From 67d15bd5e2980ab4c2270ddfc667c80c4a40c2ec Mon Sep 17 00:00:00 2001 From: Guaqamole Date: Tue, 23 Jul 2024 11:00:06 +0900 Subject: [PATCH] Quarts Sync at: 2024-07-23 11:00:00 KST --- ...beflow on EKS \354\204\244\354\271\230.md" | 56 +++++--- content/03. Data/03. Data.md | 2 +- .../Data Science/MLOps/BentoML/BentoML.md | 131 ++++++++++++++++++ ...0\353\263\270 \354\213\244\354\212\265.md" | 4 + content/Pasted image 20240722140915.png | Bin 0 -> 90045 bytes 5 files changed, 172 insertions(+), 21 deletions(-) create mode 100644 content/03. Data/Data Science/MLOps/BentoML/BentoML.md create mode 100644 content/Pasted image 20240722140915.png diff --git "a/content/02. Infra/Kubernetes/Kubeflow on EKS \354\204\244\354\271\230.md" "b/content/02. Infra/Kubernetes/Kubeflow on EKS \354\204\244\354\271\230.md" index aa07e52..0fb4596 100644 --- "a/content/02. Infra/Kubernetes/Kubeflow on EKS \354\204\244\354\271\230.md" +++ "b/content/02. Infra/Kubernetes/Kubeflow on EKS \354\204\244\354\271\230.md" @@ -1239,26 +1239,6 @@ EOF kustomize build upstream/apps/profiles/upstream/overlays/kubeflow | kubectl apply -f - ``` -#### 매우중요!!!!! -vi upstream/common/user-namespace/overlay/profile.yaml -- 해당 프로파일에 role arn을 넣지 않으면 kfp 파이프라인이 돌지 않는다.. -- 이후 아래 user 배포. kustomize build upstream/common/user-namespace/overlay | kubectl apply -f - -```python -apiVersion: kubeflow.org/v1beta1 -kind: Profile -metadata: - name: $(profile-name) -spec: - owner: - kind: User - name: $(user) - plugins: - - kind: AwsIamForServiceAccount - spec: - awsIamRole: arn:aws:iam::... # 여기 추가해야함 - annotateOnly: true -``` - #### nodeSelector ```python @@ -1295,6 +1275,42 @@ kustomize build upstream/apps/training-operator/upstream/overlays/kubeflow | kub ### user +user-example service account +user namespace에서 s3 접근하려면 service account 만들어야한다. +```python +eksctl create iamserviceaccount --cluster=my-cluster --name=kubeflow-user-example-com-sa --namespace=kubeflow-user-example-com --attach-policy-arn=arn:aws:iam::aws:policy/AmazonS3FullAccess --approve + +kubectl get sa -n kubeflow-user-example-com +NAME SECRETS AGE +default 0 129m +default-editor 0 129m +default-viewer 0 129m +kubeflow-user-example-com-sa 0 59s. # 이게있어야 pipeline 구성할때 s3 접근할수있다. +``` + +#### 매우중요!!!!! +- 해당 프로파일에 profile role arn을 넣지 않으면 kfp 파이프라인이 돌지 않는다.. (backend-role 집어넣으면안됨) +- 이후 아래 user 배포. kustomize build upstream/common/user-namespace/overlay | kubectl apply -f - +```python +vi upstream/common/user-namespace/overlay/profile.yaml + +apiVersion: kubeflow.org/v1beta1 +kind: Profile +metadata: + name: $(profile-name) +spec: + owner: + kind: User + name: $(user) + plugins: + - kind: AwsIamForServiceAccount + spec: + awsIamRole: arn:aws:iam::123456789:role/kf-pipeline-profile-role-my-cluster # 여기 추가해야함 + annotateOnly: true +``` + + +#### install ```python cp -R awsconfigs/common/user-namespace/overlay upstream/common/user-namespace diff --git a/content/03. Data/03. Data.md b/content/03. Data/03. Data.md index 5dc542f..55b8cf8 100644 --- a/content/03. Data/03. Data.md +++ b/content/03. Data/03. Data.md @@ -94,7 +94,7 @@ tags: [] - [[Random Variable & Probability Distribution]] - [[ML vs DL]] - **MLOps** - - **BentoML** + - **[[BentoML]]** - [[BentoML Examples]] - **Kubeflow** - [[Kubeflow 기본 실습]] diff --git a/content/03. Data/Data Science/MLOps/BentoML/BentoML.md b/content/03. Data/Data Science/MLOps/BentoML/BentoML.md new file mode 100644 index 0000000..31e441d --- /dev/null +++ b/content/03. Data/Data Science/MLOps/BentoML/BentoML.md @@ -0,0 +1,131 @@ +--- +title: BentoML +date: 2024-07-22 +draft: false +tags: + - MLOps + - BentoML +complete: true +--- +## Overview +[BentoML](https://github.com/bentoml/BentoML) 은 안정적이고 확장 가능하며 비용 효율적인 AI 애플리케이션을 구축하기 위한 통합 AI 애플리케이션 프레임워크입니다. 배포 프로세스를 간소화하고 모델 제공, 애플리케이션 패키징 및 프로덕션 배포에 필요한 모든 것을 통합하기 위한 엔드투엔드 솔루션을 제공합니다. + +### features +- 통합된 포맷으로 배포를 간소화 +- 모든 AI 모델로 애플리케이션 구축 +- AI 애플리케이션을 위한 Inference 최적화 +- 한 번만 빌드하면 어디든 배포 가능 + +### architecture +1. 모델 정의 : tensorflow, pytorch 다양한 라이브러리 제공 +2. 