TeamCity is a continuous integration server from Czeck company JetBrains.
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TensorFlow
Score7.6 out of 10
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TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
TeamCity is very quick and straightforward to get up and running. A new server and a handful of agents could be brought online in easily under an hour. The professional tier is completely free, full-featured, and offers a huge amount of growth potential. TeamCity does exceptionally well in a small-scale business or enterprise setting.
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
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
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
The customization is still fairly complex and is best managed by a dev support team. There is great flexibility, but with flexibility comes responsibility. It isn't always obvious to a developer how to make simple customizations.
Sometimes the process for dealing with errors in the process isn't obvious. Some paths to rerunning steps redo dependencies unnecessarily while other paths that don't are less obvious.
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.
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
TeamCity runs really well, even when sharing a small instance with other applications. The user interface adequately conveys important information without being overly bloated, and it is snappy. There isn't any significant overhead to build agents or unit test runners that we have measured.
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
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
TeamCity is a great on-premise Continuous Integration tool. Visual Studio Team Services (VSTS) is a hosted SAAS application in Microsoft's Cloud. VSTS is a Source Code Repository, Build and Release System, and Agile Project Management Platform - whereas TeamCity is a Build and Release System only. TeamCity's interface is easier to use than VSTS, and neither have a great deployment pipeline solution. But VSTS's natural integration with Microsoft products, Microsoft's Cloud, Integration with Azure Active Directory, and free, private, Source Code repository - offer additional features and capabilities not available with Team City alone.
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
TeamCity has greatly improved team efficiency by streamlining our production and pre-production pipelines. We moved to TeamCity after seeing other teams have more success with it than we had with other tools.
TeamCity has helped the reliability of our product by easily allowing us to integrate unit testing, as well as full integration testing. This was not possible with other tools given our corporate firewall.
TeamCity's ability to include Docker containers in the pipeline steps has been crucial in improving our efficiency and reliability.
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
