The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.
Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.
In today’s hyper-connected technological world, there is a need for geographically widespread technical teams to facilitate global growth. Businesses that scale to this level need global teams to handle that reach. However, as development teams continue to ramp, it quickly becomes infeasible for them to solve operational problems while also scaling the product. That’s where SREs, whose primary job is to develop software to solve operational problems, come in. Investing in geographically-distributed (GD) SRE teams is key to achieve the goal of scaling a business or product to a global audience.
In part one of this series, we discussed some of the key principles to consider when developing geographically distributed (GD) SRE teams. Similar to the first article, we’re leveraging the journey of LinkedIn’s SRE team as the point of reference for the topics discussed here in part two. Within this post, we’ll discuss growth planning, the challenges associated with being part of a remote team, and some of the unexpected advantages geographically distributed SRE teams can offer.
The Lambda programming model for Node.js 8.10 now supports defining a function handler using the async/await pattern.
Asynchronous or non-blocking calls are an inherent and important part of applications, as user and human interfaces are asynchronous by nature. If you decide to have a coffee with a friend, you usually order the coffee then start or continue a conversation with your friend while the coffee is getting ready. You don’t wait for the coffee to be ready before you start talking. These activities are asynchronous, because you can start one and then move to the next without waiting for completion. Otherwise, you’d delay (or block) the start of the next activity.
Asynchronous calls used to be handled in Node.js using callbacks. That presented problems when they were nested within other callbacks in multiple levels, making the code difficult to maintain and understand.
Promises were implemented to try to solve issues caused by “callback hell.” They allow asynchronous operations to call their own methods and handle what happens when a call is successful or when it fails. As your requirements become more complicated, even promises become harder to work with and may still end up complicating your code.
Async/await is the new way of handling asynchronous operations in Node.js, and makes for simpler, easier, and cleaner code for non-blocking calls. It still uses promises but a callback is returned directly from the asynchronous function, just as if it were a synchronous blocking function.
During the past year our team is building Bitwhichmakes it simpler to build software using components. As part of our work, we develop ML and NLP algorithms to better understand how code is written, organized and used.