Executor File Structure#
Besides organizing your Executor code inline (i.e. with Flow.add()
in the same file), you can also write it as an “external” module and then use it via YAML. This is useful when your Executor’s logic is too complicated to fit into a single file.
foo.py
from docarray import Document
from jina import Executor, Flow, requests
class MyExecutor(Executor):
@requests
def foo(self, **kwargs):
print(kwargs)
config.yml
jtype: MyExecutor
metas:
py_modules:
- foo.py
name: awesomeness
description: my first awesome executor
requests:
/random_work: foo
flow.py
from docarray import Document
from jina import Flow
f = Flow().add(uses='config.yml')
with f:
f.post(on='/random_work', inputs=Document(), on_done=print)
from jina import Executor, Flow, Document, requests
class MyExecutor(Executor):
@requests
def foo(self, **kwargs):
print(kwargs)
f = Flow().add(uses=MyExecutor)
with f:
f.post(on='/random_work', inputs=Document(), on_done=print)
Best practice#
Use
jina hub new
in the terminal to create an Executor bundle.
Single Python file#
When you are only working with a single python file (let’s call it my_executor.py
), you can simply put it at the root of the repository, and import it directly in config.yml
jtype: MyExecutor
metas:
py_modules:
- my_executor.py
Multiple Python files#
When you are working with multiple python files, you should organize them as a Python package and put them in a special folder inside your repository (as you would normally do with Python packages). Specifically, you should do the following:
put all your Python files inside a special folder (call it
executor
, as a convention), and put an__init__.py
file inside itbecause of how Jina registers executors, make sure to import your executor in this file (see the contents of
executor/__init__.py
in the example below).
use relative imports (
from .bar import foo
, and notfrom bar import foo
) inside the python modules in this folderOnly list
executor/__init__.py
underpy_modules
inconfig.yml
- this way Python knows that you are importing a package, and makes sure that all the relative imports within your package work properly
To make things more specific, take this repository structure as an example:
.
├── config.yml
└── executor
├── helper.py
├── __init__.py
└── my_executor.py
The contents of executor/__init__.py
is
from .my_executor import MyExecutor
the contents of executor/helper.py
is
def print_something():
print('something')
and the contents of executor/my_executor.py
is
from jina import Executor, requests
from .helper import print_something
class MyExecutor(Executor):
@requests
def foo(self, **kwargs):
print_something()
Finally, the contents of config.yml
- notice that only the executor/__init__.py
file needs to be listed under py_modules
jtype: MyExecutor
metas:
py_modules:
- executor/__init__.py
This was a relatively simple example, but this way of structuring python modules works for any python package structure, however complex. Consider this slightly more complicated example
.
├── config.yml # Remains exactly the same as before
└── executor
├── helper.py
├── __init__.py
├── my_executor.py
└── utils/
├── __init__.py # Required inside all executor sub-folders
├── data.py
└── io.py
Here you can then import from utils/data.py
in my_executor.py
like this: from .utils.data import foo
, and do any other kinds of relative imports that python enables.
The best thing is that no matter how complicated your package structure, “importing” it in your config.yml
file is super easy - you always put only executor/__init__.py
under py_modules
.