lightning-bolts | Toolbox of models , callbacks , and datasets for AI/ML | Machine Learning library

by PyTorchLightning Python Version: 0.5.0 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | lightning-bolts Summary

lightning-bolts is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. lightning-bolts 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 lightning-bolts' or download it from GitHub, PyPI.

Bolts provides a variety of components to extend PyTorch Lightning such as callbacks & datasets, for applied research and production.

Support

Quality

Security

License

Reuse

Support

lightning-bolts has a medium active ecosystem.

It has 1287 star(s) with 256 fork(s). There are 25 watchers for this library.

It had no major release in the last 12 months.

There are 46 open issues and 264 have been closed. On average issues are closed in 162 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of lightning-bolts is 0.5.0

Quality

lightning-bolts has 0 bugs and 0 code smells.

Security

lightning-bolts has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

lightning-bolts code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

lightning-bolts is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

lightning-bolts releases are available to install and integrate.

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.

It has 16803 lines of code, 1318 functions and 236 files.

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

Top functions reviewed by kandi - BETA

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

Calculate the loss for the given boxes .
Read a configuration file .
Forward computation .
Convert a list of dicts into a table .
Generate trajectories .
Filter detections based on confidence threshold .
Return the literal arguments for the given class .
Balance the class of each class .
Add model specific arguments to argparse .
Parse devkit directory .

Get all kandi verified functions for this library.

lightning-bolts Key Features

No Key Features are available at this moment for lightning-bolts.

lightning-bolts Examples and Code Snippets

SimCLR,4. Acknowledgement

Python

Lines of Code : 8

License : Permissive (MIT)

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@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine le

SimCLR,4. Acknowledgement

Python

Lines of Code : 8

License : Permissive (MIT)

Copy

@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine le

bolts.rst

Python

Lines of Code : 0

License : No License

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pip install lightning-bolts
Quality control
---------------
The Lightning community builds bolts and contributes them to Bolts.
The lightning team guarantees that contributions are:

- Rigorously Tested (CPUs, GPUs, TPUs).
- Rigorously Documented.
-

Community Discussions

Trending Discussions on lightning-bolts

Pretrained lightning-bolts VAE not doing proper inference on training dataset

AttributeError: 'tuple' object has no attribute 'train_dataloader'

How to use numpy dataset in Pytorch Lightning

Conda - how to update only cudatoolkit in an existing environment?

QUESTION

Pretrained lightning-bolts VAE not doing proper inference on training dataset

Asked 2022-Feb-01 at 20:11

I'm using the CIFAR-10 pre-trained VAE from lightning-bolts. It should be able to regenerate images with the quality shown on this picture taken from the docs (LHS are the real images, RHS are the generated)

However, when I write a simple script that loads the model, the weights, and tests it over the training set, I get a much worse reconstruction (top row are real images, bottom row are the generated ones):

Here is a link to a self-contained colab notebook that reproduces the steps I've followed to produce the pictures.

Am I doing something wrong on my inference process? Could it be that the weights are not as "good" as the docs claim?

Thanks!

...

ANSWER

Answered 2022-Feb-01 at 20:11

First, the image from the docs you show is for the AE, not the VAE. The results for the VAE look much worse:

https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-cifar10/vae_output.png

Second, the docs state "Both input and generated images are normalized versions as the training was done with such images." So when you load the data you should specify normalize=True. When you plot your data, you will need to 'unnormalize' the data as well:

Source https://stackoverflow.com/questions/70197274

QUESTION

AttributeError: 'tuple' object has no attribute 'train_dataloader'

Asked 2021-May-08 at 21:04

I have a 3 file. In the datamodule file, I have created data and used the basic format of the PyTorch Lightning. In the linear_model I made a linear regression model based on this page. Finally, I have a train file, I am calling the model and trying to fit the data. But I am getting this error

...

ANSWER

Answered 2021-May-08 at 21:04

Most of the things were correct, except few things like:

Source https://stackoverflow.com/questions/67449926

QUESTION

How to use numpy dataset in Pytorch Lightning

Asked 2021-May-08 at 13:13

I want to make a dataset using NumPy and then want to train and test a simple model like 'linear, or logistic`.

I am trying to learn Pytorch Lightning. I have found a tutorial that we can use the NumPy dataset and can use uniform distribution here. As a newcomer, I am not getting the full idea, how can I do that!

My code is given below

...

ANSWER

Answered 2021-May-07 at 16:25

This code will return you label as y and a,b as 2 features of 500 random examples merged into X.

Source https://stackoverflow.com/questions/67437448

QUESTION

Conda - how to update only cudatoolkit in an existing environment?

Asked 2021-Mar-22 at 03:02

This is a specific instance of a general problem that I run into when updating packages using conda. I have an environment that is working great on machine A. I want to transfer it to machine B. But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2. Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1

I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml. However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B. I try

...

ANSWER

Answered 2021-Mar-22 at 03:02

Overly-Restrictive Constraints

I'd venture the issue is that recreating from a YAML that includes versions and builds will establish those versions and builds as explicit specifications for that environment moving forward. That is, Conda will regard explicit specifications as hard requirements that it cannot mutate and so if even a single one of the dependencies of cudatoolkit also needs to be updated in order to use version 11, Conda will not know how to satisfy it without violating those previously specified constraints.

Specifically, this is what I see when searching (assuming linux-64 platform):

Source https://stackoverflow.com/questions/66734416

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

Vulnerabilities

No vulnerabilities reported

Install lightning-bolts

You can install using 'pip install lightning-bolts' or download it from GitHub, PyPI.
You can use lightning-bolts like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

We've deprecated a bunch of specific model research, primarily because they've grown outdated or support for them was not possible. This also means in the future we'll not accept any model specific research. We'd like to encourage users to contribute general components that will help a broad range of problems, however components that help specifics domains will also be welcomed!. For example a callback to help train SSL models would be a great contribution, however the next greatest SSL model from your latest paper would be a good contribution to Lightning Flash. Use Lightning Flash to train, predict and serve state-of-the-art models for applied research. We suggest looking at our VISSL Flash integration for SSL based tasks. See Deprecated Modules for more information.

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