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Unstructured Sedna Lifelong Learning Architecture #392
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@@ -17,7 +17,7 @@ | |||
from utils.args import TrainingArguments, EvaluationArguments | |||
from estimators.train import Trainer | |||
from estimators.eval import Validator, load_my_state_dict | |||
from accuracy import robo_accuracy | |||
from accuracy import accuracy | |||
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os.environ["BACKEND_TYPE"] = '' |
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env var's default value is ""
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done
@@ -0,0 +1,30 @@ | |||
""" | |||
Mining tasks of inference sample base on task attribute extractor |
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base on -> based on
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done
Before this, user must provide an s3 directory for storage which is denoted as "s3_prefix" in this example, by setting environment variable s3_prefix. | ||
``` | ||
s3_prefix=$s3_prefix | ||
cloud_image=swr.cn-south-1.myhuaweicloud.com/sedna/sedna-robo:v0.1.1 |
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When the user needs to change the image, it shall be done in the configuration instead of after dataset preparation. Add an introduction to the image (as ``prepare image'' introduced below).
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done
My comment is addressed in the current version. |
For commit history of Sedna repository more readable and cleaner, I suggest to squash the commits by git rebase -i kubeedge/main, to keep the number of commit to 3~5, and to make the commit message more concrete, instead of "update this... imporve that" |
/lgtm |
Signed-off-by: SiqiLuo <1587295470@qq.com>
… learning Signed-off-by: SiqiLuo <1587295470@qq.com>
Signed-off-by: SiqiLuo <1587295470@qq.com>
Signed-off-by: SiqiLuo <1587295470@qq.com>
/lgtm |
/approve |
[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: jaypume The full list of commands accepted by this bot can be found here. The pull request process is described here
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Approvers can indicate their approval by writing |
In this PR, unstructured Sedna lifelong learning architecture is achieved. It surpports the following new features.