torchdiffeq | Differentiable ODE solvers with full GPU support | Machine Learning library

by rtqichen Python Version: 0.2.4 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | torchdiffeq Summary

torchdiffeq is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. torchdiffeq has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install torchdiffeq' or download it from GitHub, PyPI.

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

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Quality

Security

License

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Support

torchdiffeq has a medium active ecosystem.

It has 4658 star(s) with 848 fork(s). There are 123 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 54 open issues and 142 have been closed. On average issues are closed in 73 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of torchdiffeq is 0.2.4

Quality

torchdiffeq has 0 bugs and 0 code smells.

Security

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

torchdiffeq code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

torchdiffeq is licensed under the MIT License. This license is Permissive.

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

Reuse

torchdiffeq releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

torchdiffeq saves you 1435 person hours of effort in developing the same functionality from scratch.

It has 3204 lines of code, 269 functions and 32 files.

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

Top functions reviewed by kandi - BETA

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

Backward backward
Calculate the ODEint solution for a given function
Combine event functions
Helper function for _tol
R Evaluate an ODEint function
Max norm of a tensor
Finds the parameters of a module
Handle the adjoint norm
Integrate the solution t
Create a logger
Calculate the objective function
Simulate the trajectory
Compute the log - likelihood at t
Advance until an event is reached
Integrate at time t
Calculates a learning rate function based on the given parameters
Calculate the accuracy of a given model
An infinite generator
Integrate a fixed step
Generate a 2D spiral
Calculate ODEint solution
Calculate the gradient of a ball
Visualize the trajectories
Return data loaders
Initialize the run
Evaluate step function

Get all kandi verified functions for this library.

torchdiffeq Key Features

No Key Features are available at this moment for torchdiffeq.

torchdiffeq Examples and Code Snippets

"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms,Summary:

Python

Lines of Code : 24

License : Permissive (Apache-2.0)

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import torchdiffeq

func = ...
y0 = ...
t = ...
torchdiffeq.odeint_adjoint(func=func, y0=y0, t=t)

import torchdiffeq

def rms_norm(tensor):
    return tensor.pow(2).mean().sqrt()

def make_norm(state):
    state_size = state.numel()
    def norm(aug

Development installation

Python

Lines of Code : 14

License : Permissive (Apache-2.0)

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cd /scratch/
wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
chmod +x Anaconda3-2020.11-Linux-x86_64.sh
./Anaconda3-2020.11-Linux-x86_64.sh

source /scratch/anaconda/bin/activate

cd /scratch/
git clone https://github.com/unc

HyperFlow,Dependencies

Python

Lines of Code : 9

License : No License

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# Create a new conda environment
conda create -n HyperFlow python=3.6
conda activate HyperFlow

# Install pytorch (please refer to the commend in the official website)
conda install pytorch=1.0.1 torchvision cudatoolkit=10.0 -c pytorch -y

# Install

Community Discussions

Trending Discussions on torchdiffeq

I constantly get ResolvePackageNotFound

pip installing from git vs cloning repo

QUESTION

I constantly get ResolvePackageNotFound

Asked 2021-Jan-17 at 05:16

When I type conda env create -f environment.yml

I constantly get

...

ANSWER

Answered 2021-Jan-15 at 14:57

Conda does not work well with large environments in which everything pinned to specific versions (in contrast to other ecosystems in which pinning everything is the standard). The result of conda env export, which is what this probably is, here also includes the build numbers, which are almost always too specific (and often platform-specific) for the purpose of installing the right version of the software. It's great for things like reproducibility of scientific work (specific versions and builds of everything need to be known), but not great for installing software (there is plenty of flexibility in versions that should work with any package).

I'd start by removing the build pins (dropping everything after the second = in each line) so that only the versions are pinned. After that, I'd start removing version pins.

Source https://stackoverflow.com/questions/65735532

QUESTION

pip installing from git vs cloning repo

Asked 2020-Jan-09 at 10:25

What I did.
Following instructions, I installed the torchdiffeq package with

...

ANSWER

Answered 2020-Jan-08 at 13:32

pip did clone the git repository as you say so yourself towards the end of your question. It was cloned in a temporary directory. pip then used this temporary to build and install the Python project into your current environment. Once this is done the temporary directory containing the cloned repository has been deleted.

The directories torchdiffeq and torchdiffeq._impl are declared as packages of this project (listed in the parameter to the packages argument of the setuptools.setup() function call in setup.py) and are meant to actually be installed. Other directories are not meant to be installed, so pip didn't install them.

If you want to use the examples, then you can keep your own clone of the git repository and use them against the installed version of the project. Unless you want to modify the code of the project itself there is not much point in using the editable mode.

Source https://stackoverflow.com/questions/59646010

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