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Tomasz Dłuski
2021-12-03 22:14:24 +01:00
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@@ -15,74 +15,17 @@ The only requirement is docker installed on your system and we are going to use
- Ready to use bash scripts for python development!
- Automatically-created s3 buckets
## Simple setup guide
# 🚀 Setup guide
1. Configure `.env` file for your choice. You can put there anything you like, it will be used to configure you services
2. Run `docker compose up`
3. Open up http://localhost:5000 for MlFlow, and http://localhost:9001/ to browse your files in S3 artifact store
2. Run the Infrastructure by this one line:
```shell
$ docker-compose up -d
Creating network "mlflow-basis_A" with driver "bridge"
Creating mlflow_db ... done
Creating tracker_mlflow ... done
Creating aws-s3 ... done
```
## How to use in ML development in python
3. Create mlflow bucket. You can use my bundled script.
<details>
<summary>Click to show</summary>
Just run
```shell
bash ./run_create_bucket.sh
```
You can also do it **either using AWS CLI or Python Api**.
<details><summary>AWS CLI</summary>
1. [Install AWS cli](https://aws.amazon.com/cli/) **Yes, i know that you dont have an Amazon Web Services Subscription - dont worry! It wont be needed!**
2. Configure AWS CLI - enter the same credentials from the `.env` file
```shell
aws configure
```
> AWS Access Key ID [****************123]: AKIAIOSFODNN7EXAMPLE
> AWS Secret Access Key [****************123]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
> Default region name [us-west-2]: us-east-1
> Default output format [json]: <ENTER>
3. Run
```shell
aws --endpoint-url=http://localhost:9000 s3 mb s3://mlflow
```
</details>
<details><summary>Python API</summary>
1. Install Minio
```shell
pip install Minio
```
2. Run this to create a bucket
```python
from minio import Minio
from minio.error import ResponseError
s3Client = Minio(
'localhost:9000',
access_key='<YOUR_AWS_ACCESSS_ID>', # copy from .env file
secret_key='<YOUR_AWS_SECRET_ACCESS_KEY>', # copy from .env file
secure=False
)
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
5. Configure your client-side
1. Configure your client-side
For running mlflow files you need various environment variables set on the client side. To generate them user the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.
@@ -91,7 +34,7 @@ For running mlflow files you need various environment variables set on the clien
The script installs this variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI. All of them are needed to use mlflow from the client-side.
6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
2. 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
@@ -99,12 +42,15 @@ mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
python ./quickstart/mlflow_tracking.py
```
7. *(Optional)* If you are constantly switching your environment you can use this environment variable syntax
3. *(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
```
</details>
## Licensing
Copyright (c) 2021 Tomasz Dłuski