tinykernel - A minimal Python kernel so you can run Python in your Python

Overview

tinykernel

A minimal Python kernel, so you can run Python in your Python.

All the clever stuff in this library is provided by Python's builtin ast module and compilation/exec/eval system, along with IPython's CachingCompiler which does some deep magic. tinykernel just brings them together with a little glue.

Install

With pip:

pip install tinykernel

With conda:

conda install -c fastai tinykernel

How to use

This library provides a single class, TinyKernel, which is a tiny persistent kernel for Python code:

k = TinyKernel()

Call it, passing Python code, to have the code executed in a separate Python environment:

k("a=1")

Expressions return the value of the expression:

k('a')
1

All variables are persisted across calls:

k("a+=1")
k('a')
2

Multi-line inputs are supported. If the last line is an expression, it is returned:

k("""import types
b = types.SimpleNamespace(foo=a)
b""")
namespace(foo=2)

The original source code is stored, so inspect.getsource works and, tracebacks have full details.

k("""def f(): pass # a comment
import inspect
inspect.getsource(f)""")
'def f(): pass # a comment\n'

When creating a TinyKernel, you can pass a dict of globals to initialize the environment:

k = TinyKernel(glb={'foo':'bar'})
k('foo*2')
'barbar'

Pass name to customize the string that appears in tracebacks ("kernel" by default):

k = TinyKernel(name='myapp')
code = '''def f():
    return 1/0
print(f())'''
try: k(code)
except Exception as e: print(traceback.format_exc())
", line 5, in try: k(code) File "/home/jhoward/git/tinykernel/tinykernel/__init__.py", line 20, in __call__ if expr: return self._run(Expression(expr.value), nm, 'eval') File "/home/jhoward/git/tinykernel/tinykernel/__init__.py", line 12, in _run def _run(self, p, nm, mode='exec'): return eval(compiler(p, nm, mode), self.glb) File "", line 3, in print(f()) File "", line 2, in f return 1/0 ZeroDivisionError: division by zero ">
Traceback (most recent call last):
  File "", line 5, in 
    try: k(code)
  File "/home/jhoward/git/tinykernel/tinykernel/__init__.py", line 20, in __call__
    if expr: return self._run(Expression(expr.value), nm, 'eval')
  File "/home/jhoward/git/tinykernel/tinykernel/__init__.py", line 12, in _run
    def _run(self, p, nm, mode='exec'): return eval(compiler(p, nm, mode), self.glb)
  File "", line 3, in 
    print(f())
  File "", line 2, in f
    return 1/0
ZeroDivisionError: division by zero

Acknowledgements

Thanks to Christopher Prohm, Matthias Bussonnier, and Aaron Meurer for their helpful insights in this twitter thread.

You might also like...
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)
Exploring Image Deblurring via Blur Kernel Space (CVPR'21)

Exploring Image Deblurring via Encoded Blur Kernel Space About the project We introduce a method to encode the blur operators of an arbitrary dataset

Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021) This repository is the official PyTorc

[ICCV 2021] Official Tensorflow Implementation for
[ICCV 2021] Official Tensorflow Implementation for "Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions"

KPAC: Kernel-Sharing Parallel Atrous Convolutional block This repository contains the official Tensorflow implementation of the following paper: Singl

The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing".

BMC The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing". BibTex entry available here. B

Fuzzing the Kernel Using Unicornafl and AFL++
Fuzzing the Kernel Using Unicornafl and AFL++

Unicorefuzz Fuzzing the Kernel using UnicornAFL and AFL++. For details, skim through the WOOT paper or watch this talk at CCCamp19. Is it any good? ye

A Kernel fuzzer focusing on race bugs

Razzer: Finding kernel race bugs through fuzzing Environment setup $ source scripts/envsetup.sh scripts/envsetup.sh sets up necessary environment var

Fuzzer for Linux Kernel Drivers

difuze: Fuzzer for Linux Kernel Drivers This repo contains all the sources (including setup scripts), you need to get difuze up and running. Tested on

Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data based on Pytorch Framework
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data based on Pytorch Framework

VFedPCA+VFedAKPCA This is the official source code for the Paper: Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-

Releases(0.0.2)
Deep Learning GPU Training System

DIGITS DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, To

NVIDIA Corporation 4.1k Jan 03, 2023
AdamW optimizer for bfloat16 models in pytorch.

