Import Python modules from dicts and JSON formatted documents.

Overview

Paker

Build Version Version

Paker is module for importing Python packages/modules from dictionaries and JSON formatted documents. It was inspired by httpimporter.

Important: Since v0.6.0 paker supports importing .pyd and .dll modules directly from memory. This was achieved by using _memimporter from py2exe project. Importing .so files on Linux still requires writing them to disk.

Installation

From PyPI

pip install paker -U

From source

git clone https://github.com/desty2k/paker.git
cd paker
pip install .

Usage

In Python script

You can import Python modules directly from string, dict or bytes (without disk IO).

import paker
import logging

MODULE = {"somemodule": {"type": "module", "extension": "py", "code": "fun = lambda x: x**2"}}
logging.basicConfig(level=logging.NOTSET)

if __name__ == '__main__':
    with paker.loads(MODULE) as loader:
        # somemodule will be available only in this context
        from somemodule import fun
        assert fun(2), 4
        assert fun(5), 25
        print("6**2 is {}".format(fun(6)))
        print("It works!")

To import modules from .json files use load function. In this example paker will serialize and import mss package.

import paker
import logging

file = "mss.json"
logging.basicConfig(level=logging.NOTSET)

# install mss using `pip install mss`
# serialize module
with open(file, "w+") as f:
    paker.dump("mss", f, indent=4)

# now you can uninstall mss using `pip uninstall mss -y`
# load package back from dump file
with open(file, "r") as f:
    loader = paker.load(f)

import mss
with mss.mss() as sct:
    sct.shot()

# remove loader and clean the cache
loader.unload()

try:
    # this will throw error
    import mss
except ImportError:
    print("mss unloaded successfully!")

CLI

Paker can also work as a standalone script. To dump module to JSON dict use dump command:

paker dump mss

To recreate module from JSON dict use load:

paker load mss.json

Show all modules and packages in .json file

paker list mss.json

How it works

When importing modules or packages Python iterates over importers in sys.meta_path and calls find_module method on each object. If the importer returns self, it means that the module can be imported and None means that importer did not find searched package. If any importer has confirmed the ability to import module, Python executes another method on it - load_module. Paker implements its own importer called jsonimporter, which instead of searching for modules in directories, looks for them in Python dictionaries

To dump module or package to JSON document, Paker recursively iterates over modules and creates dict with code and type of each module and submodules if object is package.

You might also like...
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.
Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.

Self-attention building blocks for computer vision applications in PyTorch Implementation of self attention mechanisms for computer vision in PyTorch

Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

DI-HPC: Decision Intelligence - High Performance Computation DI-HPC is an acceleration operator component for general algorithm modules in reinforceme

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Weight initialization schemes for PyTorch nn.Modules

nninit Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin. ##Update This repo has been

Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems
Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

WideLinears Pytorch parallel Neural Networks A package of pytorch modules for fast paralellization of separate deep neural networks. Ideal for agent-b

Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems
Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems

Static Token And Credential Scanner CI Integrations What is it? STACS is a YARA

Comments
  • psutil example exits with module not found when using _memimporter

    psutil example exits with module not found when using _memimporter

    I pulled latest releases zip file, ran python setup.py build and attempted to run the psutil example with the compiled pyd. This resulted in the following error:

    DEBUG:jsonimporter:searching for pwd
    DEBUG:jsonimporter:searching for psutil._common
    INFO:jsonimporter:psutil._common has been imported successfully
    DEBUG:jsonimporter:searching for psutil._compat
    INFO:jsonimporter:psutil._compat has been imported successfully
    DEBUG:jsonimporter:searching for psutil._pswindows
    DEBUG:jsonimporter:searching for psutil._psutil_windows
    DEBUG:jsonimporter:searching for psutil._psutil_windows
    INFO:jsonimporter:using _memimporter to load '.pyd' file
    INFO:jsonimporter:unloaded all modules
    Traceback (most recent call last):
      File "c:\Users\User\Desktop\paker-0.7.1\paker-0.7.1\build\lib.win-amd64-cpython-310\psutil_example.py", line 20, in <module>
        import psutil
      File "c:\Users\User\Desktop\paker-0.7.1\paker-0.7.1\build\lib.win-amd64-cpython-310\paker\importers\jsonimporter.py", line 115, in load_module
        exec(jsonmod["code"], mod.__dict__)
      File "<string>", line 107, in <module>
      File "c:\Users\User\Desktop\paker-0.7.1\paker-0.7.1\build\lib.win-amd64-cpython-310\paker\importers\jsonimporter.py", line 115, in load_module
        exec(jsonmod["code"], mod.__dict__)
      File "<string>", line 35, in <module>
      File "c:\Users\User\Desktop\paker-0.7.1\paker-0.7.1\build\lib.win-amd64-cpython-310\paker\importers\jsonimporter.py", line 134, in load_module
        mod = _memimporter.import_module(fullname, path, initname, self._get_data, spec)
    ImportError: MemoryLoadLibrary failed loading psutil\_psutil_windows.pyd: The specified module could not be found. (126)
    

