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Add support for AIX360 explanations #1094
Add support for AIX360 explanations #1094
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aix api patch
Clean up example readme
update version format to match with KFServing
update v1beta1 spec and tests for rebase
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
Thanks for the awesome contribution! Can we add a e2e test like alibi explainer here ? https://github.com/kubeflow/kfserving/blob/master/test/e2e/explainer/test_tabular_explainer.py |
AIX beta controller rebase
fix containerv1 spec
remove testing logs
/retest |
update e2e test to match with the v1beta1 controller changes
}, | ||
"aix": { | ||
"image" : "aipipeline/aix-explainer", | ||
"defaultImageVersion": "0.4.0" |
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please also change here which is used for KFServing CI https://github.com/kubeflow/kfserving/blob/master/config/overlays/test/configmap/inferenceservice.yaml#L39
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Thanks for the pointer.
def __init__(self, name: str, predictor_host: str, segm_alg: str, num_samples: str, | ||
top_labels: str, min_weight: str, positive_only: str, explainer_type: str): | ||
super().__init__(name) | ||
print("INIT") |
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use proper logger?
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replaced print with logger.info
KFServing.wait_isvc_ready(service_name, namespace=KFSERVING_TEST_NAMESPACE, timeout_seconds=720) | ||
except RuntimeError as e: | ||
logging.info(KFServing.api_instance.get_namespaced_custom_object("serving.knative.dev", "v1", | ||
KFSERVING_TEST_NAMESPACE, "services", service_name + "-predictor")) |
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oh the service name is actually, service_name + "-predictor-default"
, I was trying to remove -default
but figured it breaks the backwards compatibility.
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Thanks, we added back -default
address comments on ci config
move aix example to use beta api
@yuzisun @animeshsingh |
That's awesome! |
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Thanks @drewbutlerbb4 - few nits, else looks good to go
Co-authored-by: Animesh Singh <singhan@us.ibm.com>
/retest |
/lgtm |
[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: animeshsingh The full list of commands accepted by this bot can be found here. The pull request process is described here
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What this PR does / why we need it:
Adds a new Explainer framework called Artificial Intelligence eXplainability 360 (AIX360) from the Trusted AI project. This PR contains the first explainer using a package called LIME (local interpretable model-agnostic explanations). A sample has been created which gives explanations for a random forest model's predictions on the MNIST dataset.
Which issue(s) this PR fixes (optional, in
fixes #<issue number>(, fixes #<issue_number>, ...)
format, will close the issue(s) when PR gets merged):Fixes #1093
Special notes for your reviewer:
Release note: