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<@ULVA73B9P> I have a prefect yaml file with 3 dif...
# ask-marvin
d
@Marvin I have a prefect yaml file with 3 different deployments:
Copy code
- name: xrs-hal-pipeline-deploy
  tags: []
  description:
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []

- name: mux-hal-pipeline-deploy
  tags: []
  description:
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []
What are the best practices for setting up these pipelines for my production environment which will connect to a different database
m
Your message was too long by 12 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I have a prefect yaml file with 3 different deployments:
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- name: xrs-hal-pipeline-deploy
  version:
  tags: []
  description:
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet",
                    "heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K235_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []

- name: mux-hal-pipeline-deploy
  version:
  tags: []
  description:
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet",
                  "heathrow/parquet/CTX_MUXLOGS_PARQUET/20230102/20230102_K549.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []
What are the best practices for setting up these
### Best Practices for Setting Up Prefect Deployments for Production Environments To set up these pipelines for your production environment connecting to a different database, consider the following best practices: 1. Database Configuration: - Use PostgreSQL: Prefect recommends PostgreSQL for production deployments, especially when connecting to external databases. - Database Settings: Configure your database settings appropriately. Here are some typical settings:
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     PREFECT_API_DATABASE_CONNECTION_URL='postgresql+asyncpg://<username>:<password>@<hostname>/<database_name>'
     PREFECT_API_DATABASE_ECHO='False'
     PREFECT_API_DATABASE_MIGRATE_ON_START='True'
     PREFECT_API_DATABASE_PASSWORD='<your_password>'
Ensure you install and enable the 
pg_trgm
extension in PostgreSQL. Configure Production Database 2. Environment Separation: - Use environment variables to manage different environments (development, staging, production). These variables can hold database credentials, API keys, etc. - Use different 
YAML
config files for different environments. 3. Code and Deployment Management: - Implement a CI/CD pipeline to automate deployments. Learn more from the CI/CD Guide - Version your flows and use distinct names for different environments. 4. Security: - Avoid hardcoding sensitive information in your deployment files. Use services like AWS Secrets Manager, HashiCorp Vault, etc. - Ensure secure access to database credentials. 5. Monitoring and Logging: - Use Prefect Cloud or set up your own monitoring to keep track of flow runs and logs. - Redirect logs to a centralized logging service (e.g., AWS CloudWatch, ELK Stack). ### Example Updated Deployment Configuration Here's an updated example based on best practices:
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yaml
- name: xrs-hal-pipeline-deploy
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []

- name: mux-hal-pipeline-deploy
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
  schedules: []
By following these practices, you should ensure a smooth and secure production setup.
d
@Marvin How do I ensure the production deployments have the correct environment variables to connect to the production database
m
To ensure your production deployments have the correct environment variables to connect to the production database, follow these steps: ### 1. Set Environment Variables in Deployment Configuration You can add environment variables directly to your deployment configuration in the 
prefect.yaml
file:
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yaml
- name: xrs-hal-pipeline-deploy
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        PREFECT_API_DATABASE_CONNECTION_URL: 'postgresql+asyncpg://<username>:<password>@<hostname>/<database_name>'
        PREFECT_API_DATABASE_PASSWORD: '<your_password>'
  schedules: []

- name: mux-hal-pipeline-deploy
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        PREFECT_API_DATABASE_CONNECTION_URL: 'postgresql+asyncpg://<username>:<password>@<hostname>/<database_name>'
        PREFECT_API_DATABASE_PASSWORD: '<your_password>'
  schedules: []
### 2. Use Profiles for Environment-Specific Configurations Profiles allow you to manage environment-specific configurations separately: - Create profiles for different environments. - Set the environment variable 
PREFECT_PROFILE
to specify which profile to use.
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export PREFECT_PROFILE=production
When running commands, specify the profile:
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bash
prefect --profile "production" deployment apply my-deployment.yaml
For detailed usage, refer to the Profiles Documentation. ### 3. Using External Secrets Management For better security, consider storing credentials in external secrets management services: - AWS Secrets Manager - HashiCorp Vault - Azure Key Vault Configure your Prefect deployments to fetch these secrets at runtime. ### 4. Overriding Job Variables Using the job variables to override environment settings is another way. Add environment-specific configurations directly to the job variables:
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yaml
deployments:
  - name: my-deployment
    entrypoint: path/to/your_flow.py:your_function
    work_pool:
      name: your-work-pool
      job_variables:
        env:
          EXECUTION_ENVIRONMENT: "production"
          DATABASE_URL: "{{ secret('DATABASE_URL') }}"
### Resources - Overriding Job Variables - Database Configuration - Settings and Profiles By following these steps, you'll ensure your production deployments have the correct environment variables and configurations needed to connect to your production database securely.
d
@Marvin my database config looks like this: f”postgresql//{db user}{db_password}@{db_host}:{db_port}/{symbol}” . show me what environment variables to pass to my dev and prod environments in two of the same deployments but one for dev and one for prod. show me what the prefect yaml(s) should look like
m
Below are the 
prefect.yaml
configurations for both your development and production environments, illustrating how to pass the required environment variables for database connection. ### Development Environment Configuration
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yaml
- name: xrs-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'dev_user'
        DB_PASSWORD: 'dev_password'
        DB_HOST: 'dev_host'
        DB_PORT: '5432'
        DB_NAME: 'dev_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []

- name: mux-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'dev_user'
        DB_PASSWORD: 'dev_password'
        DB_HOST: 'dev_host'
        DB_PORT: '5432'
        DB_NAME: 'dev_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []
### Production Environment Configuration
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yaml
- name: xrs-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'prod_user'
        DB_PASSWORD: 'prod_password'
        DB_HOST: 'prod_host'
        DB_PORT: '5432'
        DB_NAME: 'prod_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []

- name: mux-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'prod_user'
        DB_PASSWORD: 'prod_password'
        DB_HOST: 'prod_host'
        DB_PORT: '5432'
        DB_NAME: 'prod_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []
### Notes: 1. Environment Variables: - For development: 
DB_USER
, 
DB_PASSWORD
, 
DB_HOST
, 
DB_PORT
, and 
DB_NAME
are set to their respective development values. - For production: The same variables are set to production values. 2. Database Connection URL: - The 
DATABASE_URL
is constructed using the environment variables for both development and production. 3. Separation of Environments: - Deployment names are suffixed with 
-dev
for development and 
-prod
for production. - Tags are used to distinguish between development and production environments. This configuration helps maintain a clear and organized structure for managing different environments. Make sure to manage these secrets securely, potentially using tools like AWS Secrets Manager, HashiCorp Vault, or other secret management solutions.
d
@Marvin Should both these live in 1 yaml or 2 different yamls. If they are in 2 different yamls in the same directory how do I differentiate deploying prod and dev?
m
For best practices and to avoid any confusion, it is generally recommended to keep the configurations for the development and production environments in separate YAML files. This way, you can clearly differentiate between the two and minimize the risk of deploying the wrong configuration by mistake. ### Directory Structure
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prefect/
│
├── deployments/
│   ├── prefect_dev.yaml
│   └── prefect_prod.yaml
### Deployment Commands - Development Deployment:
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sh
  prefect deployment apply prefect/deployments/prefect_dev.yaml
- Production Deployment:
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sh
  prefect deployment apply prefect/deployments/prefect_prod.yaml
### Example Files #### 
prefect_dev.yaml
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yaml
- name: xrs-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'dev_user'
        DB_PASSWORD: 'dev_password'
        DB_HOST: 'dev_host'
        DB_PORT: '5432'
        DB_NAME: 'dev_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []

- name: mux-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'dev_user'
        DB_PASSWORD: 'dev_password'
        DB_HOST: 'dev_host'
        DB_PORT: '5432'
        DB_NAME: 'dev_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []
#### 
prefect_prod.yaml
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yaml
- name: xrs-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'prod_user'
        DB_PASSWORD: 'prod_password'
        DB_HOST: 'prod_host'
        DB_PORT: '5432'
        DB_NAME: 'prod_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []

