오픈소스 기반 완전한 LLMOps 파이프라인 구축 가이드
개요
완전한 오픈소스 스택으로 구성된 엔터프라이즈급 LLMOps 파이프라인을 구축할 수 있습니다. 이 가이드는 Hugging Face Transformers, vLLM, MLflow, ArgoCD 등을 활용해 데이터 전처리부터 모델 서빙까지 전체 AI 플라이휠을 구현하는 방법을 다룹니다.
오픈소스 LLMOps 아키텍처
1. 전체 아키텍처 개요
Data Sources → DVC + Airflow → Axolotl/Unsloth → LM Eval → vLLM → Production
↓ ↓ ↓ ↓ ↓ ↓
Git Repo → GitOps/ArgoCD → MLflow Track → Guardrails → Kubernetes
2. 스택 구성 요소
| 기능 영역 | NVIDIA | 오픈소스 대안 |
|---|---|---|
| 데이터 큐레이션 | NeMo Curator | DVC + Apache Airflow |
| 모델 훈련 | NeMo Customizer | Axolotl + Unsloth |
| 모델 평가 | NeMo Evaluator | LM Evaluation Harness |
| 모델 서빙 | NIM | vLLM + Text Generation Inference |
| 오케스트레이션 | DGX Cloud | Kubernetes + ArgoCD |
| 실험 추적 | 독점 솔루션 | MLflow + Weights & Biases |
| 안전성 보장 | NeMo Guardrails | Guardrails AI + NLTK |
파이프라인 구성 요소별 상세 가이드
1. 데이터 파이프라인: DVC + Apache Airflow
DVC를 활용한 데이터 버전 관리
# dvc.yaml
stages:
data_preprocessing:
cmd: python src/data/preprocess.py
deps:
- src/data/preprocess.py
- data/raw/
outs:
- data/processed/
data_validation:
cmd: python src/data/validate.py
deps:
- src/data/validate.py
- data/processed/
outs:
- data/validated/
Airflow DAG 구성
# airflow_dags/llmops_pipeline.py
from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'llmops-team',
'depends_on_past': False,
'start_date': datetime(2025, 6, 25),
'retries': 1,
'retry_delay': timedelta(minutes=5)
}
dag = DAG(
'llmops_pipeline',
default_args=default_args,
description='Open Source LLMOps Pipeline',
schedule_interval='@daily',
catchup=False
)
data_preprocess = BashOperator(
task_id='data_preprocess',
bash_command='cd /workspace && dvc repro data_preprocessing',
dag=dag
)
model_training = BashOperator(
task_id='model_training',
bash_command='cd /workspace && python -m axolotl.cli.train config/llama3_lora.yml',
dag=dag
)
data_preprocess >> model_training
2. 모델 훈련: Axolotl + Unsloth
Axolotl 설정 파일
# config/llama3_lora.yml
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: datasets/custom_dataset
type: sharegpt
conversation: llama-3
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/llama3-lora
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: llmops-training
wandb_entity: your-team
wandb_watch:
wandb_name:
wandb_log_model:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
Unsloth를 활용한 고속 훈련
# src/training/unsloth_trainer.py
from unsloth import FastLanguageModel
import torch
def setup_unsloth_model():
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="meta-llama/Llama-3.1-8B-Instruct",
max_seq_length=4096,
dtype=None,
load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
model,
r=16,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"],
lora_alpha=16,
lora_dropout=0,
bias="none",
use_gradient_checkpointing="unsloth",
random_state=3407,
use_rslora=False,
loftq_config=None,
)
return model, tokenizer
def train_model(model, tokenizer, dataset):
from trl import SFTTrainer
from transformers import TrainingArguments
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=4096,
dataset_num_proc=2,
packing=False,
args=TrainingArguments(
