dl-docker | one Docker image for deep learning | Continuous Deployment library

by floydhub Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | dl-docker Summary

dl-docker is a Python library typically used in Institutions, Learning, Education, Devops, Continuous Deployment, Deep Learning, Docker applications. dl-docker has no bugs, it has no vulnerabilities and it has medium support. However dl-docker build file is not available. You can download it from GitHub.

Docker itself has a great answer to this question. Docker is based on the idea that one can package code along with its dependencies into a self-contained unit. In this case, we start with a base Ubuntu 14.04 image, a bare minimum OS. When we build our initial Docker image using docker build, we install all the deep learning frameworks and its dependencies on the base, as defined by the Dockerfile. This gives us an image which has all the packages we need installed in it. We can now spin up as many instances of this image as we like, using the docker run command. Each instance is called a container. Each of these containers can be thought of as a fully functional and isolated OS with all the deep learning libraries installed in it.

Support

Quality

Security

License

Reuse

Support

dl-docker has a medium active ecosystem.

It has 3859 star(s) with 833 fork(s). There are 160 watchers for this library.

It had no major release in the last 6 months.

There are 60 open issues and 27 have been closed. On average issues are closed in 35 days. There are 10 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dl-docker is current.

Quality

dl-docker has 0 bugs and 0 code smells.

Security

dl-docker has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

dl-docker code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

dl-docker does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

dl-docker releases are not available. You will need to build from source code and install.

dl-docker has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

dl-docker saves you 3 person hours of effort in developing the same functionality from scratch.

It has 9 lines of code, 0 functions and 1 files.

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

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dl-docker Key Features

No Key Features are available at this moment for dl-docker.

dl-docker Examples and Code Snippets

Usage

Python

Lines of Code : 5

License : Permissive (MIT)

Copy

docker run -d \
    --name youtube-dl \
    -v youtube-dl_data:/config \
    -v :/downloads \
    jeeaaasustest/youtube-dl

Community Discussions

Trending Discussions on dl-docker

keras - cannot import name Conv2D

Keras with TensorFlow backend not using GPU

QUESTION

keras - cannot import name Conv2D

Asked 2020-Apr-01 at 05:55

I recently got the deep learning docker from https://github.com/floydhub/dl-docker running and while trying out the tutorials, received an error when importing the keras layers module.

...

ANSWER

Answered 2020-Apr-01 at 05:55

Try this: from keras.layers.convolutional import Conv2D

Importing changed with the new keras. Are you sure you are not using keras >= 2?

NOTE:

With tensorflow 2.0 keras is included. You can now import the layer with:

Source https://stackoverflow.com/questions/44131295

QUESTION

Keras with TensorFlow backend not using GPU

Asked 2019-Feb-27 at 06:06

I built the gpu version of the docker image https://github.com/floydhub/dl-docker with keras version 2.0.0 and tensorflow version 0.12.1. I then ran the mnist tutorial https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py but realized that keras is not using GPU. Below is the output that I have

...

ANSWER

Answered 2018-Mar-22 at 16:12

It is never a good idea to have both tensorflow and tensorflow-gpu packages installed side by side (the one single time it happened to me accidentally, Keras was using the CPU version).

I guess now I need to figure out how to have keras use the gpu version of tensorflow.

You should simply remove both packages from your system, and then re-install tensorflow-gpu [UPDATED after comment]:

Source https://stackoverflow.com/questions/46080634

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

Vulnerabilities

No vulnerabilities reported

Install dl-docker

You can download it from GitHub.
You can use dl-docker 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

Docker is supported on all the OSes mentioned here: Install Docker Engine (i.e. different flavors of Linux, Windows and OS X). The CPU version (Dockerfile.cpu) will run on all the above operating systems. However, the GPU version (Dockerfile.gpu) will only run on Linux OS. This is because Docker runs inside a virtual machine on Windows and OS X. Virtual machines don't have direct access to the GPU on the host. Unless PCI passthrough is implemented for these hosts, GPU support isn't available on non-Linux OSes at the moment.

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