From 670e449369fed4c7a59a38b9fdd3cc761b9eee3e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tomasz=20D=C5=82uski?= Date: Mon, 24 Aug 2020 16:24:52 +0200 Subject: [PATCH] Update README.md --- README.md | 37 +++++-------------------------------- 1 file changed, 5 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 8012749..8686e22 100644 --- a/README.md +++ b/README.md @@ -59,16 +59,15 @@ s3Client.make_bucket('mlflow') - --- -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