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
warmup_steps=5,
max_steps=60,
learning_rate=2e-4,
fp16=not torch.cuda.is_bf16_supported(),
bf16=torch.cuda.is_bf16_supported(),
logging_steps=1,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
seed=3407,
output_dir="outputs",
),
)
trainer.train()
return trainer
3. 모델 평가: LM Evaluation Harness
평가 설정 파일
# eval_config.yaml
model_args: "pretrained=./outputs/llama3-lora,dtype=float16"
tasks:
- gsm8k
- arc_easy
- arc_challenge
- hellaswag
- winogrande
- piqa
- custom_domain_qa
num_fewshot: 5
batch_size: 8
device: cuda
output_path: ./eval_results/
커스텀 평가 태스크
# src/evaluation/custom_tasks.py
from lm_eval.api.task import ConfigurableTask
from lm_eval.api.instance import Instance
class CustomDomainQA(ConfigurableTask):
VERSION = 0
DATASET_PATH = "datasets/domain_qa"
DATASET_NAME = None
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return True
def validation_docs(self):
return self._load_docs("validation")
def test_docs(self):
return self._load_docs("test")
def _load_docs(self, split):
# 커스텀 데이터셋 로딩 로직
pass
def doc_to_text(self, doc):
return f"Question: {doc['question']}\nAnswer:"
def doc_to_target(self, doc):
return doc['answer']
def construct_requests(self, doc, ctx, **kwargs):
return [Instance(
request_type="generate_until",
doc=doc,
arguments=(ctx, {"until": ["\n"], "max_gen_toks": 256}),
)]
def process_results(self, doc, results):
pred = results[0].strip()
target = self.doc_to_target(doc)
# 도메인별 정확도 계산 로직
exact_match = pred.lower() == target.lower()
return {
"exact_match": exact_match,
"prediction": pred,
"target": target
}
def aggregation(self):
return {
"exact_match": "mean",
}
def higher_is_better(self):
return {
"exact_match": True,
}
4. LLM-as-a-Judge 평가 시스템
# src/evaluation/llm_judge.py
from transformers import pipeline
import json
class LLMJudge:
def __init__(self, judge_model="meta-llama/Llama-3.1-8B-Instruct"):
self.judge = pipeline(
"text-generation",
model=judge_model,
torch_dtype=torch.float16,
device_map="auto"
)
def evaluate_response(self, question, response, criteria):
prompt = f"""
다음 질문에 대한 응답을 평가해주세요:
질문: {question}
응답: {response}
평가 기준:
{criteria}
1-10점 척도로 평가하고 이유를 설명해주세요.
평가:
점수:
이유:
"""
result = self.judge(prompt, max_length=512, temperature=0.1)
return self._parse_evaluation(result[0]['generated_text'])
def _parse_evaluation(self, text):
# 평가 결과 파싱 로직
lines = text.split('\n')
score_line = [l for l in lines if l.startswith('점수:')]
reason_lines = [l for l in lines if l.startswith('이유:')]
score = None
if score_line:
try:
score = int(score_line[0].split(':')[1].strip())
except:
score = 5 # 기본값
reason = reason_lines[0].split(':')[1].strip() if reason_lines else ""
return {"score": score, "reason": reason}
def run_llm_judge_evaluation(test_data, model_responses):
judge = LLMJudge()
results = []
for item in test_data:
question = item['question']
expected = item['answer']
response = model_responses[item['id']]
criteria = """
1. 정확성: 답변이 사실적으로 올바른가?
2. 완전성: 질문에 완전히 답변했는가?
3. 명확성: 답변이 이해하기 쉬운가?
4. 관련성: 질문과 관련된 답변인가?
