what I'm more looking for is

kedro run --command "input1_s3_key":"s3://foobar"

or something on that order

that you'll need to extend the default behaviour somewhat

So would I be correct in assuming that kedro currently expects to run on the same inputs every time a pipeline is run??

So we're following this philosophy https://12factor.net/config

and what we'd want you to do is to set up your

conf

folder to have someone like

conf/staging/catalog.yml

and

conf/prod/catalog.yml

and then if you switch between then with

--env

CLI arg or

KEDRO_ENV

environment variable

Sure, I understand that for static configuration.. but generally the purpose of a (production) machine learning pipeline is to output new predictions or other insights based on future executions with new and different data.

I guess that’s a question of orchestration - you’re able to run kedro with any inputs you want. If you would like to add more control in the CLI it’s possible to do so. It’s an open question how much dynamism we allow in the configuration space. It’s something we want to keep readable and declarative. This may change in the future and some of our thinking is on this page - feel free to add your thoughts as it will drive the future of the product: https://github.com/quantumblacklabs/kedro/issues/891

Yeah I read through that, that's a really nice investigation.

I'm just trying to better under the world as it is today, etc.

Sure in which case your options are: - good use of environments - jinja - extensions to the CLI for your own purposes

And if folks are using kedro in production, I'm curious how they are typically doing that w/o regenerating the same outputs every time.

Oh - lots of pipelines are in production

If the raw source is a dynamic source then of course outputs will be different

Where do you get your source data and how often does it refresh?

Ideally I'd like to run it with varying S3 keys

which I imagine could be fairly simple to vary via ENV variables.

So there are two ways of doing that in Kedro

I’m going to drop that in the thread above so it’s easier to track

word

Hi @ende! I've just seen this and is is similar to what I was also trying to achieve. If you just want to inject some new variables another alternative is to provide the parameters with the --params. Like: kedro run --pipeline=amazing_pipeline --params "key1: value1,key2:value2". These params will be available in the register_config_loader in the extra_params. You can then pass them to the global_dict of the TemplatedConfigLoader

Hey, thanks! I was wondering about that... so can you then inject those parameters into the data catalog configs ?

Yes, you just need to set the variable in the catalog using this notation ${cool_variable} and the --params would be something --params "cool_variable:cool_variable_value, ..." and in the extra_params you would get a dict {cool_variable : cool_variable_value} wich can be passed to the the TemplatedConfigLoader in the globals_dict. Whatever is passed in the globals_dict will be used to fill the catalog and will have precedence to the globals.yml which is used by default.

Was there a recent update, which prior to the update allowed for duplicate output config values to nodes, then after the update prevents this option? Specifically, we have two pipelines created which effectively do the same thing, however one is an incremental update pipeline while the other is not. There are several nodes in the incremental pipeline which are not eligible for incremental changes and thus get rewritten (and utilize the same code as the non-incremental pipeline). These nodes use to work fine, however just recently upgrading the kedro version we are getting an issue where utilizing the same node output conf value is throwing an error.

Which versions did you upgrade between? Even I'm not sure any of that logic has changed since 0.15.x

Hi! Is there a way to get the kedro current session like using get_current_session() when using the parallel runner without having to recreate it each time ? I'm getting runtime error: "there is no active kedro session". Thanks!

So this gets tricky with the parallel runner due to limitation of how multi-processing works in Python - what do you need from the current one that's not available in an old one?

Realistically hooks are the way that we encourage users to do this sort of thing

The problem is that there is no current session. I was trying to load a dataset from the catalog. The old one would also be ok