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Answered
Question About Artifacts, Dynamic Vs Static And Their Relationship To Experiments Under

Question about artifacts, dynamic vs static and their relationship to experiments

Under https://allegro.ai/docs/concepts_arch/concepts_arch/ in the Artifacts section, and also in step 3 and 4 in the https://allegro.ai/docs/tutorials/tutorial_explicit_reporting/ tutorial the notation of dynamid and static artifacts are used.
It is not clear not me what is the meaning of "If a registered artifact chages in an experiment, the change is update on the backend" and the overall nature of dynamic artifacts.

I assume static artifact are somewhat similar to object stores, where I store files and then I can look them up using the task identifiers nad the artifact name - correct? What does the "backend update" mean on dynamic artifacts?
TL;DR it is not really clear to me form the docs what is the right use case for dynamic vs static and how do their behavior differ in the context of tasks and generally

I hope this channel will have the answers 🙂

  
  
Posted 4 years ago
Votes Newest

Answers 6


Mmm maybe, lets see if I get this straight

A static artifact is a one-upload object, a dynamic artifact is an object I can change during the experiment -> this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?

  
  
Posted 4 years ago

So dynamic or static are basically the same thing, just in dynamic, I can edit the artifact while running the expriment?

Second, why would it be overwritten if I run a different run of the same experiment? As I saw, each object is stored under a directory with the task ID which is unique per run, so I assume I won't be overriding artifacts which are saved under the same name in different runs (regardless of static or dynamic)

  
  
Posted 4 years ago

Thanks a lot, that clarifies things

  
  
Posted 4 years ago

So dynamic or static are basically the same thing, just in dynamic, I can edit the artifact while running the experiment?

Correct

Second, why would it be overwritten if I run a different run of the same experiment?

Sorry, I meant in the same run, if you reuse the artifact name you will be overwriting it. Obviously different runs different artifacts :)

  
  
Posted 4 years ago

Hi WackyRabbit7
Yes, we definitely need to work on wording there ...
"Dynamic" means you register a pandas object that you are constantly logging into while training, think for example the image files you are feeding into the network. Then Trains will make sure it is constantly updated & uploaded so you have a way to later verify/compare different runs and detect dataset contemplation etc.
"Static" is just, this is my object/file upload and store it as an artifact for me ...
Make sense ?

  
  
Posted 4 years ago

this results at the end of an experiment in an object to be saved under a given name regardless if it was dynamic or not?

Yes, at the end the name of the artifact is what it will be stored under (obviously if you reuse the name you basically overwrites the artifact)

  
  
Posted 4 years ago