"""
evaluation = judge.evaluate_response(question, response, criteria)
results.append({
"question": question,
"response": response,
"expected": expected,
"evaluation": evaluation
})
return results
5. 모델 서빙: vLLM + Kubernetes
vLLM 서빙 설정
# src/serving/vllm_server.py
from vllm import LLM, SamplingParams
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn
app = FastAPI()
# vLLM 모델 로드
llm = LLM(
model="./outputs/llama3-lora",
tensor_parallel_size=1,
gpu_memory_utilization=0.9,
max_model_len=4096,
quantization="bitsandbytes",
load_format="bitsandbytes"
)
class ChatRequest(BaseModel):
prompt: str
max_tokens: int = 256
temperature: float = 0.7
top_p: float = 0.9
@app.post("/chat")
async def chat(request: ChatRequest):
sampling_params = SamplingParams(
temperature=request.temperature,
top_p=request.top_p,
max_tokens=request.max_tokens
)
outputs = llm.generate([request.prompt], sampling_params)
response = outputs[0].outputs[0].text
return {"response": response}
@app.get("/health")
async def health():
return {"status": "healthy"}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
Kubernetes 배포 매니페스트
# k8s/vllm-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: vllm-llm-server
namespace: llmops
spec:
replicas: 2
selector:
matchLabels:
app: vllm-llm-server
template:
metadata:
labels:
app: vllm-llm-server
spec:
containers:
- name: vllm-server
image: your-registry/vllm-server:latest
ports:
- containerPort: 8000
resources:
requests:
nvidia.com/gpu: 1
memory: "16Gi"
cpu: "4"
limits:
nvidia.com/gpu: 1
memory: "32Gi"
cpu: "8"
env:
- name: CUDA_VISIBLE_DEVICES
value: "0"
volumeMounts:
- name: model-storage
mountPath: /workspace/outputs
volumes:
- name: model-storage
persistentVolumeClaim:
claimName: model-pvc
---
apiVersion: v1
kind: Service
metadata:
name: vllm-service
namespace: llmops
spec:
selector:
app: vllm-llm-server
ports:
- port: 80
targetPort: 8000
type: LoadBalancer
6. GitOps 오케스트레이션: ArgoCD
ArgoCD Application 설정
# argocd/llmops-app.yaml
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
name: llmops-pipeline
namespace: argocd
spec:
project: default
source:
repoURL: https://github.com/your-org/llmops-manifests
targetRevision: HEAD
path: k8s/
destination:
server: https://kubernetes.default.svc
namespace: llmops
syncPolicy:
automated:
prune: true
selfHeal: true
syncOptions:
- CreateNamespace=true
Workflow Template (Argo Workflows)
# workflows/training-workflow.yaml
apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
name: llm-training-template
namespace: llmops
spec:
entrypoint: llm-training-pipeline
templates:
- name: llm-training-pipeline
dag:
tasks:
- name: data-preprocessing
template: data-preprocess
- name: model-training
template: train-model
dependencies: [data-preprocessing]
- name: model-evaluation
template: evaluate-model
dependencies: [model-training]
- name: model-deployment
template: deploy-model
dependencies: [model-evaluation]
when: " > 0.8"
- name: data-preprocess
container:
image: your-registry/data-processor:latest
command: ["python"]
args: ["src/data/preprocess.py"]
volumeMounts:
- name: data-volume
mountPath: /data
- name: train-model
container:
image: your-registry/axolotl-trainer:latest
command: ["python"]
args: ["-m", "axolotl.cli.train", "config/llama3_lora.yml"]
resources:
requests:
nvidia.com/gpu: 2
limits:
nvidia.com/gpu: 2
volumeMounts:
- name: model-volume
mountPath: /workspace/outputs
- name: evaluate-model
container:
image: your-registry/lm-eval:latest
command: ["lm_eval"]
args: ["--config", "eval_config.yaml"]
outputs:
parameters:
- name: accuracy
valueFrom:
path: /tmp/accuracy.txt
