UIF1.1

Signed-off-by: amuralee <[email protected]>

LICENSE

@@ -1,6 +1,6 @@
 Apache License
 
-   * Copyright (c) 2018-2022 Advanced Micro Devices, Inc. All Rights Reserved.
+   * Copyright (c) 2018 Advanced Micro Devices, Inc. All Rights Reserved.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

docs/1_installation/installation.md

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+<table width="100%">
+  <tr width="100%">
+    <td align="center"><img src="https://raw.githubusercontent.com/Xilinx/Image-Collateral/main/xilinx-logo.png" width="30%"/><h1>Unified Inference Frontend (UIF) 1.1 User Guide </h1>
+    </td>
+ </tr>
+ <tr>
+ <td align="center"><h1>Step 1.0: Installation</h1>
+ </td>
+ </tr>
+</table>
+
+
+# Table of Contents
+
+- [1.1: Pull PyTorch TensorFlow Docker (for GPU Users)](#11-pull-pytorchtensorflow-docker-for-gpu-users)
+  - [1.1.1: Set up Host with ROCm/GPU](#111-set-up-host-with-rocmgpu)
+  - [1.1.2: Install Docker](#112-install-docker)
+  - [1.1.3: Pull a UIF Docker Image](#113-pull-a-uif-docker-image)
+- [1.2: Pull PyTorch/TensorFlow Docker (for FPGA Users)](#12-pull-pytorchtensorflow-docker-for-fpga-users)
+   - [1.2.1: Install Docker](#121-install-docker)  
+   - [1.2.2: Pull a Vitis AI Docker Image](#122-pull-a-vitis-ai-docker-image)
+ - [1.3: Install ZenDNN Package (for CPU Users)](#13-install-zendnn-package-for-cpu-users)
+    - [1.3.1: TensorFlow+ZenDNN](#131-tensorflowzendnn)
+    - [1.3.2: PyTorch+ZenDNN](#132-pytorchzendnn)
+ - [1.4: Get the Inference Server Docker Image (for Model Serving)](#14-get-the-inference-server-docker-image-for-model-serving)
+    - [1.4.1: Use a Prebuilt Docker Image](#141-use-a-prebuilt-docker-image)
+    - [1.4.2: Build an Inference Server Docker Image](#142-build-an-inference-server-docker-image)
+
+
+  _Click [here](/README.md#implementing-uif-11) to go back to the UIF User Guide home page._
+
+
+# 1.1: Pull PyTorch/TensorFlow Docker (for GPU Users)
+
+## 1.1.1: Set up Host with ROCm/GPU 
+
+### ROCm Userspace and Kernel Driver Compatibility
+
+ROCm™ Userspace API is guaranteed to be compatible with specific older and newer ROCm base driver installations. 
+
+**Note**: The ROCm userspace is delivered using a Docker® container based on ROCm v5.4. Consult this matrix when running a Docker container with a different version of ROCm than installed on the host.
+
+**Legend**
+
+* Green: Shows compatibility between the same versions.
+
+* Blue: Shows compatibility tested versions. 
+
+* Gray: Not tested. 
+
+**Note:** The color in the figures may vary slightly.
+
+![](./images/5.4Matrix1.png)
+
+Kernel space compatibility meets the following condition:
+
+* Userspace works with -/+ 2 releases of kernel space
+
+### Framework Compatibility
+
+The UIF v1.1 release supports the most recent and two prior releases of PyTorch and TensorFlow.
+
+* UIF v1.1 is based on TensorFlow 2.10 and PyTorch 1.12.
+
+* UIF v1.1 has been tested with TensorFlow 2.3 to 2.10 and PyTorch 1.2 to 1.12.
+
+### ROCm Installation 
+
+For general information on ROCm installation, refer to the [ROCm Installation Guide](https://docs.amd.com). 
+
+### Verifying ROCm Installation
+
+Verify the ROCm installation using the `rocminfo` command.
+
+        $ /opt/rocm-<version>/bin/rocminfo
+
+
+## 1.1.2: Install Docker
+
+Install the Docker software. If Docker is not installed on your machine, see the [official Docker documentation](https://docs.docker.com/engine/install/) for installation instructions.
+
+## 1.1.3: Pull a UIF Docker Image
+
