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‎.gitignore

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language_modeling/pytorch/bert_large/training/dlsa # Remove this to change submodule
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transfer_learning/tensorflow/resnet50/inference/transfer-learning-inference # Remove this to change submodule
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transfer_learning/tensorflow/resnet50/training/transfer-learning-training # Remove this to change submodule

‎LICENSE

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‎README.md

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# AI Workflow Infrastructure for Intel® architecture
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## Description
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On this page you will find details and instructions on how to set up an environment that supports Intel's AI Pipelines container build and test infrastructure.
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## Dependency Requirements
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Only Linux systems are currently supported. Please make sure the following are installed in your package manager of choice:
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- `make`
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- `docker.io`
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A full installation of [docker engine](https://docs.docker.com/engine/install/) with docker CLI is required. The recommended docker engine version is `19.03.0+`.
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- `docker-compose`
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The Docker Compose CLI can be [installed](https://docs.docker.com/compose/install/compose-plugin/#installing-compose-on-linux-systems) both manually and via package manager.
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```
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$ DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker}
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$ mkdir -p $DOCKER_CONFIG/cli-plugins
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$ curl -SL https://github.com/docker/compose/releases/download/v2.7.0/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
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$ chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
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$ docker compose version
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Docker Compose version v2.7.0
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```
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## Development Container
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Rather than installing the dependencies above on a bare-metal system, a base development container with the relevant dependencies for execution of MLOps validation can be built:
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```docker build -f ./.github/utils/Dockerfile.compose .```
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## Build and Run Workflows
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Each pipeline will contain specific requirements and instructions for how to provide its specific dependencies and what customization options are possible. Generally, pipelines are run with the following format:
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```git submodule update --init --recursive```
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This will pull the dependent repo containing the scripts to run the end2end pipeline's inference and/or training.
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```<KEY>=<VALUE> ... <KEY>=<VALUE> make <PIPELINE_NAME>```
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Where `KEY` and `VALUE` pairs are environment variables that can be used to customize both the pipeline's script options and the resulting container. For more information about the valid `KEY` and `VALUE` pairs, see the README.md file in the folder for each workflow container:
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|AI Workflow|Framework/Tool|Mode|
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|-|-|-|
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|Language Modeling|PyTorch*|[Training](./language_modeling/pytorch/bert_large/training/)|
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|Vision Based Transfer Learning|TensorFlow|[Training](./transfer_learning/tensorflow/resnet50/training/) \| [Inference](./transfer_learning/tensorflow/resnet50/inference/)|
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### Cleanup
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Each pipeline can remove all resources allocated by executing `make clean`.

‎language_modeling/pytorch/bert_large/training/DEVCATALOG.md

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# **Document Level Sentiment Analysis Using Hugging Face Transformers - Training**
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## **Use Hugging Face API for Natural Language Processing Tasks**
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DLSA is Intel optimized representative End-to-end Fine-Tuning & Inference pipeline for Document level sentiment analysis using BERT model implemented with Hugging Face transformer API. For detailed information about the workflow, go to [Document Level Sentiment Analysis](https://github.com/intel/document-level-sentiment-analysis) GitHub repository.
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## **Pull Docker Image**
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```
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docker pull intel/ai-workflows:document-level-sentiment-analysis
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```
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## **Download Model**
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Clone [End-to-End DLSA](https://github.com/intel/document-level-sentiment-analysis) repository into your working directory.
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```
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git clone https://github.com/intel/document-level-sentiment-analysis.git .
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git checkout dlsa_multinode_ipex_v1.2
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```
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## **Download Dataset**
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### **SST-2 dataset**
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Download and extract SST-2 dataset
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```
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wget https://dl.fbaipublicfiles.com/glue/data/SST-2.zip && unzip SST-2.zip && mv SST-2 sst
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```
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### **IMDB dataset**
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Download and extract IMDB dataset
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```
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wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz && tar -zxf aclImdb_v1.tar.gz
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```
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### **Prepare dataset**
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Set dataset directory
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For SST-2 dataset
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```
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export DATASET_DIR=$(pwd)/sst
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```
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For IMDB dataset
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```
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export DATASET_DIR=$(pwd)/aclImdb
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```
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Copy dataset
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```
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mkdir -p profiling-transformers/datasets && cp -r ${DATASET_DIR} profiling-transformers/datasets
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```
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## **Customization Options**
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| Script Name | Description |
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| --- | --- |
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| DATASET | `sst2` and `imdb`. Names of datasets. For SST-2 dataset, use `DATASET=sst2` and for IMDB dataset, use `DATASET=imdb` |
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| MODEL | `bert-large-uncased`. Name of model on [Hugging Face](https://huggingface.co/) |
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## **Running with Docker**
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The snippet below shows a quick start running with the following options: `DATASET=sst2` and `OUTPUT_DIR=/output`.
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```
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export DATASET=sst2
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export MODEL=bert-large-uncased
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export OUTPUT_DIR=/output
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docker run \
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--env DATASET=${DATASET} \
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--env MODEL_NAME_OR_PATH=${MODEL} \
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--env ${OUTPUT_DIR}:${OUTPUT_DIR}/fine_tuned \
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--env http_proxy=${http_proxy} \
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--env https_proxy=${https_proxy} \
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--env no_proxy=${no_proxy} \
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--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
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--volume ${PWD}:/workspace \
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--workdir /workspace/profiling-transformers \
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--privileged --init -it \
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intel/ai-workflows:document-level-sentiment-analysis \
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fine-tuning/run_dist.sh -np 1 -ppn 1 fine-tuning/run_ipex_native.sh
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```
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## **Documentation and Sources**
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### **Get Started**
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[Docker* Repository](https://hub.docker.com/u/intel) <br>
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[Main GitHub*](https://github.com/intel/document-level-sentiment-analysis)<br>
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[Readme](https://github.com/intel/document-level-sentiment-analysis/blob/main/README.md)<br>
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[Release Notes]()<br>
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### **Code Sources**
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[Dockerfile](https://github.com/intel/ai-workflows/blob/v0.1.0/pipelines/language_modeling/pytorch/bert_large/training/Dockerfile.hugging-face-dlsa)<br>
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[Report Issue](https://community.intel.com/t5/Intel-Optimized-AI-Frameworks/bd-p/optimized-ai-frameworks)<br>
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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/intel/ai-workflows/blob/main/LICENSE) for additional details.
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## **Related Containers and Solutions**
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[View All Containers and Solutions 🡢](https://www.intel.com/content/www/us/en/developer/tools/software-catalog/containers.html)

‎language_modeling/pytorch/bert_large/training/Dockerfile.hugging-face-dlsa

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ARG BASE_IMAGE_NAME="intel/intel-optimized-pytorch"
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ARG BASE_IMAGE_TAG="latest"
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# Inherit IPEX
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FROM ${BASE_IMAGE_NAME}:${BASE_IMAGE_TAG}
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && apt-get install --no-install-recommends --fix-missing -y \
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ca-certificates \
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git \
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libgomp1 \
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numactl \
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patch \
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wget \
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mpich
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# Default Workspace
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RUN mkdir -p /workspace
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ENV LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib
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# Install reqs via pip for transformers
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RUN pip install --upgrade pip && \
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pip install astunparse \
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cffi \
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cmake \
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dataclasses \
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datasets==2.3.2 \
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intel-openmp \
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mkl \
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mkl-include \
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ninja \
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numpy \
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future \
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pyyaml \
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requests \
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setuptools \
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six \
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transformers==4.20.1 \
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typing_extensions

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