mlearn | Benchmark Suite for Machine Learning Interatomic Potentials | Machine Learning library

by materialsvirtuallab Python Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | mlearn Summary

mlearn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. mlearn 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.

NOTE: This package has been deprecated and all code has been moved to the updated maml package. Please use maml from henceforth. This package is retained for reference but it is archived and will no longer be updated or maintained. The mlearn package is a benchmark suite for machine learning interatomic potentials for materials science. It enables a seamless way to develop various potentials and provides LAMMPS-driven properties predictor with developed potentials as plugins.

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Quality

Security

License

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Support

mlearn has a low active ecosystem.

It has 94 star(s) with 27 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 8 have been closed. On average issues are closed in 4 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of mlearn is current.

Quality

mlearn has no bugs reported.

Security

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

License

mlearn is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

mlearn 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.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

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

Train the lattice model
Write the cfg files
Construct a data pool from a set of structures
Generate a doc from a structure
Evaluate the model
Convert a list of dictionaries into energies
Read csv files
Write the parameter settings
Train RuNNer
Write the cfg_pools to a file
Load scaling parameter from file
Load weights from file
Evaluate the neural network
Write weights to file
Read data from a csv file
Train the model
Generate the start_CFG
Writes a configuration file to disk
Return the energy of a structure
Parses the amino acid file
Number ofbs scripts
Return a list of subscripts
Create a new NNPotential from a config file
Set up the CMDS
Evaluate a set of test structures
Parse energy and stress

Get all kandi verified functions for this library.

mlearn Key Features

No Key Features are available at this moment for mlearn.

mlearn Examples and Code Snippets

No Code Snippets are available at this moment for mlearn.

Community Discussions

Trending Discussions on mlearn

Failed to load the native TensorFlow runtime

Sagemaker with tensorflow 2 not saving model

What needs to be in a setup.py to create a wheel?

how to change default python version of container at docker?

Java: How to automatically open pdf after Download

QUESTION

Failed to load the native TensorFlow runtime

Asked 2020-May-03 at 18:18

I have that error after running that code.

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ANSWER

Answered 2018-Oct-07 at 13:08

@rpath/libcudart.8.0.dylib

I think this is your problem maybe. You have not got this library yet. IF you want to learn ML only, maybe switching back to tensorflow CPU is a better option.

Source https://stackoverflow.com/questions/43356578

QUESTION

Sagemaker with tensorflow 2 not saving model

Asked 2020-Feb-14 at 23:54

I am working with Keras and I am trying to train a model using Sagemaker. I have the following issue: When I train my model using TensorFlow 1.12 everything works fine:

...

ANSWER

Answered 2020-Feb-14 at 23:54

you're correct, there has been a major, beneficial change last year in the SageMaker TensorFlow experience named the Script Mode formalism. As you can see in the SDK Documentation:

"Warning. We have added a new format of your TensorFlow training script with TensorFlow version 1.11. This new way gives the user script more flexibility. This new format is called Script Mode, as opposed to Legacy Mode, which is what we support with TensorFlow 1.11 and older versions. In addition we are adding Python 3 support with Script Mode. The last supported version of Legacy Mode will be TensorFlow 1.12. Script Mode is available with TensorFlow version 1.11 and newer. Make sure you refer to the correct version of this README when you prepare your script. You can find the Legacy Mode README here."

with TensorFlow 2, you need to follow that Script Mode formalism and save your model in the opt/ml/model path, otherwise nothing will be sent to S3. Script Mode is quite straightforward to implement and gives better flexibility and portability, and this spec is shared with SageMaker Sklearn container, SageMaker Pytorch container and SageMaker MXNet container so definitely worth adopting

Source https://stackoverflow.com/questions/60190365

QUESTION

What needs to be in a setup.py to create a wheel?

Asked 2019-Jan-24 at 01:48

I have a Python project prj that we can describe as a collection of folders and a requirements.txt file:

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ANSWER

Answered 2017-Feb-21 at 13:56

You don't need to write anything special in your setup.py to be able to create a wheel. As long as your setup.py is using setuptools (which it should be anyway), you just write a normal setup.py, install the wheel package on your system, and run python setup.py bdist_wheel.

Source https://stackoverflow.com/questions/42369006

QUESTION

how to change default python version of container at docker?

Asked 2018-Oct-18 at 17:07

I installed docker on background of python 3.7 and built container and image, which is /c/Users/conkj/sample:/sample mlearn:init /bin/bash

To run tensorflow, i had to install python 3.6. After i installed python 3.6, at the first page of docker, if i checked version of python, it shown python 3.6. But at the container, as "docker run -it -v /c/Users/conkj/sample:/sample mlearn:init /bin/bash", it shown python 3.7. I'd tried "pip install python 3.6" and built other images and containers.(Maybe it wasn't right way to build.) But still stuck in...

Could you guys give me solutions for changing python 3.7 to 3.6 at docker container?

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ANSWER

Answered 2018-Oct-18 at 17:07

You should use a proper basic image for your container, like:

Source https://stackoverflow.com/questions/52877561

QUESTION

Java: How to automatically open pdf after Download

Asked 2018-Jul-25 at 00:22

I've been looking for various tutorials but no one can solve my case. I hope here is something that can make me understand, about how to open PDF file automatically after finished download from database to android.

This is my download script.

...

ANSWER

Answered 2018-Jul-24 at 13:24

Here is the solution please use ACTION_VIEW Intent to open all you need the path of downloaded pdf.

Source https://stackoverflow.com/questions/51499372

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

Vulnerabilities

No vulnerabilities reported

Install mlearn

The usage of mlearn requires installation of specific packages and the plugins in LAMMPS. Please see detailed installation instructions for all descriptors.

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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