+The UIF/TensorFlow Docker image provides a superset functionality from ROCm/TensorFlow, and the UIF/PyTorch Docker image provides a superset functionality from ROCm/PyTorch.  When the UIF Docker images were created, no items were deleted from underlying PyTorch or TensorFlow Docker images. The items that have been added in the superset include:
+
+* Quantizer and pruner tools as plugins to TensorFlow/PyTorch to enable the use of UIF Docker images to quantize models on a ROCm platform (for GPU or CPU). **Note:** To use the pruner, use the Vitis&trade; AI 3.0 ROCm Dockers. See the [Host Installation Instructions](https://xilinx.github.io/Vitis-AI/docs/install/install.html#docker-install-and-verification) in the Vitis AI documentation for details. 
+* MIGraphX to enable the use of UIF Docker images for GPU inference
+
+### PyTorch
+
+A prebuilt Docker image is used to run UIF tools using PyTorch.
+
+Follow these steps:
+
+1.	Obtain the latest Docker image.
+
+      		docker pull amdih/uif-pytorch:uif1.1_rocm5.4.1_vai3.0_py3.7_pytorch1.12
+
+
+	The above instruction will download the UIF container, including PyTorch and optimization tools. 
+
+
+2.	Start a Docker container using the image.
+
+      		docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G 	amdih/uif-pytorch:uif1.1_rocm5.4.1_vai3.0_py3.7_pytorch1.12
+
+	You can also pass the `-v` argument to mount any data directories from the host onto the container.
+
+### TensorFlow
+
+A prebuilt Docker image is used to run UIF tools using TensorFlow.
+
+Follow these steps:
+
+1.	Obtain the latest Docker image.
+
+      		docker pull amdih/uif-tensorflow:uif1.1_rocm5.4.1_vai3.0_tf2.10
+
+
+	The above instruction will download the UIF container, including TensorFlow and optimization tools. 
+
+
+2.	Start a Docker container using the image.
+
+      		docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G 	amdih/uif-tensorflow:uif1.1_rocm5.4.1_vai3.0_tf2.10
+
+	You can also pass the `-v` argument to mount any data directories from the host onto the container.
+
+# 1.2: Pull PyTorch/TensorFlow Docker (for FPGA Users)
+
+## 1.2.1: Install Docker
+
+Install the Docker software. If Docker is not installed on your machine yet, see the [official Docker documentation](https://docs.docker.com/engine/install/) for installation instructions.
+
+## 1.2.2: Pull a Vitis AI Docker Image
+
+For instuctions on how to pull a Docker image for the Vitis AI development environment, see the [Vitis AI Docker Installation](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/blob/vai3.0_update/docs/docs/install/install.html).
+
+# 1.3: Install ZenDNN Package (for CPU Users)
+
+Perform the following steps to install TensorFlow and PyTorch built with ZenDNN:
+
+### 1.3.1: TensorFlow+ZenDNN
+
+To run inference on the TensorFlow model using ZenDNN, download and install the TensorFlow+ZenDNN package. Perform the following steps to complete the TensorFlow+ZenDNN installation:
+
+1. Download the TensorFlow+ZenDNN v4.0 release package from the [AMD ZenDNN page](https://www.amd.com/en/developer/zendnn.html).
+
+2. Unzip the package. For example: `TF_v2.10_ZenDNN_v4.0_Python_v3.8.zip`.
+
+    ```
+   unzip TF_v2.10_ZenDNN_v4.0_Python_v3.8.zip
+    ```
+3. Ensure that you have the conda environment installed, and execute the following commands:
+
+    ```
+    cd TF_v2.10_ZenDNN_v4.0_Python_v*/
+    source scripts/TF_ZenDNN_setup_release.sh
+    ```
+TensorFlow+ZenDNN installation completes.
+
+### 1.3.2: PyTorch+ZenDNN
+
+To run inference on the PyTorch model using ZenDNN, download and install the PyTorch+ZenDNN package. Perform the following steps to complete the PyTorch+ZenDNN installation:
+
+1. Download PTv1.12+ZenDNN_v4.0 release package from the [AMD ZenDNN page](https://www.amd.com/en/developer/zendnn.html).
+
+2. Unzip the package. For example: `PT_v1.12.0_ZenDNN_v4.0_Python_v3.8.zip`.
+
+    ```
+    unzip PT_v1.12.0_ZenDNN_v4.0_Python_v3.8.zip
+    ```
+3. Ensure that you have the conda environment installed, and execute the following commands:
+
+    ```
+    cd PT_v1.12.0_ZenDNN_v4.0_Python_v*/ZenDNN/
+    source scripts/PT_ZenDNN_setup_release.sh
+    ```
+    PyTorch+ZenDNN installation completes.
+
+# 1.4: Get the Inference Server Docker Image (for Model Serving)
+
+The AMD Inference Server is integrated with [ZenDNN](https://www.amd.com/en/developer/zendnn.html), [MIGraphX](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX) and [Vitis AI](https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html) and can be used for [model serving](/docs/4_deploy_your_own_model/serve_model/servingmodelwithinferenceserver.md). To use the inference server, you need a Docker image for it, which you can get by using a prebuilt image or building one from the [inference server repository](https://github.com/Xilinx/inference-server) on GitHub.
+
+The instructions provided here are an overview, but you can see more complete information about the AMD Inference Server in the [documentation](https://xilinx.github.io/inference-server/0.3.0/index.html).
+
+## 1.4.1: Use a Prebuilt Docker Image
+
+You can pull the appropriate deployment Docker image(s) from DockerHub using:
+
+```
+docker pull amdih/serve:uif1.1_zendnn_amdinfer_0.3.0
+docker pull amdih/serve:uif1.1_migraphx_amdinfer_0.3.0
+```
+
+The Vitis AI deployment image and development images for all platforms are not prebuilt and must be built by the user.
+
+## 1.4.2: Build an Inference Server Docker Image
+
+You need Docker (18.09+) to build the image.
+
+1. Clone the `inference-server` repo.
+
+```
+git clone https://github.com/Xilinx/inference-server
+cd inference-server
+# version 0.3.0 corresponds to this documentation
+git checkout v0.3.0
+python3 docker/generate.py
+./amdinfer dockerize <flags>
+```
+
+2. Use flags to control the image building, such as:
+
+- `--production`: Builds the deployment version of the image instead of the default development one.
+- `--vitis`: Enables FPGAs with Vitis AI in the image.
+- `--migraphx`: Enables GPUs with MIGraphX in the image.
+- `--tfzendnn=<path to zip>`: Enables CPUs with TF+ZenDNN in the image. You need to download [TF_v2.10_ZenDNN_v4.0_C++_API.zip](https://www.amd.com/en/developer/zendnn.html) and pass the path to it.
+- `--ptzendnn=<path to zip>`: Enables CPUs with PT+ZenDNN in the image. You need to download [PT_v1.12_ZenDNN_v4.0_C++_API.zip](https://www.amd.com/en/developer/zendnn.html) and pass the path to it.
+
+ **Note:** The downloaded ZenDNN package(s) must be inside the inference-server folder since the Docker will not be able to access files outside the repository.
+
+You can pass these flags in any combination. Use `./amdinfer dockerize --help` for the full documentation on the available flags.
+
+<hr/>
+
+[< Previous](/README.md) | [Next >](/docs/2_model_setup/uifmodelsetup.md)
+
+<hr/>
+
+ # License
+
+UIF is licensed under [Apache License Version 2.0](/LICENSE). Refer to the [LICENSE](/LICENSE) file for the full license text and copyright notice.
+
+# Technical Support
+
+Contact [email protected] for questions, issues, and feedback on UIF.
+
+Submit your questions, feature requests, and bug reports on the [GitHub issues](https://github.com/amd/UIF/issues) page.
+

docs/2_model_setup/AMD-license-agreement-for-non-commercial-models.md

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+ADVANCED MICRO DEVICES, INC. 
+LICENSE AGREEMENT FOR NON-COMMERCIAL MODELS 
+
+Trained Models: 
+
+inception-resnetv2, 
+inceptionv1, 
+inceptionv1 pruned0.09, 
+inceptionv1 pruned0.16, 
+inceptionv2, 
+inceptionv3, 
+inceptionv3 pruned0.2, 
+inceptionv3 pruned0.4, 
+inceptionv4, 
+inceptionv4 pruned0.2, 
+inceptionv4 pruned0.4, 
+mobilenetv1_1.0, 
+mobilenetv1_1.0 pruned0.11, 
+mobilenetv1_1.0 pruned0.12, 
+resnetv1_50, 
+resnetv1_50 pruned0.38, 
+resnetv1_50 pruned0.65, 
+resnetv1_101, 
+resnetv1_101 pruned0.35, 
+resnetv1_101 pruned0.57, 
+resnetv1_152, 
+resnetv1_152 pruned0.51, 
+resnetv1_152 pruned0.60, 
+vgg16, 
+vgg16 pruned0.43, 
+vgg16 pruned0.50, 
+vgg19, 
+vgg19 pruned0.24, 
+vgg19 pruned0.39, 
+efficientnet-edgetpu-S, 
+efficientnet-edgetpu-M, 
+efficientnet-edgetpu-L, 
+resnet50, 
+mobilenetv1,  
+inceptionv3 pruned0.3, 
+inceptionv3 pruned0.4, 
+inceptionv3 pruned0.5, 
+inceptionv3 pruned0.6,  
+resnet50_v1.5, 
+resnet50_v1.5 pruned0.3, 
+resnet50_v1.5 pruned0.4, 
+resnet50_v1.5 pruned0.5, 
+resnet50_v1.5 pruned0.6, 
+resnet50_v1.5 pruned0.7, 
+OFA-resnet50, 
+OFA-resnet50 pruned0.45, 
+OFA-resnet50 pruned0.60, 
+OFA-resnet50 pruned0.74, 
+OFA-resnet50 pruned0.88,    
+Resnet34-ssd, 
+Resnet34-ssd pruned0.19, 
+Resnet34-ssd pruned0.29, 
+Reid_resnet50, 
+Reid_resnet50 pruned0.4, 
+Reid_resnet50 pruned0.5, 
+Reid_resnet50 pruned0.6, 
+Reid_resnet50 pruned0.7, 
+DLRM  
+
+This License Agreement for Non-Commercial Models (“Agreement”) is a legal agreement between you (either an individual or an entity) and Advanced Micro Devices, Inc. (“AMD”). DO NOT USE THE TRAINED MODELS IDENTIFIED ABOVE UNTIL YOU HAVE CAREFULLY READ AND AGREE TO THE AGREEMENT. BY USING, INSTALLING, MODIFYING, COPYING, TRAINING, BENCHMARKING, OR DISTRIBUTING THE TRAINED MODELS, YOU ACCEPT ALL TERMS AND CONDITIONS OF THIS AGREEMENT.  If you do not accept these terms, do not use the Trained Models. 
+
+1.	Subject to Your compliance with this Agreement, AMD grants you a license to use, modify, and distribute the Trained Models solely for non-commercial and research purposes. This means you may use the Trained Models for benchmarking, testing, and evaluating the Trained Models (including non-commercial research undertaken by or funded by a commercial entity) but you cannot use the Trained Models in any commercial offering, including as part of a product or service You use or provide to others in exchange for money or other consideration. 
+
+2.	Your use of the Trained Models are subject to the following conditions: (a) You cannot alter any copyright, trademark, or other notice in the Trained Models; (b) You cannot sublicense or distribute the Trained Models under any other terms or conditions; (c) You cannot use AMD’s trademarks in your applications or technologies in a way that suggests you applications or technologies are endorsed by AMD; (d) if you distribute the Trained Model, You must provide corresponding source code for Trained Model; and (e) if the Trained Models include any code or content subject to a open source license or third party license (“Third Party Materials”), You agree to comply with such license terms. 
+
+3.	THE TRAINED MODELS (INCLUDING THIRD PARTY MATERIALS, IF ANY) ARE PROVIDED “AS IS” AND WITHOUT A WARRANTY OF ANY KIND, WHETHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. YOU BEAR ALL RISK OF USING THE TRAINED MODELS (INCLUDING THIRD PARTY PART MATERIALS, IF ANY) AND YOU AGREE TO RELEASE AMD FROM ANY LIABILITY OR DAMAGES FOR ANY CLAIM  OR ACTION ARISING OUT OF OR IN CONNECTION WITH YOUR USE OF THE TRAINED MODEL AND/OR THIRD PARTY MATERIALS.