provide caddyfile basic auth for the mlflow

.env

@@ -1,5 +1,5 @@
-AWS_ACCESS_KEY_ID=admin
+AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE
-AWS_SECRET_ACCESS_KEY=sample_key
+AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
 AWS_REGION=us-east-1
 AWS_BUCKET_NAME=mlflow
 MYSQL_DATABASE=mlflow

.github/workflows/verify-docker-compose.yml

@@ -4,7 +4,7 @@ jobs:
   verify:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v2
       - name: Show the config
         run: docker-compose config
       - name: Run

Caddyfile

@@ -0,0 +1,22 @@
+# Minio Console
+s3.localhost:9001 {
+    handle_path /* {
+        reverse_proxy s3:9001
+    }
+}
+
+# Minio API
+s3.localhost:9000 {
+    handle_path /* {
+        reverse_proxy s3:9000
+    }
+}
+
+mlflow.localhost {
+    basicauth /* {
+	    root JDJhJDEwJEVCNmdaNEg2Ti5iejRMYkF3MFZhZ3VtV3E1SzBWZEZ5Q3VWc0tzOEJwZE9TaFlZdEVkZDhX # root hiccup
+    }
+    handle_path /* {
+        reverse_proxy mlflow:5000
+    }
+}

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Tomasz Dłuski
+Copyright (c) 2020 Tomasz Dłuski
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,41 +1,95 @@
 # MLFlow Docker Setup [![Actions Status](https://github.com/Toumash/mlflow-docker/workflows/VerifyDockerCompose/badge.svg)](https://github.com/Toumash/mlflow-docker/actions)
 
-> If you want to boot up mlflow project with one-liner - this repo is for you. 
+If you want to boot up mlflow project with one-liner - this repo is for you. 
-> The only requirement is docker installed on your system and we are going to use Bash on linux/windows.
 
-# 🚀 1-2-3! Setup guide 
+The only requirement is docker installed on your system and we are going to use Bash on linux/windows.
-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
 
-
+[![Youtube tutorial](https://img.youtube.com/vi/ma5lA19IJRA/0.jpg)](https://www.youtube.com/watch?v=ma5lA19IJRA)
-**👇Video tutorial how to set it up + BONUS with Microsoft Azure 👇**
-
-[![Youtube tutorial](https://user-images.githubusercontent.com/9840635/144674240-f1ede224-410a-4b77-a7b8-450f45cc79ba.png)](https://www.youtube.com/watch?v=ma5lA19IJRA)
 
 # Features
- - One file setup (.env)
+ - Setup by one file (.env)
- - Minio S3 artifact store with GUI
+ - Production-ready docker volumes
- - MySql mlflow storage
+ - Separate artifacts and data containers
- - Ready to use bash scripts for python development!
+ - [Artifacts GUI](https://min.io/)
- - Automatically-created s3 buckets
+ - Ready bash scripts to copy and paste for colleagues to use your server!
 
 
-## How to use in ML development in python
+## Simple setup guide
+1. Configure `.env` file for your choice. You can put there anything you like, it will be used to configure you services
 
-<details>
+2. Run the Infrastructure by this one line:
-<summary>Click to show</summary>
+```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
+```
 
-1. Configure your client-side
+3. Create mlflow bucket. You can use my bundled script.
 
-For running mlflow files you need various environment variables set on the client side. To generate them use the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.
+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
+
+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.
 
 > $ ./bashrc_install.sh   
 > [ OK ] Successfully installed environment variables into your .bashrc!
 
 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.
 
