---
name: thread-utilization-profiling-and-validation
description: Use when when deploying Mass2SMILES inference on CPU-only hardware (e.g., when GPU/CUDA support is unavailable), you need to verify that the InferenceModel cpu_threads parameter (e.g., cpu_threads=128) actually controls thread allocation during execution.
license: CC-BY-4.0
metadata:
  edam_topics: []
  tools:
  - Python
  - Docker
  - TensorFlow (CPU build)
  - Mass2SMILES (delser292/mass2smiles:final)
  techniques:
  - LC-MS
derived_from:
- doi: 10.1101/2023.07.06.547963v1
  title: Mass2SMILES
evidence_spans:
- open-source Python based deep learning approach
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_mass2smiles_cq
    doi: 10.1101/2023.07.06.547963v1
    title: Mass2SMILES
  dedup_kept_from: coll_mass2smiles_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1101/2023.07.06.547963v1
  all_source_dois:
  - 10.1101/2023.07.06.547963v1
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# thread-utilization-profiling-and-validation

## Summary

Validate that a configurable CPU-thread parameter in TensorFlow-CPU inference correctly maps requested thread counts to observed thread allocation during model execution. This skill ensures that performance tuning parameters are actually honored by the runtime, enabling reproducible inference optimization on CPU-only deployments.

## When to use

When deploying Mass2SMILES inference on CPU-only hardware (e.g., when GPU/CUDA support is unavailable), you need to verify that the InferenceModel cpu_threads parameter (e.g., cpu_threads=128) actually controls thread allocation during execution. Use this skill after modifying the cpu_threads configuration and before running production inference pipelines on new hardware, to confirm that thread count expectations match observed behavior.

## When NOT to use

- Input MGF file is empty or contains malformed spectra — validation will fail spuriously due to inference failure, not thread misconfiguration.
- Hardware or Docker environment enforces thread limits (e.g., cgroup CPU quota) lower than the requested cpu_threads value — observed count will be capped and comparison will not reflect InferenceModel configuration fidelity.
- You have GPU/CUDA support available — use GPU inference (delser292/mass2smiles:transformer_v1) instead, as CPU thread tuning is not relevant and may mask performance optimization opportunities.

## Inputs

- Mass2SMILES InferenceModel instance (instantiated with cpu_threads parameter)
- MGF file containing MS/MS spectral data (GNPS-style format)
- System resource monitoring access (process-level thread counts)

## Outputs

- Configured thread count (integer)
- Observed thread count during inference (integer)
- Verification report comparing expected vs. actual thread utilization
- Inference execution log with timestamp and thread state

## How to apply

Load the Mass2SMILES TensorFlow-CPU Docker container (delser292/mass2smiles:final) and instantiate InferenceModel with a specific cpu_threads value. Execute a single inference run on a representative sample MGF file (GNPS-style MS/MS spectral data) while capturing system-level thread activity (e.g., via process monitoring tools like `ps`, `/proc/[pid]/status`, or Python's `os.cpu_count()` and `threading` module). Log the configured thread count alongside the observed thread count used during inference execution. Compare these values and generate a verification report that confirms the parameter was honored; thread count drift or unresponsiveness indicates misconfiguration or runtime constraints that require adjustment (e.g., physical core limits, Docker resource caps).

## Related tools

- **Docker** (Container runtime for Mass2SMILES TensorFlow-CPU environment, isolates inference execution and enables reproducible thread-level profiling across systems)
- **TensorFlow (CPU build)** (Deep learning framework that interprets the cpu_threads parameter and allocates thread pools for inference execution)
- **Python** (Language for instantiating InferenceModel, executing inference, and instrumenting thread count capture via os and threading modules)
- **Mass2SMILES (delser292/mass2smiles:final)** (Docker image containing the InferenceModel class and pre-trained model for MS/MS structure prediction) — https://github.com/volvox292/mass2smiles

## Examples

```
from mass2smiles import InferenceModel; model = InferenceModel(cpu_threads=128); results = model.predict('sample.mgf'); print(f'Configured: 128, Observed: {get_thread_count(model.pid)}')
```

## Evaluation signals

- Observed thread count matches or is within ±2 of the configured cpu_threads parameter (accounting for system overhead threads).
- Inference execution completes without errors; output SMILES structures are generated for all spectra in the sample MGF file.
- Thread count remains stable throughout the inference run (no spurious spikes or drops after initialization).
- Verification report is reproducible across multiple runs with the same configuration and MGF input, confirming deterministic thread allocation.
- When cpu_threads parameter is increased in subsequent runs, observed thread count increases correspondingly (monotonic relationship).

## Limitations

- Observed thread count may not exactly equal the requested cpu_threads value due to system-level constraints: Docker cgroups CPU quotas, physical core limits on the host, or TensorFlow internal thread pool overhead. Validation should define acceptable tolerance ranges.
- CPU inference speed improvement from thread tuning is hardware-dependent; systems with fewer physical cores than requested cpu_threads will not see proportional speedup and may exhibit contention.
- This skill validates thread allocation only for the TensorFlow-CPU build; the GPU variant (transformer_v1) does not expose or respond to cpu_threads and should not be profiled using this method.
- MGF file size and spectral complexity affect inference duration; validation on a single small sample may not reflect thread utilization patterns under production workload (batch inference on large spectral libraries).

## Evidence

- [readme] cpu_threads parameter control: "can be speed up by changing the number of cores: e.g. InferenceModel(cpu_threads=128)"
- [readme] TensorFlow-CPU rationale: "the cddd does not seem to work on newer cuda drivers, therefore it is build using tensorflow cpu"
- [intro] MGF input format: "Spectral data can be provided as MGF files (GNPS-syle)"
- [intro] Docker container deployment: "model inference is most effciently performed via the provided docker container"
- [readme] Performance improvement claim: "Using this setup inference speed is highly improved"