cuBERT | Fast implementation of BERT inference | Machine Learning library

 by   zhihu C++ Version: v0.0.5 License: MIT

kandi X-RAY | cuBERT Summary

kandi X-RAY | cuBERT Summary

cuBERT is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Bert, Transformer applications. cuBERT has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

|batch size|128 (ms) |32 (ms) | |--- |--- |--- | |tensorflow|255.2 |70.0 | |cuBERT |184.6|54.5|.
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              cuBERT has a low active ecosystem.
              It has 496 star(s) with 83 fork(s). There are 20 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 8 open issues and 34 have been closed. On average issues are closed in 29 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of cuBERT is v0.0.5

            kandi-Quality Quality

              cuBERT has 0 bugs and 0 code smells.

            kandi-Security Security

              cuBERT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              cuBERT code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              cuBERT is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              cuBERT releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              It has 777 lines of code, 38 functions and 16 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            cuBERT Key Features

            No Key Features are available at this moment for cuBERT.

            cuBERT Examples and Code Snippets

            No Code Snippets are available at this moment for cuBERT.

            Community Discussions

            QUESTION

            Box-Cox Tranformation Error: object 'x' not found
            Asked 2020-May-13 at 02:24

            hopefully a relatively easy one for those more experienced than me!

            Trying to perform a Box-Cox transformation using the following code:

            ...

            ANSWER

            Answered 2020-May-13 at 02:24

            I thought 'x' was being defined in line 3?

            Line 3 is lambda<-with(bc, x[which.max(y)]). It doesn't define x, it defines lambda. It does use x, which it looks for within the bc environment. If you're using boxcox() from the MASS package, bc should indeed include x and y components, so bc$x shouldn't give you the same error message. I'd expect an error about the replacement lengths. Because...

            bc$x are the potential lambda values tried by boxcox - you're using the default seq(-2, 2, 1/10), and it would be an unlikely coincidence if your data had a multiple of 41 rows needed to not give an error when assigning 41 values to a new column.

            Line 3 picks out the lambda value that maximizes the likelihood, so you shouldn't need the rest of the values in bc ever again. I'd expect you to use that lambda values to transform your response variable, as that's what the Box Cox transformation is for. ((x^lambda)-1/lambda) doesn't make any statistical or programmatic sense. Use this instead:

            Source https://stackoverflow.com/questions/61745309

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install cuBERT

            If you would like to run tfBERT_benchmark for performance comparison, please first install tensorflow C API from https://www.tensorflow.org/install/lang_c.
            Pre-built python binary package (currently only with MKL on Linux) can be installed as follows:.
            Download and install [MKL](https://github.com/intel/mkl-dnn/releases) to system path.
            Download the wheel package and pip install cuBERT-xxx-linux_x86_64.whl
            run python -c 'import libcubert' to verify your installation.

            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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          • HTTPS

            https://github.com/zhihu/cuBERT.git

          • CLI

            gh repo clone zhihu/cuBERT

          • sshUrl

            git@github.com:zhihu/cuBERT.git

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