From aa3328b54ca2748c9934d64f7379aef8b05a21fd Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Thu, 28 Dec 2023 16:23:14 +0100 Subject: [PATCH 1/7] Add initial code for ML chapter. --- .gitignore | 3 + chapterml/dags/01_docker.py | 62 + chapterml/docker-compose.yml | 388 +++ chapterml/images/airflow-docker/Dockerfile | 18 + chapterml/images/fashion-model/.env | 10 + chapterml/images/fashion-model/Dockerfile | 29 + chapterml/images/fashion-model/README.md | 2 + .../images/fashion-model/docker-compose.yml | 66 + .../fashion-model/notebooks/example.ipynb | 224 ++ chapterml/images/fashion-model/poetry.lock | 2772 +++++++++++++++++ chapterml/images/fashion-model/pyproject.toml | 33 + .../src/fashion_model/__init__.py | 0 .../fashion-model/src/fashion_model/cli.py | 34 + .../src/fashion_model/evaluate.py | 15 + .../fashion-model/src/fashion_model/fetch.py | 56 + .../fashion-model/src/fashion_model/train.py | 62 + .../images/fashion-model/tests/__init__.py | 0 17 files changed, 3774 insertions(+) create mode 100644 chapterml/dags/01_docker.py create mode 100644 chapterml/docker-compose.yml create mode 100644 chapterml/images/airflow-docker/Dockerfile create mode 100644 chapterml/images/fashion-model/.env create mode 100644 chapterml/images/fashion-model/Dockerfile create mode 100644 chapterml/images/fashion-model/README.md create mode 100644 chapterml/images/fashion-model/docker-compose.yml create mode 100644 chapterml/images/fashion-model/notebooks/example.ipynb create mode 100644 chapterml/images/fashion-model/poetry.lock create mode 100644 chapterml/images/fashion-model/pyproject.toml create mode 100644 chapterml/images/fashion-model/src/fashion_model/__init__.py create mode 100644 chapterml/images/fashion-model/src/fashion_model/cli.py create mode 100644 chapterml/images/fashion-model/src/fashion_model/evaluate.py create mode 100644 chapterml/images/fashion-model/src/fashion_model/fetch.py create mode 100644 chapterml/images/fashion-model/src/fashion_model/train.py create mode 100644 chapterml/images/fashion-model/tests/__init__.py diff --git a/.gitignore b/.gitignore index 7db69727..ffb2e19b 100644 --- a/.gitignore +++ b/.gitignore @@ -153,3 +153,6 @@ logs # Data data + +# Mpypy +.mypy_cache diff --git a/chapterml/dags/01_docker.py b/chapterml/dags/01_docker.py new file mode 100644 index 00000000..2ee5f112 --- /dev/null +++ b/chapterml/dags/01_docker.py @@ -0,0 +1,62 @@ +import datetime as dt + +from airflow import DAG +from airflow.providers.docker.operators.docker import DockerOperator + + +with DAG( + dag_id="01_docker", + description="Fetches ratings from the Movielens API using Docker.", + start_date=dt.datetime(2019, 1, 1), + end_date=dt.datetime(2019, 1, 1), + schedule_interval="@daily", +) as dag: + + environment= { + "MLFLOW_TRACKING_URI": "http://mlflow:9002", + "MLFLOW_S3_ENDPOINT_URL": "http://minio:9000", + "AWS_ENDPOINT_URL_S3": "http://minio:9000", + "AWS_ACCESS_KEY_ID": "{{ conn.minio.login }}", + "AWS_SECRET_ACCESS_KEY": "{{ conn.minio.password }}" + } + + fetch_dataset = DockerOperator( + task_id="fetch_dataset", + image="jrderuiter/fashion-model", + command=[ + "fetch", + "s3://data/{{ds}}/train", + "s3://data/{{ds}}/test", + ], + network_mode="chapterml_airflow", + environment=environment + ) + + + train_model = DockerOperator( + task_id="train_model", + image="jrderuiter/fashion-model", + command=[ + "train", + "s3://data/{{ds}}/train", + "--epochs", + "5" + ], + network_mode="chapterml_airflow", + environment=environment + ) + + evaluate_model = DockerOperator( + task_id="evaluate_model", + image="jrderuiter/fashion-model", + command=[ + "evaluate", + "s3://data/{{ds}}/test", + "{{ task_instance.xcom_pull(task_ids='train_model', key='return_value') }}" + ], + network_mode="chapterml_airflow", + environment=environment + ) + + fetch_dataset >> train_model + [fetch_dataset, train_model] >> evaluate_model diff --git a/chapterml/docker-compose.yml b/chapterml/docker-compose.yml new file mode 100644 index 00000000..93bf581c --- /dev/null +++ b/chapterml/docker-compose.yml @@ -0,0 +1,388 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL. +# +# WARNING: This configuration is for local development. Do not use it in a production deployment. +# +# This configuration supports basic configuration using environment variables or an .env file +# The following variables are supported: +# +# AIRFLOW_IMAGE_NAME - Docker image name used to run Airflow. +# Default: apache/airflow:2.7.3 +# AIRFLOW_UID - User ID in Airflow containers +# Default: 50000 +# AIRFLOW_PROJ_DIR - Base path to which all the files will be volumed. +# Default: . +# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode +# +# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account (if requested). +# Default: airflow +# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account (if requested). +# Default: airflow +# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers. +# Use this option ONLY for quick checks. Installing requirements at container +# startup is done EVERY TIME the service is started. +# A better way is to build a custom image or extend the official image +# as described in https://airflow.apache.org/docs/docker-stack/build.html. +# Default: '' +# +# Feel free to modify this file to suit your needs. +--- +version: '3.8' +x-airflow-common: + &airflow-common + # Build an image with the Docker provider pre-installed. + build: + context: images/airflow-docker + args: + AIRFLOW_BASE_IMAGE: apache/airflow:2.8.0-python3.11 + environment: + &airflow-common-env + AIRFLOW__CORE__EXECUTOR: CeleryExecutor + AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + # For backward compatibility, with Airflow <2.3 + AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0 + AIRFLOW__CORE__FERNET_KEY: '' + AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true' + AIRFLOW__CORE__LOAD_EXAMPLES: 'false' + AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session' + AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL : 60 + AIRFLOW__CORE__TEST_CONNECTION: 'Enabled' + + # yamllint disable rule:line-length + # Use simple http server on scheduler for health checks + # See https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/check-health.html#scheduler-health-check-server + # yamllint enable rule:line-length + AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true' + # WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks + # for other purpose (development, test and especially production usage) build/extend Airflow image. + # _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-} + _PIP_ADDITIONAL_REQUIREMENTS: apache-airflow-providers-docker==3.9.1 + volumes: + - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags + - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs + - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config + - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins + networks: + - airflow + user: "${AIRFLOW_UID:-50000}:0" + depends_on: + &airflow-common-depends-on + redis: + condition: service_healthy + postgres: + condition: service_healthy + +networks: + airflow: + driver: bridge + +services: + postgres: + image: postgres:13 + environment: + POSTGRES_USER: airflow + POSTGRES_PASSWORD: airflow + POSTGRES_DB: airflow + volumes: + - postgres-db-volume:/var/lib/postgresql/data + healthcheck: + test: ["CMD", "pg_isready", "-U", "airflow"] + interval: 10s + retries: 5 + start_period: 5s + restart: always + networks: + - airflow + + redis: + image: redis:latest + expose: + - 6379 + healthcheck: + test: ["CMD", "redis-cli", "ping"] + interval: 10s + timeout: 30s + retries: 50 + start_period: 30s + restart: always + networks: + - airflow + + airflow-webserver: + <<: *airflow-common + command: webserver + ports: + - "8080:8080" + healthcheck: + test: ["CMD", "curl", "--fail", "http://localhost:8080/health"] + interval: 30s + timeout: 10s + retries: 5 + start_period: 30s + restart: always + depends_on: + <<: *airflow-common-depends-on + airflow-init: + condition: service_completed_successfully + + airflow-scheduler: + <<: *airflow-common + command: scheduler + healthcheck: + test: ["CMD", "curl", "--fail", "http://localhost:8974/health"] + interval: 30s + timeout: 10s + retries: 5 + start_period: 30s + restart: always + depends_on: + <<: *airflow-common-depends-on + airflow-init: + condition: service_completed_successfully + + airflow-worker: + <<: *airflow-common + command: celery worker + healthcheck: + # yamllint disable rule:line-length + test: + - "CMD-SHELL" + - 'celery --app airflow.providers.celery.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}" || celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"' + interval: 30s + timeout: 10s + retries: 5 + start_period: 30s + environment: + <<: *airflow-common-env + # Required to handle warm shutdown of the celery workers properly + # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation + DUMB_INIT_SETSID: "0" + restart: always + volumes: + - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags + - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs + - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config + - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins + - /var/run/docker.sock:/var/run/docker.sock + user: "0:0" # TODO: Set this from env var. + depends_on: + <<: *airflow-common-depends-on + airflow-init: + condition: service_completed_successfully + + airflow-triggerer: + <<: *airflow-common + command: triggerer + healthcheck: + test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"'] + interval: 30s + timeout: 10s + retries: 5 + start_period: 30s + restart: always + depends_on: + <<: *airflow-common-depends-on + airflow-init: + condition: service_completed_successfully + + airflow-init: + <<: *airflow-common + entrypoint: /bin/bash + # yamllint disable rule:line-length + command: + - -c + - | + function ver() { + printf "%04d%04d%04d%04d" $${1//./ } + } + airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && gosu airflow airflow version) + airflow_version_comparable=$$(ver $${airflow_version}) + min_airflow_version=2.2.0 + min_airflow_version_comparable=$$(ver $${min_airflow_version}) + if (( airflow_version_comparable < min_airflow_version_comparable )); then + echo + echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m" + echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!" + echo + exit 1 + fi + if [[ -z "${AIRFLOW_UID}" ]]; then + echo + echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m" + echo "If you are on Linux, you SHOULD follow the instructions below to set " + echo "AIRFLOW_UID environment variable, otherwise files will be owned by root." + echo "For other operating systems you can get rid of the warning with manually created .env file:" + echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user" + echo + fi + one_meg=1048576 + mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg)) + cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat) + disk_available=$$(df / | tail -1 | awk '{print $$4}') + warning_resources="false" + if (( mem_available < 4000 )) ; then + echo + echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m" + echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))" + echo + warning_resources="true" + fi + if (( cpus_available < 2 )); then + echo + echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m" + echo "At least 2 CPUs recommended. You have $${cpus_available}" + echo + warning_resources="true" + fi + if (( disk_available < one_meg * 10 )); then + echo + echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m" + echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))" + echo + warning_resources="true" + fi + if [[ $${warning_resources} == "true" ]]; then + echo + echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m" + echo "Please follow the instructions to increase amount of resources available:" + echo " https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#before-you-begin" + echo + fi + mkdir -p /sources/logs /sources/dags /sources/plugins + chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins} + exec /entrypoint airflow version + # yamllint enable rule:line-length + environment: + <<: *airflow-common-env + _AIRFLOW_DB_MIGRATE: 'true' + _AIRFLOW_WWW_USER_CREATE: 'true' + _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-admin} + _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-admin} + _PIP_ADDITIONAL_REQUIREMENTS: '' + user: "0:0" + volumes: + - ${AIRFLOW_PROJ_DIR:-.