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# Model Zoo for Intel® Architecture
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This repository contains **links to pre-trained models, sample scripts, best practices, and step-by-step tutorials** for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors.
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This repository contains **links to pre-trained models, sample scripts, best practices, and step-by-step tutorials** for many popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs.
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Model packages and containers for running the Model Zoo's workloads can be found at the [Intel® Developer Catalog](https://software.intel.com/containers).
# Model Zoo for Intel® Architecture Workloads Optimized for the Intel® Data Center GPU Flex Series
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This document provides links to step-by-step instructions on how to leverage Model Zoo docker containers to run optimized open-source Deep Learning inference workloads using Intel® Extension for PyTorch* and Intel® Extension for TensorFlow* on the [Intel® Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/data-center-gpu/flex-series/overview.html).
The ResNet50 v1-5 inference container includes scripts,model and libraries need to run int8 inference. To run the `inference_block_format.sh` quickstart script using this container, you'll need to provide volume mounts for the ImageNet dataset. You will need to provide an output directory where log files will be written.
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```
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export PRECISION=int8
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export OUTPUT_DIR=<path to output directory>
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export DATASET_DIR=<path to the preprocessed imagenet dataset>
Support for Intel® Extension for PyTorch* is found via the [Intel® AI Analytics Toolkit.](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html#gs.qbretz) Additionally, the Intel® Extension for PyTorch* team tracks both bugs and enhancement requests using [GitHub issues](https://github.com/intel/intel-extension-for-pytorch/issues). Before submitting a suggestion or bug report, please search the GitHub issues to see if your issue has already been reported.
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## License Agreement
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LEGAL NOTICE: By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the [license file](https://github.com/IntelAI/models/tree/master/third_party) for additional details.
The ResNet50 v1-5 inference container includes scripts,model and libraries need to run int8 inference. To run one of the inference quickstart scripts using this container, you'll need to provide volume mounts for the ImageNet dataset for running `accuracy.sh` script. For `online_inference.sh` and `batch_inference.sh` dummy dataset will be used. You will need to provide an output directory where log files will be written.
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### Run Docker Image
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The ResNet50 v1-5 inference container includes scripts,model and libraries need to run int8 inference. To run one of the inference quickstart scripts using this container, you'll need to provide volume mounts for the ImageNet dataset for running `accuracy.sh` script. For `online_inference.sh` and `batch_inference.sh` dummy dataset will be used. You will need to provide an output directory where log files will be written.
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```
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export PRECISION=int8
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export OUTPUT_DIR=<path to output directory>
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export DATASET_DIR=<path to the preprocessed imagenet dataset>
Support for Intel® Extension for TensorFlow* is found via the [Intel® AI Analytics Toolkit.](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html#gs.qbretz) Additionally, the Intel® Extension for TensorFlow* team tracks both bugs and enhancement requests using [GitHub issues](https://github.com/intel/intel-extension-for-tensorflow/issues). Before submitting a suggestion or bug report, please search the GitHub issues to see if your issue has already been reported.
Support for Intel® Extension for PyTorch* is found via the [Intel® AI Analytics Toolkit.](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html#gs.qbretz) Additionally, the Intel® Extension for PyTorch* team tracks both bugs and enhancement requests using [GitHub issues](https://github.com/intel/intel-extension-for-pytorch/issues). Before submitting a suggestion or bug report, please search the GitHub issues to see if your issue has already been reported.
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