-2. 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
 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
@@ -43,16 +97,8 @@ mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
 python ./quickstart/mlflow_tracking.py
 ```
 
-3. *(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
 ```
-
-</details>
- 
-
-## Licensing
-Copyright (c) 2021 Tomasz Dłuski
-
-Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License by reviewing the file [LICENSE](./LICENSE) in the repository.

create_bucket.py

@@ -9,20 +9,20 @@ minioUrl =  os.environ.get('MLFLOW_S3_ENDPOINT_URL')
 bucketName =  os.environ.get('AWS_BUCKET_NAME')
 
 if accessID == None:
-    print('[!] AWS_ACCESS_KEY_ID environment variable is empty! run \'source .env\' to load it from the .env file')
+    print('[!] AWS_ACCESS_KEY_ID environemnt variable is empty! run \'source .env\' to load it from the .env file')
     exit(1)
 
 if accessSecret == None:
-    print('[!] AWS_SECRET_ACCESS_KEY environment variable is empty! run \'source .env\' to load it from the .env file')
+    print('[!] AWS_SECRET_ACCESS_KEY environemnt variable is empty! run \'source .env\' to load it from the .env file')
     exit(1)
 
 if minioUrl == None:
-    print('[!] MLFLOW_S3_ENDPOINT_URL environment variable is empty! run \'source .env\' to load it from the .env file')
+    print('[!] MLFLOW_S3_ENDPOINT_URL environemnt variable is empty! run \'source .env\' to load it from the .env file')
     exit(1)
 
     
 if bucketName == None:
-    print('[!] AWS_BUCKET_NAME environment variable is empty! run \'source .env\' to load it from the .env file')
+    print('[!] AWS_BUCKET_NAME environemnt variable is empty! run \'source .env\' to load it from the .env file')
     exit(1)
 
 minioUrlHostWithPort = minioUrl.split('//')[1]

docker-compose.yml

@@ -1,96 +1,69 @@
-version: "3.9"
+version: '3.2'
 services:
-  s3:
+  caddy:
-    image: minio/minio:RELEASE.2023-11-01T18-37-25Z
+    image: caddy:2-alpine
-    restart: unless-stopped
+    container_name: caddy
+    volumes:
+      - ./Caddyfile:/etc/caddy/Caddyfile
+      - /caddy/data:/data
+      - /caddy/config:/config
     ports:
-      - "9000:9000"
+      - 80:80
-      - "9001:9001"
+      - 443:443
+      - 9000:9000
+      - 9001:9001
+    restart: unless-stopped
+  s3:
+    restart: always
+    image: minio/minio:latest
+    container_name: aws-s3
+    ports:
+      - 9000
+      - 9001
     environment:
       - MINIO_ROOT_USER=${AWS_ACCESS_KEY_ID}
       - MINIO_ROOT_PASSWORD=${AWS_SECRET_ACCESS_KEY}
-    command: server /data --console-address ":9001"
+    command:
-    networks:
+      server /date --console-address ":9001"
-      - internal
-      - public
     volumes:
-      - minio_new_volume:/data
+      - ./s3:/date
+    networks:
+      - default
+      - proxy-net
   db:
-    image: mysql:8-oracle # -oracle tag supports arm64 architecture!
+      restart: always
-    restart: unless-stopped
+      image: mysql/mysql-server:5.7.28
-    container_name: mlflow_db
+      container_name: mlflow_db
-    expose:
+      expose:
-      - "3306"
+          - "3306"
-    environment:
+      environment:
-      - MYSQL_DATABASE=${MYSQL_DATABASE}
+          - MYSQL_DATABASE=${MYSQL_DATABASE}
-      - MYSQL_USER=${MYSQL_USER}
+          - MYSQL_USER=${MYSQL_USER}
-      - MYSQL_PASSWORD=${MYSQL_PASSWORD}
+          - MYSQL_PASSWORD=${MYSQL_PASSWORD}
-      - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
+          - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
-    volumes:
+      volumes:
-      - db_new_volume:/var/lib/mysql
+          - ./dbdata:/var/lib/mysql
-    networks:
+      networks:
-      - internal
+          - default
   mlflow:
-    image: ubuntu/mlflow:2.1.1_1.0-22.04
+      restart: always
-    container_name: tracker_mlflow
+      container_name: tracker_mlflow
-    restart: unless-stopped
+      image: tracker_ml
-    ports:
+      build:
-      - "5000:5000"
+          context: ./mlflow
-    environment:
+          dockerfile: Dockerfile
-      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
+      ports:
-      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
+          - "5000:5000"
-      - AWS_DEFAULT_REGION=${AWS_REGION}
+      environment:
-      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
+          - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
-    networks:
+          - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
-      - public
+          - AWS_DEFAULT_REGION=${AWS_REGION}
-      - internal
+          - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
-    entrypoint: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ --artifacts-destination s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
+      entrypoint: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
-    depends_on:
+      networks:
-      wait-for-db:
+          - proxy-net
-        condition: service_completed_successfully
+          - default
-  create_s3_buckets:
+
-    image: minio/mc
-    depends_on:
-      - "s3"
-    entrypoint: >
-      /bin/sh -c "
-      until (/usr/bin/mc alias set minio http://s3:9000 '${AWS_ACCESS_KEY_ID}' '${AWS_SECRET_ACCESS_KEY}') do echo '...waiting...' && sleep 1; done;
-      /usr/bin/mc mb minio/${AWS_BUCKET_NAME};
-      exit 0;
-      "
-    networks:
-      - internal
-  wait-for-db:
-    image: atkrad/wait4x
-    depends_on:
-      - db
-    command: tcp db:3306 -t 90s -i 250ms
-    networks:
-      - internal
-  run_test_experiment:
-    build:
-      context: ./test_experiment
-      dockerfile: Dockerfile
-    platform: linux/amd64  # once continuumio/miniconda3:latest image work on native aarch64 (arm), remove this line 
-    depends_on:
-      - "mlflow"
-    environment:
-      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
-      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
-      - AWS_DEFAULT_REGION=${AWS_REGION}
-      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
-      - MLFLOW_TRACKING_URI=http://mlflow:5000
-    entrypoint: >
-      /bin/sh -c "
-      python3 mlflow_tracking.py;
-      exit 0;
-      "
-    networks:
-      - internal
 networks:
-  internal:
+  default:
-  public:
+  proxy-net:
-    driver: bridge
-volumes:
-  db_new_volume:
-  minio_new_volume:

mlflow/Dockerfile

@@ -0,0 +1,10 @@
+FROM continuumio/miniconda3:latest
+
+ADD . /app
+WORKDIR /app
+
+COPY wait-for-it.sh wait-for-it.sh 
+RUN chmod +x wait-for-it.sh
+
+RUN pip install mlflow boto3 pymysql
+

mlflow/wait-for-it.sh

@@ -0,0 +1,182 @@
+#!/usr/bin/env bash
+# Use this script to test if a given TCP host/port are available
+
+WAITFORIT_cmdname=${0##*/}
+
+echoerr() { if [[ $WAITFORIT_QUIET -ne 1 ]]; then echo "$@" 1>&2; fi }
+
+usage()
+{
+    cat << USAGE >&2
+Usage:
+    $WAITFORIT_cmdname host:port [-s] [-t timeout] [-- command args]
+    -h HOST | --host=HOST       Host or IP under test
+    -p PORT | --port=PORT       TCP port under test
+                                Alternatively, you specify the host and port as host:port
+    -s | --strict               Only execute subcommand if the test succeeds
+    -q | --quiet                Don't output any status messages
+    -t TIMEOUT | --timeout=TIMEOUT
+                                Timeout in seconds, zero for no timeout
+    -- COMMAND ARGS             Execute command with args after the test finishes
+USAGE
+    exit 1
+}
+
+wait_for()
+{
+    if [[ $WAITFORIT_TIMEOUT -gt 0 ]]; then
+        echoerr "$WAITFORIT_cmdname: waiting $WAITFORIT_TIMEOUT seconds for $WAITFORIT_HOST:$WAITFORIT_PORT"
+    else
+        echoerr "$WAITFORIT_cmdname: waiting for $WAITFORIT_HOST:$WAITFORIT_PORT without a timeout"
+    fi
+    WAITFORIT_start_ts=$(date +%s)
+    while :
+    do
+        if [[ $WAITFORIT_ISBUSY -eq 1 ]]; then
+            nc -z $WAITFORIT_HOST $WAITFORIT_PORT
+            WAITFORIT_result=$?
+        else
+            (echo -n > /dev/tcp/$WAITFORIT_HOST/$WAITFORIT_PORT) >/dev/null 2>&1
+            WAITFORIT_result=$?
+        fi
+        if [[ $WAITFORIT_result -eq 0 ]]; then
+            WAITFORIT_end_ts=$(date +%s)
+            echoerr "$WAITFORIT_cmdname: $WAITFORIT_HOST:$WAITFORIT_PORT is available after $((WAITFORIT_end_ts - WAITFORIT_start_ts)) seconds"
+            break
+        fi
+        sleep 1
+    done
+    return $WAITFORIT_result
+}
+
+wait_for_wrapper()
+{
+    # In order to support SIGINT during timeout: http://unix.stackexchange.com/a/57692
+    if [[ $WAITFORIT_QUIET -eq 1 ]]; then
+        timeout $WAITFORIT_BUSYTIMEFLAG $WAITFORIT_TIMEOUT $0 --quiet --child --host=$WAITFORIT_HOST --port=$WAITFORIT_PORT --timeout=$WAITFORIT_TIMEOUT &
+    else
+        timeout $WAITFORIT_BUSYTIMEFLAG $WAITFORIT_TIMEOUT $0 --child --host=$WAITFORIT_HOST --port=$WAITFORIT_PORT --timeout=$WAITFORIT_TIMEOUT &
+    fi
+    WAITFORIT_PID=$!
+    trap "kill -INT -$WAITFORIT_PID" INT
+    wait $WAITFORIT_PID
+    WAITFORIT_RESULT=$?
+    if [[ $WAITFORIT_RESULT -ne 0 ]]; then
+        echoerr "$WAITFORIT_cmdname: timeout occurred after waiting $WAITFORIT_TIMEOUT seconds for $WAITFORIT_HOST:$WAITFORIT_PORT"