}:/sources + + airflow-cli: + <<: *airflow-common + profiles: + - debug + environment: + <<: *airflow-common-env + CONNECTION_CHECK_MAX_COUNT: "0" + # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252 + command: + - bash + - -c + - airflow + + # You can enable flower by adding "--profile flower" option e.g. docker-compose --profile flower up + # or by explicitly targeted on the command line e.g. docker-compose up flower. + # See: https://docs.docker.com/compose/profiles/ + flower: + <<: *airflow-common + command: celery flower + profiles: + - flower + ports: + - "5555:5555" + healthcheck: + test: ["CMD", "curl", "--fail", "http://localhost:5555/"] + interval: 30s + timeout: 10s + retries: 5 + start_period: 30s + restart: always + depends_on: + <<: *airflow-common-depends-on + airflow-init: + condition: service_completed_successfully + + # Services for the ML use case. + minio: + image: minio/minio:RELEASE.2023-12-20T01-00-02Z + volumes: + - minio:/data + networks: + - airflow + ports: + - "9000:9000" + - "9001:9001" + command: server /data --console-address ":9001" + environment: + MINIO_ROOT_USER: AKIAIOSFODNN7EXAMPLE + MINIO_ROOT_PASSWORD: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + + minio_init: + image: minio/mc:RELEASE.2023-12-20T07-14-22Z + depends_on: + - minio + entrypoint: > + /bin/sh -c " + while ! /usr/bin/mc config host add minio http://minio:9000 AKIAIOSFODNN7EXAMPLE wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY; do echo 'MinIO not up and running yet...' && sleep 1; done; + echo 'Added mc host config.'; + /usr/bin/mc mb minio/data || true; + /usr/bin/mc mb minio/mlflow || true; + exit 0; + " + restart: "on-failure" + networks: + - airflow + + postgres-mlflow: + image: postgres:16.1 + environment: + POSTGRES_USER: mlflow + POSTGRES_PASSWORD: mlflow123 + POSTGRES_DB: mlflowdb + networks: + - airflow + ports: + - 5432:5432 + volumes: + - postgres-mlflow:/var/lib/postgresql/data + + mlflow: + image: bitnami/mlflow:latest + environment: + MLFLOW_S3_ENDPOINT_URL: http://minio:9000 + AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE + AWS_SECRET_ACCESS_KEY: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + networks: + - airflow + ports: + - 9002:9002 + entrypoint: mlflow + command: server --backend-store-uri postgresql://mlflow:mlflow123@postgres-mlflow:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port 9002 + + # These aren't actual services, but we include them to make sure that the + # corresponding images are built by when pulling up docker-compose. + fashion-model: + build: images/fashion-model + image: jrderuiter/fashion-model + # container_name: chapter10_docker_movielens-fetch_1 + restart: "no" + +volumes: + postgres-db-volume: + postgres-mlflow: + minio: diff --git a/chapterml/images/airflow-docker/Dockerfile b/chapterml/images/airflow-docker/Dockerfile new file mode 100644 index 00000000..2d4ca266 --- /dev/null +++ b/chapterml/images/airflow-docker/Dockerfile @@ -0,0 +1,18 @@ +ARG AIRFLOW_BASE_IMAGE="apache/airflow:2.8.0-python3.11" +FROM ${AIRFLOW_BASE_IMAGE} + +RUN pip install --user --no-cache-dir \ + apache-airflow-providers-docker==3.9.1 + +# Note we need to run Airflow as root in this case, as Airflow needs to +# have sufficient priviledges to access /var/run/docker.sock. +# +# In principle, it would be better to (1) create a docker group, +# (2) add the airflow user to that group and (3) chmod +# /var/run/docker.sock so that the docker group has access. However, +# this is tricky to do properly in this kind of docker-in-docker setup. + +# # To make sure that the root user can find the packages that were +# # installed as the airflow user, we amend the Python path to include +# # the airflow user's site-packages. +# ENV PYTHONPATH=/home/airflow/.local/lib/python3.11/site-packages diff --git a/chapterml/images/fashion-model/.env b/chapterml/images/fashion-model/.env new file mode 100644 index 00000000..cd0fc509 --- /dev/null +++ b/chapterml/images/fashion-model/.env @@ -0,0 +1,10 @@ +# FSSPEC_S3_KEY=9ZHAg1aQwX0HV1RWf6WZ +# FSSPEC_S3_SECRET=6DF46i1pFxK88cu1oeVaEItCqkQ6s5nQoSfPbhHf +# FSSPEC_S3_ENDPOINT_URL="http://localhost:9000" + +MLFLOW_TRACKING_URI="http://localhost:9002" +MLFLOW_S3_ENDPOINT_URL="http://localhost:9000" + +AWS_ENDPOINT_URL_S3="http://localhost:9000" +AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE +AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY diff --git a/chapterml/images/fashion-model/Dockerfile b/chapterml/images/fashion-model/Dockerfile new file mode 100644 index 00000000..0ae01289 --- /dev/null +++ b/chapterml/images/fashion-model/Dockerfile @@ -0,0 +1,29 @@ +FROM python:3.11-buster as builder + +RUN pip install poetry==1.7.1 + +ENV POETRY_NO_INTERACTION=1 \ + POETRY_VIRTUALENVS_IN_PROJECT=1 \ + POETRY_VIRTUALENVS_CREATE=1 \ + POETRY_CACHE_DIR=/tmp/poetry_cache + +WORKDIR /app + +COPY pyproject.toml poetry.lock ./ +RUN touch README.md + +RUN --mount=type=cache,target=$POETRY_CACHE_DIR poetry install --without dev --no-root + +COPY src ./src +RUN poetry install --only-root + +FROM python:3.11-slim-buster as runtime + +ENV VIRTUAL_ENV=/app/.venv \ + APP_SRC=/app/src \ + PATH="/app/.venv/bin:$PATH" + +COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} +COPY --from=builder ${APP_SRC} ${APP_SRC} + +ENTRYPOINT ["fashion-model"] diff --git a/chapterml/images/fashion-model/README.md b/chapterml/images/fashion-model/README.md new file mode 100644 index 00000000..bea42973 --- /dev/null +++ b/chapterml/images/fashion-model/README.md @@ -0,0 +1,2 @@ +https://docs.astronomer.io/learn/airflow-mlflow +https://airflow-provider-mlflow.readthedocs.io/en/latest/index.html diff --git a/chapterml/images/fashion-model/docker-compose.yml b/chapterml/images/fashion-model/docker-compose.yml new file mode 100644 index 00000000..c4c3a43e --- /dev/null +++ b/chapterml/images/fashion-model/docker-compose.yml @@ -0,0 +1,66 @@ +version: '3.7' + +networks: + app-tier: + driver: bridge + +services: + minio: + image: minio/minio:RELEASE.2023-12-20T01-00-02Z + volumes: + - s3:/data + networks: + - app-tier + ports: + - "9000:9000" + - "9001:9001" + command: server /data --console-address ":9001" + environment: + MINIO_ROOT_USER: AKIAIOSFODNN7EXAMPLE + MINIO_ROOT_PASSWORD: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + + minio_init: + image: minio/mc:RELEASE.2023-12-20T07-14-22Z + depends_on: + - minio + entrypoint: > + /bin/sh -c " + while ! /usr/bin/mc config host add minio http://minio:9000 AKIAIOSFODNN7EXAMPLE wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY; do echo 'MinIO not up and running yet...' && sleep 1; done; + echo 'Added mc host config.'; + /usr/bin/mc mb minio/data || true; + /usr/bin/mc mb minio/mlflow || true; + exit 0; + " + restart: "on-failure" + networks: + - app-tier + + postgres: + image: postgres:16.1 + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres123 + POSTGRES_DB: mlflowdb + networks: + - app-tier + ports: + - 5432:5432 + volumes: + - postgres:/var/lib/postgresql/data + + mlflow: + image: bitnami/mlflow:latest + environment: + MLFLOW_S3_ENDPOINT_URL: http://minio:9000 + AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE + AWS_SECRET_ACCESS_KEY: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + networks: + - app-tier + ports: + - 9002:9002 + entrypoint: mlflow + command: server --backend-store-uri postgresql://postgres:postgres123@postgres:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port 9002 + +volumes: + postgres: + s3: diff --git a/chapterml/images/fashion-model/notebooks/example.ipynb b/chapterml/images/fashion-model/notebooks/example.ipynb new file mode 100644 index 00000000..fa6d3350 --- /dev/null +++ b/chapterml/images/fashion-model/notebooks/example.