Use Conda XGBoost

[jakirkham]

Description

Switch to Conda XGBoost in Conda-based test of third-party dependencies.

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.

[Matt711]

So this should close #17952 by getting rid of the warnings?

[jakirkham]

I don't think so

That likely needs a change in this test

cudf/python/cudf/cudf_pandas_tests/third_party_integration_tests/tests/test_xgboost.py

Lines 116 to 140 in 61e47bb

	@pytest.mark.parametrize("device", ["cpu", "cuda"])
	def test_predict(device: str) -> np.ndarray:
	reg = xgb.XGBRegressor(n_estimators=2, device=device)
	X, y = make_regression(n_samples, n_features, random_state=11)
	X_df = pd.DataFrame(X)
	reg.fit(X_df, y)
	booster = reg.get_booster()
	predt0 = reg.predict(X_df)
	predt1 = booster.inplace_predict(X_df)
	# After https://github.com/dmlc/xgboost/pull/11014, .inplace_predict()
	# returns a real cupy array when called on a cudf.pandas proxy dataframe.
	# So we need to ensure we have a valid numpy array.
	if not isinstance(predt1, np.ndarray):
	predt1 = predt1.get()
	np.testing.assert_allclose(predt0, predt1)
	predt2 = booster.predict(xgb.DMatrix(X_df))
	np.testing.assert_allclose(predt0, predt2)
	predt3 = booster.inplace_predict(X)
	np.testing.assert_allclose(predt0, predt3)
	return predt0

[jakirkham]

This is just following up on your's and Bradley's suggestions in PR ( #17947 ) starting with comment ( #17947 (comment) )

[galipremsagar]

/merge

[jakirkham]

Thanks Matthew and Prem! 🙏