Show resources on the example's training node

The example's `fit` is what a real training node looks like, so it is where
`resources={"cpus": 2}` belongs -- beside the `device="gpu"` it already
carries, since the two answer different questions about the same node.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

examples/myresearch/train.py

@@ -19,6 +19,7 @@ from fluksio import Port, node
     device="gpu",
     device_policy="prefer",
     timeout=600,
+    resources={"cpus": 2},
 )
 def fit(dataset, lr, epochs=25):
     """Train, reporting the loss as it goes.
@@ -28,6 +29,12 @@ def fit(dataset, lr, epochs=25):
     why there is no `log_metric()` to call. `device="gpu"` with
     `device_policy="prefer"` sends this to a worker carrying that label when
     one is attached, and runs it here when none is.
+
+    `resources` is what a real training node needs and the rest of a flow does
+    not: two cores held for the length of the call, and a worker started with
+    thread limits saying so — otherwise a numerical library sizes itself to
+    every core on the machine, and a sweep of these starves the engine. A GPU
+    one would say `{"gpus": 1}` and hold the card exclusively.
     """
     rows = json.loads(open(fluksio.load_artifact(dataset)).read())["rows"]
     loss = 1.0