gce9 Kubeflow 1.0 기능 #6 (Metadata) 2020.03.23 1. Kubeflow Metadata ? - Tracking and managing metadata of machine learning workflows in Kubeflow - metadata means information about executions (runs), models, datasets, and other artifacts. Artifacts are the files and objects that form the inputs and outputs of the components in your ML workflow 2. Try the Metadata SDK in a sample Jupyter notebook - demo.ipynb download from Github Te.. 2021. 9. 25. Kubeflow 1.0 기능 #5 (KFServing, TFServing) 2020.03.13 1. Model serving overview - https://v1-0-branch.kubeflow.org/docs/reference/pytorchjob/v1/pytorch/ - Kubeflow supports two model serving systems that allow multi-framework model serving: KFServing and Seldon Core. Alternatively, you can use a standalone model serving system. a. Multi-framework model serving - A check mark (✓) indicates that the system (KFServing or Seldon Core) suppor.. 2021. 9. 25. Kubeflow 1.0 기능 #4 (PyTorch Training) 2020.03.09 1. PyTorchJob ? - Kubeflow에서 PyTorch training할 때 사용되는 Kubernetes custom resource - https://v1-0-branch.kubeflow.org/docs/reference/pytorchjob/v1/pytorch/ 2. PyTorch training 하기 - https://v1-0-branch.kubeflow.org/docs/components/training/pytorch/components/training/pytorch/ a. Cloud shell 기동 b. Verify that PyTorch support is included in your Kubeflow deployment $ kubectl get crd | head.. 2021. 9. 25. Kubeflow 1.0 기능 #3 (Katib) 2020.03.09 1. Kubeflow Katib ? - Katib uses for automated tuning of ML model’s hyperparameters. Hyperparameters are the variables that control the model training process. For example: ✓ Learning rate. ✓ Number of layers in a neural network. ✓ Number of nodes in each layer. Hyperparameter values are not learned. Hyperparameter tuning is the process of optimizing the hyperparameter values to maxim.. 2021. 9. 25. Kubeflow 1.0 기능 #2 (TF-Job, TF-Serving, Kubeflow pipeline) 2020.02.26 1. 개요 - GKE에 설치한 Kubeflow의 Pipeline 기능을 이해하기 위해 아래 사이트를 참조하여 사용 해 봄 - Using Kubeflow for Financial Time Series (https://github.com/kubeflow/examples/tree/master/financial_time_series) - This example covers the following concepts: a. Deploying Kubeflow to a GKE cluster b. Exploration via JupyterHub (prospect data, preprocess data, develop ML model) c. Training several tensorflow models.. 2021. 9. 24. Kubeflow 1.0 기능 #1 (Jupyter notebook) 2020.03.12 1. 참고 문서 - https://v1-0-branch.kubeflow.org/docs/notebooks/setup/ 2. Notebook server 생성 a. Cloud shell 기동 b. URL 확인 $ kubectl -n istio-system get ingress NAME HOSTS ADDRESS PORTS AGE envoy-ingress my-kubeflow.endpoints.my-kubeflow-269301.cloud.goog 34.107.211.135 80 6m42s$ $ c. Kubeflow 접속 (URL: my-kubeflow.endpoints.my-kubeflow-269301.cloud.goog) d. Create a Jupyter notebook server a.. 2021. 9. 24. Kubeflow 1.0 in GCE 구성 2020.02.20 1. 개요 - 본 문서에서는 GCP(Google Cloud Platform)에서 Kubernetes 기반의 End 2 End ML Platform인 Kubeflow를 구성하는 절차를 설명하고자 함 - Ref. Page: https://www.kubeflow.org/docs/gke/deploy/deploy-cli/ 2. What is Kubeflow ? - Kubeflow is the ML toolkit for Kubernetes. The following diagram shows Kubeflow as a platform for arranging the components of your ML system on top of Kubernetes. - https://www.kubeflow.o.. 2021. 9. 24. Dashboard on GCE 2020.04.10 1. Kubernetes dashboard install on Google K8s Engine Ref. https://github.com/kubernetes/dashboard#kubernetes-dashboard $ kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.0.0-beta8/aio/deploy/recommended.yaml namespace/kubernetes-dashboard created serviceaccount/kubernetes-dashboard created service/kubernetes-dashboard created secret/kubernetes-dashboard-certs.. 2021. 9. 15. K8s 구성 - Single on GCE 2020.07.14 1. K8s 설치 개요 - 내용: K8s 1.18.1/1.15.11 (Single control plane) install, K8s Dashboard v2.0.0 / Weave scope install - 환경 : Google Compute Engine, Centos 7.7 - 참조: https://futurecreator.github.io/2019/02/25/kubernetes-cluster-on-google-compute-engine-for-developers/ https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/ - 참고: Kubeflow v1.0 설치 기준 요구사항 compat.. 2021. 9. 14. 이전 1 다음
