h2o4gpu | H2O4GPU is a collection of GPU solvers by H2Oai with APIs | GPU library

by h2oai Python Version: rel-0.4.1 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | h2o4gpu Summary

h2o4gpu is a Python library typically used in Hardware, GPU, Pytorch applications. h2o4gpu has no vulnerabilities, it has a Permissive License and it has high support. However h2o4gpu has 259 bugs and it build file is not available. You can download it from GitHub.

H2O4GPU is a collection of GPU solvers by H2Oai with APIs in Python and R. The Python API builds upon the easy-to-use scikit-learn API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn (i.e. import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option. The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling. Daal library added for CPU, currently supported only x86_64 architecture.

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Quality

Security

License

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Support

h2o4gpu has a highly active ecosystem.

It has 424 star(s) with 100 fork(s). There are 120 watchers for this library.

It had no major release in the last 12 months.

There are 145 open issues and 241 have been closed. On average issues are closed in 149 days. There are 7 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of h2o4gpu is rel-0.4.1

Quality

h2o4gpu has 259 bugs (0 blocker, 0 critical, 238 major, 21 minor) and 673 code smells.

Security

h2o4gpu has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

h2o4gpu code analysis shows 0 unresolved vulnerabilities.

There are 42 security hotspots that need review.

License

h2o4gpu 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

h2o4gpu releases are available to install and integrate.

h2o4gpu has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

h2o4gpu saves you 10208 person hours of effort in developing the same functionality from scratch.

It has 20767 lines of code, 820 functions and 150 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed h2o4gpu and discovered the below as its top functions. This is intended to give you an instant insight into h2o4gpu implemented functionality, and help decide if they suit your requirements.

Fit the factorization algorithm
Get sparse matrixes
Load the GPU library
Fits the prediction at the given position
Internal helper function for FitorPredict
Perform a prediction on the current device
Free the model
Fit the objective function
Change settings
Helper function to get the usage of the subprocess
Raises an error if variable is not a type
Set parameters
Create an H2OgrSettings object
Prints a 3D tensor
Fit the model
Get compute capability for a given subprocess
Calculate ridge regression
Calculate the area under the posterior distribution
Returns a numpy ndarray
Prints a TensorDescriptor
Compute computeability for a given GPU
Set the device device
Calculate the log loss between actual and predicted and predicted samples
Root mean squared error
Print a formatted table
Mean squared error

Get all kandi verified functions for this library.

h2o4gpu Key Features

No Key Features are available at this moment for h2o4gpu.

h2o4gpu Examples and Code Snippets

No Code Snippets are available at this moment for h2o4gpu.

Community Discussions

Trending Discussions on h2o4gpu

Why are deep learning libraries so huge?

QUESTION

Why are deep learning libraries so huge?

Asked 2020-Jan-18 at 13:42

I've recently downloaded all packages from PyPI. One interesting observation was that of the Top-15 of the biggest packages, all execept one are deep learning packages:

mxnet: mxnet-cu90 (600 MB), mxnet-cu92, mxnet-cu101mkl, mxnet-cu101 (and 6 more mxnet versions)
cntk: cntk-gpu (493MB)
H2O4GPU (366MB)
tensorflow: tensorflow-gpu (357MB), tensorflow

I looked at mxnet-cu90. It has exactly one huge file: libmxnet.so (936.7MB). What does this file contain? Is there any way to make it smaller?

I'm especially astonished that those libraries are so huge considering that one usually uses them on top of CUDA + cuDNN, which I thought would do the heavy lifting.

As a comparison, I looked at related libraries with which you can also build deep learning libraries:

numpy: 6MB
sympy: 6MB
pycuda: 3.6MB
tensorflow-cpu: 116MB (so the GPU version needs 241 MB more or around 3x the size!)

...

ANSWER

Answered 2020-Jan-18 at 13:42

Deep learning frameworks are large because they package CuDNN from NVIDIA into their wheels. This is done for the convenience of downstream users.

CuDNN are the primitives that the frameworks call to execute highly optimised neural network ops (e.g. LSTM)

The unzipped version of CuDNN for windows 10 is 435MB.

Source https://stackoverflow.com/questions/59721109

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

Vulnerabilities

No vulnerabilities reported

Install h2o4gpu

Note: Installation steps mentioned below are for users planning to use H2O4GPU. See DEVEL.md for developer installation. H2O4GPU can be installed using either PIP or Conda.
Download the Python wheel file (For Python 3.6):. Start a fresh pyenv or virtualenv session. Install the Python wheel file. NOTE: If you don't use a fresh environment, this will overwrite your py3nvml and xgboost installations to use our validated versions.
Stable: CUDA10 - linux_x86_64 CUDA10 - linux_ppc64le
Bleeding edge (changes with every successful master branch build): CUDA10.0 - linux_x86_64 CUDA10.0 - linux_ppc64le
Ensure you meet the Requirements and have installed the Prerequisites. If not already done you need to install conda package manager. Ensure you test your conda installation. H204GPU packages for CUDA8, CUDA 9 and CUDA 9.2 are available from h2oai channel in anaconda cloud. Create a new conda environment with H2O4GPU based on CUDA 9.2 and all its dependencies using the following command. For other cuda versions substitute the package name as needed. Note the requirement for h2oai and conda-forge channels. Once the environment is created activate it source activate h2o4gpuenv.
To test your installation of the Python package, the following code:.

Support

Please refer to our CONTRIBUTING.md and DEVEL.md for instructions on how to build and test the project and how to contribute. The h2o4gpu Gitter chatroom can be used for discussion related to open source development. GitHub issues are used for bugs, feature and enhancement discussion/tracking.

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