#############################
Quick Start Guide for VCK5000
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The AMD **DPUCVDX8H** for Versal |trade| VCK5000 is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support VCK5000.
.. image:: ../reference/images/vck5000.png
:width: 1300
*************
Prerequisites
*************
System Requirements
===================
- Confirm that your development machine meets the minimum :doc:`Host System Requirements <../reference/system_requirements>`.
- Confirm that you have at least **100GB** of free space in the target partition.
Applicable Targets
==================
- This quickstart is applicable to the `VCK5000 <https://www.xilinx.com/applications/data-center/vck5000.html>`__
* For additional information on the VCK5000 see the `VCK5000 User Guide <https://docs.xilinx.com/r/en-US/ug1531-vck5000-install/Card-Interfaces-and-Details>`__
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Quickstart
**********
As a first step, it is recommended that you select the appropriate DPU configuration for your application:
=== ================ ===============================
No. Accelerator Card DPU IP
=== ================ ===============================
1 VCK5000-PROD DPUCVDX8H_4pe_miscdwc
2 VCK5000-PROD DPUCVDX8H_ISA1_F2W4_6PE_aieDWC
3 VCK5000-PROD DPUCVDX8H_ISA1_F2W4_6PE_aieMISC
4 VCK5000-PROD DPUCVDX8H_ISA1_F2W2_8PE
=== ================ ===============================
This selection depends on several factors:
- `misc` is selected if the models to be deployed leverage pooling and element-wise layers
- `dwc` is selected if the models to be deployed leverage Depthwise Convolution (eg. MobileNets)
- The number of `pe` or "processing engines" determines both performance and programmable logic resource utilization
If you are just getting started and are uncertain which to choose, you may wish to start with the `DPUCVDX8H_4pe_miscdwc` as it provides the most extensive operator support. Please refer to `this user guide <https://docs.xilinx.com/r/en-US/pg403-dpucvdx8h/Configuration-Options>`__ for more extensive details regarding selection.
VCK5000-PROD Card Setup
=======================
A script is provided to drive the VCK5000-PROD card setup process.
.. note:: You should run this script on the host machine, OUTSIDE of the Docker container. After the script has executed successfully, manually reboot the host server once. For data center DPUs, Vitis |trade| AI 3.0 specifically leverages the 2022.2 versions of the Vitis tools, VCK5000 platform, XRT and XRM.
This script will detect the operating system of the host, and will download and install the appropriate packages for that operating system. Please refer to :doc:`Host System Requirements <../reference/system_requirements>` prior to proceeding.
Execute this script as follows:
.. code-block::
[Host] $ cd <Vitis-AI install path>/Vitis-AI/board_setup/vck5000
[Host] $ source ./install.sh
The following installation steps are performed by this script:
1. XRT Installation. The `Xilinx RunTime (XRT) <https://github.com/Xilinx/XRT>`__ is a combination of userspace and kernel driver components supporting PCIe accelerator cards such as the VCK5000.
2. XRM Installation. The `Xilinx Resource Manager (XRM) <https://github.com/Xilinx/XRM/>`__ manages and controls FPGA resources on the host. It is required by the runtime.
3. Installation of the VCK5000-PROD platform.
4. Installation of the DPU xclbin for the VCK5000-PROD platform.
After the script is executed successfully, use the XRT `xbutil` command to check that the installation was successful. The result should contain the correct information for System Configuration, XRT and Devices present.
.. code-block::
[Host] $ /opt/xilinx/xrt/bin/xbutil examine
.. note:: Vitis AI 3.0 requires the use of a VCK5000-PROD card. Support for the pre-production VCK5000-ES1 card is not available in this release. If you do not have a production release card, you must use `Vitis AI 1.4.1 <https://github.com/Xilinx/Vitis-AI/tree/v1.4.1>`__.
Clone the Vitis AI Repository
=============================
.. code-block:: Bash
[Host] $ git clone https://github.com/Xilinx/Vitis-AI
Install Docker
==============
- Make sure that the Docker engine is installed according to the official Docker `documentation <https://docs.docker.com/engine/install/>`__.