모델 생성 및 저장 : 모델을 간단하고 쉽게 저장하고 불러올수있음 +3. 서비스 생성 : 모델을 래핑하고 추상화하여 외부 엔드포인트를 쉽게 구성할 수 있음 +4. 벤토 만들기 : 서비스, 파이썬 패키지, 모델, 도커 설정과 같은 모든 빌드 옵션이 포함된 yaml 파일을 통해 밴토로 패키징 가능. +5. 벤토 배포 : Dockerfile 사용하여 컨테이너화 하거나, bentocloud에 배포하여 바로 서비스에 적용가능. +![[Pasted image 20240722140915.png|775]] + + +## Components +### Models +BentoML은 모델을 저장하고 관리하기 위한 로컬 모델 스토어를 제공하는데, 이는 BentoML이 유지 관리하는 로컬 파일 디렉토리입니다. 혹은, remote storage로 mlflow와 통합해서 사용할 수 있습니다. + +#### 1. save model +download_iris.py +```python +import bentoml + +from sklearn import svm +from sklearn import datasets + +# Load training data set +iris = datasets.load_iris() +X, y = iris.data, iris.target + +# Train the model +clf = svm.SVC(gamma='scale') +clf.fit(X, y) + +# Save model to the BentoML local Model Store +saved_model = bentoml.sklearn.save_model("iris_clf", clf) +``` + +```python +python download_iris.py +``` + +##### 자세히 설정하고 싶으면… +```python +bentoml.pytorch.save_model( + "demo_mnist", # Model name in the local Model Store + trained_model, # Model instance being saved + labels={ # User-defined labels for managing models in BentoCloud + "owner": "nlp_team", + "stage": "dev", + }, + metadata={ # User-defined additional metadata + "acc": acc, + "cv_stats": cv_stats, + "dataset_version": "20210820", + }, + custom_objects={ # Save additional user-defined Python objects + "tokenizer": tokenizer_object, + } +) +``` +#### 2. list model +```python +bentoml models list + + Tag Module Size Creation Time + iris_clf:6r7hwuch46fighsm bentoml.sklearn 5.98 KiB 2024-07-22 14:05:03 +``` + +#### 3. load model +모델 인스턴스를 메모리로 다시 로드하려면 프레임워크별 `load_model`메서드를 사용합니다. +두가지 명령어를 사용할 수 있습니다. +##### load_model() +```python +import bentoml +from sklearn.base import BaseEstimator + +model: BaseEstimator = bentoml.sklearn.load_model("iris_clf:latest") +print(model.__dict__) +``` +##### get() +모델 정보를 검색하고 `to_runner`API에 액세스하려면 다음 메서드를 사용하세요 +```python +import bentoml +bento_model: bentoml.Model = bentoml.models.get("iris_clf:latest") + +print(bento_model.tag) +print(bento_model.path) +print(bento_model.custom_objects) +print(bento_model.info.metadata) +print(bento_model.info.labels) + +my_runner: bentoml.Runner = bento_model.to_runner() +``` + +#### 4. mlflow +##### mlflow.load_model +```python +import bentoml + +bentoml.mlflow.import_model( + 'my_mlflow_model', + model_uri="runs://run-relative/path/to/model", + signatures={ + "predict": {"batchable": True}, + } +) +``` + +##### mlflow.get +```python +import bentoml +# target model must be from the BentoML model store +model = bentoml.mlflow.get("my_mlflow_model") +``` + + +### Runners diff --git "a/content/03. Data/Data Science/MLOps/Kubeflow/Kubeflow \352\270\260\353\263\270 \354\213\244\354\212\265.md" "b/content/03. Data/Data Science/MLOps/Kubeflow/Kubeflow \352\270\260\353\263\270 \354\213\244\354\212\265.md" index 6583cb3..c5e9421 100644 --- "a/content/03. Data/Data Science/MLOps/Kubeflow/Kubeflow \352\270\260\353\263\270 \354\213\244\354\212\265.md" +++ "b/content/03. Data/Data Science/MLOps/Kubeflow/Kubeflow \352\270\260\353\263\270 \354\213\244\354\212\265.md" @@ -51,6 +51,7 @@ client = kfp.Client( # V1 https://kubeflow-pipelines.readthedocs.io/en/1.8.22/ ## Hello World +### func_to_container_op https://kubeflow-pipelines.readthedocs.io/en/1.8.22/source/kfp.components.html#kfp.components.func_to_container_op ```python from kfp import dsl, components @@ -72,6 +73,9 @@ if __name__ == "__main__": hello_world_pipeline, arguments={}, experiment_name="hello-world-experiment") ``` +### + + ## Passing TEXT Data ```python import kfp diff --git a/content/Pasted image 20240722140915.png b/content/Pasted image 20240722140915.png new file mode 100644 index 0000000000000000000000000000000000000000..e22a5b798c3993b903c95475b83f00d5fb1080d6 GIT binary patch literal 90045 zcma&OcQ}^+`v-i>$_m*dLS_SHmX(<;+*YzjWK;Ht$d+U#L}X>}?7gx|$d;YGBYMuu z=leU3=fCH1e5U)p*86>q*Ep|2)Kuh%@M-Z;C=`+W18H>>3acH3x^Qq22Y!?LDd`ak 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