Create performance.yml (#18929)
* Create performance.yml create new performance issue template for customers to report issue that related to performance * Update performance.yml Amend based on latest comment * Update performance.yml * Update performance.yml amend based on suggestion
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HARI CHAND BALASUBRAMANIAM
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.github/ISSUE_TEMPLATE/performance.yml
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.github/ISSUE_TEMPLATE/performance.yml
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name: Performance Issue Report
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description: This report is for the performance-related issue
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title: "[Performance]: "
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labels: ["performance", "support_request"]
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body:
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- type: markdown
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attributes:
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value: |
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Please provide all the necessary information to expedite the response.
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- type: input
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id: ov_version
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attributes:
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label: OpenVINO Version
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description: OpenVINO version, branch, or tag in OpenVINO GitHub
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placeholder: 2021.4.0 LTS / Master Branch / tag 2022.3.0
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validations:
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required: false
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- type: dropdown
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id: os
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attributes:
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label: Operating System
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description: What OS are you using?
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options:
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- Ubuntu 18.04 (LTS)
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- Ubuntu 20.04 (LTS)
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- Ubuntu 22.04 (LTS)
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- Windows System
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- Red Hat Enterprise Linux 8
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- OpenSUSE
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- Android System
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- Raspbian Stretch OS
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- macOS Systems for Intel CPU
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- macOS Systems for Apple Silicon
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- WebAssembly
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- WSL2 on Windows
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- Other (Please specify in description)
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validations:
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required: true
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- type: dropdown
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id: device_use
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attributes:
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label: Device used for inference
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description: What hardware are you using for inference?
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options:
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- CPU
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- iGPU
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- dGPU
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- NPU
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validations:
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required: false
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- type: dropdown
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id: openvino_installation
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attributes:
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label: OpenVINO installation
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description: How do you install OpenVINO on your system?
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options:
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- PyPi
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- Docker
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- Build from source
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- Other virtual machines
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validations:
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required: true
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- type: dropdown
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id: openvino_api
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attributes:
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label: Programming Language
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description: What is the programming language you use in your performance test?
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options:
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- Python
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- C++
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- Other
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validations:
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required: true
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- type: dropdown
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id: architecture
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attributes:
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label: Hardware Architecture
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description: What is your hardware architecture used in this test?
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options:
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- x86 (64 bits)
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- x86 (32 bits)
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- ARM (64 bits)
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- ARM (32 bits)
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- RISC-V
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- Other (please specify in the description)
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validations:
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required: true
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- type: input
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id: model_name
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attributes:
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label: Model used
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description: Please provide us the link to your model in the description
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placeholder: ResNet50 / YOLOv4
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validations:
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required: true
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- type: dropdown
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id: model_quantized
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attributes:
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label: Quantized model?
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description: Is your model quantized with model optimized?
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options:
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- 'Yes'
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- 'No'
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validations:
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required: true
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- type: textarea
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id: target_platform
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attributes:
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label: Target Platform
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description: |
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You can also provide us full system log with the following command
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Windows cmd - "systeminfo"
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Linux terminal - "lscpu" and "lscpu -e"
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placeholder: Paste your full platform/system information here
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validations:
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required: false
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- type: textarea
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id: performance_description
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attributes:
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label: Performance issue description
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description: What issue are you having, and what did you expect to happen instead?
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placeholder: Please provide a detailed description of what happened
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value: |
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let us know the application you use for performance tests
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e.g., hello_classification.py / benchmark_app / own test script
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if you are using your own test script, could this reproduce using benchmark_app?
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validations:
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required: true
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- type: textarea
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id: step_by_step
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attributes:
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label: Step-by-step reproduction
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description: How can we reproduce your issue?
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placeholder: Please provide detailed instructions on how to reproduce the issue
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validations:
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required: false
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- type: checkboxes
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id: terms
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attributes:
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label: Issue submission checklist
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description: By submitting this issue, you agree to follow our [Code of Conduct](https://github.com/intel/intel-one-mono/blob/main/CODE_OF_CONDUCT.md)
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options:
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- label: I report the performance issue. It's not a question
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required: true
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- label: I checked the problem with the documentation, FAQ, open issues, Stack Overflow, etc., and have not found the solution
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required: true
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- label: There is reproducer code and related data files such as images, videos, models, etc.
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required: true
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