- name: deploy-model
container:
image: your-registry/kubectl:latest
command: ["kubectl"]
args: ["apply", "-f", "k8s/vllm-deployment.yaml"]
7. 실험 추적 및 모니터링: MLflow + Weights & Biases
MLflow 추적 설정
# src/tracking/mlflow_tracker.py
import mlflow
import mlflow.pytorch
from mlflow.tracking import MlflowClient
class LLMOpsTracker:
def __init__(self, experiment_name="llmops-experiments"):
mlflow.set_experiment(experiment_name)
self.client = MlflowClient()
def start_run(self, run_name=None):
return mlflow.start_run(run_name=run_name)
def log_training_params(self, config):
mlflow.log_params({
"base_model": config.get("base_model"),
"lora_r": config.get("lora_r"),
"lora_alpha": config.get("lora_alpha"),
"learning_rate": config.get("learning_rate"),
"batch_size": config.get("micro_batch_size"),
"num_epochs": config.get("num_epochs")
})
def log_evaluation_metrics(self, metrics):
mlflow.log_metrics(metrics)
def log_model(self, model, model_name):
mlflow.pytorch.log_model(model, model_name)
def register_model(self, model_uri, model_name):
mlflow.register_model(model_uri, model_name)
def transition_model_stage(self, model_name, version, stage):
self.client.transition_model_version_stage(
name=model_name,
version=version,
stage=stage
)
# 사용 예시
def train_with_tracking():
tracker = LLMOpsTracker()
with tracker.start_run("llama3-lora-experiment"):
# 훈련 파라미터 로깅
config = load_config("config/llama3_lora.yml")
tracker.log_training_params(config)
# 모델 훈련
model = train_model(config)
# 평가 실행
eval_results = evaluate_model(model)
tracker.log_evaluation_metrics(eval_results)
# 모델 저장 및 등록
tracker.log_model(model, "llama3-lora")
if eval_results["accuracy"] > 0.8:
model_uri = f"runs:/{mlflow.active_run().info.run_id}/llama3-lora"
tracker.register_model(model_uri, "production-llm")
tracker.transition_model_stage("production-llm", "1", "Production")
8. 안전성 보장: Guardrails AI
# src/safety/guardrails_config.py
from guardrails import Guard
from guardrails.validators import (
ValidLength,
NoToxicLanguage,
NoSecrets,
ValidJson
)
def create_response_guard():
guard = Guard.from_rail_string("""
<rail version="0.1">
<output>
<string
name="response"
description="AI model response"
validators="valid-length:1 100000;no-toxic-language;no-secrets"
/>
</output>
</rail>
""")
return guard
def validate_response(response_text):
guard = create_response_guard()
try:
validated_response = guard.parse(response_text)
return {
"is_valid": True,
"response": validated_response.validated_output,
"errors": []
}
except Exception as e:
return {
"is_valid": False,
"response": None,
"errors": [str(e)]
}
# FastAPI 미들웨어로 통합
from fastapi import FastAPI, HTTPException
from fastapi.middleware.base import BaseHTTPMiddleware
class GuardrailsMiddleware(BaseHTTPMiddleware):
async def dispatch(self, request, call_next):
response = await call_next(request)
if request.url.path == "/chat":
# 응답 내용 검증
response_body = await response.body()
response_text = response_body.decode()
validation_result = validate_response(response_text)
if not validation_result["is_valid"]:
raise HTTPException(
status_code=400,
detail=f"Response validation failed: {validation_result['errors']}"
)
return response
비용 효율성 및 성능 최적화
1. 리소스 최적화 전략
# k8s/resource-optimization.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
name: llmops-quota
namespace: llmops
spec:
hard:
requests.nvidia.com/gpu: "8"
limits.nvidia.com/gpu: "8"
requests.memory: "64Gi"
limits.memory: "128Gi"
---
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: vllm-pdb
namespace: llmops
spec:
minAvailable: 1
selector:
matchLabels:
app: vllm-llm-server
