gRPC Reviews

NumPy Reviews

gRPC mentions (92)

• Performance and Scalability for Database-Backed Applications

  We can take the previously mentioned idea of partitioning the database further by breaking up an application into multiple applications, each with its own database. In this case each application will communicate with the others via something like REST, RPC (e.g. gRPC), or a message queue (e.g. Redis, Kafka, or RabbitMQ). - Source: dev.to / 26 days ago

• Text-based language processing enhanced with AI/ML

  Aside from the obvious differences in client library nomenclature and use of different credentials, API usage is fairly similar. Not visually obvious is that the older platform client library calls the REST versions of the GCP APIs whereas the newer product client libraries call the gRPC versions which generally perform better... Yet another reason why the product client libraries are always recommended. - Source: dev.to / 10 days ago

• Fastly and the Linux kernel

  The open source projects Fastly uses and the foundations we partner with are vital to Fastly’s mission and success. Here's an unscientific list of projects and organizations supported by the Linux Foundation that we use and love include: The Linux Kernel, Kubernetes, containerd, eBPF, Falco, OpenAPI Initiative, ESLint, Express, Fastify, Lodash, Mocha, Node.js, Prometheus, Jenkins, OpenTelemetry, Envoy, etcd, Helm,... - Source: dev.to / 15 days ago

• Best Practices for Building Microservices with NestJS

  Choose a consistent communication protocol for inter-service communication. Common protocols include HTTP, gRPC, and message brokers like RabbitMQ or Kafka. NestJS supports various communication strategies, allowing you to choose the one that best fits your needs. - Source: dev.to / 28 days ago

• Why Did Google Choose To Implement gRPC Using HTTP/2?

  gRPC is an open-source high-performance RPC framework developed by Google. The design goal of gRPC is to run in any environment, supporting pluggable load balancing, tracing, health checking, and authentication. It not only supports service calls within and across data centers but is also suitable for the last mile of distributed computing, connecting devices, mobile applications, and browsers to backend services.... - Source: dev.to / about 1 month ago

NumPy mentions (112)

• Essential Deep Learning Checklist: Best Practices Unveiled

  How to Accomplish: Develop a script that iterates over the image database, preprocesses each image according to the model's requirements (e.g., resizing, normalization), and feeds them into the model for prediction. Ensure the script can handle large datasets efficiently by implementing batch processing. Use libraries like NumPy or Pandas for data management and TensorFlow or PyTorch for model inference. Include... - Source: dev.to / 20 days ago

• Documenting my pin collection with Segment Anything: Part 3

  NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations. - Source: dev.to / 20 days ago

• Awesome List

  NumPy - The fundamental package for scientific computing with Python. NumPy Documentation - Official documentation. - Source: dev.to / 25 days ago

• NumPy for Beginners: A Basic Guide to Get You Started

  This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / 27 days ago

• 2 Minutes to JupyterLab Notebook on Docker Desktop

  Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 10 months ago