dalphinloser/mlflow-docker-minio
1
0
Fork 0
mirror of https://github.com/Toumash/mlflow-docker synced 2025-11-04 07:09:22 +01:00
Code Issues Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Tomasz Dłuski
2020-08-24 16:24:52 +02:00
committed by GitHub
parent 3d97b2f1f7
commit 670e449369
1 changed files with 5 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
README.md
View File

@@ -59,16 +59,15 @@ s3Client.make_bucket('mlflow')
</details>
---
4. Open up http://localhost:5000/#/ for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
4. Open up http://localhost:5000 for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
5. Configure your client-side
For running mlflow files you AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables present on the client-side.
Also, you will need to specify the address of your S3 server (minio) and mlflow tracking server
Also, you will need to specify the address of your S3 server (minio) and mlflow tracking server. For that, run following script
```shell
export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
@@ -77,43 +76,17 @@ export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000
export MLFLOW_TRACKING_URI=http://localhost:5000
```
You can load them from the .env file. But i recommend putting it in the .bashrc as below
```
AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
AWS_REGION=us-east-1
AWS_BUCKET_NAME=mlflow
MYSQL_DATABASE=mlflow
MYSQL_USER=mlflow_user
MYSQL_PASSWORD=mlflow_password
MYSQL_ROOT_PASSWORD=toor
MLFLOW_S3_ENDPOINT_URL=http://localhost:9000
MLFLOW_TRACKING_URI=http://localhost:5000
```
Then run
```shell
source .env
```
or add them as `export X=Y` to the .bashrc file and then run
```shell
source ~/.bashrc
```
or paste it into your .bashrc file and then run `source ~/.bashrc`
6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
```shell
mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
```
Optionally you can run
```shell
# or
python ./quickstart/mlflow_tracking.py
```
7. (Optional) If you are constantly switching your environment you can use this environment variable syntax
7. *(Optional)* If you are constantly switching your environment you can use this environment variable syntax
```shell
MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 MLFLOW_TRACKING_URI=http://localhost:5000 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5