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 2023-08-28 11:29:04 +08:00 committed by GitHub
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name: Performance Issue Report
description: This report is for the performance-related issue
title: "[Performance]: "
labels: ["performance", "support_request"]
body:
- type: markdown
attributes:
value: |
Please provide all the necessary information to expedite the response.
- type: input
id: ov_version
attributes:
label: OpenVINO Version
description: OpenVINO version, branch, or tag in OpenVINO GitHub
placeholder: 2021.4.0 LTS / Master Branch / tag 2022.3.0
validations:
required: false
- type: dropdown
id: os
attributes:
label: Operating System
description: What OS are you using?
options:
- Ubuntu 18.04 (LTS)
- Ubuntu 20.04 (LTS)
- Ubuntu 22.04 (LTS)
- Windows System
- Red Hat Enterprise Linux 8
- OpenSUSE
- Android System
- Raspbian Stretch OS
- macOS Systems for Intel CPU
- macOS Systems for Apple Silicon
- WebAssembly
- WSL2 on Windows
- Other (Please specify in description)
validations:
required: true
- type: dropdown
id: device_use
attributes:
label: Device used for inference
description: What hardware are you using for inference?
options:
- CPU
- iGPU
- dGPU
- NPU
validations:
required: false
- type: dropdown
id: openvino_installation
attributes:
label: OpenVINO installation
description: How do you install OpenVINO on your system?
options:
- PyPi
- Docker
- Build from source
- Other virtual machines
validations:
required: true
- type: dropdown
id: openvino_api
attributes:
label: Programming Language
description: What is the programming language you use in your performance test?
options:
- Python
- C++
- Other
validations:
required: true
- type: dropdown
id: architecture
attributes:
label: Hardware Architecture
description: What is your hardware architecture used in this test?
options:
- x86 (64 bits)
- x86 (32 bits)
- ARM (64 bits)
- ARM (32 bits)
- RISC-V
- Other (please specify in the description)
validations:
required: true
- type: input
id: model_name
attributes:
label: Model used
description: Please provide us the link to your model in the description
placeholder: ResNet50 / YOLOv4
validations:
required: true
- type: dropdown
id: model_quantized
attributes:
label: Quantized model?
description: Is your model quantized with model optimized?
options:
- 'Yes'
- 'No'
validations:
required: true
- type: textarea
id: target_platform
attributes:
label: Target Platform
description: |
You can also provide us full system log with the following command
Windows cmd - "systeminfo"
Linux terminal - "lscpu" and "lscpu -e"
placeholder: Paste your full platform/system information here
validations:
required: false
- type: textarea
id: performance_description
attributes:
label: Performance issue description
description: What issue are you having, and what did you expect to happen instead?
placeholder: Please provide a detailed description of what happened
value: |
let us know the application you use for performance tests
e.g., hello_classification.py / benchmark_app / own test script
if you are using your own test script, could this reproduce using benchmark_app?
validations:
required: true
- type: textarea
id: step_by_step
attributes:
label: Step-by-step reproduction
description: How can we reproduce your issue?
placeholder: Please provide detailed instructions on how to reproduce the issue
validations:
required: false
- type: checkboxes
id: terms
attributes:
label: Issue submission checklist
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)
options:
- label: I report the performance issue. It's not a question
required: true
- label: I checked the problem with the documentation, FAQ, open issues, Stack Overflow, etc., and have not found the solution
required: true
- label: There is reproducer code and related data files such as images, videos, models, etc.
required: true