https://github.com/nomnomnonono/myautoml
Train, infer and deploy suitable machine learning models for any uploaded dataset, and visualise the experimental results (MLflow) easily from the GUI.
https://github.com/nomnomnonono/myautoml
automl deep-learning docker docker-compose google-cloud-platform machine-learning mlflow mlops poetry python streamlit vertex-ai
Last synced: about 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/nomnomnonono/myautoml
- Owner: nomnomnonono
- Created: 2023-09-05T10:37:02.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-23T12:33:15.000Z (over 2 years ago)
- Last Synced: 2025-02-07T07:32:06.553Z (over 1 year ago)
- Topics: automl, deep-learning, docker, docker-compose, google-cloud-platform, machine-learning, mlflow, mlops, poetry, python, streamlit, vertex-ai
- Language: Python
- Homepage:
- Size: 92.8 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MyAutoML
## Requirements
- Poetry
- gcloud CLI
- docker compose
## Setup
### GCP Authentification
```bash
$ gcloud auth login
$ gcloud components install pubsub-emulator
```
### Install Dependencies
```bash
$ make install
```
### Environmental Variables
```bash
$ vi .env
```
- 以下の情報を記入＋環境変数としてexportしておく
```bash
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
```bash
$ gsutil mb -l $LOCATION $DATA_BUCKET
$ gsutil mb -l $LOCATION $ARTIFACT_BUCKET
```
## Build & Push Docker Image
```bash
$ 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
```bash
$ make streamlit
```
## MLflow
### Boot MLflow
```bash
$ make mlflow
```
### Update Experiment Results on MLflow
```bash
$ make update_mlflow
```