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: vllm-hpa
namespace: llmops
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: vllm-llm-server
minReplicas: 1
maxReplicas: 5
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
2. 모델 최적화 기법
# src/optimization/model_optimization.py
from transformers import BitsAndBytesConfig
import torch
def setup_quantization_config():
"""8비트 및 4비트 양자화 설정"""
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
return bnb_config
def optimize_model_for_inference(model_path):
"""추론 최적화된 모델 생성"""
from optimum.bettertransformer import BetterTransformer
from torch.jit import script
# BetterTransformer 적용
model = BetterTransformer.transform(model_path)
# TorchScript 컴파일 (옵션)
example_input = torch.randint(0, 1000, (1, 512))
scripted_model = script(model, example_input)
return scripted_model
def setup_flash_attention():
"""Flash Attention 활성화"""
import flash_attn
# Flash Attention이 사용 가능한지 확인
if flash_attn.flash_attn_func:
return True
return False
모니터링 및 관찰성
1. Prometheus + Grafana 메트릭
# src/monitoring/metrics.py
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time
# 메트릭 정의
REQUEST_COUNT = Counter('llm_requests_total', 'Total LLM requests', ['model', 'status'])
REQUEST_DURATION = Histogram('llm_request_duration_seconds', 'LLM request duration')
ACTIVE_CONNECTIONS = Gauge('llm_active_connections', 'Active connections')
MODEL_LOAD_TIME = Histogram('llm_model_load_seconds', 'Model loading time')
class MetricsCollector:
def __init__(self):
start_http_server(8001) # Prometheus 메트릭 서버
def record_request(self, model_name, status, duration):
REQUEST_COUNT.labels(model=model_name, status=status).inc()
REQUEST_DURATION.observe(duration)
def update_active_connections(self, count):
ACTIVE_CONNECTIONS.set(count)
def record_model_load_time(self, duration):
MODEL_LOAD_TIME.observe(duration)
# FastAPI와 통합
from fastapi import FastAPI
import time
app = FastAPI()
metrics = MetricsCollector()
@app.middleware("http")
async def add_metrics_middleware(request, call_next):
start_time = time.time()
response = await call_next(request)
duration = time.time() - start_time
status = "success" if response.status_code < 400 else "error"
metrics.record_request("llama3-lora", status, duration)
return response
2. 로깅 및 트레이싱
# src/monitoring/logging_config.py
import logging
import json
from datetime import datetime
class JSONFormatter(logging.Formatter):
def format(self, record):
log_entry = {
"timestamp": datetime.utcnow().isoformat(),
"level": record.levelname,
"message": record.getMessage(),
"module": record.module,
"function": record.funcName,
"line": record.lineno
}
if hasattr(record, 'request_id'):
log_entry['request_id'] = record.request_id
if hasattr(record, 'user_id'):
log_entry['user_id'] = record.user_id
return json.dumps(log_entry)
def setup_logging():
logger = logging.getLogger("llmops")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)
return logger
# OpenTelemetry 트레이싱
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
def setup_tracing():
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
jaeger_exporter = JaegerExporter(
agent_host_name="jaeger",
agent_port=6831,
)
span_processor = BatchSpanProcessor(jaeger_exporter)
trace.get_tracer_provider().add_span_processor(span_processor)
return tracer
보안 및 컴플라이언스
1. 데이터 보안
# src/security/data_security.py
from cryptography.fernet import Fernet
import hashlib
import os
class DataEncryption:
def __init__(self):
self.key = os.environ.get('ENCRYPTION_KEY') or Fernet.generate_key()
self.cipher_suite = Fernet(self.key)
def encrypt_sensitive_data(self, data):