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-22 11:52:20.985883: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]],\n", + "\n", + " [[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]],\n", + "\n", + " [[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]],\n", + "\n", + " ...,\n", + "\n", + " [[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]],\n", + "\n", + " [[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]],\n", + "\n", + " [[0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " ...,\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0],\n", + " [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import keras\n", + "\n", + "fashion_mnist = keras.datasets.fashion_mnist\n", + "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", + "\n", + "train_images\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from fashion_model.prepare import fetch_datasets\n", + "\n", + "datasets = fetch_datasets()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from fashion_model.prepare import fetch_dataset\n", + "\n", + "#fetch_dataset(output_path=\"_local\")\n", + "fetch_dataset(output_path=\"s3://data/test\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'fsspec' has no attribute 'Filesystem'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mfsspec\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFilesystem\u001b[49m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'fsspec' has no attribute 'Filesystem'" + ] + } + ], + "source": [ + "import fsspec\n", + "\n", + "fsspec.AbstractFileSystem" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('uint8')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_labels.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import numpy.typing as npt\n", + "\n", + "npt.NDArray[np.float64]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": 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b/chapterml/images/fashion-model/pyproject.toml new file mode 100644 index 00000000..0cb5d409 --- /dev/null +++ b/chapterml/images/fashion-model/pyproject.toml @@ -0,0 +1,33 @@ +[tool.poetry] +name = "fashion-model" +version = "0.1.0" +description = "" +authors = ["Julian de Ruiter "] +readme = "README.md" +packages = [{include = "fashion_model", from = "src"}] + +[tool.poetry.dependencies] +python = ">=3.10,<3.12" +tensorflow = {version = ">=2.15.0", markers = "sys_platform != 'darwin' or platform_machine != 'arm64'"} +tensorflow-macos = {version = ">=2.15.0", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'"} +fsspec = "^2023.12.2" +s3fs = "^2023.12.2" +python-dotenv = "^1.0.0" +typer = "^0.9.0" +#aiobotocore = {extras = ["boto3"], version = "^2.9.0"} +aiobotocore = {extras = ["boto3"], version = "^2.9.0"} +mlflow-skinny = "^2.9.2" +# Keras dependency for MLflow. +pandas = "^2.1.4" + +[tool.poetry.group.dev.dependencies] +ruff = "^0.1.8" +ipykernel = "^6.27.1" +mypy = "^1.8.0" + +[tool.poetry.scripts] +fashion-model = 'fashion_model.cli:app' + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/chapterml/images/fashion-model/src/fashion_model/__init__.py b/chapterml/images/fashion-model/src/fashion_model/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/chapterml/images/fashion-model/src/fashion_model/cli.py b/chapterml/images/fashion-model/src/fashion_model/cli.py new file mode 100644 index 00000000..94ce39a2 --- /dev/null +++ b/chapterml/images/fashion-model/src/fashion_model/cli.py @@ -0,0 +1,34 @@ +import dotenv +import typer + +from .evaluate import evaluate_model +from .fetch import fetch_datasets, Dataset +from .train import train_model + +dotenv.load_dotenv() + +app = typer.Typer() + + +@app.command() +def fetch(train_output_path: str, test_output_path: str): + train_dataset, test_dataset = fetch_datasets() + train_dataset.save(train_output_path) + test_dataset.save(test_output_path) + + +@app.command() +def train(train_dataset_path: str, epochs: int = 5): + dataset = Dataset.load(train_dataset_path) + result = train_model(dataset, epochs=epochs) + print(result.mlflow_run_id) + + +@app.command() +def evaluate(test_dataset_path: str, mlflow_run_id: str): + dataset = Dataset.load(test_dataset_path) + evaluate_model(test_dataset=dataset, mlflow_run_id=mlflow_run_id) + + +if __name__ == "__main__": + app() diff --git a/chapterml/images/fashion-model/src/fashion_model/evaluate.py b/chapterml/images/fashion-model/src/fashion_model/evaluate.py new file mode 100644 index 00000000..39706d90 --- /dev/null +++ b/chapterml/images/fashion-model/src/fashion_model/evaluate.py @@ -0,0 +1,15 @@ +from .fetch import Dataset + + +def evaluate_model(test_dataset: Dataset, mlflow_run_id: str): + # Delay imports to speed up CLI. + import mlflow.keras + + test_images = test_dataset.images / 255.0 + test_images = test_images.reshape(test_images.shape[0], 28, 28, 1) + + model = mlflow.keras.load_model(f"runs:/{mlflow_run_id}/model") + + with mlflow.start_run(run_id=mlflow_run_id): + _, test_acc = model.evaluate(test_images, test_dataset.labels) + mlflow.log_metrics({"accuracy": test_acc}) diff --git a/chapterml/images/fashion-model/src/fashion_model/fetch.py b/chapterml/images/fashion-model/src/fashion_model/fetch.py new file mode 100644 index 00000000..a6f2fb43 --- /dev/null +++ b/chapterml/images/fashion-model/src/fashion_model/fetch.py @@ -0,0 +1,56 @@ +from dataclasses import dataclass +from typing import Tuple + +import fsspec + +import numpy as np +import numpy.typing as npt + + +@dataclass +class