Inference model cpu thread configuration

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v2/skills/inference-model-cpu-thread-configuration

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Install

npx -y skills add HolobiomicsLab/asb-skill-collections --skill inference-model-cpu-thread-configuration

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Use when when running Mass2SMILES inference on a TensorFlow-CPU build (e.g., delser292/mass2smiles:final container) and you need to optimize inference speed by controlling CPU core allocation.

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SKILL.md

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InferenceModel CPU-thread configuration

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Configure the number of CPU cores allocated to TensorFlow-CPU inference in Mass2SMILES by setting the cpu_threads parameter on the InferenceModel class. This skill enables performance tuning on CPU-bound inference runs without GPU acceleration.

When to use

When running Mass2SMILES inference on a TensorFlow-CPU build (e.g., delser292/mass2smiles:final container) and you need to optimize inference speed by controlling CPU core allocation. This is particularly relevant when GPU-based acceleration is unavailable or when inference speed is limited by CPU parallelism on multi-core systems.

When NOT to use

Input MGF file is already preprocessed or does not contain valid MS/MS spectral data; cpu_threads tuning assumes the inference bottleneck is CPU parallelism, not data quality or format errors.
The Mass2SMILES container is already built on GPU (e.g., delser292/mass2smiles:gpu or CUDA-enabled variant); cpu_threads applies only to TensorFlow-CPU builds.
System has fewer available CPU cores than the configured cpu_threads value; operating system thread scheduling may not achieve the desired parallelism.

Inputs

InferenceModel instance (from Mass2SMILES TensorFlow-CPU build)
MGF file (GNPS-style spectral data) with MS/MS fragment intensities and m/z values
cpu_threads integer parameter (number of CPU cores to allocate)

Outputs

Inference execution with specified thread count allocated
Thread usage logs or system observation confirming cpu_threads allocation
Verification report comparing configured vs. observed thread count

How to apply

Instantiate the InferenceModel class with an explicit cpu_threads parameter set to the desired number of CPU cores (e.g., cpu_threads=128). Execute a test inference run on sample MGF spectral data to verify the configured thread count is actually utilized during execution. Monitor system thread usage via logs or system tools to confirm that the InferenceModel respects the cpu_threads setting. The rationale is that TensorFlow-CPU inference can be accelerated by distributing work across multiple cores; the parameter directly controls this parallelism without requiring code changes to the inference pipeline itself.

Related tools

Mass2SMILES TensorFlow-CPU Docker container (Execution environment providing the InferenceModel class with TensorFlow-CPU backend; cpu_threads parameter is exposed at this level) — https://github.com/volvox292/mass2smiles
Docker (Container runtime for isolating and executing the Mass2SMILES inference with controlled resource allocation)
TensorFlow (CPU build) (Underlying deep learning framework that respects cpu_threads for thread pool configuration in inference execution)
Python (Language for instantiating InferenceModel, passing cpu_threads parameter, and logging/verifying thread usage)

Examples

from mass2smiles import InferenceModel; model = InferenceModel(cpu_threads=128); predictions = model.predict('sample.mgf')

Evaluation signals

Configured cpu_threads value is accepted by InferenceModel constructor without error
Inference execution on sample MGF data completes without crashing or hanging
System-level thread monitoring (e.g., htop, ps, or TensorFlow logs) shows actual thread count matches or approaches configured cpu_threads value
Inference latency is lower when cpu_threads is increased on multi-core systems, compared to single-threaded or under-provisioned runs
Verification report documents expected vs. observed thread count with ≤10% deviation tolerance

Limitations

TensorFlow-CPU inference speed is fundamentally slower than GPU-accelerated inference; cpu_threads tuning is a mitigation, not a replacement for GPU acceleration.
Actual thread utilization depends on system load, OS scheduling, and physical core availability; configured cpu_threads may not be fully realized if the system is resource-constrained.
CDDD model component in Mass2SMILES does not work on newer CUDA drivers, hence the TensorFlow-CPU build; this constraint is fixed at container build time and cannot be changed via cpu_threads alone.
Increasing cpu_threads beyond available physical cores may cause thread oversubscription and reduce performance rather than improve it.

Evidence

[other] cpu_threads parameter controls thread allocation in TensorFlow-CPU Mass2SMILES: "it can be speed up by changing the number of cores: e.g. InferenceModel(cpu_threads=128)"
[readme] TensorFlow-CPU build is used because CDDD does not work on newer CUDA drivers: "the cddd does not seem to work on newer cuda drivers, therefore it is build using tensorflow cpu"
[readme] Inference speed improvement is achieved via cpu_threads tuning on CPU build: "Using this setup inference speed is highly improved"
[other] InferenceModel class accepts cpu_threads as a configurable parameter: "The InferenceModel accepts a configurable cpu_threads parameter (e.g., cpu_threads=128) to adjust the number of CPU cores used during inference"