- The Docker daemon always runs as the root user. Non-root users must be `added <https://docs.docker.com/engine/install/linux-postinstall/>`__ to the docker group. Do this now.
Verify Docker Installation
==========================
- Perform a quick and simple test of your Docker installation by executing the following command. This command will download a test image from Docker Hub and run it in a container. When the container runs successfully, it prints a "Hello World" message and exits.
.. code-block:: Bash
[Host] $ docker run hello-world
- Finally, verify that the version of Docker that you have installed meets the minimum :doc:`Host System Requirements <../reference/system_requirements>` by running the following command
.. code-block:: Bash
[Host] $ docker --version
Pull Vitis AI Docker
====================
In order to simplify this quickstart tutorial, we will utilize the Vitis-AI PyTorch CPU Docker to assess pre-built Vitis-AI examples, and subsequently perform quantization and compilation of our own model. The CPU docker image is generic, does not require the user to build the container, and has no specific GPU enablement requirements. More advanced users can optionally skip this step and jump to the :doc:`Full Install Instructions <../install/install>` but we would recommend that new users start with this simpler first step.
Pull and start the latest Vitis AI Docker using the following commands:
.. code-block:: Bash
[Host] $ docker pull xilinx/vitis-ai-pytorch-cpu:latest
[Host] $ ./docker_run.sh xilinx/vitis-ai-pytorch-cpu:latest
Docker Container Environment Variable Setup
-------------------------------------------
From inside the docker container, execute one of the following commands to set the required environment variables for the DPU. Note that the chosen xclbin file must be in the ``/opt/xilinx/overlaybins`` directory prior to execution. There are four xclbin files to choose from. Select the xclbin that matches your chosen DPU configuration.
- For the 4PE 350MHz configuration with pooling, elementwise and depthwise convolution support:
.. code-block::
[Docker] $ source /workspace/board_setup/vck5000/setup.sh DPUCVDX8H_4pe_miscdwc
- For the 6PE 350MHz configuration with depthwise convolution support:
.. code-block::
[Docker] $ source /workspace/board_setup/vck5000/setup.sh DPUCVDX8H_6pe_dwc
- For the 6PE 350MHz configuration with pooling and elementwise support:
.. code-block::
[Docker] $ source /workspace/board_setup/vck5000/setup.sh DPUCVDX8H_6PE_misc
- For the 8PE 350MHz base configuration:
.. code-block::
[Docker] $ source /workspace/board_setup/vck5000/setup.sh DPUCVDX8H_8pe_normal
.. note:: You must execute this setup script each time to re-enter Docker.
Run the Vitis AI Examples
=========================
1. Download `vitis_ai_runtime_r3.0.0_image_video.tar.gz <https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_runtime_r3.0.0_image_video.tar.gz>`__ to your host.
2. Extract the ``vitis_ai_runtime_r3.0.0_image_video.tar.gz`` package.
.. code-block:: Bash
[Docker] $ cd ~
[Docker] $ tar -xzvf vitis_ai_runtime_r3.0.0_image_video.tar.gz -C Vitis-AI/examples/vai_runtime
3. Navigate to the example directory. Take ``resnet50`` as an example.
.. code-block:: Bash
[Docker] $ cd /worksapce/examples/vai_runtime/resnet50
4. Run the example.
.. code-block:: Bash
[Docker] $ ./resnet50 /usr/share/vitis_ai_library/models/resnet50/resnet50.xmodel
5. The console should reflect the following output:
.. code-block:: Bash
Image : 001.jpg
top[0] prob = 0.982662 name = brain coral
top[1] prob = 0.008502 name = coral reef
top[2] prob = 0.006621 name = jackfruit, jak, jack
top[3] prob = 0.000543 name = puffer, pufferfish, blowfish, globefish
top[4] prob = 0.000330 name = eel
These results reflect the classification of a single test image located in the ``~/Vitis-AI/examples/vai_library/images`` directory.
