Extended register artifact docs #3165
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| @step(enable_cache=True) | ||
| def load_v1(): |
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Is that best practice really to do this in a step? This rather seems like a one-time execution, which happens manually?
| @@ -46,6 +46,61 @@ with open(os.path.join(temp_artifact_folder_path,test_file_name),"r") as f: | |||
| The artifact produced from the preexisting data will have a `pathlib.Path` type, once loaded or passed as input to another step. The path will be pointing to a temporary location in the executing environment and ready for use as a normal local `Path` (passed into `from_pretrained` or `open` functions to name a few examples). | |||
| {% endhint %} | |||
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| ## Register Existing folder as a ZenML Artifact and use in Pipeline | |||
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| For datasets in a folder structure, that you don't want to materialize in the pipeline, you can register the folder as a ZenML Artifact and use it in subsequent steps or other pipelines. | |||
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What's the meaning of don't want to materialize in the pipeline here?
- IMO
loadis not just shorter, it's also the better and more clear verb that people actually understand - The data gets loaded in the same pipeline. I think this refers to the first step, but I think in general this probably should not happen in the pipeline at all, see my other comment
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