Dataset: + images: npt.NDArray[np.uint8] + labels: npt.NDArray[np.uint8] + + # test_images: npt.NDArray[np.uint8] + # test_labels: npt.NDArray[np.uint8] + + @classmethod + def load(cls, dataset_path: str): + fs, base_path = fsspec.core.url_to_fs(dataset_path) + + return cls( + images=_load_array(fs, base_path + "/images.npy"), + labels=_load_array(fs, base_path + "/labels.npy"), + ) + + def save(self, output_path: str): + fs, base_path = fsspec.core.url_to_fs(output_path) + + fs.makedirs(base_path, exist_ok=True) + + _write_array(fs, base_path + "/images.npy", self.images) + _write_array(fs, base_path + "/labels.npy", self.labels) + + +def fetch_datasets() -> Tuple[Dataset, Dataset]: + # Delay import to speed up CLI. + import keras + + fashion_mnist = keras.datasets.fashion_mnist + (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() + + train_dataset = Dataset(images=train_images, labels=train_labels) + test_dataset = Dataset(images=test_images, labels=test_labels) + + return train_dataset, test_dataset + + +def _load_array(fs, path) -> npt.NDArray[np.uint8]: + with fs.open(path, "rb") as file_: + return np.load(file_) + + +def _write_array(fs: fsspec.AbstractFileSystem, path: str, array: npt.NDArray[np.uint8]): + with fs.open(path, "wb") as file_: + np.save(file_, array) diff --git a/chapterml/images/fashion-model/src/fashion_model/train.py b/chapterml/images/fashion-model/src/fashion_model/train.py new file mode 100644 index 00000000..ed832559 --- /dev/null +++ b/chapterml/images/fashion-model/src/fashion_model/train.py @@ -0,0 +1,62 @@ +from dataclasses import dataclass + +from .fetch import Dataset + + +@dataclass +class TrainResult: + mlflow_run_id: str + mlflow_artifact_path: str + + +def train_model(dataset: Dataset, epochs: int = 5, mlflow_artifact_path: str ="model") -> TrainResult: + # Delay imports to speed up CLI. + import keras + import mlflow.keras + import tensorflow as tf + + with mlflow.start_run() as run: + # Log model hyperparameters. + mlflow.log_params({"epochs": epochs}) + + # Scale the values to 0.0 to 1.0 + train_images = dataset.images / 255.0 + + # Reshape for feeding into the model + train_images = train_images.reshape(train_images.shape[0], 28, 28, 1) + + # Define the model architecture. + model = keras.Sequential( + [ + keras.layers.Conv2D( + input_shape=(28, 28, 1), + filters=8, + kernel_size=3, + strides=2, + activation="relu", + name="Conv1", + ), + keras.layers.Flatten(), + keras.layers.Dense(10, name="Dense"), + ] + ) + model.summary() + + # Fit the model to the training data. + model.compile( + optimizer="adam", + loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), + metrics=[keras.metrics.SparseCategoricalAccuracy()], + ) + model.fit(train_images, dataset.labels, epochs=epochs) + + # Store model in MLflow. + mlflow.keras.log_model(model, artifact_path=mlflow_artifact_path) + + return TrainResult( + mlflow_run_id=run.info.run_id, + mlflow_artifact_path=mlflow_artifact_path, + ) + + +# def evaluate_model(): diff --git a/chapterml/images/fashion-model/tests/__init__.py b/chapterml/images/fashion-model/tests/__init__.py new file mode 100644 index 00000000..e69de29b From 9b1fce8c8f9d3e0d687497435f928eea827268cc Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Fri, 29 Dec 2023 11:00:25 +0100 Subject: [PATCH 2/7] Add Docker socket proxy, add Airflow connections for MLflow/Minio. --- chapterml/.env | 12 +++ chapterml/dags/01_docker.py | 18 ++-- chapterml/docker-compose.yml | 88 +++++++++---------- chapterml/images/airflow-docker/Dockerfile | 18 ---- .../images/fashion-model/docker-compose.yml | 66 -------------- 5 files changed, 65 insertions(+), 137 deletions(-) create mode 100644 chapterml/.env delete mode 100644 chapterml/images/airflow-docker/Dockerfile delete mode 100644 chapterml/images/fashion-model/docker-compose.yml diff --git a/chapterml/.env b/chapterml/.env new file mode 100644 index 00000000..f0066689 --- /dev/null +++ b/chapterml/.env @@ -0,0 +1,12 @@ +_PIP_ADDITIONAL_REQUIREMENTS='apache-airflow-providers-docker==3.9.1' + +MINIO_ID='AKIAIOSFODNN7EXAMPLE' +MINIO_KEY='wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY' + +# URL-encoded version of the above key. +MINIO_KEY_ENCODED='wJalrXUtnFEMI%2FK7MDENG%2FbPxRfiCYEXAMPLEKEY' + +# Ports for the extra services. +MINIO_API_PORT='8081' +MINIO_UI_PORT='8082' +MLFLOW_PORT='8083' diff --git a/chapterml/dags/01_docker.py b/chapterml/dags/01_docker.py index 2ee5f112..70b2e39d 100644 --- a/chapterml/dags/01_docker.py +++ b/chapterml/dags/01_docker.py @@ -12,29 +12,32 @@ schedule_interval="@daily", ) as dag: + docker_url = "tcp://docker-socket-proxy:2375" + environment= { - "MLFLOW_TRACKING_URI": "http://mlflow:9002", - "MLFLOW_S3_ENDPOINT_URL": "http://minio:9000", - "AWS_ENDPOINT_URL_S3": "http://minio:9000", + "MLFLOW_TRACKING_URI": "{{ conn.mlflow.get_uri() }}", + "MLFLOW_S3_ENDPOINT_URL": "{{ conn.minio.extra_dejson.get('endpoint_url') }}", + "AWS_ENDPOINT_URL_S3": "{{ conn.minio.extra_dejson.get('endpoint_url') }}", "AWS_ACCESS_KEY_ID": "{{ conn.minio.login }}", "AWS_SECRET_ACCESS_KEY": "{{ conn.minio.password }}" } fetch_dataset = DockerOperator( task_id="fetch_dataset", + docker_url=docker_url, image="jrderuiter/fashion-model", command=[ "fetch", "s3://data/{{ds}}/train", "s3://data/{{ds}}/test", ], - network_mode="chapterml_airflow", + network_mode="chapterml_default", environment=environment ) - train_model = DockerOperator( task_id="train_model", + docker_url=docker_url, image="jrderuiter/fashion-model", command=[ "train", @@ -42,19 +45,20 @@ "--epochs", "5" ], - network_mode="chapterml_airflow", + network_mode="chapterml_default", environment=environment ) evaluate_model = DockerOperator( task_id="evaluate_model", + docker_url=docker_url, image="jrderuiter/fashion-model", command=[ "evaluate", "s3://data/{{ds}}/test", "{{ task_instance.xcom_pull(task_ids='train_model', key='return_value') }}" ], - network_mode="chapterml_airflow", + network_mode="chapterml_default", environment=environment ) diff --git a/chapterml/docker-compose.yml b/chapterml/docker-compose.yml index 93bf581c..030ac9c4 100644 --- a/chapterml/docker-compose.yml +++ b/chapterml/docker-compose.yml @@ -47,11 +47,11 @@ version: '3.8' x-airflow-common: &airflow-common - # Build an image with the Docker provider pre-installed. - build: - context: images/airflow-docker - args: - AIRFLOW_BASE_IMAGE: apache/airflow:2.8.0-python3.11 + # In order