[ML] Periodically report pytorch_inference RSS

[edsavage]

Summary

Adds a periodic memory reporter to pytorch_inference so Elasticsearch can track the process's real resident set size (RSS) for a trained model deployment, independent of inference traffic.

• A dedicated timer thread emits process stats every 10s (a single RSS read; negligible cost), so memory is observed even when a deployment is idle or between requests.
• Each report carries both the current RSS (memory_rss) and the OS peak / high-water mark (memory_max_rss). The peak is transmitted explicitly rather than derived on the ES side from the sample stream, so transient spikes between reports are not lost — this is the signal used to keep model assignment and adaptive scaling OOM-safe.
• The process-stats document key is renamed from process_stats to stats to match the Elasticsearch PyTorchResult / InferenceProcessStats parser.
• The reporter thread is shut down promptly (condition-variable signalled) before the rest of the process tears down, so it can never write to a closing stream.

Relates #2885

Release-ordering note (important)

ES's InferenceProcessStats parser is strict and treats an unknown field as a fatal parse error for the result stream. Because this PR (re)introduces memory_max_rss, the Elasticsearch-side change that accepts memory_max_rss as an optional field must be released before the ml-cpp version emitting it is bumped into Elasticsearch. See the companion Elasticsearch PR. Merging this PR in the ml-cpp repo is safe on its own; the constraint applies to the ml-cpp version bump inside ES.

Test plan

• CResultWriterTest.testWriteProcessStats updated for the stats schema + memory_max_rss; passes.
• Manual: run pytorch_inference with a model and confirm periodic stats docs are emitted ~every 10s with memory_rss and memory_max_rss.
• Confirm prompt shutdown (no stray writes / hangs on exit).

Made with Cursor