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Update README.md

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Tomasz Dłuski
2020-08-24 16:24:52 +02:00
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commit 670e449369
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@@ -59,16 +59,15 @@ s3Client.make_bucket('mlflow')
</details> </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 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. 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 ```shell
export AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE 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 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 or paste it into your .bashrc file and then run `source ~/.bashrc`
```
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
```
6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda` 6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
```shell ```shell
mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
``` # or
Optionally you can run
```shell
python ./quickstart/mlflow_tracking.py 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 ```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 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