Let a node say how much of the machine it takes

Five concurrent training nodes, each sizing its thread pool to every core,
left the engine's own event loop unscheduled: the API stopped answering
within 10 s and every client died. The same shape on a GPU deadlocked a run
for 21 minutes at 0% utilisation with nothing failing and nothing to read --
it just sat in `running`.

@node(resources={"cpus": 2}) is the declaration. The engine holds that much
for the length of the execution, so more of them than the machine has room
for wait their turn rather than oversubscribing it, and a `gpus` node holds
its card exclusively. FLOW_CPUS defaults to every core but two, and those two
are what keeps the engine answering.

Because a thread cap is read when the process imports the library, a warm
worker cannot be told a different one -- so an environment gets a pool of its
own and nodes deriving the same one share it, rather than paying a cold start
per call on exactly the nodes whose imports are slowest. XLA_FLAGS is never
derived: it is a composed, version-dependent string, so it travels in
resources.env where it is visible.

A node that declares nothing is not accounted for and behaves as it always
did -- it just gets FLOW_CPUS/FLOW_MAX_WORKERS as a thread cap, which is the
half of this that fixes the reported incident without anybody declaring
anything. An operator who set OMP_NUM_THREADS themselves still wins.

Resources are claimed strictly before a worker slot, so the two blocking
waits cannot deadlock. A node queued for them publishes node_queued and shows
on GET /workers/resources, because waiting and hanging looked identical.

Accounted, not enforced: no cgroups, no rlimits. Scheduling across machines,
flavours and enforcement are the next steps.

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

docs/reference/configuration.md

@@ -125,8 +125,18 @@ warning into a refusal to start.
 | `FLOW_MAX_WORKERS` | `4` | node-code subprocesses run in parallel |
 | `FLOW_MAX_CASCADES` | `4` | cascades in flight at once; throughput is this over the mean cascade time, so raise it where nodes wait on a network rather than a CPU |
 | `FLOW_NODE_TIMEOUT` | `0` | seconds a node may be silent, unless it sets its own; 0 is no limit |
+| `FLOW_CPUS` | `0` | cores nodes that declare `resources` may be given; 0 works it out as every core but two, which are what keeps the engine answering while the machine is busy |
+| `FLOW_GPUS` | `0` | GPUs on this machine, each held by one node at a time. Not detected — say how many there are |
 | `OBS_RETENTION_DAYS` | `30` | how long metrics, events and run records are kept |
 
+A node that declares nothing is not accounted against `FLOW_CPUS`; it runs on
+the shared pool and is given `FLOW_CPUS / FLOW_MAX_WORKERS` as a thread cap, so
+several at once cannot each size themselves to the whole machine. Setting
+`OMP_NUM_THREADS` (or any of its siblings) on the engine yourself overrides
+that default. What is free, and which nodes are queued for it, is
+`GET /api/v1/workers/resources`. See
+[declaring resources](../getting-started/data-science.md#declaring-what-a-node-needs).
+
 ## Agents
 
 | Variable | Default | Notes |