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Running a Compiled MATLAB Training Script on Databricks

1. Overview

It is technically feasible to take a MATLAB script that implements a deep neural network training algorithm, compile it into a standalone application using MATLAB Compiler, and execute that compiled application on a Databricks cluster. The workflow relies on MATLAB Runtime for execution in the Databricks environment, and must be built with specific constraints in mind.

MathWorks and Databricks document this as part of the supported integration for deploying MATLAB/Simulink algorithms to Databricks clusters for execution. While common use cases focus on inference, training is also supported for single-node execution when the training algorithm is compatible with MATLAB Compiler.

2. Feasibility and Requirements

2.1 Feasibility

  • Supported: MATLAB Compiler can package scripts and functions that perform deep learning training (using trainnet, trainNetwork, or custom loops) into standalone executables. These run in MATLAB Runtime without needing a MATLAB license on the target system.
  • Databricks Execution: Databricks can execute these Linux-native compiled artifacts on a cluster, provided MATLAB Runtime is installed or included in a custom container image.

2.2 Platform Requirements

  • OS Specific: Compiler outputs are platform-specific; a binary compiled on Windows will not run on Linux. For Databricks, the executable must be built on Linux.
  • MATLAB Runtime: The exact MATLAB Runtime version matching your MATLAB release must be available on the Databricks nodes. This can be installed manually or baked into a custom Docker image.
  • GPU Support (optional): For GPU-accelerated training, the Databricks node must have a supported NVIDIA driver and CUDA version compatible with the MATLAB Runtime for your MATLAB release. [Inference]

3. Limitations

3.1 Execution Model

  • Single-node only: Out-of-the-box, compiled MATLAB applications run on a single Databricks node. Multi-node or distributed training requires MATLAB Parallel Server and additional configuration, which is not part of the standard Databricks integration.
  • No interactive UIs: Training progress plots and interactive GUIs are not supported in headless execution environments like Databricks jobs. Logging must be redirected to files or stdout. [Inference]

3.2 Data Access

  • Integration with Databricks storage: Input data must be loaded from DBFS, object storage, or external sources accessible to the Databricks cluster. Reading directly from Spark DataFrames is possible only if implemented using MATLAB-Spark integration or intermediate file export.

4.1 Refactor MATLAB Script

  1. Encapsulate training code into a function, e.g., train_model(dataPath, outputPath).
  2. Implement data loading from DBFS or external storage paths accessible on Databricks.
  3. Use noninteractive training routines with file or console logging.
  4. Save checkpoints and final model artifacts to DBFS or a configured output path.

4.2 Build the Standalone Application

  1. Set up a Linux environment (VM, physical machine, or Docker container) with MATLAB and MATLAB Compiler installed.
  2. Run mcc -m train_model.m to create a standalone Linux executable.
  3. Package the executable with the MATLAB Runtime installer for the same release.

4.3 Deploy to Databricks

  1. Prepare a custom Databricks container image with:

  2. Compiled MATLAB application files.

  3. Installed MATLAB Runtime.
  4. Any required GPU drivers and CUDA libraries.
  5. Upload the image to a container registry accessible by Databricks.
  6. Create a Databricks cluster using this custom image.
  7. Launch the compiled training executable as part of a Databricks job or notebook command.

5. References

  • MathWorks documentation on compiling and deploying deep learning training scripts with MATLAB Compiler
  • MathWorks–Databricks integration for deploying MATLAB and Simulink algorithms
  • MATLAB Compiler platform-specific executable behavior
  • Creating custom container images for MATLAB Runtime deployment on Databricks
  • Databricks data access from MATLAB workflows

  • Parallel and distributed execution requirements for MATLAB in cluster environments

  • Feasibility

  • MATLAB Compiler can build a standalone executable that performs neural network training, and that executable runs on MATLAB Runtime (no MATLAB license on the cluster). MathWorks explicitly documents deploying functions that train neural networks via MATLAB Compiler. (MathWorks)

  • Databricks supports running compiled MATLAB/Simulink algorithms on clusters using MATLAB Runtime; this is the documented MathWorks-Databricks integration path. (MathWorks, Databricks)

  • Platform/build requirements

  • Build a Linux artifact on Linux; Compiler outputs are OS-specific. Use a Linux VM or container to compile, then run the binary on Databricks. (MathWorks)

  • You can base your Databricks cluster on a custom container image that includes MATLAB Runtime; MathWorks-linked examples exist. (GitHub)

  • What works in practice

  • Training APIs: Deep Learning Toolbox training via command-line functions (e.g., trainnet/trainNetwork or custom loops) can be used in compiled apps, per MathWorks’ guidance on deploying training. (MathWorks)

  • Single-node execution: Runs as a normal Linux process on a Databricks node. GPU training is possible if the node has a supported NVIDIA driver/CUDA stack that matches the MATLAB Runtime version. [Inference]

  • Important limitations

  • Cross-OS: You cannot compile on Windows and run that binary on Linux; compile on Linux for Databricks. (MathWorks)

  • Interactive UIs/plots: Training progress windows and interactive apps are not appropriate in headless Runtime jobs. [Inference]

  • Multi-node/distributed training: Parallel execution in deployed apps is limited and requires Parallel Computing Toolbox and, for cluster-scale, MATLAB Parallel Server with a valid cluster profile. This is nontrivial on Databricks and not the documented path; expect single-node training unless you engineer and license a PCT/MPS setup. (MathWorks)

  • Minimal workflow

  • Refactor your training script into a function that loads data from DBFS/Spark (e.g., via files or pre-materialized parquet/CSV), performs training with trainnet or a custom loop, saves checkpoints/artifacts to DBFS, and exits. (MathWorks)

  • On a Linux machine/container with MATLAB + Compiler: build a standalone app. Deploy the resulting files plus the matching MATLAB Runtime to a Databricks cluster (ideally via a custom Docker image). Launch from a notebook or a job. (MathWorks)
  • Possible: compile your MATLAB training code and run it on Databricks under MATLAB Runtime. Expect single-node, headless training; plan for Linux-native build, careful Runtime/GPU matching, and noninteractive logging/checkpointing. (MathWorks)