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Small edits (#670)
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@@ -42,7 +42,7 @@ Remember ClearML also stores your code environment, making it reproducible. So w
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Back to the overview. One of the output types you can add to your task is what’s called an artifact.
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An artifact can be a lot of things, mostly they’re files like model weights or pandas dataframes containing preprocessed features for example. Our documentation lists all supported data types.
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An artifact can be a lot of things, mostly they’re files like model weights or Pandas DataFrames containing preprocessed features for example. Our documentation lists all supported data types.
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You can download the artifacts your code produced from the web UI to your local computer if you want to, but artifacts can also be retrieved programmatically.
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@@ -148,7 +148,7 @@ status, it isn't completed this should not happen but. If it is completed, we ar
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functions that I won't go deeper into. Basically, they format the dictionary of the state of the task scalars into
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markdown that we can actually use. Let me just go into this though one quick time. So we can basically do `Task.get_last_scalar_metrics()`,
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and this function is built into ClearML, which basically gives you a dictionary with all the metrics on your task.
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We'll just get that formatted into a table, make it into a pandas DataFrame, and then tabulate it with this cool package
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We'll just get that formatted into a table, make it into a Pandas DataFrame, and then tabulate it with this cool package
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that turns it into MarkDown. So now that we have marked down in the table, we then want to return results table. You can
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view the full task. This is basically the comment content we want to be in the comment that will later end up in the PR.
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If something else went wrong, we want to log it here. It will also end up in a comment, by the way, so then we know that
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