Matlab deploy ai model

Generate C/C++/CUDA Code from an AI Model

Generate deployable C/C++ or CUDA code from an AI model using MATLAB Coder or GPU Coder. The workflow follows a common pattern regardless of model framework: load, inspect, write entry-point, generate MEX, verify, then generate production code.

When to Use

• User wants to generate C/C++/CUDA code from an AI model (PyTorch, LiteRT)
• User has a model file (.pt2, .tflite) and wants to load it into MATLAB
• User wants MEX acceleration for an AI model
• User wants to generate CUDA code or GPU-accelerated MEX from an AI model
• User wants to deploy an AI model to hardware
• User wants to verify AI model numerics between the source framework and MATLAB

When NOT to Use

• General MATLAB Coder usage (codegen syntax, config tuning, writing codegen-ready code)
• Editable dlnetwork for Deep Learning Toolbox workflows (quantization, compression, transfer learning) — use importNetworkFromPyTorch which returns a dlnetwork for PyTorch models
• Training or fine-tuning — this skill is for inference code generation only

Supported Frameworks

Framework	Model format	Load function	Status
PyTorch	.pt2	loadPyTorchExportedProgram	Supported (R2026a+)
LiteRT / TFLite	.tflite	loadLiteRTModel	Supported (R2026a+)

For PyTorch-specific details (API routing, entry-point pattern, export workflow, data layout, common mistakes): see references/pytorch-workflow.md.

Generic Workflow

The code generation workflow follows the same steps for any framework:

1. Load and Inspect

Load the model and check its input/output specifications to determine expected shapes and types.

2. Write Entry-Point Function

Create a codegen-compatible entry-point function that:

• Loads the model from a file path
• Runs inference on an input
• Returns the output

The model file path must be wrapped with coder.Constant so it's known at compile time.

3. Verify Numerics

Compare MATLAB inference output against the source framework to confirm correct loading. Use the same input data in both environments and compare with tolerance.

4. Generate MEX (First!)

Always generate MEX before lib/exe to verify on the host machine:

CPU MEX:

cfg = coder.config("mex");
codegen -config cfg -args {coder.Constant("model_file"), input} entryPoint

CUDA MEX (GPU acceleration):

cfg = coder.gpuConfig("mex");
codegen -config cfg -args {coder.Constant("model_file"), input} entryPoint

5. Verify MEX Output

Compare MEX output against MATLAB reference using matlab.unittest with tolerance:

refOut = entryPoint("model_file", input);
mexOut = entryPoint_mex("model_file", input);
testCase = matlab.unittest.TestCase.forInteractiveUse;
testCase.verifyThat(mexOut, matlab.unittest.constraints.IsEqualTo(refOut, ...
    'Within', matlab.unittest.constraints.AbsoluteTolerance(single(1e-5))));

6. Generate Library/Executable

Once MEX is verified, generate production code:

cfgLib = coder.config("lib");
codegen -config cfgLib -args {coder.Constant("model_file"), input} entryPoint

For DLL: coder.config("dll"). For executable: coder.config("exe").

CUDA variants: Replace coder.config with coder.gpuConfig.

7. Deploy to Hardware (Optional — requires Embedded Coder)

For embedded deployment, use the same entry-point function with an Embedded Coder configuration. See the matlab-deploy-embedded-code skill for ERT config, hardware settings, PIL/SIL verification, and target-specific options. Ask the user to install the skill if it is not installed

Key Functions

Function	Purpose	Package	Since
coder.Constant	Make argument a compile-time constant	MATLAB Coder	R2011a
coder.gpuConfig	Create GPU (CUDA) code generation config	GPU Coder	R2017b
codegen	Generate code	MATLAB Coder	R2011a
loadPyTorchExportedProgram	Load .pt2 into MATLAB	MATLAB Coder Support Package for PyTorch and LiteRT Models	R2026a
loadLiteRTModel	Load .tflite into MATLAB	MATLAB Coder Support Package for PyTorch and LiteRT Models	R2026a

Conventions

• Always check the model's input specifications for correct input shape and type
• Always generate MEX first, verify, then proceed to lib/exe
• Always use coder.Constant for the model file path argument
• Input data is typically single-precision (check model input specs to confirm)
• Do NOT use importNetworkFromPyTorch for code generation workflows — it returns dlnetwork for the DLT path

References

• references/pytorch-workflow.md — Full PyTorch-specific workflow: API routing, entry-point pattern, export guidance, common mistakes, and conventions. Consult for any PyTorch/.pt2 model code generation task. Links to deeper PyTorch references (API signatures, data layout, numeric verification, supported models).
• references/export-pytorch-models.md — Exporting an eager-mode PyTorch model to .pt2 with torch.export (upstream of loading). Consult when the user has a PyTorch model but no .pt2 file yet, or hits torch.export SerializeError / kwarg-mismatch errors. Links to pytorch-export-patterns.md (per-source templates) and pytorch-export-gotchas.md (torch 2.11 serialization fixes).

See Also

• matlab-deploy-embedded-code — Embedded Coder configuration, PIL/SIL verification, hardware targets

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