to add custom dependencies or upgrade provider packages you can use your extended image. + # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml + # and uncomment the "build" line below, Then run `docker-compose build` to build the images. + image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.7.3} + # build: . environment: &airflow-common-env AIRFLOW__CORE__EXECUTOR: CeleryExecutor @@ -67,6 +67,10 @@ x-airflow-common: AIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL : 60 AIRFLOW__CORE__TEST_CONNECTION: 'Enabled' + # Provide Minio/MLFlow connection details to Airflow. + AIRFLOW_CONN_MINIO: aws://${MINIO_ID}:${MINIO_KEY_ENCODED}@?endpoint_url=http%3A%2F%2Fminio%3A${MINIO_API_PORT} + AIRFLOW_CONN_MLFLOW: http://mlflow:${MLFLOW_PORT} + # yamllint disable rule:line-length # Use simple http server on scheduler for health checks # See https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/logging-monitoring/check-health.html#scheduler-health-check-server @@ -74,15 +78,12 @@ x-airflow-common: AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true' # WARNING: Use _PIP_ADDITIONAL_REQUIREMENTS option ONLY for a quick checks # for other purpose (development, test and especially production usage) build/extend Airflow image. - # _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-} - _PIP_ADDITIONAL_REQUIREMENTS: apache-airflow-providers-docker==3.9.1 + _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-} volumes: - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins - networks: - - airflow user: "${AIRFLOW_UID:-50000}:0" depends_on: &airflow-common-depends-on @@ -91,10 +92,6 @@ x-airflow-common: postgres: condition: service_healthy -networks: - airflow: - driver: bridge - services: postgres: image: postgres:13 @@ -110,8 +107,6 @@ services: retries: 5 start_period: 5s restart: always - networks: - - airflow redis: image: redis:latest @@ -124,8 +119,6 @@ services: retries: 50 start_period: 30s restart: always - networks: - - airflow airflow-webserver: <<: *airflow-common @@ -177,13 +170,6 @@ services: # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation DUMB_INIT_SETSID: "0" restart: always - volumes: - - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags - - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs - - ${AIRFLOW_PROJ_DIR:-.}/config:/opt/airflow/config - - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins - - /var/run/docker.sock:/var/run/docker.sock - user: "0:0" # TODO: Set this from env var. depends_on: <<: *airflow-common-depends-on airflow-init: @@ -322,15 +308,13 @@ services: image: minio/minio:RELEASE.2023-12-20T01-00-02Z volumes: - minio:/data - networks: - - airflow ports: - - "9000:9000" - - "9001:9001" - command: server /data --console-address ":9001" + - "${MINIO_API_PORT}:${MINIO_API_PORT}" + - "${MINIO_UI_PORT}:${MINIO_UI_PORT}" + command: server /data --address :${MINIO_API_PORT} --console-address ":${MINIO_UI_PORT}" environment: - MINIO_ROOT_USER: AKIAIOSFODNN7EXAMPLE - MINIO_ROOT_PASSWORD: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + MINIO_ROOT_USER: ${MINIO_ID} + MINIO_ROOT_PASSWORD: ${MINIO_KEY} minio_init: image: minio/mc:RELEASE.2023-12-20T07-14-22Z @@ -338,15 +322,13 @@ services: - minio entrypoint: > /bin/sh -c " - while ! /usr/bin/mc config host add minio http://minio:9000 AKIAIOSFODNN7EXAMPLE wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY; do echo 'MinIO not up and running yet...' && sleep 1; done; + while ! /usr/bin/mc config host add minio http://minio:${MINIO_API_PORT} ${MINIO_ID} ${MINIO_KEY}; do echo 'MinIO not up and running yet...' && sleep 1; done; echo 'Added mc host config.'; /usr/bin/mc mb minio/data || true; /usr/bin/mc mb minio/mlflow || true; exit 0; " restart: "on-failure" - networks: - - airflow postgres-mlflow: image: postgres:16.1 @@ -354,32 +336,46 @@ services: POSTGRES_USER: mlflow POSTGRES_PASSWORD: mlflow123 POSTGRES_DB: mlflowdb - networks: - - airflow - ports: - - 5432:5432 + # ports: + # - 5432:5432 volumes: - postgres-mlflow:/var/lib/postgresql/data mlflow: image: bitnami/mlflow:latest environment: - MLFLOW_S3_ENDPOINT_URL: http://minio:9000 - AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE - AWS_SECRET_ACCESS_KEY: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY - networks: - - airflow + MLFLOW_S3_ENDPOINT_URL: http://minio:${MLFLOW_PORT} + AWS_ACCESS_KEY_ID: ${MINIO_ID} + AWS_SECRET_ACCESS_KEY: ${MINIO_KEY} ports: - - 9002:9002 + - ${MLFLOW_PORT}:${MLFLOW_PORT} entrypoint: mlflow - command: server --backend-store-uri postgresql://mlflow:mlflow123@postgres-mlflow:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port 9002 + command: server --backend-store-uri postgresql://mlflow:mlflow123@postgres-mlflow:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port ${MLFLOW_PORT} + + docker-socket-proxy: + image: bobrik/socat + command: "TCP4-LISTEN:2375,fork,reuseaddr UNIX-CONNECT:/var/run/docker.sock" + ports: + - "2376:2375" + volumes: + - /var/run/docker.sock:/var/run/docker.sock:ro + + # docker-socket-proxy: + # image: tecnativa/docker-socket-proxy:latest + # environment: + # CONTAINERS: 1 + # IMAGES: 1 + # AUTH: 1 + # POST: 1 + # volumes: + # - /var/run/docker.sock:/var/run/docker.sock:ro + # restart: always # These aren't actual services, but we include them to make sure that the # corresponding images are built by when pulling up docker-compose. fashion-model: build: images/fashion-model image: jrderuiter/fashion-model - # container_name: chapter10_docker_movielens-fetch_1 restart: "no" volumes: diff --git a/chapterml/images/airflow-docker/Dockerfile b/chapterml/images/airflow-docker/Dockerfile deleted file mode 100644 index 2d4ca266..00000000 --- a/chapterml/images/airflow-docker/Dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -ARG AIRFLOW_BASE_IMAGE="apache/airflow:2.8.0-python3.11" -FROM ${AIRFLOW_BASE_IMAGE} - -RUN pip install --user --no-cache-dir \ - apache-airflow-providers-docker==3.9.1 - -# Note we need to run Airflow as root in this case, as Airflow needs to -# have sufficient priviledges to access /var/run/docker.sock. -# -# In principle, it would be better to (1) create a docker group, -# (2) add the airflow user to that group and (3) chmod -# /var/run/docker.sock so that the docker group has access. However, -# this is tricky to do properly in this kind of docker-in-docker setup. - -# # To make sure that the root user can find the packages that were -# # installed