Skip to content

Latest commit

 

History

History
executable file
·
191 lines (134 loc) · 6.45 KB

README.md

File metadata and controls

executable file
·
191 lines (134 loc) · 6.45 KB
Raw

Build Machine Learning Models for DPU

This folder helps users recompile their own DPU models so they can be deployed on the board. The recompilation is needed if users want to retarget a different DPU configuration.

We provide a compile.sh script that helps users compile their own deployable models from Vitis AI model zoo. For advanced users, a jupyter notebook train_mnist_model.ipynb is provided to show how to train a ML model, quantize it, and compile it using Vitis AI tools.

Prerequisites

1. Repository

Make sure you have cloned this repository onto your host machine:

git clone --recursive --shallow-submodules https://github.com/Xilinx/DPU-PYNQ.git

2. Docker

If you have not installed docker on your host machine, please refer to the Vitis AI getting started page to install docker.

3. (optional) dpu.hwh

There are 3 cases for different boards:

  1. For ZCU104, our DPU configuration is consistent with the ZCU104 design of the Vitis AI release.
  2. For ZCU111, our DPU configuration is consistent with the ZCU102 design of the Vitis AI release.
  3. For Ultra96 or other boards, the Vitis AI release does not contain necessary files to recompile the DPU models directly.

For case 1 and 2, users can leverage existing files on the released docker image. No action is required.

For case 3 (Ultra96 or any other board), the DPU configuration file (Ultra96.dcf or <Board>.dcf) does not exist on the released docker image. We need to prepare the dpu.hwh so we can compile it into a DPU configuration file.

One way to get the dpu.hwh is to download it for Ultra96; remember to rename it to dpu.hwh if necessary.

Alternatively, if you have rebuilt the DPU hardware design by yourself, you should see 3 overlay files (dpu.hwh, dpu.bit, and dpu.xclbin) inside folder DPU-PYNQ/Boards/<Board>. You can take the dpu.hwh there as well.

Note: if you have changed the DPU configurations in your hardware design, you must prepare your new dpu.hwh.

4. (optional) Docker

If you want to run the jupyter notebook train_mnist_model.ipynb on the host machine, you will need to make sure you have an AVX2 and FMA compatible machine. To check that:

grep avx2 /proc/cpuinfo
grep fma /proc/cpuinfo

Both commands should return a list of available supported instruction sets. If one of the 2 commands returns nothing, your machine will have difficulties importing tensorflow package.

Also, on the docker image, you need to do the following before running any Jupyter notebook.

conda activate vitis-ai-tensorflow
yes | pip install matplotlib keras==2.2.5

Build DPU Models from Vitis AI Model Zoo

On your host machine, as mentioned in the previous section, if you are building models for Ultra96, you need to put the corresponding dpu.hwh file in folder DPU-PYNQ/host.

We can run the following commands now.

cd DPU-PYNQ/host
mkdir -p docker
cp -rf ../vitis-ai-git/docker_run.sh .
cp -rf ../vitis-ai-git/docker/PROMPT.txt docker
chmod u+x docker_run.sh
./docker_run.sh xilinx/vitis-ai-cpu:latest

The docker_run.sh will download a Vitis AI docker image after users accept the license agreements. It may take a long time to download since the image is about 8GB. After the download is complete, the docker_run.sh script will help you log onto that docker image.

The Vitis AI docker image gives users access to the Vitis AI utilities and compilation tools. If you have run docker_run.sh before, it will simply launch the docker image without downloading again.

Once you are in the docker environment, you can run the compile.sh script.

./compile.sh <Board> <model_name>

Here Board can be Ultra96, ZCU104, and ZCU111. For model_name, users can check the model information page as shown below.

The compile.sh eases the compilation of existing DPU models. Users can also adjust the script to compile their own models. The compile.sh does the following things.

(1) Adding Ultra96 support

We prepare the Ultra96.dcf using the dpu.hwh file.

(2) Downloading a model from model zoo

We download a deployable model as a zip file from Vitis AI Model Zoo. The contents of the extracted zip file (e.g., cf_resnet50_imagenet_224_224_7.7G) will contain multiple versions of the model:

  • floating point frozen graph (under float)
  • quantized evaluation model (under quantized)
  • quantized deployment model (under quantized)

In our case we only need the following files inside the quantized directory: (1) deploy.caffemodel and (2) deploy.prototxt.

(3) Compiling the model

We will compile the model into a *.elf file. If everything is successful, you should see a screen as shown below.

A new model file (e.g. dpu_resnet50_0.elf) should appear in your working directory; this is the model file that can be deployed on the board.

After you are done with the docker environment, type in:

exit

to exit.

Train Your Own DPU Models from Scratch

Instead of using the deployable models from the Vitis AI model zoo, advanced users may even train their own machine learning models. We will show one example in train_mnist_model.ipynb.

Once you are in the docker environment, if you have not done the following, make sure you do it before running the notebook:

conda activate vitis-ai-tensorflow
yes | pip install matplotlib keras==2.2.5

Then launch jupyter notebook and run the train_mnist_model.ipynb step-by-step.

jupyter notebook --ip=0.0.0.0 --port=8080

For more information such as training your own model, please refer to the Vitis AI user guide and Vitis AI Tutorials.

References

Copyright (C) 2020 Xilinx, Inc