ngx_http_js_module | nginx javascript module | Runtime Evironment library
kandi X-RAY | ngx_http_js_module Summary
kandi X-RAY | ngx_http_js_module Summary
This module main goal is to make nginx as scriptable as it could to be. Every scripting language known to me has its own standard library. This is a very good opportunity for a programmer that uses a language to write standard (i.e. multithreaded, synchronous, apache-based) web applications. But it is no good for the embedding into an async application like nginx. For example the [node.js][] developers have to re-implement almost all existing libraries from scratch (and they do so). If you don’t fully understand the difference between the applications based on sync and async principles please read about [asynchronous I/O][asynchronous] first. And SpiderMonkey doesn’t have any library. And nobody expect to have it in this language. More of that, the browser interface is also async and event-based. So we can relax and just script nginx instead of making another Web 2.0 framework. In other words the nxg_http_js_module tries to reflect the nginx functionality in JS. You can touch the ground running if you have ever seen [ngx_http_perl_module][] or [mod_perl][] before. [node.js]: [asynchronous]: [ngx_http_perl_module]: [mod_perl]: This module tries to mimic the perl modules in most cases.
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QUESTION
Why is my instance of Nginx Plus unable to access a JSON Web Key (JWK) file from Okta?
This instance of Nginx Plus was containerized with a Dockerfile
similar to this official Dockerfile.alpine
direct from Nginx, with the following differences:
ANSWER
Answered 2022-Jan-13 at 01:09Under the "Troubleshooting" header from the nginx-openid-connect
repository you linked, the final bullet underneath "Authentication is successful but browser shows too many redirects" instructs you to ensure that the nginx
user has access to the JWK file.
This is not referring to an Okta user or account; this is referring to the nginx
user within your Docker image. If you:
QUESTION
I am trying to replicate https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_serving_using_elastic_inference_with_your_own_model/tensorflow_serving_pretrained_model_elastic_inference.ipynb
My elastic inference accelerator is attached to notebook instance. I am using conda_amazonei_tensorflow_p36 kernel. According to documentation I made the changes for local EI:
...ANSWER
Answered 2020-Jun-23 at 01:37Solved it. The error I was getting is due to roles/permission of elastic inference attached to notebook. Once fixed these permissions by our devops team. It worked as expected. See https://github.com/aws/sagemaker-tensorflow-serving-container/issues/142
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