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floydhub
dl-docker
Python2016–2017
floydhub / dl-docker

dl-docker

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

AI & Machine LearningSystems & InfrastructureCreative & MediaLearning & ReferenceDevOps & Cloud
Explore on GitHubRead Hacker News discussions
3Distinct people
5Mentions
0Explicit recommenders
3.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month5 dated mentions

Observed mentions: 2016-06-07 14:18:09 UTC to 2017-08-18 16:33:56 UTC.

0122016-06-01 00:00:00 UTC to 2016-07-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsJun 20162016-07-01 00:00:00 UTC to 2016-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-08-01 00:00:00 UTC to 2016-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-09-01 00:00:00 UTC to 2016-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-10-01 00:00:00 UTC to 2016-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-11-01 00:00:00 UTC to 2016-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2016-12-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2017-02-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsJan 20172017-02-01 00:00:00 UTC to 2017-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-03-01 00:00:00 UTC to 2017-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-04-01 00:00:00 UTC to 2017-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-05-01 00:00:00 UTC to 2017-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-06-01 00:00:00 UTC to 2017-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-01 00:00:00 UTC to 2017-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-08-01 00:00:00 UTC to 2017-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 2017

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jun 20162016-06-01 00:00:00 UTC2016-07-01 00:00:00 UTC220
Jul 20162016-07-01 00:00:00 UTC2016-08-01 00:00:00 UTC000
Aug 20162016-08-01 00:00:00 UTC2016-09-01 00:00:00 UTC000
Sep 20162016-09-01 00:00:00 UTC2016-10-01 00:00:00 UTC000
Oct 20162016-10-01 00:00:00 UTC2016-11-01 00:00:00 UTC000
Nov 20162016-11-01 00:00:00 UTC2016-12-01 00:00:00 UTC000
Dec 20162016-12-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
Jan 20172017-01-01 00:00:00 UTC2017-02-01 00:00:00 UTC202
Feb 20172017-02-01 00:00:00 UTC2017-03-01 00:00:00 UTC000
Mar 20172017-03-01 00:00:00 UTC2017-04-01 00:00:00 UTC000
Apr 20172017-04-01 00:00:00 UTC2017-05-01 00:00:00 UTC000
May 20172017-05-01 00:00:00 UTC2017-06-01 00:00:00 UTC000
Jun 20172017-06-01 00:00:00 UTC2017-07-01 00:00:00 UTC000
Jul 20172017-07-01 00:00:00 UTC2017-08-01 00:00:00 UTC000
Aug 20172017-08-01 00:00:00 UTC2017-09-01 00:00:00 UTC101

What Hacker News says

5 sources
saip·Jan 27, 2017
Agreed. The tooling around deep learning is not as mature as the tooling around software development. There is a fair amount of engineering and grunt work needed to even get started, let alone build on others' research. A few problems from top of mind: - Setup: Installing DL frameworks, Nvidia drivers and CUDA is an exercise in dependency hell. Trying to run someone's project, which has different dependencies than what you have is difficult to get right. Docker images [1] and nvidia-docker make this simple, but are still not the norm. - Reproducibility: This is big as Denny mentions. Folks still use Github for sharing code. But DL pipelines need versioning of more than just code. It's code, environment, parameters, data and results. - Sharing and collaboration: I've noticed that most collaboration on deep learning research, unlike softwar
Read the rest
e, happens only when the folks are co-located (e.g. part of the same school or company). This likely links back to reproducibility, but there are not many good tools for effective collaboration currently IMHO. [1] https://github.com/floydhub/dl-docker (Disclaimer: I created this)
In Engineering is the bottleneck in deep learning research
saip·Jan 26, 2017
Agreed. The tooling around deep learning is not as mature as the tooling around software development. There is a fair amount of engineering and grunt work needed to even get started, let alone build on others' research. A few problems from top of mind: - Setup: Installing DL frameworks, Nvidia drivers and CUDA is an exercise in dependency hell. Trying to run someone's project, which has different dependencies than what you have is difficult to get right. Docker images [1] and nvidia-docker make this simple, but are still not the norm. - Reproducibility: This is big as Denny mentions. Folks still use Github for sharing code. But DL pipelines need versioning of more than just code. It's code, environment, parameters, data and results. - Sharing and collaboration: I've noticed that most collaboration on deep learning research, unlike softwar
Read the rest
e, happens only when the folks are co-located (e.g. part of the same school or company). This likely links back to reproducibility, but there are not many good tools for effective collaboration currently IMHO. [1] https://github.com/floydhub/dl-docker (Disclaimer: I created this)
In Engineering is the bottleneck in Deep Learning research

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