model_analyzer | Triton Model Analyzer is a CLI tool | Machine Learning library
kandi X-RAY | model_analyzer Summary
kandi X-RAY | model_analyzer Summary
model_analyzer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Docker applications. model_analyzer has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.
Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.
Support
Quality
Security
License
Reuse
Support
model_analyzer has a low active ecosystem.
It has 260 star(s) with 69 fork(s). There are 11 watchers for this library.
It had no major release in the last 6 months.
There are 12 open issues and 89 have been closed. On average issues are closed in 66 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of model_analyzer is current.
Quality
model_analyzer has 0 bugs and 0 code smells.
Security
model_analyzer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
model_analyzer code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
model_analyzer is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
model_analyzer releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
It has 20638 lines of code, 1531 functions and 223 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed model_analyzer and discovered the below as its top functions. This is intended to give you an instant insight into model_analyzer implemented functionality, and help decide if they suit your requirements.
- Add custom configs for profiles models .
- Visit given path .
- Build a PDF report .
- Add configs to model spec .
- Returns a remote server handle .
- Run the profiler .
- Profile the model .
- Generate default range configs
- Plot the latency bar chart .
- Creates the metrics tables .
Get all kandi verified functions for this library.
model_analyzer Key Features
No Key Features are available at this moment for model_analyzer.
model_analyzer Examples and Code Snippets
No Code Snippets are available at this moment for model_analyzer.
Community Discussions
Trending Discussions on model_analyzer
QUESTION
Printing model summaries for rllib models
Asked 2022-Jan-04 at 08:20
I have not seen anything in the rllib documentation that would allow me to print a quick summary of the model like print(model.summary()) in keras. I tried using tf-slim and
ANSWER
Answered 2022-Jan-04 at 08:20The training agent can return the policy which gives you access to the model:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install model_analyzer
You can download it from GitHub.
You can use model_analyzer 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 model_analyzer 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
InstallationQuick StartModel Analyzer CLILaunch ModesConfiguring Model AnalyzerModel Analyzer MetricsModel Config SearchCheckpointingModel Analyzer ReportsDeployment with Kubernetes
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
