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Finally, we add the supporting of GPT-2 model, which is an important and popular model for decoder. In FasterTransformer v4. Compare to usual framework to train giant model like Megatron, FasterTransformer provides 1. We also add supporting of FP16 fused multi-head attention kernel for V Finally, we optimize the decoding module. Compare to v3.

You can choose the tensorflow version and python version you want. Here, we list some possible images:. To achieve best performance, we recommand to use the latest image. For example, running image nvcr. Note: xx is the compute capability of your GPU.

Uses need to set the path of TensorFlow. For example, if we use nvcr. This will build the TorchScript custom class. Note: From FasterTransformer 3.

From FasterTransformer 4. We implement two INT8 pipelines. Note that there are different checkpoint version of Megatron. The version of the checkpoint above is 0. If users have trained a model by themselves, the default version of latest Megatron is 3. It controls the model path, model size, tensor parallelism size, and some hyper-parameters. And then run gpt by following script:. To see the full list of available options and their descriptions, use the -h or --help command-line option with the Python file, for example:.

This subsection provides the details about how to use the encoder, the decoder and the decoding. In order to run the following benchmark, we need to install the unix computing tool "bc" by. Using Effective FasterTransformer and int8v2 at the same time can bring about 3. Skip to content. Star Branches Tags. Could not load branches. Could not load tags.

Latest commit. Small code cleanup to remove unnecessary print statement. Git stats 18 commits. Failed to load latest commit information. View code. Prerequisites The code for this project uses python 3. Releases No releases published. Packages 0 No packages published. Dec 3, Dec 2, Nov 15, Nov 9, Nov 5, Oct 21, Oct 20, Oct 10, Download the file for your platform. If you're not sure which to choose, learn more about installing packages. Warning Some features may not work without JavaScript.

Please try enabling it if you encounter problems. Search PyPI Search. Latest version Released: Feb 3, Clustering based on density with variable density clusters. Navigation Project description Release history Download files. Project links Homepage. Maintainers lmcinnes. Performance Significant effort has been put into making the hdbscan implementation as fast as possible. Additional functionality The hdbscan package comes equipped with visualization tools to help you understand your clustering results.

After fitting data the clusterer object has attributes for: The condensed cluster hierarchy The robust single linkage cluster hierarchy The reachability distance minimal spanning tree All of which come equipped with methods for plotting and converting to Pandas or NetworkX for further analysis.

Based on the paper: R. Moulavi, A. Zimek and J. Robust single linkage The hdbscan package also provides support for the robust single linkage clustering algorithm of Chaudhuri and Dasgupta.

Example usage: import hdbscan from sklearn. Chaudhuri and S. Installing Easiest install, if you have Anaconda thanks to conda-forge which is awesome! Python Version The hdbscan library supports both Python 2 and Python 3.

Contributing We welcome contributions in any form! Citing If you have used this codebase in a scientific publication and wish to cite it, please use the Journal of Open Source Software article. McInnes, J. Healy, S. Project details Project links Homepage. Download files Download the file for your platform.