- name: mux-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: '{{ build_image.image }}'
      env:
        DB_USER: 'prod_user'
        DB_PASSWORD: 'prod_password'
        DB_HOST: 'prod_host'
        DB_PORT: '5432'
        DB_NAME: 'prod_db'
        DATABASE_URL: 'postgresql://{{ env('DB_USER') }}:{{ env('DB_PASSWORD') }}@{{ env('DB_HOST') }}:{{ env('DB_PORT') }}/{{ env('DB_NAME') }}'
  schedules: []
### Advantages of Separate Files 1. Clarity: It's clear which file and configuration is for which environment. 2. Safety: Reduces the risk of deploying development settings to production. 3. Simplicity: Easier to manage and update configurations for each environment separately.
By keeping separate YAML files for different environments and using structured directories, you can manage deployments for both development and production more effectively and with reduced risk.
d
@Marvin what does the prefect deployment apply command do?
m
The 
prefect deployment apply
command in Prefect is used to create a deployment from a deployment YAML file. This command registers the deployment with the Prefect API, making it available for execution and visible in the Prefect UI. ### Purpose of 
prefect deployment apply
- Registers the Deployment: It takes the configuration defined in your 
prefect.yaml
(or any other YAML file containing deployment specifications) and creates the deployment on the Prefect API. - Visibility in UI: Once applied, the deployment is visible in the Prefect UI where it can be managed, triggered, or inspected. - Optional File Upload: The command can optionally upload deployment files to remote storage if specified. ### Syntax
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prefect deployment apply <path-to-yaml-file>
### Example
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prefect deployment apply ./deployments/prefect_prod.yaml
This command reads the 
prefect_prod.yaml
file and registers the specified deployments as managed entities within the Prefect orchestration environment. ### More Details - Build and Apply: - Before applying, you may need to run 
prefect deployment build
to generate your deployment YAML file. - Example:
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    prefect deployment build -n my-deployment -q test-queue flows/my_flow.py:my_func -o deployments/prefect_prod.yaml
- Then apply the generated deployment file:
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    prefect deployment apply deployments/prefect_prod.yaml
### Documentation Links - Prefect Deployment Concepts - Running Deployment Apply Command This allows your Prefect server or Cloud to recognize the deployment, enabling you to schedule and trigger the flow runs as configured.
d
@Marvin if my prefect yaml uses a custom dockerfile can I just set the env variables in the dockerfile rather than having to pass each of my deployments env variables?
m
Yes, you can set environment variables directly in your Dockerfile, which can simplify the deployment configuration by avoiding the need to pass environment variables individually in each deployment. This is especially useful when you have common environment variables across multiple deployments. ### Configuring Environment Variables in Dockerfile Here’s how you can configure your Dockerfile to set environment variables: #### Example Dockerfile:
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dockerfile
# Use a base image
FROM python:3.10

# Set environment variables
ENV DB_USER='your_db_user'
ENV DB_PASSWORD='your_db_password'
ENV DB_HOST='your_db_host'
ENV DB_PORT='5432'
ENV DB_NAME='your_db_name'

# Optional: You can also set DATABASE_URL using other environment variables
ENV DATABASE_URL="postgresql://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:${DB_PORT}/${DB_NAME}"

# Copy your requirements.txt and install any dependencies
COPY ./requirements.txt /requirements.txt
RUN pip install -r /requirements.txt

# Copy the rest of your application code
COPY . /app

# Set the working directory
WORKDIR /app

# Command to run your application
CMD ["python", "your_flow_script.py"]
### Prefect YAML Configuration With the environment variables set in the Dockerfile, you can simplify the Prefect deployment YAML files since the necessary environment variables are already baked into the Docker image. #### 
prefect_dev.yaml
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yaml
- name: xrs-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:dev'
  schedules: []

- name: mux-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:dev'
  schedules: []
#### 
prefect_prod.yaml
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yaml
- name: xrs-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:prod'
  schedules: []

