…CR (#6741)

Allow scheduling the operator-managed Deployment on tainted or specific nodes via spec.services.tolerations and spec.services.nodeSelector, alongside the existing affinity and topologySpreadConstraints fields.

Signed-off-by: Shumin <shumin.zheng@outlook.com>

* fix: feature freshness logic updated

Signed-off-by: Jitendra Yejare <11752425+jyejare@users.noreply.github.com>

* fix: Avoid redundant _get_max_timestamp query in _compute_for_feature_view

Move the _get_max_timestamp call to compute_metrics and pass the result
into _compute_for_feature_view, eliminating a duplicate data-source query
per feature view.

Signed-off-by: Jitendra Yejare <11752425+jyejare@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

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Signed-off-by: Jitendra Yejare <11752425+jyejare@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>

test_get_online_features computes `device` from torch.cuda.is_available()
and passes it when building the expected tensors for `driver_id` and
`trips`, but the `avg_orders_day` assertion omits it.

OnlineResponse.to_tensor() places its output on cuda when a GPU is
present (sdk/python/feast/online_response.py), so on a GPU host the
expected tensor stays on cpu and torch.equal raises:

    RuntimeError: Expected all tensors to be on the same device, but got
    other is on cpu, different from other tensors on cuda:0
    (when checking argument in method wrapper_CUDA__equal)

CI runs CPU-only, where `device` resolves to "cpu" and the omission has
no effect, so this only reproduces on machines with CUDA available.

Verified on a CUDA host: the unmodified test fails with the GPU visible
and passes under CUDA_VISIBLE_DEVICES="", while the patched test passes
in both cases.

Signed-off-by: Piyush Mondal <piyushmondal182004@gmail.com>
Co-authored-by: Francisco Javier Arceo <arceofrancisco@gmail.com>