \
rm -rf /var/lib/apt/lists/*
COPY requirements.txt /requirements.txt
RUN pip3 install -r requirements.txt --no-cache-dir
COPY finetune.py /finetune.py
ENV PYTHONUNBUFFERED 1
CMD python3 /finetune.py --device cuda - 在 Artifact Registry 存放區中建構容器：
gcloud builds submit \
--tag $REGION-docker.pkg.dev/$PROJECT_ID/$AR_REPO/$IMAGE_NAME \
--region $REGION
5. 部署及執行工作
在這個步驟中，您將為工作建立 YAML 設定，並使用直接 VPC 出口，以便更快將資料上傳至 Google Cloud Storage。
請注意，這個檔案包含您稍後會更新的變數。
- 建立名為
finetune-job.yaml.tmpl的檔案：apiVersion: run.googleapis.com/v1
kind: Job
metadata:
name: $JOB_NAME
labels:
cloud.googleapis.com/location: $REGION
annotations:
run.googleapis.com/launch-stage: ALPHA
spec:
template:
metadata:
annotations:
run.googleapis.com/execution-environment: gen2
run.googleapis.com/network-interfaces: '[{"network":"default","subnetwork":"default"}]'
spec:
parallelism: 1
taskCount: 1
template:
spec:
serviceAccountName: $SERVICE_ACCOUNT_ADDRESS
containers:
- name: $IMAGE_NAME
image: $REGION-docker.pkg.dev/$PROJECT_ID/$AR_REPO/$IMAGE_NAME
env:
- name: MODEL_NAME
value: "google/gemma-2b"
- name: NEW_MODEL
value: "gemma-2b-sql-finetuned"
- name: BUCKET_NAME
value: "$BUCKET_NAME"
- name: LORA_R
value: "8"
- name: LORA_ALPHA
value: "16"
- name: GRADIENT_ACCUMULATION_STEPS
value: "2"
- name: DATASET_LIMIT
value: "1000"
- name: LOGGING_STEPS
value: "5"
- name: HF_TOKEN
valueFrom:
secretKeyRef:
key: 'latest'
name: HF_TOKEN
resources:
limits:
cpu: 8000m
nvidia.com/gpu: '1'
memory: 32Gi
volumeMounts:
- mountPath: /finetune/new_model
name: finetuned_model
volumes:
- name: finetuned_model
csi:
driver: gcsfuse.run.googleapis.com
readOnly: false
volumeAttributes:
bucketName: $BUCKET_NAME
maxRetries: 3
timeoutSeconds: '3600'
nodeSelector:
run.googleapis.com/accelerator: nvidia-l4 - 執行下列指令，將 YAML 中的變數替換為環境變數：
envsubst < finetune-job.yaml.tmpl > finetune-job.yaml - 建立 Cloud Run 工作：
gcloud alpha run jobs replace finetune-job.yaml - 執行工作：
gcloud alpha run jobs execute $JOB_NAME --region $REGION --async
這項作業大約 10 分鐘就能完成。您可以使用上一個指令輸出內容中提供的連結，查看狀態。
6. 使用 Cloud Run 服務，透過 vLLM 提供經過微調的模型
在這個步驟中，您將部署 Cloud Run 服務。這項設定會透過私人網路使用直接虛擬私有雲存取 Cloud Storage 值區，以便加快下載速度。
請注意，這個檔案包含您稍後會更新的變數。
- 建立
service.yaml.tmpl檔案：apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: serve-gemma-sql
labels:
cloud.googleapis.com/location: $REGION
annotations:
run.googleapis.com/launch-stage: BETA
run.googleapis.com/ingress: all
run.googleapis.com/ingress-status: all
spec:
template:
metadata:
labels:
annotations:
autoscaling.knative.dev/maxScale: '1'
run.googleapis.com/cpu-throttling: 'false'
run.googleapis.com/gpu-zonal-redundancy-disabled: 'true'
run.googleapis.com/network-interfaces: '[{"network":"default","subnetwork":"default"}]'
spec:
containers:
- name: serve-finetuned
image: us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-vllm-serve:20250505_0916_RC00
ports:
- name: http1
containerPort: 8000
resources:
limits:
cpu: 8000m
nvidia.com/gpu: '1'
memory: 32Gi
volumeMounts:
- name: fuse
mountPath: /finetune/new_model
command: ["python3", "-m", "vllm.entrypoints.api_server"]
args:
- --model=/finetune/new_model
- --tensor-parallel-size=1
env:
- name: MODEL_ID
value: 'new_model'
- name: HF_HUB_OFFLINE
value: '1'
volumes:
- name: fuse
csi:
driver: gcsfuse.run.googleapis.com
volumeAttributes:
bucketName: $BUCKET_NAME
nodeSelector:
run.googleapis.com/accelerator: nvidia-l4 - 使用值區名稱更新
service.yaml檔案。envsubst < service.yaml.tmpl > service.yaml - 部署 Cloud Run 服務：
gcloud alpha run services replace service.yaml
7. 測試微調後的模型
在這個步驟中，您會提示模型測試精細調整。
- 取得 Cloud Run 服務的服務網址：
SERVICE_URL=$(gcloud run services describe serve-gemma-sql --platform managed --region $REGION --format 'value(status.url)') - 為模型建立提示。
USER_PROMPT="Question: What are the first name and last name of all candidates? Context: CREATE TABLE candidates (candidate_id VARCHAR); CREATE TABLE people (first_name VARCHAR, last_name VARCHAR, person_id VARCHAR)" - 使用 CURL 呼叫服務，以便提示模型：
curl -X POST $SERVICE_URL/generate \
-H "Content-Type: application/json" \
-H "Authorization: bearer $(gcloud auth print-identity-token)" \
-d @- <<EOF
{
"prompt": "${USER_PROMPT}"
}
EOF
畫面會顯示類似以下的回應：
{"predictions":["Prompt:\nQuestion: What are the first name and last name of all candidates? Context: CREATE TABLE candidates (candidate_id VARCHAR); CREATE TABLE people (first_name VARCHAR, last_name VARCHAR, person_id VARCHAR)\nOutput:\n CREATE TABLE people_to_candidates (candidate_id VARCHAR, person_id VARCHAR) CREATE TABLE people_to_people (person_id VARCHAR, person_id VARCHAR) CREATE TABLE people_to_people_to_candidates (person_id VARCHAR, candidate_id"]}
9. 清除所用資源
為避免產生意外費用，如果 Cloud Run 服務不小心叫用次數超過 免付費層級的 Cloud Run 叫用次數配額，您可以刪除在步驟 6 中建立的 Cloud Run 服務。
如要刪除 Cloud Run 服務，請前往 Cloud Run 控制台 (https://console.cloud.google.com/run) 並刪除 serve-gemma-sql 服務。
如要刪除整個專案，請前往「Manage Resources」，選取您在步驟 2 中建立的專案，然後選擇「Delete」(刪除)。如果您刪除專案，就必須在 Cloud SDK 中變更專案。您可以執行 gcloud projects list 來查看所有可用專案的清單。