+    fi
+    return $WAITFORIT_RESULT
+}
+
+# process arguments
+while [[ $# -gt 0 ]]
+do
+    case "$1" in
+        *:* )
+        WAITFORIT_hostport=(${1//:/ })
+        WAITFORIT_HOST=${WAITFORIT_hostport[0]}
+        WAITFORIT_PORT=${WAITFORIT_hostport[1]}
+        shift 1
+        ;;
+        --child)
+        WAITFORIT_CHILD=1
+        shift 1
+        ;;
+        -q | --quiet)
+        WAITFORIT_QUIET=1
+        shift 1
+        ;;
+        -s | --strict)
+        WAITFORIT_STRICT=1
+        shift 1
+        ;;
+        -h)
+        WAITFORIT_HOST="$2"
+        if [[ $WAITFORIT_HOST == "" ]]; then break; fi
+        shift 2
+        ;;
+        --host=*)
+        WAITFORIT_HOST="${1#*=}"
+        shift 1
+        ;;
+        -p)
+        WAITFORIT_PORT="$2"
+        if [[ $WAITFORIT_PORT == "" ]]; then break; fi
+        shift 2
+        ;;
+        --port=*)
+        WAITFORIT_PORT="${1#*=}"
+        shift 1
+        ;;
+        -t)
+        WAITFORIT_TIMEOUT="$2"
+        if [[ $WAITFORIT_TIMEOUT == "" ]]; then break; fi
+        shift 2
+        ;;
+        --timeout=*)
+        WAITFORIT_TIMEOUT="${1#*=}"
+        shift 1
+        ;;
+        --)
+        shift
+        WAITFORIT_CLI=("$@")
+        break
+        ;;
+        --help)
+        usage
+        ;;
+        *)
+        echoerr "Unknown argument: $1"
+        usage
+        ;;
+    esac
+done
+
+if [[ "$WAITFORIT_HOST" == "" || "$WAITFORIT_PORT" == "" ]]; then
+    echoerr "Error: you need to provide a host and port to test."
+    usage
+fi
+
+WAITFORIT_TIMEOUT=${WAITFORIT_TIMEOUT:-15}
+WAITFORIT_STRICT=${WAITFORIT_STRICT:-0}
+WAITFORIT_CHILD=${WAITFORIT_CHILD:-0}
+WAITFORIT_QUIET=${WAITFORIT_QUIET:-0}
+
+# Check to see if timeout is from busybox?
+WAITFORIT_TIMEOUT_PATH=$(type -p timeout)
+WAITFORIT_TIMEOUT_PATH=$(realpath $WAITFORIT_TIMEOUT_PATH 2>/dev/null || readlink -f $WAITFORIT_TIMEOUT_PATH)
+
+WAITFORIT_BUSYTIMEFLAG=""
+if [[ $WAITFORIT_TIMEOUT_PATH =~ "busybox" ]]; then
+    WAITFORIT_ISBUSY=1
+    # Check if busybox timeout uses -t flag
+    # (recent Alpine versions don't support -t anymore)
+    if timeout &>/dev/stdout | grep -q -e '-t '; then
+        WAITFORIT_BUSYTIMEFLAG="-t"
+    fi
+else
+    WAITFORIT_ISBUSY=0
+fi
+
+if [[ $WAITFORIT_CHILD -gt 0 ]]; then
+    wait_for
+    WAITFORIT_RESULT=$?
+    exit $WAITFORIT_RESULT
+else
+    if [[ $WAITFORIT_TIMEOUT -gt 0 ]]; then
+        wait_for_wrapper
+        WAITFORIT_RESULT=$?
+    else
+        wait_for
+        WAITFORIT_RESULT=$?
+    fi
+fi
+
+if [[ $WAITFORIT_CLI != "" ]]; then
+    if [[ $WAITFORIT_RESULT -ne 0 && $WAITFORIT_STRICT -eq 1 ]]; then
+        echoerr "$WAITFORIT_cmdname: strict mode, refusing to execute subprocess"
+        exit $WAITFORIT_RESULT
+    fi
+    exec "${WAITFORIT_CLI[@]}"
+else
+    exit $WAITFORIT_RESULT
+fi