- name: mux-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:prod'
  schedules: []
### Deployment Commands - Development Deployment:
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sh
  prefect deployment apply prefect/deployments/prefect_dev.yaml
- Production Deployment:
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  prefect deployment apply prefect/deployments/prefect_prod.yaml
### Summary - Environment Variables in Dockerfile: By setting environment variables in the Dockerfile, you streamline deployment configuration. - Simplified YAML: The Prefect YAML configurations are cleaner, focusing solely on deployment-specific parameters. - Deployment: Use the 
prefect deployment apply
command to register the deployments. This approach simplifies managing environment variables and ensures consistency across different environments.
d
@Marvin if you run
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prefect deploy --all
in the directory of both prefect_dev.yaml and prefect_prod.yaml will it deploy all the deployments from both of the yamls?
m
Running 
prefect deploy --all
in a directory where both 
prefect_dev.yaml
and 
prefect_prod.yaml
are present will deploy all the deployments specified within those files. ### Clarification on 
prefect deploy --all
The 
prefect deploy --all
command scans the current directory for all deployments defined in YAML files and deploys them. It does not require you to specify individual files, making it convenient for deploying multiple configurations at once. ### Example Directory Structure Ensure your directory structure is organized as follows:
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prefect/
│
├── deployments/
│   ├── prefect_dev.yaml
│   └── prefect_prod.yaml
### Example Deployment Command Navigate to the 
deployments
directory and run:
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sh
cd prefect/deployments
prefect deploy --all
### What Happens - The command will search the directory for all 
.yaml
files. - It will read 
prefect_dev.yaml
and 
prefect_prod.yaml
. - All deployments defined in these files will be registered with the Prefect server. ### Example Deployments in YAML Files #### 
prefect_dev.yaml
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yaml
- name: xrs-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:dev'
  schedules: []

- name: mux-hal-pipeline-deploy-dev
  tags: ["development"]
  description: Development deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:dev'
  schedules: []
#### 
prefect_prod.yaml
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yaml
- name: xrs-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for XRS HAL pipeline
  entrypoint: flows/xrs_flow.py:process_xrs_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_JSON_PARQUET/XRS/2023-01-01_K204_JSON_XRS.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:prod'
  schedules: []

- name: mux-hal-pipeline-deploy-prod
  tags: ["production"]
  description: Production deployment for MUX HAL pipeline
  entrypoint: flows/mux_flow.py:process_mux_data
  parameters:
    new_file_list: ["heathrow/parquet/CTX_MUXLOGS_PARQUET/20230101/20230101_K204.parquet"]
  work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    job_variables:
      image: 'your-docker-repo/your-image:prod'
  schedules: []
### Documentation Links For more detailed information: - Prefect Deployment Documentation - Prefect Deploy Guide This setup will ensure that both your development and production deployments are registered and ready to be run.
d
@Marvin I ran prefect deploy --all in that directory and this message appear: prefect deploy --all Unable to read the specified config file. Reason: [Errno 2] No such file or directory: ‘prefect.yaml’. Skipping. ? Select a flow to deploy [Use arrows to move; enter to select; n to select none] ┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ Flow Name ┃ Location ┃ ┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩ │ > │ process_fdr_data │ fdr_flow.py │ │ │ process_mux_data │ mux_flow.py │ │ │ process_xrs_data │ xrs_flow.py │ │ │ alert_check_and_generate │ src/common/util.py │ └────┴──────────────────────────┴────────────────────┘ Enter a flow entrypoint manually Aborted. how do I make it deploy all flows in a file named prefect_dev.yaml
m
It appears that by default, 
prefect deploy --all
looks for a config file named 
prefect.yaml
in the current directory. To specify a different config file, you need to use the 
--prefect-file
flag followed by the path to your YAML file. ### Steps to Deploy All Flows in 
prefect_dev.yaml
1. Navigate to the Directory: Ensure you are in the directory containing your deployment YAML file.
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sh
   cd path/to/your/deployments
2. Run the Deployment Command: Use the 
--prefect-file
flag to specify the YAML file.
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sh
   prefect deploy --prefect-file prefect_dev.yaml --all
### Example Directory Structure To clarify, if your directory structure looks like this:
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plaintext
prefect/
│
├── deployments/
│   ├── prefect_dev.yaml
│   └── prefect_prod.yaml
Run the command from within the 
deployments
directory:
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sh
cd prefect/deployments
prefect deploy --prefect-file prefect_dev.yaml --all
### Explanation - 
--prefect-file
flag: This flag allows you to specify an alternative to the default 
prefect.yaml
file. - 
--all
flag: This will deploy all deployments defined in the specified YAML file. ### Documentation Links For more details, you can refer to the official documentation: - Prefect Deploy Guide By following these steps, you should be able to deploy all flows defined in your 
prefect_dev.yaml
file without encountering the error related to 
prefect.yaml
.
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