as the airflow user, we amend the Python path to include -# # the airflow user's site-packages. -# ENV PYTHONPATH=/home/airflow/.local/lib/python3.11/site-packages diff --git a/chapterml/images/fashion-model/docker-compose.yml b/chapterml/images/fashion-model/docker-compose.yml deleted file mode 100644 index c4c3a43e..00000000 --- a/chapterml/images/fashion-model/docker-compose.yml +++ /dev/null @@ -1,66 +0,0 @@ -version: '3.7' - -networks: - app-tier: - driver: bridge - -services: - minio: - image: minio/minio:RELEASE.2023-12-20T01-00-02Z - volumes: - - s3:/data - networks: - - app-tier - ports: - - "9000:9000" - - "9001:9001" - command: server /data --console-address ":9001" - environment: - MINIO_ROOT_USER: AKIAIOSFODNN7EXAMPLE - MINIO_ROOT_PASSWORD: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY - - minio_init: - image: minio/mc:RELEASE.2023-12-20T07-14-22Z - depends_on: - - minio - entrypoint: > - /bin/sh -c " - while ! /usr/bin/mc config host add minio http://minio:9000 AKIAIOSFODNN7EXAMPLE wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY; do echo 'MinIO not up and running yet...' && sleep 1; done; - echo 'Added mc host config.'; - /usr/bin/mc mb minio/data || true; - /usr/bin/mc mb minio/mlflow || true; - exit 0; - " - restart: "on-failure" - networks: - - app-tier - - postgres: - image: postgres:16.1 - environment: - POSTGRES_USER: postgres - POSTGRES_PASSWORD: postgres123 - POSTGRES_DB: mlflowdb - networks: - - app-tier - ports: - - 5432:5432 - volumes: - - postgres:/var/lib/postgresql/data - - mlflow: - image: bitnami/mlflow:latest - environment: - MLFLOW_S3_ENDPOINT_URL: http://minio:9000 - AWS_ACCESS_KEY_ID: AKIAIOSFODNN7EXAMPLE - AWS_SECRET_ACCESS_KEY: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY - networks: - - app-tier - ports: - - 9002:9002 - entrypoint: mlflow - command: server --backend-store-uri postgresql://postgres:postgres123@postgres:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port 9002 - -volumes: - postgres: - s3: From 4a131f82e0a739ce8d760718ede546d5f12922ef Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Fri, 29 Dec 2023 13:00:50 +0100 Subject: [PATCH 3/7] Remove old proxy entry. --- chapterml/docker-compose.yml | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/chapterml/docker-compose.yml b/chapterml/docker-compose.yml index 030ac9c4..526f3f10 100644 --- a/chapterml/docker-compose.yml +++ b/chapterml/docker-compose.yml @@ -353,24 +353,13 @@ services: command: server --backend-store-uri postgresql://mlflow:mlflow123@postgres-mlflow:5432/mlflowdb --default-artifact-root s3://mlflow/ --host 0.0.0.0 --port ${MLFLOW_PORT} docker-socket-proxy: - image: bobrik/socat + image: alpine/socat command: "TCP4-LISTEN:2375,fork,reuseaddr UNIX-CONNECT:/var/run/docker.sock" ports: - "2376:2375" volumes: - /var/run/docker.sock:/var/run/docker.sock:ro - # docker-socket-proxy: - # image: tecnativa/docker-socket-proxy:latest - # environment: - # CONTAINERS: 1 - # IMAGES: 1 - # AUTH: 1 - # POST: 1 - # volumes: - # - /var/run/docker.sock:/var/run/docker.sock:ro - # restart: always - # These aren't actual services, but we include them to make sure that the # corresponding images are built by when pulling up docker-compose. fashion-model: From 03e4c8c9251c5ce17a86669e79c7ee6c71908169 Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Fri, 29 Dec 2023 13:06:37 +0100 Subject: [PATCH 4/7] Fix Minio port in MLflow. --- chapterml/docker-compose.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/chapterml/docker-compose.yml b/chapterml/docker-compose.yml index 526f3f10..9b551b87 100644 --- a/chapterml/docker-compose.yml +++ b/chapterml/docker-compose.yml @@ -344,7 +344,7 @@ services: mlflow: image: bitnami/mlflow:latest environment: - MLFLOW_S3_ENDPOINT_URL: http://minio:${MLFLOW_PORT} + MLFLOW_S3_ENDPOINT_URL: http://minio:${MINIO_API_PORT} AWS_ACCESS_KEY_ID: ${MINIO_ID} AWS_SECRET_ACCESS_KEY: ${MINIO_KEY} ports: From 0a95c72e2b4f74fa13fcf9591b1e5b45d7f1a570 Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Fri, 29 Dec 2023 17:05:05 +0100 Subject: [PATCH 5/7] Fix Tensorflow dependency issues on Mac M1. --- chapterml/images/fashion-model/poetry.lock | 596 ++++++++++-------- chapterml/images/fashion-model/pyproject.toml | 16 +- 2 files changed, 359 insertions(+), 253 deletions(-) diff --git a/chapterml/images/fashion-model/poetry.lock b/chapterml/images/fashion-model/poetry.lock index fcdf0f09..c61f61ad 100644 --- a/chapterml/images/fashion-model/poetry.lock +++ b/chapterml/images/fashion-model/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand. 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a/chapterml/images/fashion-model/pyproject.toml +++ b/chapterml/images/fashion-model/pyproject.toml @@ -8,17 +8,25 @@ packages = [{include = "fashion_model", from = "src"}] [tool.poetry.dependencies] python = ">=3.10,<3.12" -tensorflow = {version = ">=2.15.0", markers = "sys_platform != 'darwin' or platform_machine != 'arm64'"} -tensorflow-macos = {version = ">=2.15.0", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'"} fsspec = "^2023.12.2" s3fs = "^2023.12.2" python-dotenv = "^1.0.0" typer = "^0.9.0" -#aiobotocore = {extras = ["boto3"], version = "^2.9.0"} aiobotocore = {extras = ["boto3"], version = "^2.9.0"} mlflow-skinny = "^2.9.2" -# Keras dependency for MLflow. pandas = "^2.1.4" +tensorflow = {version = "~2.13.0" } +tensorflow-macos = { version = "~2.13.0", platform = "darwin", markers = "platform_machine=='arm64'" } +tensorflow-intel = { version = "~2.13.0", platform = "win32" } +tensorflow-cpu = [ + { version = "~2.13.0", platform = "linux", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" }, + { version = "~2.13.0", platform = "darwin", markers = "platform_machine!='arm64' and platform_machine!='aarch64'" }, +] +tensorflow-cpu-aws = { version = "~2.13.0", platform = "linux", markers = "platform_machine=='arm64' or platform_machine=='aarch64'" } +tensorflow-io-gcs-filesystem = [ + { version = ">= 0.23.1", markers = "platform_machine!='arm64' or platform_system!