"""민감한 데이터 암호화"""
return self.cipher_suite.encrypt(data.encode()).decode()
def decrypt_sensitive_data(self, encrypted_data):
"""암호화된 데이터 복호화"""
return self.cipher_suite.decrypt(encrypted_data.encode()).decode()
def hash_user_data(self, user_data):
"""사용자 데이터 해싱 (개인정보 보호)"""
return hashlib.sha256(user_data.encode()).hexdigest()
class DataAnonymizer:
def __init__(self):
self.pii_patterns = [
r'\b\d{3}-\d{2}-\d{4}\b', # SSN
r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', # Email
r'\b\d{3}-\d{3}-\d{4}\b', # Phone
]
def anonymize_text(self, text):
"""텍스트에서 개인정보 제거/마스킹"""
import re
anonymized = text
for pattern in self.pii_patterns:
anonymized = re.sub(pattern, '[REDACTED]', anonymized)
return anonymized
2. 접근 제어 및 인증
# k8s/rbac.yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: llmops
name: llmops-operator
rules:
- apiGroups: [""]
resources: ["pods", "services", "configmaps", "secrets"]
verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
- apiGroups: ["apps"]
resources: ["deployments", "replicasets"]
verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: llmops-operator-binding
namespace: llmops
subjects:
- kind: ServiceAccount
name: llmops-service-account
namespace: llmops
roleRef:
kind: Role
name: llmops-operator
apiGroup: rbac.authorization.k8s.io
실전 배포 체크리스트
1. 프로덕션 준비 사항
## 배포 전 체크리스트
### 인프라
- [ ] Kubernetes 클러스터 준비 (GPU 노드 포함)
- [ ] ArgoCD 설치 및 구성
- [ ] Prometheus + Grafana 모니터링 스택
- [ ] ELK/EFK 로깅 스택
- [ ] 네트워크 정책 및 보안 그룹 설정
### 데이터
- [ ] DVC 리포지터리 초기화
- [ ] 데이터 파이프라인 검증
- [ ] 데이터 품질 체크
- [ ] 백업 및 복원 절차 확립
### 모델
- [ ] 기본 모델 다운로드 및 검증
- [ ] LoRA 어댑터 훈련 및 테스트
- [ ] 모델 평가 자동화
- [ ] A/B 테스트 환경 준비
### 보안
- [ ] RBAC 권한 설정
- [ ] 시크릿 관리 (Vault 등)
- [ ] 네트워크 보안 정책
- [ ] 데이터 암호화 활성화
### 운영
- [ ] 알람 및 모니터링 룰 설정
- [ ] 백업 및 재해복구 계획
- [ ] 용량 계획 및 스케일링 정책
- [ ] 운영 매뉴얼 작성
2. 성능 튜닝 가이드
# src/performance/tuning_guide.py
def optimize_vllm_config():
"""vLLM 성능 최적화 설정"""
config = {
# GPU 메모리 사용률 (80-95% 권장)
"gpu_memory_utilization": 0.9,
# 텐서 병렬화 (GPU 수에 따라 조정)
"tensor_parallel_size": 2,
# 배치 크기 최적화
"max_num_batched_tokens": 8192,
"max_num_seqs": 256,
# KV 캐시 최적화
"block_size": 16,
"swap_space": 4, # GB
# 양자화 설정
"quantization": "bitsandbytes",
"load_format": "bitsandbytes",
# 메모리 효율성
"enable_prefix_caching": True,
"disable_log_stats": False,
}
return config
def kubernetes_resource_tuning():
"""Kubernetes 리소스 최적화"""
recommendations = {
"node_affinity": {
"gpu_nodes": "nvidia.com/gpu=true",
"memory_nodes": "memory-optimized=true"
},
"resource_requests": {
"memory": "16Gi",
"cpu": "4",
"nvidia.com/gpu": "1"
},
"resource_limits": {
"memory": "32Gi",
"cpu": "8",
"nvidia.com/gpu": "1"
},
"pod_anti_affinity": True, # 고가용성
"horizontal_pod_autoscaler": {
"min_replicas": 2,
"max_replicas": 10,
"target_cpu_utilization": 70
}
}
return recommendations
결론 및 다음 단계
1. 오픈소스 LLMOps의 장점
- 비용 효율성: 라이센스 비용 제거로 50-70% 비용 절감
- 투명성: 전체 소스코드 접근으로 완전한 커스터마이징 가능
- 유연성: 벤더 종속성 없이 다양한 모델과 도구 조합 가능
- 커뮤니티: 활발한 오픈소스 생태계와 빠른 혁신
- 표준화: 업계 표준 도구들의 조합으로 이식성 높음
2. 고려사항
- 기술적 복잡성: 더 많은 통합 작업과 운영 노하우 필요
- 지원: 상업적 지원 대신 커뮤니티 기반 지원
- 안정성: 엔터프라이즈급 안정성 확보를 위한 추가 노력 필요
3. 로드맵
graph TD
A[Phase 1: 기본 파이프라인 구축] --> B[Phase 2: 고급 기능 추가]
B --> C[Phase 3: 운영 최적화]
C --> D[Phase 4: 고도화 및 확장]
A --> A1[DVC + Airflow 데이터 파이프라인]
A --> A2[Axolotl 기반 LoRA 훈련]
A --> A3[vLLM 기본 서빙]
B --> B1[LM Eval Harness 통합]
B --> B2[MLflow 실험 추적]
B --> B3[ArgoCD GitOps]
C --> C1[Prometheus 모니터링]
C --> C2[Guardrails 안전성]
C --> C3[자동 스케일링]
D --> D1[멀티모달 지원]
D --> D2[분산 훈련 확장]
D --> D3[엣지 배포]
이 오픈소스 LLMOps 파이프라인은 유연성과 비용 효율성을 제공합니다. 각 조직의 요구사항에 맞게 점진적으로 구축하여 안정적이고 확장 가능한 AI 인프라를 만들어보세요.