****************
PyTorch Tutorial
****************
This tutorial assumes that Vitis AI has been installed and that the board has been configured as explained in the installation instructions above. For additional information on the Vitis AI Quantizer, Optimizer, or Compiler, please refer to the Vitis AI User Guide.
Quantizing the Model
====================
Quantization reduces the precision of network weights and activations to optimize memory usage and computational efficiency while maintaining acceptable levels of accuracy. Inference is computationally expensive and requires high memory bandwidths to satisfy the
low-latency and high-throughput requirements of Edge applications. Quantization and channel pruning techniques are employed to address these issues while achieving high performance and high energy efficiency with little degradation in accuracy. The Vitis AI Quantizer takes a
floating-point model as an input and performs pre-processing (folds batchnorms and removes nodes not required for inference), and finally quantizes the weights/biases and activations to the given bit width.
1. Navigate to the cloned Vitis-AI directory and create a new workspace for your project. Here you will store the test dataset, models, and python scripts required for quantization.
.. code-block:: Bash
[Host] $ cd ~/Vitis-AI
[Host] $ mkdir -p resnet18/model
2. Download the `ImageNet 1000 (mini) <https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000/download?datasetVersionNumber=1>`__ dataset from Kaggle. This dataset is subset of the ILSVRC 2012-2017 dataset and comprises 1000 object classes, and contains 1,281,167 training, 50,000 validation, and 100,000 test images. You will need to create a Kaggle account to access this dataset. Move the downloaded `archive.zip` file into the created `/Vitis-AI/resnet18` folder and unzip the dataset.
.. code-block:: Bash
[Host] $ cd resnet18
[Host] $ unzip archive.zip
- Your workspace directory should reflect the following:
::
├── archive.zip
│
├── model
│
└── imagenet-mini
├── train # Training images folder. Will not be used in this tutorial.
│ └─── n01440764 # Class folders to group images.
└── val # Validation images that will be used for quantization and evaluation of the floating point model.
└─── n01440764
3. Navigate to the Vitis-AI directory and execute the following command to start Docker.
.. code-block:: Bash
[Host] $ cd ..
[Host] ./docker_run.sh xilinx/vitis-ai-pytorch-cpu:latest
* Note that when you start Docker appropriate as shown above, your ``/workspace`` folder will correspond to ``/Vitis-AI`` and your initial path in Docker will be ``/workspace``. If you inspect ``docker_run.sh`` you can see that the -v option is leveraged which links the Docker file system to your Host file system. Verify that you see the created ``/resnet18`` subfolder in your workspace:
.. code-block:: Bash
[Docker] $ ls
4. Activate the vitis-ai-pytorch conda enviornment.
.. code-block:: Bash
[Docker] $ conda activate vitis-ai-pytorch
5. Download the pre-trained resnet18 model from PyTorch to the docker environment and store it in the ``model`` folder . This is the floating point (FP32) model that will be quantized to INT8 precision for deployment on the target.
.. code-block:: Bash
[Docker] $ cd resnet18/model
[Docker] $ wget https://download.pytorch.org/models/resnet18-5c106cde.pth -O resnet18.pth
.. note:: The `Vitis AI Model Zoo <../workflow-model-zoo.html>`__ also provides optimized deep learning models to speed up the deployment of deep learning inference on adaptable AMD platforms. For this tutorial we have chosen to use an open-source PyTorch model to showcase that models from the community can also be deployed.
6. Copy the example Vitis AI ResNet18 quantization script to your workspace. This script contains the Quantizer API calls that will be executed in order to quantize the model.
.. code-block:: Bash
[Docker] $ cd ..
[Docker] $ cp ../src/vai_quantizer/vai_q_pytorch/example/resnet18_quant.py ./
* Your ``workspace/resnet18`` directory should reflect the following:
::
├── archive.zip
│
├── model
│ └── resnet18.pth # ResNet18 floating point model downloaded from PyTorch.
