TensorRT-Inference-Server-Tutorial | 服务侧深度学习部署案例
kandi X-RAY | TensorRT-Inference-Server-Tutorial Summary
kandi X-RAY | TensorRT-Inference-Server-Tutorial Summary
TensorRT-Inference-Server-Tutorial is a Python library. TensorRT-Inference-Server-Tutorial has no vulnerabilities and it has low support. However TensorRT-Inference-Server-Tutorial has 1 bugs and it build file is not available. You can download it from GitHub.
服务侧深度学习部署案例
服务侧深度学习部署案例
Support
Quality
Security
License
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Support
TensorRT-Inference-Server-Tutorial has a low active ecosystem.
It has 326 star(s) with 49 fork(s). There are 9 watchers for this library.
It had no major release in the last 6 months.
There are 11 open issues and 4 have been closed. On average issues are closed in 7 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of TensorRT-Inference-Server-Tutorial is current.
Quality
TensorRT-Inference-Server-Tutorial has 1 bugs (0 blocker, 0 critical, 0 major, 1 minor) and 58 code smells.
Security
TensorRT-Inference-Server-Tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
TensorRT-Inference-Server-Tutorial code analysis shows 0 unresolved vulnerabilities.
There are 1 security hotspots that need review.
License
TensorRT-Inference-Server-Tutorial does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
Reuse
TensorRT-Inference-Server-Tutorial releases are not available. You will need to build from source code and install.
TensorRT-Inference-Server-Tutorial has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
TensorRT-Inference-Server-Tutorial saves you 1011 person hours of effort in developing the same functionality from scratch.
It has 2297 lines of code, 149 functions and 23 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed TensorRT-Inference-Server-Tutorial and discovered the below as its top functions. This is intended to give you an instant insight into TensorRT-Inference-Server-Tutorial implemented functionality, and help decide if they suit your requirements.
- Convert torch graph to onnx
- Dumps data_def into a string
- Simplify ONNX model
- Generate trti config file
- Simplify a model
- Convert a model into an OrderedDict
- Checks the given model
- Apply preprocessing
- Performs preprocessing
- Convert torch model to Tensorflow
- Build an engine
- Deprecated
- Convert a graphdef file into a graph definition
- Create a convolution layer
- Evaluate the image speed
- Preprocess image
- DLA - 34
- Convenience function for the DLA46 - C model
- Get the next batch of data
- Construct a DLA60 model
- Construct a DLA60X model
- Get a batch of data
- Convenience model for DLA60X - C
- Define a DLA -102 model
- Convenience constructor for DLA 2 0
- Convenience constructor for DLA102x2x2x2
- Parse a model
Get all kandi verified functions for this library.
TensorRT-Inference-Server-Tutorial Key Features
No Key Features are available at this moment for TensorRT-Inference-Server-Tutorial.
TensorRT-Inference-Server-Tutorial Examples and Code Snippets
No Code Snippets are available at this moment for TensorRT-Inference-Server-Tutorial.
Community Discussions
No Community Discussions are available at this moment for TensorRT-Inference-Server-Tutorial.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install TensorRT-Inference-Server-Tutorial
You can download it from GitHub.
You can use TensorRT-Inference-Server-Tutorial like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use TensorRT-Inference-Server-Tutorial like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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