TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
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Travis CI
Score7.3 out of 10
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Travis CI is an open source continuous integration platform, that enables users to run and test simultaneously on different environments, and automatically catch code failures and bugs.
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
TravisCI is suited for workflows involving typical software development but unfortunately I think the software needs more improvement to be up to date with current development systems and TravisCI hasn't been improving much in that space in terms of integrations.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
I think they could have a cheaper personal plan. I'd love to use Travis on personal projects, but I don't want to publish them nor I can pay $69 a month for personal projects that I don't want to be open source.
There is no interface for configuring repos on Travis CI, you have to do it via a file in the repo. This make configuration very flexible, but also makes it harder for simpler projects and for small tweaks in the configuration.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
TravisCI hasn't had much changes made to its software and has thus fallen behind compared to many other CI/CD applications out there. I can only give it a 5 because it does what it is supposed to do but lacks product innovation.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
After the private equity firm had bought this company the innovation and support has really gone downhill a lot. I am not a fan that they have gutted the software trying to make money from it and put innovation and product development second.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Jenkins is much more complicated to configure and start using. Although, one you have done that, it's extremely powerful and full of features. Maybe many more than Travis CI. As per TeamCity, I would never go back to using it. It's also complicated to configure but it is not worth the trouble. Codeship supports integration with GitHub, GitLab and BitBucket. I've only used it briefly, but it seems to be a nice tool.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
It's improved my ability to deliver working code, increasing my development velocity.
It increases confidence that your own work (and those of external contributors) does not have any obvious bugs, provided you have sufficient test coverage.
It helps to ensure consistent standards across a team (you can integrate process elements like "go lint" and other style checks as part of your build).
It's zero-cost for public/open source projects, so the only investment is a few minutes setting up a build configuration file (hence the return is very high).
The .travis.yml file is a great way for onboarding new developers, since it shows how to bootstrap a build environment and run a build "from scratch".
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