│
├── imagenet-mini
│ ├── train # Training images folder. Will not be used in this tutorial.
│ │ └─── n01440764 # Class folders to group images.
│ └── val # Validation images that will be used for quantization and evaluation of the floating point model.
│ └─── n01440764
│
└── resnet18_quant.py # Quantization python script.
* Inspect ``resnet18_quant.py``. Observe the parser arguments that can be passed to the script via command line switches ``subset_len`` ``quant_mode`` ``data_dir`` and ``model_dir``. We will set the ``data_dir`` and ``model_dir`` arguments to align with our directory structure. If you wish to avoid extraneous typing and are manually entering these commands, you can simply edit the script to suit your use case.
.. code-block:: Bash
[Docker] $ vim resnet18_quant.py
* Use the sequence ``<esc> :q!`` to exit vim without saving.
7. Run the command below to evaluate the accuracy of the floating point model.
.. code-block:: Bash
[Docker] $ python resnet18_quant.py --quant_mode float --data_dir imagenet-mini --model_dir model
* You should observe that the accuracy reported is similar to ``top-1 / top-5 accuracy: 69.9975 / 88.7586``
8. Next, let's run the Model Inspector to confirm that this model should be compatible with the target DPU architecture. Replace ``DPUCVDX8H_4pe_miscdwc`` with the appropriate architecture based on your original DPU architecture selection.
.. code-block:: Bash
[Docker] $ python resnet18_quant.py --quant_mode float --inspect --target DPUCVDX8H_4pe_miscdwc --model_dir model
9. Run the command below to start quantization. Generally, 100-1000 images are required for quantization and the number of iterations can be controlled through the the ``subset_len`` data loading argument. In this case, 200 images are forward propagated through the network, and these images are chosen randomly from the validation image set. Note that the displayed loss and accuracy that are output from this process are not representative of final model accuracy.
.. code-block:: Bash
[Docker] $ python resnet18_quant.py --quant_mode calib --data_dir imagenet-mini --model_dir model --subset_len 200
* On most host machines this command should complete in less than 1 minute even with the CPU-only Docker. If you leverage the CUDA or ROCm Dockers on a compatible machine, the Quantization process will be accelerated considerably. Let's take a look at the output:
.. code-block:: Bash
[Docker] $ cd quantize_result
[Docker] $ ls
* If the command ran successfully, the output directory ``quantize_result`` will be generated, containing two important files:
-``ResNet.py``
The quantized vai_q_pytorch format model.
-``Quant_info.json``
Quantization steps of tensors. Retain this file for evaluation of the quantized model./
10. To evaluate the accuracy of the quantized model, return to the ``/resnet18`` directory run the following commands. Note that on CPU-only host machines this command will take some time to complete (~20 minutes). If you are in a hurry, you can skip this step and move to the next.
.. code-block:: Bash
[Docker] $ cd ..
[Docker] $ python resnet18_quant.py --model_dir model --data_dir imagenet-mini --quant_mode test
You should observe that the accuracy reported will be similar to ``top-1 / top-5 accuracy: 69.1308 / 88.7076``. The net accuracy loss due to quantization is less than 1%.
11. To generate the quantized ``.xmodel`` file that will subsequently be compiled for the DPU, run the following command with ``batch_size`` and ``subset_len`` arguments set to `1`. For model export, both of these parameters should be set `1` as multiple iterations are not required.
.. code-block:: Bash
[Docker] $ python resnet18_quant.py --quant_mode test --subset_len 1 --batch_size=1 --model_dir model --data_dir imagenet-mini --deploy
The resultant model `resnet18_pt.xmodel` can now be found in the `resnet18/resnet18_pt` folder.
Compile the model
=================
The Vitis AI Compiler compiles the graph operators as a set of micro-coded instructions that are executed by the DPU. In this step, we will compile the ResNet18 model that we quantized in the previous step.