Image source AdamW optimizer for bfloat16 models in pytorch. Bfloat16 is currently an optimal tradeoff between range and relative error for deep netwo

Alex Rogozhnikov 8 Nov 20, 2022
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Project Page | Paper A Shading-Guided Generative Implicit Model

Xingang Pan 115 Dec 18, 2022
Compute execution plan: A DAG representation of work that you want to get done. Individual nodes of the DAG could be simple python or shell tasks or complex deeply nested parallel branches or embedded DAGs themselves.

Hello from magnus Magnus provides four capabilities for data teams: Compute execution plan: A DAG representation of work that you want to get done. In

12 Feb 08, 2022
Benchmark for the generalization of 3D machine learning models across different remeshing/samplings of a surface.

Discretization Robust Correspondence Benchmark One challenge of machine learning on 3D surfaces is that there are many different representations/sampl

Nicholas Sharp 10 Sep 30, 2022
MultiTaskLearning - Multi Task Learning for 3D segmentation

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

2 Sep 22, 2022
⚡ H2G-Net for Semantic Segmentation of Histopathological Images

H2G-Net This repository contains the code relevant for the proposed design H2G-Net, which was introduced in the manuscript "Hybrid guiding: A multi-re

André Pedersen 8 Nov 24, 2022
A list of multi-task learning papers and projects.

This page contains a list of papers on multi-task learning for computer vision. Please create a pull request if you wish to add anything. If you are interested, consider reading our recent survey pap

svandenh 297 Dec 17, 2022
TransVTSpotter: End-to-end Video Text Spotter with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 66 Dec 26, 2022
YOLOV4运行在嵌入式设备上

在嵌入式设备上实现YOLO V4 tiny 在嵌入式设备上实现YOLO V4 tiny 目录结构 目录结构 |-- YOLO V4 tiny |-- .gitignore |-- LICENSE |-- README.md |-- test.txt |-- t

Liu-Wei 6 Sep 09, 2021
Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

tflite2tensorflow Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite. 1. Supported Layers No. TFLite Layer TF

Katsuya Hyodo 214 Dec 29, 2022
Unsupervised Discovery of Object Radiance Fields

Unsupervised Discovery of Object Radiance Fields by Hong-Xing Yu, Leonidas J. Guibas and Jiajun Wu from Stanford University. arXiv link: https://arxiv

Hong-Xing Yu 148 Nov 30, 2022
Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

Note: this repo has been discontinued, please check code for newer version of the paper here Weight Normalized GAN Code for the paper "On the Effects

Sitao Xiang 182 Sep 06, 2021
An Intelligent Self-driving Truck System For Highway Transportation

Inceptio Intelligent Truck System An Intelligent Self-driving Truck System For Highway Transportation Note The code is still in development. OS requir

InceptioResearch 11 Jul 13, 2022
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator

involution Official implementation of a neural operator as described in Involution: Inverting the Inherence of Convolution for Visual Recognition (CVP

Duo Li 1.3k Dec 28, 2022
Code for How To Create A Fully Automated AI Based Trading System With Python

AI Based Trading System This code works as a boilerplate for an AI based trading system with yfinance as data source and RobinHood or Alpaca as broker

Rubén 196 Jan 05, 2023
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

MLP Mixer Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. Give us a star if you like this repo. Author: Github: bangoc123 Emai

Ngoc Nguyen Ba 86 Dec 10, 2022
A tiny, friendly, strong baseline code for Person-reID (based on pytorch).

Pytorch ReID Strong, Small, Friendly A tiny, friendly, strong baseline code for Person-reID (based on pytorch). Strong. It is consistent with the new

Zhedong Zheng 3.5k Jan 08, 2023
Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

Weakly_detector Tensorflow implementation of "Learning Deep Features for Discriminative Localization" B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and

Taeksoo Kim 363 Jun 29, 2022
CVPR2020 Counterfactual Samples Synthesizing for Robust VQA

CVPR2020 Counterfactual Samples Synthesizing for Robust VQA This repo contains code for our paper "Counterfactual Samples Synthesizing for Robust Visu

72 Dec 22, 2022