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Report commands

The cfsai utility can generate two types of reports to help you prepare machine learning models for deployment:

  • Compatibility Analyzer report (cfsai compat) — Validates operator support, memory constraints, and data type compatibility on the selected target.
  • Resource Profiling report (cfsai profile) — Estimates memory usage, compute cycles, latency, and optimization opportunities.

These reports do not modify your model or block code generation. Instead, they highlight potential issues and provide data to guide optimizations. You can re-run the tools after making changes to confirm that your model is both compatible and efficient on the target hardware.

Note

This is an early version of the reporting feature, and it may evolve in future releases. Currently, only TFLM models are supported. CNN accelerator models on MAX78002, which use PyTorch, are not supported for reporting.

Compatibility command

The cfsai compat command checks whether a machine learning model can run on a supported processor. To resolve issues, you may need to replace unsupported operators, apply memory optimizations, or convert unsupported data types. After applying changes, re-run the command to confirm compatibility.

Tip

The analyzer logic is located in analyze_compatibility.py. This script can be adapted or extended to suit your own use cases.

To run the cfsai compat command, you must provide:

  • The model path with --model (or -m)
  • The target string with --target (or -t) 
cfsai compat --model <path/to/model_file> --target <soc>?([<package>]).<core>?(.<accelerator>)

Optional compat outputs

Flag Description
--json-file Path to a file where results are written in JSON format. Useful for parsing.

Example

cfsai compat  --model m4/resnet_float32.tflite --target MAX32690.CM4 --json-file m4/output.json

Tip

For details on interpreting the report, see Compatibility Report.

Profile command

The cfsai profile command estimates memory usage, hardware performance, per-layer efficiency, and optimization opportunities for a model on the target device. Unlike the cfsai compat command (which flags unsupported features), it focuses on performance characteristics and optimization opportunities. You can apply the suggested optimizations to improve efficiency and better fit the model to your hardware.

Tip

The cfsai profile logic is located in profile_resources.py. This script can be adapted or extended to suit your own use cases.

To run the cfsai profile command, you must provide:

  • The model path with --model (or -m)
  • The target string with --target (or -t) 
cfsai profile --model <path/to/model_file> --target <soc>?([<package>]).<core>?(.<accelerator>)

Optional profile outputs

Flag Description
--json-file Path to a file where results are written in JSON format. Useful for parsing.
--text-file Path to a file where results are written in plain text.

Example

cfsai profile  --model m4/customized_resnet_float32.tflite --target MAX32690.CM4 --json-file m4/output.json

Tip

For details on interpreting the report, see Resource Profiling Report