1. The compiler takes the quantized ``INT8.xmodel`` and generates the deployable ``DPU.xmodel`` by running the command below. Note that you must modify the command to specify the appropriate ``arch.json`` file for your target. For VCK5000 targets, these are located in the folder ``/opt/vitis_ai/compiler/arch/DPUCVDX8H`` inside the Docker container.
.. code-block:: Bash
[Docker] $ cd /workspace/resnet18
[Docker] $ vai_c_xir -x quantize_result/ResNet_int.xmodel -a /opt/vitis_ai/compiler/arch/DPUCVDX8H/VCK5000<XXX>/arch.json -o resnet18_pt -n resnet18_pt
- If compilation is successful, the ``resnet18_pt.xmodel`` file should be generated according to the specified DPU architecture.
2. Create a new file with your text editor of choice and name the file ``resnet18_pt.prototxt``. Copy and paste the following lines of code:
.. code-block:: Bash
model {
name : "resnet18_pt"
kernel {
name: "resnet18_pt_0"
mean: 103.53
mean: 116.28
mean: 123.675
scale: 0.017429
scale: 0.017507
scale: 0.01712475
}
model_type : CLASSIFICATION
classification_param {
top_k : 5
test_accuracy : false
preprocess_type : VGG_PREPROCESS
}
}
- The ``.prototxt`` file is a Vitis |trade| AI configuration file that facilitates the uniform configuration management of model parameters. Please refer to the Vitis AI User Guide to learn more.
- We can now deploy the quantized and compiled model on the VCK5000 accelerator card.
Model Deployment
================
1. Copy the ``resnet18_pt`` folder into the ``/usr/share/vitis_ai_library/models/`` directory. This will locate your compiled model in the default Vitis AI Library example model directory, alongside the other Vitis AI example models. Our purpose in doing this is to simplify the commands that follow, in which we will execute the Vitis AI Library samples with our model.
2. The `vitis_ai_library_r3.0.0_images.tar.gz <https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_library_r3.0.0_images.tar.gz>`__ and `vitis_ai_library_r3.0.0_video.tar.gz <https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_library_r3.0.0_video.tar.gz>`__ packages
contain test images and videos that can be leveraged to evaluate our quantized model and other pre-built Vitis-AI Library examples.
a. Download the packages.
.. code-block:: Bash
[Docker] $ cd /workspace
[Docker] $ wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_library_r3.0.0_images.tar.gz -O vitis_ai_library_r3.0.0_images.tar.gz
[Docker] $ wget https://www.xilinx.com/bin/public/openDownload?filename=vitis_ai_library_r3.0.0_video.tar.gz -O vitis_ai_library_r3.0.0_video.tar.gz
b. Untar the files.
.. code-block:: Bash
[Docker] $ tar -xzvf vitis_ai_library_r3.0.0_images.tar.gz -C examples/vai_library/
[Docker] $ tar -xzvf vitis_ai_library_r3.0.0_video.tar.gz -C examples/vai_library/
3. Enter the directory of the sample and then compile it.
.. code-block:: Bash
[Docker] $ cd /workspace/examples/vai_library/samples/classification
[Docker] $ ./build.sh
4. Execute the single-image test application.
.. code-block:: Bash
[Docker] $ ./test_jpeg_classification resnet18_pt /workspace/examples/vai_library/samples/classification/images/002.jpg
If you wish to do so, you can review the ``result.jpg file``. OpenCV function calls have been used to overlay the predictions.
5. To run the video example, run the following command:
.. code-block:: Bash
[Docker] $ ./test_video_classification resnet18_pt /workspace/examples/vai_library/seg_and_pose_detect/pose_960_540.avi -t 8
7. The output should be as follows:
.. image:: ../reference/images/Wallace.png
:width: 1300
- Congratulations! You have successfully quantized, compiled, and deployed a pre-trained model onto the VCK5000 accelerator card.
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.. |reg| unicode:: U+000AE .. REGISTERED TRADEMARK SIGN
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