    Is this an issue with how I compiled memimporter, or something else?

    opened by rkbennett 1
Releases(v0.7.1)
Owner
Wojciech Wentland
Wojciech Wentland
Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Ro

Meta Research 1.2k Jan 02, 2023
Trains an agent with stochastic policy gradient ascent to solve the Lunar Lander challenge from OpenAI

Introduction This script trains an agent with stochastic policy gradient ascent to solve the Lunar Lander challenge from OpenAI. In order to run this

Momin Haider 0 Jan 02, 2022
A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".

Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y

hoshi-hiyouga 85 Dec 26, 2022
Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

LEXA Benchmark Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models

Oleg Rybkin 36 Dec 22, 2022
Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport

Non-Homogeneous Poisson Process Intensity Modeling and Estimation using Measure Transport This GitHub page provides code for reproducing the results i

Andrew Zammit Mangion 1 Nov 08, 2021
PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Lip to Speech Synthesis with Visual Context Attentional GAN This repository contains the PyTorch implementation of the following paper: Lip to Speech

6 Nov 02, 2022
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers

DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generative Transformers Authors: Jaemin Cho, Abhay Zala, and Mohit Bansal (

Jaemin Cho 98 Dec 15, 2022
GRF: Learning a General Radiance Field for 3D Representation and Rendering

GRF: Learning a General Radiance Field for 3D Representation and Rendering [Paper] [Video] GRF: Learning a General Radiance Field for 3D Representatio

Alex Trevithick 243 Dec 29, 2022
Towards Representation Learning for Atmospheric Dynamics (AtmoDist)

Towards Representation Learning for Atmospheric Dynamics (AtmoDist) The prediction of future climate scenarios under anthropogenic forcing is critical

Sebastian Hoffmann 4 Dec 15, 2022
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

HiPAL Code for KDD'22 Applied Data Science Track submission -- HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electro

Hanyang Liu 4 Aug 08, 2022
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
PyGCL: A PyTorch Library for Graph Contrastive Learning

PyGCL is a PyTorch-based open-source Graph Contrastive Learning (GCL) library, which features modularized GCL components from published papers, standa

PyGCL 588 Dec 31, 2022
Weight estimation in CT by multi atlas techniques

maweight A Python package for multi-atlas based weight estimation for CT images, including segmentation by registration, feature extraction and model

György Kovács 0 Dec 24, 2021
Secure Distributed Training at Scale

Secure Distributed Training at Scale This repository contains the implementation of experiments from the paper "Secure Distributed Training at Scale"

Yandex Research 9 Jul 11, 2022
Aydin is a user-friendly, feature-rich, and fast image denoising tool

Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.

Royer Lab 99 Dec 14, 2022
这是一个facenet-pytorch的库,可以用于训练自己的人脸识别模型。

Facenet:人脸识别模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download 预测步骤 How2predict 训练步骤 How2train 参考资料 Reference 性能情况 训练数据

Bubbliiiing 210 Jan 06, 2023
URIE: Universal Image Enhancementfor Visual Recognition in the Wild

URIE: Universal Image Enhancementfor Visual Recognition in the Wild This is the implementation of the paper "URIE: Universal Image Enhancement for Vis

Taeyoung Son 43 Sep 12, 2022
基于深度强化学习的原神自动钓鱼AI

原神自动钓鱼AI由YOLOX, DQN两部分模型组成。使用迁移学习,半监督学习进行训练。 模型也包含一些使用opencv等传统数字图像处理方法实现的不可学习部分。

4.2k Jan 01, 2023
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集,准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022