pytorch-lightning-bolts | Toolbox of models , callbacks , and datasets | Machine Learning library

 by   PyTorchLightning Python Version: 0.3.0 License: Apache-2.0

kandi X-RAY | pytorch-lightning-bolts Summary

kandi X-RAY | pytorch-lightning-bolts Summary

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

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              pytorch-lightning-bolts has a medium active ecosystem.
              It has 784 star(s) with 132 fork(s). There are 24 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 70 open issues and 128 have been closed. On average issues are closed in 35 days. There are 19 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-lightning-bolts is 0.3.0

            kandi-Quality Quality

              pytorch-lightning-bolts has 0 bugs and 0 code smells.

            kandi-Security Security

              pytorch-lightning-bolts has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pytorch-lightning-bolts code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pytorch-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.

            kandi-Reuse Reuse

              pytorch-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 13435 lines of code, 1043 functions and 196 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            pytorch-lightning-bolts Key Features

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

            pytorch-lightning-bolts Examples and Code Snippets

            No Code Snippets are available at this moment for pytorch-lightning-bolts.

            Community Discussions

            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

            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 pytorch-lightning-bolts

            Simple installation from PyPI. Install bleeding-edge (no guarantees). In case you want to have full experience you can install all optional packages at once.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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