quickstart/mlflow_tracking.py

@@ -1,22 +1,22 @@
 import os
 from random import random, randint
 
-import mlflow
+from mlflow import mlflow,log_metric, log_param, log_artifacts
 
 if __name__ == "__main__":
     with mlflow.start_run() as run:
-        mlflow.set_tracking_uri('http://localhost:5000')
+        mlflow.set_tracking_uri('https://mlflow.localhost')
         print("Running mlflow_tracking.py")
 
-        mlflow.log_param("param1", randint(0, 100))
+        log_param("param1", randint(0, 100))
         
-        mlflow.log_metric("foo", random())
+        log_metric("foo", random())
-        mlflow.log_metric("foo", random() + 1)
+        log_metric("foo", random() + 1)
-        mlflow.log_metric("foo", random() + 2)
+        log_metric("foo", random() + 2)
 
         if not os.path.exists("outputs"):
             os.makedirs("outputs")
         with open("outputs/test.txt", "w") as f:
             f.write("hello world!")
 
-        mlflow.log_artifacts("outputs")
+        log_artifacts("outputs")

test_experiment/Dockerfile

@@ -1,6 +0,0 @@
-FROM continuumio/miniconda3:latest
-
-RUN pip install mlflow boto3
-
-WORKDIR /app
-COPY . .

test_experiment/mlflow_tracking.py

@@ -1,22 +0,0 @@
-import os
-from random import random, randint
-
-import mlflow
-
-if __name__ == "__main__":
-    with mlflow.start_run() as run:
-        mlflow.set_tracking_uri('http://mlflow:5000')
-        print("Running mlflow_tracking.py")
-
-        mlflow.log_param("param1", randint(0, 100))
-        
-        mlflow.log_metric("foo", random())
-        mlflow.log_metric("foo", random() + 1)
-        mlflow.log_metric("foo", random() + 2)
-
-        if not os.path.exists("outputs"):
-            os.makedirs("outputs")
-        with open("outputs/test.txt", "w") as f:
-            f.write("hello world!")
-
-        mlflow.log_artifacts("outputs")