nuclio | Performance Serverless event and data processing platform | Serverless library
kandi X-RAY | nuclio Summary
kandi X-RAY | nuclio Summary
Nuclio is a high-performance "serverless" framework focused on data, I/O, and compute intensive workloads. It is well integrated with popular data science tools, such as Jupyter and Kubeflow; supports a variety of data and streaming sources; and supports execution over CPUs and GPUs. The Nuclio project began in 2017 and is constantly and rapidly evolving; many start-ups and enterprises are now using Nuclio in production. You can use Nuclio as a standalone Docker container or on top of an existing Kubernetes cluster; see the deployment instructions in the Nuclio documentation. You can also use Nuclio through a fully managed application service (in the cloud or on-prem) in the Iguazio Data Science Platform, which you can try for free. If you wish to create and manage Nuclio functions through code - for example, from Jupyter Notebook - see the Nuclio Jupyter project, which features a Python package and SDK for creating and deploying Nuclio functions from Jupyter Notebook. Nuclio is also an integral part of the new open-source MLRun library for data science automation and tracking and of the open-source Kubeflow Pipelines framework for building and deploying portable, scalable ML workflows. Nuclio is extremely fast: a single function instance can process hundreds of thousands of HTTP requests or data records per second. This is 10-100 times faster than some other frameworks. To learn more about how Nuclio works, see the Nuclio architecture documentation, read this review of Nuclio vs. AWS Lambda, or watch the Nuclio serverless and AI webinar. You can find links to additional articles and tutorials on the Nuclio web site. Nuclio is secure: Nuclio is integrated with Kaniko to allow a secure and production-ready way of building Docker images at run time. For further questions and support, click to join the Nuclio Slack workspace.
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QUESTION
what is the difference between mlrun.code_to_function and mlrun.new_project? for example we can deploy a function with code_to_function
...ANSWER
Answered 2021-Jul-14 at 18:46One of the core concepts of MLRun is creating a serverless function out of a piece of code. You can specify a Python file, entrypoint function, Docker image, K8s resources, and more. code_to_function
is how this is accomplished. See this page in the docs for more info.
QUESTION
I need an example of deploying a Nuclio function using MLRun.
- I have my code in a .py file
- How do I use MLRun to deploy this function to Nuclio?
- How do I configure the HTTP endpoint? (using a specified port)
ANSWER
Answered 2021-Jul-07 at 15:06You should follow these steps:
- Call to
code_to_function
as in this example:
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