Setup and Quick Start

Getting started with hls4ml is very easy. There are several installation options available and once installed, it takes only a few lines of code to run your first synthesis.


The latest release of hls4ml can be installed with pip:

pip install hls4ml

If you want to use our profiling toolbox, you might need to install extra dependencies:

pip install hls4ml[profiling]

hls4ml is also available as a conda package in the conda-forge repository. To install, run:


Version of hls4ml available on conda-forge is outdated, we recommend installing with pip to get the latest version.

conda install -c conda-forge hls4ml

Development version

hls4ml is rapidly evolving and many experimental features and bugfixes are available on the development branch. Development version can be installed directly from git:

pip install git+


The hls4ml library depends on a number of Python packages and external tools for synthesis and simulation. Python dependencies are automatically managed by pip or conda.

  • TensorFlow (version 2.4 and newer) and QKeras are required by the Keras converter.

  • ONNX (version 1.4.0 and newer) is required by the ONNX converter.

  • PyTorch package is optional. If not installed, the PyTorch converter will not be available.

Running C simulation from Python requires a C++11-compatible compiler. On Linux, a GCC C++ compiler g++ is required. Any version from a recent Linux should work. On MacOS, the clang-based g++ is enough.

To run FPGA synthesis, installation of following tools is required:

  • Xilinx Vivado HLS 2018.2 to 2020.1 for synthesis for Xilinx FPGAs

    • Vitis HLS 2022.1 or newer is required for synthesis for Xilinx FPGAs using the experimental Vitis backend.

  • Intel Quartus 20.1 to 21.4 for the synthesis for Intel FPGAs

Quick Start

For basic concepts to understand the tool, please visit the Concepts chapter. Here we give line-by-line instructions to demonstrate the general workflow.

import hls4ml

# Fetch a keras model from our example repository
# This will download our example model to your working directory and return an example configuration file
config = hls4ml.utils.fetch_example_model('KERAS_3layer.json')

# You can print it to see some default parameters

# Convert it to a hls project
hls_model = hls4ml.converters.keras_to_hls(config)

# Print full list of example model if you want to explore more

After that, you can use Vivado HLS to synthesize the model:

# Use Vivado HLS to synthesize the model
# This might take several minutes

# Print out the report if you want'my-hls-test')

Done! You’ve built your first project using hls4ml! To learn more about our various API functionalities, check out our tutorials here.

If you want to configure your model further, check out our Configuration page.

Apart from our main API, we also support model conversion using a command line interface, check out our next section to find out more:

Getting started with hls4ml CLI (deprecated)

As an alternative to the recommended Python PI, the command-line interface is provided via the hls4ml command.

To follow this tutorial, you must first download our example-models repository:

git clone

Alternatively, you can clone the hls4ml repository with submodules

git clone --recurse-submodules

The model files, along with other configuration parameters, are defined in the .yml files. Further information about .yml files can be found in Configuration page.

In order to create an example HLS project, first go to example-models/ from the main directory:

cd example-models/

And use this command to translate a Keras model:

hls4ml convert -c keras-config.yml

This will create a new HLS project directory with an implementation of a model from the example-models/keras/ directory. To build the HLS project, do:

hls4ml build -p my-hls-test -a

This will create a Vivado HLS project with your model implementation!

NOTE: For the last step, you can alternatively do the following to build the HLS project:

cd my-hls-test
vivado_hls -f build_prj.tcl

vivado_hls can be controlled with:

vivado_hls -f build_prj.tcl "csim=1 synth=1 cosim=1 export=1 vsynth=1"

Setting the additional parameters from 1 to 0 disables that step, but disabling synth also disables cosim and export.

Further help

  • For further information about how to use hls4ml, do: hls4ml --help or hls4ml -h

  • If you need help for a particular command, hls4ml command -h will show help for the requested command

  • We provide a detailed documentation for each of the command in the Command Help section

Existing examples

  • Examples of model files and weights can be found in example_models directory.

  • Training codes and examples of resources needed to train the models can be found in the tutorial.


To uninstall hls4ml:

pip uninstall hls4ml

If installed with conda, remove the package with:

conda remove hls4ml