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[td] Remove extraneous files
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.github/workflows/build_and_deploy_to_aws_ecs.yml

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Dockerfile

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@@ -6,22 +6,16 @@ ARG USER_CODE_PATH=${MAGE_CODE_PATH}/${PROJECT_NAME}
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WORKDIR ${MAGE_CODE_PATH}
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# Replace [project_name] with the name of your project (e.g. demo_project)
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COPY ${PROJECT_NAME} ${PROJECT_NAME}
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# Set the USER_CODE_PATH variable to the path of user project.
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# The project path needs to contain project name.
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# Replace [project_name] with the name of your project (e.g. demo_project)
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ENV USER_CODE_PATH=${USER_CODE_PATH}
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# RUN apt-get update && apt-get install graphviz
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# Install custom Python libraries
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# Install custom Python libraries and dependencies for your project.
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RUN pip3 install -r ${USER_CODE_PATH}/requirements.txt
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ENV PYTHONPATH="${PYTHONPATH}:${MAGE_CODE_PATH}/${PROJECT_NAME}"
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# Installing necessary utilities and Terraform
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# Installing necessary utilities and Terraform.
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RUN apt-get update && \
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apt-get install -y wget unzip && \
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wget https://releases.hashicorp.com/terraform/1.8.3/terraform_1.8.3_linux_amd64.zip && \

docker-compose.yml

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@@ -8,14 +8,16 @@ services:
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ports:
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- 6789:6789
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volumes:
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- .:/$MAGE_CODE_PATH # /home/mage_code by default
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# Store the data output on local machine to easily debug
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- ~/.mage_data:/$MAGE_CODE_PATH/mage_data # /home/mage_code/mage_data by default
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# Initial credentials to create an IAM user with only deployment permissions.
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# Mount your local codebase to the container.
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- .:/$MAGE_CODE_PATH
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# Store the data output on local machine to easily debug (optional).
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- ~/.mage_data:/$MAGE_CODE_PATH/mage_data
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# Initial credentials to create an IAM user with limited permissions for deployment.
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- ~/.aws:/root/.aws
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# Local machine’s SSH keys to pull and push to your GitHub repository.
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- ~/.ssh:/root/.ssh:ro
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# Local machine’s GitHub configs
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- ~/.gitconfig:/root/.gitconfig:ro
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- /Users/dangerous/Code/materia/mage-ai/mage_ai/api/resources:/usr/local/lib/python3.10/site-packages/mage_ai/api/resources
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restart: on-failure:5
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networks:
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- app-network
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- 5432:5432
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volumes:
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- ~/.postgres/data:/var/lib/postgresql/data
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# Custom database initialization scripts.
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# Custom database initialization scripts (optional).
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- ./scripts/database:/docker-entrypoint-initdb.d
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restart: always
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networks:

mlops/metadata.yaml

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notebook_block_output_split_view: true
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operation_history: true
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polars: true
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project_uuid: 36404d0ffc214b8a89f598f3522c1a20
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help_improve_mage: true
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# https://docs.mage.ai/integrations/observability/alerting-email
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# https://support.google.com/mail/answer/185833?hl=en
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notification_config:
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alert_on:
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- trigger_failure
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- trigger_passed_sla
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- trigger_success
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email_config:
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smtp_host: smtp.gmail.com
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smtp_user: "{{ env_var('SMTP_EMAIL') }}"
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smtp_password: "{{ env_var('SMTP_PASSWORD') }}"
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smtp_mail_from: "{{ env_var('SMTP_EMAIL') }}"
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to_emails:
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- "{{ env_var('SMTP_EMAIL') }}"

mlops/settings.yaml

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unit_1_data_preparation: {}
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unit_2_training: {}
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unit_3_observability: {}
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unit_5_deploying: {}

mlops/unit_1_data_preparation/.gitignore

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mlops/unit_1_data_preparation/__init__.py

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mlops/unit_1_data_preparation/charts/__init__.py

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mlops/unit_1_data_preparation/charts/feature_profiles_for_ingest.py

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mlops/unit_1_data_preparation/charts/ingest_time_series_bar_chart_d1.py

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mlops/unit_1_data_preparation/charts/missing_values_for_ingest.py

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mlops/unit_1_data_preparation/charts/most_frequent_values_for_ingest.py

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mlops/unit_1_data_preparation/charts/prepare_histogram_u9.py

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mlops/unit_1_data_preparation/charts/summary_overview_for_ingest.py

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mlops/unit_1_data_preparation/charts/unique_values_for_ingest.py

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mlops/unit_1_data_preparation/custom/__init__.py

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mlops/unit_1_data_preparation/data_exporters/__init__.py

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mlops/unit_1_data_preparation/data_exporters/build.py

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