='Darwin'" }, + { version = "< 0.32.0", markers = "platform_system == 'Windows'" } +] [tool.poetry.group.dev.dependencies] ruff = "^0.1.8" From 635d62521d1f3495871b73525cb92ce15beaa80f Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Fri, 29 Dec 2023 17:24:36 +0100 Subject: [PATCH 6/7] Fix dep. --- chapterml/images/fashion-model/.env | 8 ++------ chapterml/images/fashion-model/pyproject.toml | 1 - 2 files changed, 2 insertions(+), 7 deletions(-) diff --git a/chapterml/images/fashion-model/.env b/chapterml/images/fashion-model/.env index cd0fc509..6747b69b 100644 --- a/chapterml/images/fashion-model/.env +++ b/chapterml/images/fashion-model/.env @@ -1,10 +1,6 @@ -# FSSPEC_S3_KEY=9ZHAg1aQwX0HV1RWf6WZ -# FSSPEC_S3_SECRET=6DF46i1pFxK88cu1oeVaEItCqkQ6s5nQoSfPbhHf -# FSSPEC_S3_ENDPOINT_URL="http://localhost:9000" - MLFLOW_TRACKING_URI="http://localhost:9002" MLFLOW_S3_ENDPOINT_URL="http://localhost:9000" AWS_ENDPOINT_URL_S3="http://localhost:9000" -AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE -AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY +AWS_ACCESS_KEY_ID="AKIAIOSFODNN7EXAMPLE" +AWS_SECRET_ACCESS_KEY="wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY" diff --git a/chapterml/images/fashion-model/pyproject.toml b/chapterml/images/fashion-model/pyproject.toml index 9c401af2..3be8c5ca 100644 --- a/chapterml/images/fashion-model/pyproject.toml +++ b/chapterml/images/fashion-model/pyproject.toml @@ -15,7 +15,6 @@ typer = "^0.9.0" aiobotocore = {extras = ["boto3"], version = "^2.9.0"} mlflow-skinny = "^2.9.2" pandas = "^2.1.4" -tensorflow = {version = "~2.13.0" } tensorflow-macos = { version = "~2.13.0", platform = "darwin", markers = "platform_machine=='arm64'" } tensorflow-intel = { version = "~2.13.0", platform = "win32" } tensorflow-cpu = [ From 9ce7e0839d59df2cb713e80f1cb28434e559931c Mon Sep 17 00:00:00 2001 From: Julian de Ruiter Date: Mon, 11 Mar 2024 14:57:21 +0100 Subject: [PATCH 7/7] Add notebook. --- .../fashion-model/notebooks/example.ipynb | 161 +++++++++++------- .../fashion-model/src/fashion_model/fetch.py | 3 - 2 files changed, 97 insertions(+), 67 deletions(-) diff --git a/chapterml/images/fashion-model/notebooks/example.ipynb b/chapterml/images/fashion-model/notebooks/example.ipynb index fa6d3350..220664a0 100644 --- a/chapterml/images/fashion-model/notebooks/example.ipynb +++ b/chapterml/images/fashion-model/notebooks/example.ipynb @@ -2,105 +2,132 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "__main__.Dataset" ] }, - "execution_count": 1, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from dotenv import load_dotenv\n", + "from dataclasses import dataclass\n", + "from typing import Tuple, NewType\n", "\n", - "load_dotenv()" + "import keras\n", + "import numpy as np\n", + "import numpy.typing as npt\n", + "\n", + "\n", + "@dataclass\n", + "class Dataset:\n", + " images: npt.NDArray[np.uint8]\n", + " labels: npt.NDArray[np.uint8]\n", + "\n", + "\n", + "TrainDataset = NewType(\"TrainDataset\", Dataset)\n", + "TestDataset = NewType(\"TestDataset\", Dataset)\n", + "\n", + "\n", + "def fetch_datasets() -> Tuple[TrainDataset, TestDataset]:\n", + " fashion_mnist = keras.datasets.fashion_mnist\n", + " (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", + "\n", + " train_dataset = TrainDataset(Dataset(images=train_images, labels=train_labels))\n", + " test_dataset = TestDataset(Dataset(images=test_images, labels=test_labels))\n", + "\n", + " return train_dataset, test_dataset\n", + "\n", + "\n", + "train_dataset, test_dataset = fetch_datasets()" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2023-12-22 11:52:20.985883: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " Conv1 (Conv2D) (None, 13, 13, 8) 80 \n", + " \n", + " flatten_3 (Flatten) (None, 1352) 0 \n", + " \n", + " Dense (Dense) (None, 10) 13530 \n", + " \n", + "=================================================================\n", + "Total params: 13610 (53.16 KB)\n", + "Trainable params: 13610 (53.16 KB)\n", + "Non-trainable params: 0 (0.00 Byte)\n", + "_________________________________________________________________\n", + "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5314 - sparse_categorical_accuracy: 0.8141\n" ] }, { "data": { "text/plain": [ - "array([[[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " ...,\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8)" + "" ] }, - "execution_count": 2, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import keras\n", + "import tensorflow as tf\n", + "\n", + "\n", + "def train_model(dataset: TrainDataset, epochs: int = 5) -> keras.Model:\n", + " # Scale the values to 0.0 to 1.0\n", + " train_images = dataset.images / 255.0\n", + "\n", + " # Reshape for feeding into the model\n", + " train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\n", "\n", - "fashion_mnist = keras.datasets.fashion_mnist\n", - "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", + " # Define the model architecture.\n", + " model = keras.Sequential(\n", + " [\n", + " keras.layers.Conv2D(\n", + " input_shape=(28, 28, 1),\n", + " filters=8,\n", + " kernel_size=3,\n", + " strides=2,\n", + " activation=\"relu\",\n", + " name=\"Conv1\",\n", + " ),\n", + " keras.layers.Flatten(),\n", + " keras.layers.Dense(10, name=\"Dense\"),\n", + " ]\n", + " )\n", + " model.summary()\n", "\n", - "train_images\n" + " # Fit the model to the training data.\n", + " model.compile(\n", + " optimizer=\"adam\",\n", + " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=[keras.metrics.SparseCategoricalAccuracy()],\n", + " )\n", + " model.fit(train_images, dataset.labels, epochs=epochs)\n", + "\n", + " return model\n", + "\n", + "\n", + "model = train_model(train_dataset, epochs=1)\n", + "model" ] }, { @@ -109,9 +136,15 @@ "metadata": {}, "outputs": [], "source": [ - "from fashion_model.prepare import fetch_datasets\n", + "from typing import Dict\n", + "\n", + "def evaluate_model(test_dataset: TestDataset, model: keras.Model) -> Dict[str, float]:\n", + " test_images = test_dataset.images / 255.0\n", + " test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)\n", + "\n", + " _, test_acc = model.evaluate(test_images, test_dataset.labels)\n", "\n", - "datasets = fetch_datasets()" + " return {\"accuracy\": test_acc}\n" ] }, { diff --git a/chapterml/images/fashion-model/src/fashion_model/fetch.py b/chapterml/images/fashion-model/src/fashion_model/fetch.py index a6f2fb43..bc0f017d 100644 --- a/chapterml/images/fashion-model/src/fashion_model/fetch.py +++ b/chapterml/images/fashion-model/src/fashion_model/fetch.py @@ -12,9 +12,6 @@ class Dataset: images: npt.NDArray[np.uint8] labels: npt.NDArray[np.uint8] - # test_images: npt.NDArray[np.uint8] - # test_labels: npt.NDArray[np.uint8] - @classmethod def load(cls, dataset_path: str): fs, base_path = fsspec.core.url_to_fs(dataset_path)