CircleCI is a software delivery engine from the company of the same name in San Francisco, that helps teams ship software faster, offering their platform for Continuous Integration and Continuous Delivery (CI/CD). Ultimately, the solution helps to map every source of change for software teams, so they can accelerate innovation and growth.
$0
for up to 6,000 build minutes and up to 5 active users per month
TensorFlow
Score7.6 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
Based on our experience, CircleCI is well-suited for automating mobile app release cycles. For example, to release an iOS app, you would need to build, sign, and upload it to TestFlight, which requires a dedicated Mac in the office. But with CircleCI, you can have macOS executors, so you don't have to manage a physical build machine. Another benefit is that CircleCI's certified AWS Orbs abstract away complex authentication and deployment logic, allowing us to build, push, and deploy Docker containers to Amazon ECS with minimal configuration and high reliability. CircleCI is less suited for smaller projects where the development and deployment are not that extensive, for example, a static site. Once you have built a static site, you probably won't make any further changes, so there's no point in paying for it.
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
Automated builds! This is really why you get CircleCI, to automate the build process. This makes building your application far more reliable and repeatable. It can also run tests and verify your application is working as expected.
Simple. Unlike Jenkins, Teamcity, or other platforms, CircleCI doesn't need a lot of setup. It's completely hosted, so there's no infrastructure to set up. The config file does take a bit to understand, but if you follow their example and start with something small and add to it, you can get it up and going quicker than it first looks.
Scales easily. Again, since it's all cloud-based, you don't have to manage or scale infrastructure. Simply subscribe to the number of containers you want, and scaling up just means buying more containers.
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.
The reliability & speed, it just works. The ability to spin up macOS runners and Docker containers on demand without managing hardware is a huge win. The Orbs system makes integrating with AWS and Slack incredibly easy, saving us weeks of custom scripting and providing real-time updates in our Slack channel. This makes it easy for us to track and ensures that everyone involved knows the status. Of course, it has drawbacks related to configuration complexity and, in some cases, cost transparency, but overall, it is an industry-standard, robust tool that solves our core infrastructure problems well.
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 pretty snappy, even with using workflows with multiple steps and different docker images. I've seen builds take a long time if it's really involved, but from what I can tell, it's still at least on par if not faster than other build tools.
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
Unless you have a reasonably large account, you're going to be mainly stuck reading their documentation. Which has improved somewhat over the years but is still extremely limited compared to a platform like Digital Ocean who invested in the documentation and a community to ensure it's kept up to date. If you can't find your answer there, you can be stuck.
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
Jenkins is usually self-hosted, Travis CI's infrastructure is largely unreliable (lots of tests time out for no discernable reason), and Semaphore encourages you to configure your CI/CD from a web UI. We like CircleCI because its hosted, our tests run largely as expected on their infrastructure, and we can configure it from a config file that we track in GitHub.
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
We pay over $5K/ month and we have high expectations for service. Sometimes I feel that we don't get the value, but only sometimes.
We have had to build our own application to keep state and broker releases and deployments. We call our app deployer. I feel that CircleCI could do more to understand our needs and possibly build additional features that would enable us to invest less in build and deployment infrastructure and justify paying more for Circle.
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
