Hi @freezing-airport-6809 @glamorous-carpet-83516, a lot of our users are confused why do we need to install the requirement.txt locally since we already have the remote image built. We are trying to explain the difference between local dependency and remote dependency to our users (DS and MLE) and want to know if the following statement makes sense. Thanks in advance for helping with the review.

## Local Dependency <-> Workflow Compilation

### Workflow Compilation and Validation
Flyte compiles and validates workflows into a Directed Acyclic Graph (DAG) locally before they are registered with the Flyte platform. This local compilation process involves transforming the defined tasks and workflows into a structured format that clearly outlines the execution order and dependencies between tasks. The validation step ensures that the workflow is logically sound, i.e., there are no cyclic dependencies and all required inputs for each task are properly defined.

### Role of Flyte Local Dependency Management
During this local compilation phase, Flyte local dependency management plays a crucial role. It ensures that all necessary libraries, packages, and other resources are available and properly configured. This is essential because the accuracy of the DAG compilation relies on the ability to execute pieces of code that define task functionalities and their interactions. If any package or library is missing or not correctly installed, the local validation and compilation process may fail or produce incorrect DAGs.

## Remote Dependency <-> Task Execution

### Task Execution in Remote Environments
Once the DAG is successfully compiled and validated, the workflow is registered with the Flyte backend. *Flyte tasks, as part of the workflow, are executed in remote environments*. To facilitate this remote execution and ensure that the environment in which tasks run is consistent with the environment expected by the workflow's logic, Flyte uses Docker images. These Docker images encapsulate all system and Python dependencies required by the application. Therefore, when defining a Flyte task, it's necessary to specify the Docker image that contains all the needed dependencies. This encapsulation ensures that regardless of where the task is executed, it has access to a consistent set of dependencies that match those used during the local development and testing phases.

### Importance of Dependency Management in Docker Images
To ensure tasks are successfully executed in the same remote environments, it is critical that all necessary dependencies are installed and correctly configured within the Docker image used by the tasks. This includes system libraries, Python packages, and any other tools or frameworks required by the tasks. Managing these dependencies effectively avoids runtime errors and ensures that the tasks perform as expected, regardless of the execution cluster/platform.

@brief-boots-17198 this is awesome, we should contribute it to the flyte docs itself. I would add 2 more points Point 1: Local execution

The local environment can also be used to execute the entire workflow/DAG locally. For this you will need all dependencies to be installed locally. In the absence it would have to run container and this would be heavy, slow and cumbersome. When developing Flyte, we wanted the capability of running code locally to feel almost pythonic, simple and light weight. The only way to achieve this is to have all dependencies available in local environment

2: ImageSpec

You do not need to always build a dockerfile, but use imagespec and a = imagespec(); b= a.with_packages(...), will allow you to compose multiple images based on a common base. this makes programmatic/declarative creation of environments.

TODO: we are infact working on making imagespec.install()

work to help with creating a local environment and also

pyflyte install test.py wf

somthing like that <- or

pyflyte dump dev-requirements test.py wf

Awesome. Thanks for the review!