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Train, infer and deploy suitable machine learning models for any uploaded dataset, and visualise the experimental results (MLflow) easily from the GUI.

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MyAutoML

Requirements

  • Poetry
  • gcloud CLI
  • docker compose

Setup

GCP Authentification

$ gcloud auth login
$ gcloud components install pubsub-emulator

Install Dependencies

$ make install

Environmental Variables

$ vi .env
  • 以下の情報を記入+環境変数としてexportしておく
GCP_PROJECT_ID=your project id
LOCATION=asia-northeast1
AR_REPOSITORY_NAME=artifact registory repository name
DATA_BUCKET=gs://xxx
ARTIFACT_BUCKET=gs://yyy

Create Cloud Storage Bucket

$ gsutil mb -l $LOCATION $DATA_BUCKET
$ gsutil mb -l $LOCATION $ARTIFACT_BUCKET

Build & Push Docker Image

$ gcloud auth configure-docker asia-northeast1-docker.pkg.dev
$ gcloud artifacts repositories create $AR_REPOSITORY_NAME --location=$LOCATION --repository-format=docker
$ docker compose build
$ docker compose push

Boot Streamlit GUI

$ make streamlit

MLflow

Boot MLflow

$ make mlflow

Update Experiment Results on MLflow

$ make update_mlflow

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Train, infer and deploy suitable machine learning models for any uploaded dataset, and visualise the experimental results (MLflow) easily from the GUI.

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