Getting started🔗
Install the library🔗
Install the latest version from PyPI using pip:
pip install bandsaw
Define the individual tasks of your workflow🔗
Import the @task decorator from the bandsaw package and decorate a function
with it:
import bandsaw
...
@bandsaw.task
def my_function(x):
...
return x
Configure bandsaw🔗
Create a new python module bandsaw_config and add first advice that just adds some
additional logging when a task is executed:
import bandsaw
configuration = bandsaw.Configuration().add_advice_chain(
bandsaw.advices.log.LoggingAdvice(),
)
Run your workflow🔗
When you now run your workflow, bandsaw intercepts the execution of my_function and
its LoggingAdvice prints out additional log messages before() and after() it, e.g.:
$ python my_script.py
...
2021-10-27 13:13:51,940 2290 bandsaw.advices.log INFO: BEFORE 0d268ac0..4213:76560cb4..a37d with context {}
...
2021-10-27 13:13:52,127 2290 bandsaw.advices.log INFO: AFTER 0d268ac0..4213:76560cb4..a37d with context {}
...
The log messages contain the task_id (0d268ac0..4213), which is derived from the code
that is decorated, and the run_id (76560cb4..a37d), derived from the arguments that
my_function was called with.
Where to go from here?🔗
Read the user guide for some more in-depth explanation about bandsaw and its concepts.
Alternatively, bandsaw brings with it a couple of useful advice classes, that can be used just by adding them to its configuration:
- Running tasks in a subprocess or with a different python interpreter:
bandsaw.advices.subprocess.SubprocessAdvice - Caching the result of a task:
bandsaw.advices.cache.CachingAdvice - Running tasks on a different machine using SSH (Coming soon):
bandsaw.advices.remote.SshAdvice - Capturing the resource usage of a task (Coming soon):
bandsaw.advices.profile.ProfilingAdvice - Running tasks on asynchronously (Coming soon):
bandsaw.advices.async.AsyncAdvice - Creating your own advice: How to write your own advice?