[GSoC] Add unit tests for tune API

[helenxie-bit]

What this PR does / why we need it:
This PR adds unit tests for tune API.

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 #

Checklist:

• Docs included if any changes are user facing

[helenxie-bit]

/area gsoc

[helenxie-bit]

Ref: #2339

[andreyvelich]

Thank you for doing this @helenxie-bit!

[helenxie-bit]

@andreyvelich The unit test for tune API is ready. I have also fixed some errors in this PR. Please have a look when you have time. Thanks!

[andreyvelich]

Thank you for doing this @helenxie-bit!
/assign @tenzen-y @johnugeorge @deepanker13 @droctothorpe @Electronic-Waste

[helenxie-bit]

Thanks for your great contribution @helenxie-bit ! I left some suggestions about the name of UTs for your reference

I guess it will be more friendly for other developers to understand these UTs if we could specify the scope of each UTs:)

@Electronic-Waste Thank you so much for your suggestions! I have incorporated your advice into the UTs.

[helenxie-bit]

@andreyvelich I’ve added the kubeflow-training dependency to the unit tests, allowing values and parameters to be fetched from there instead of redefining them within the Katib unit tests. You can find the change here:

katib/test/unit/v1beta1/requirements.txt

Line 3 in b24b44f

kubeflow-training[huggingface]==1.8.1

Additionally, I have added verification for the experiment object in two valid flow cases in the following section:

katib/sdk/python/v1beta1/kubeflow/katib/api/katib_client_test.py

Lines 587 to 639 in b24b44f

	call_args = mock_create_experiment.call_args
	experiment = call_args[0][0]
	if test_name == "valid flow with custom objective tuning":
	# Verify input_params
	args_content = "".join(
	experiment.spec.trial_template.trial_spec.spec.template.spec.containers[
	0
	].args
	)
	assert "'a': '${trialParameters.a}'" in args_content
	# Verify trial_params
	assert experiment.spec.trial_template.trial_parameters == [
	V1beta1TrialParameterSpec(name="a", reference="a"),
	]
	# Verify experiment_params
	assert experiment.spec.parameters == [
	V1beta1ParameterSpec(
	name="a",
	parameter_type="int",
	feasible_space=V1beta1FeasibleSpace(min="10", max="100"),
	),
	]
	elif test_name == "valid flow with external model tuning":
	# Verify input_params
	args_content = "".join(
	experiment.spec.trial_template.trial_spec.spec.pytorch_replica_specs[
	"Master"
	]
	.template.spec.containers[0]
	.args
	)
	assert (
	'"learning_rate": "${trialParameters.learning_rate}"'
	in args_content
	)
	# Verify trial_params
	assert experiment.spec.trial_template.trial_parameters == [
	V1beta1TrialParameterSpec(
	name="learning_rate", reference="learning_rate"
	),
	]
	# Verify experiment_params
	assert experiment.spec.parameters == [
	V1beta1ParameterSpec(
	name="learning_rate",
	parameter_type="double",
	feasible_space=V1beta1FeasibleSpace(
	min="1e-05", max="5e-05"
	),
	),
	]

Please review when you have time. Thanks!

[helenxie-bit]

@andreyvelich Please review this PR when you have time 👀. I'll adjust the e2e tests for the tune API after this PR is merged, as it fixes some small bugs in the tune API.

[helenxie-bit]

Since @mahdikhashan is currently working on the notebook with instructions for this API, and this PR also includes some bug fixes for the API, @andreyvelich, would it be better to separate the bug fixes into a standalone PR to make the review process more convenient? Please let me know your thoughts.

[andreyvelich]

Since @mahdikhashan is currently working on the notebook with instructions for this API, and this PR also includes some bug fixes for the API, @andreyvelich, would it be better to separate the bug fixes into a standalone PR to make the review process more convenient? Please let me know your thoughts.

Sure, please create separate PR for the bug fixes.

[Electronic-Waste]

Basically LGTM! I left a few comments concerned with objective_metric_name for you.

Thanks for your great contribution! @helenxie-bit

[Electronic-Waste]

It would be better if we also verify the objective_metric_name parameter.

[helenxie-bit]

Thank you for your suggestions! I will add that.

[helenxie-bit]

/rerun-all

[helenxie-bit]

@andreyvelich @Electronic-Waste This PR is ready for review. But I'm not sure why the ci test Publusi Katib Core images/Publish Image (katib-ui, cmd/ui/v1beta1/Dockerfile) kept failing due to network connectivity issue. Do you have any ideas?

[Electronic-Waste]

LGTM! Thanks for your great contribution! As for the CI error, it occurred occasionally. You could rerun it after a while:)

/lgtm
/rerun-all

[andreyvelich]

Thank you for this great contribution @helenxie-bit!
/lgtm
/approve

[google-oss-prow]

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