Metadata-Version: 2.1
Name: neuralsampler
Version: 0.0.13
Summary: neural sampler
Home-page: https://github.com/JiahaoYao/neuralsampler
Author: Jimmy
Author-email: jiahaoyao.math@gmail.com
License: MIT
Description: [![PyPI version](https://badge.fury.io/py/neuralsampler.svg)](https://badge.fury.io/py/neuralsampler)
        [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gEUjfsmoinemOb3P6p0UCI3zfYx-T8uI?usp=sharing)
        
        ## NeuralSampler - Pytorch
        
        Implementation of Neural Sampler. 
        
        ## Install
        
        ``` bash
        $ pip install neuralsampler
        ```
        
        or install the latest version by 
        ``` bash 
        pip install -U git+https://JiahaoYao:{password}@github.com/JiahaoYao/neuralsampler.git@main
        ```
        
        Install the jax (follow the official instruction [here](https://github.com/google/jax#installation))
        ```bash 
        pip install jax jaxlib==0.1.64+[YOUR_CUDA_VERSION] -f https://storage.googleapis.com/jax-releases/jax_releases.html
        ```
        e.g. 
        ```bash
        pip install --upgrade jax jaxlib==0.1.64+cuda101 -f https://storage.googleapis.com/jax-releases/jax_releases.html
        ```
        
        
        ## Usage
        
        ```python
        import torch
        from neuralsampler import neuralsampler
        ```
        
        ## Run the code 
        
        ```bash 
        python main.py 
        ```
        
        ## Run the scripts 
        
        ```bash 
        python scripts/test_job.py 
        ```
        
        
        ## Demonstrations and tutorials
        | Link | Description|
        |:----:|:-----|
        |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/154kY7PE4dzXERUw_bkm-Vu-W10nhoKXd?usp=sharing)  | Train the neural sampler for the **double well potential** in PyTorch |
        |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17lTrPLTt_0EDXa4hkbHmbAFQEkpRDZnh?usp=sharing) | Train the neural sampler for the **Müller-Brown Model** in PyTorch |
        |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SeXMpILhkJPjXUaesvzEhc3Ke6Zl_zxJ?usp=sharing)  | Train the neural sampler for the **periodic potential** in PyTorch |
        |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/120kYYBOVa1i0TD85RjlEkFjaWDxSFUx3?usp=sharing)| Train the neural sampler for the **Ginzburg-Landau potential** in PyTorch |
        
        
        ## Ignore me (random things)
        ```
        this repo is to collect all the random results and reproduce the experiments here 
        
        and then jax 
        
        i will use jax and flax, like shown here: https://github.com/yang-song/score_sde
        
        there are the templates of building the jax neural networks (quite interesting to try this functional programming )
        ```
        
        
        ## Todo list 
        - [x] this library is on the pytorch
        - [x] i am also going to prepare the colab notebook 
        - [x] the dataset is through the gdown: you can download the dataset from the google drive  
        - [ ] plz check the abf-mmd can reproduce the results! I have checked the codes are the same? I guessed the only issue might be just run enough runs! (Sun 09/05/2021 21:06)
        - [x] at least lots of things are connected now! 
        - [ ] update the colab module, going to download the dataset from the cloud!
        
Keywords: artificial intelligence,generative models,transformers
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
