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There are even more changes under the hood: multi-layer caching, gzipand ETag support, support for audio and video uploads, BitTorrent links,HTML and CSS filtering, flexible access control, new moderationfeatures, new UI translations and themes, code reorganization,simplified installation, and more.


The main advantage of using NEST in this way is that it decouples thesimulation backend in the form of the NEST simulation kernel from thefrontend, i.e., the code that controls the simulation. In such ascenario, only the backend (the server) depends on NEST, while thefrontend could really be anything that can talk HTTP. Under the hood,NEST Server forwards the commands it receives to PyNEST, and sendsback the result data in response packets.

The NEST Server Client is able to send complete simulation scripts tothe NEST Server using the functions exec_script and from_file.The following listing shows a Python snippet using the NEST ServerClient to execute a simple script on the Server using theexec_script function:

In a more realistic scenario, you probably already have yoursimulation script stored in a file. Such scripts can be sent to theNEST Server for execution using the from_file function provided bythe NEST Server Client.

As explained above, the /exec route of the NEST Server API allowsyou to run custom Python scripts within the NEST Server context. Thiscan greatly simplify your workflow in situations where you alreadyhave the simulation description in the form of a Python script. On thetechnical side, however, this route exposes a potential riskfor the remote execution of malicious code.

In order to protect the execution environment from such securitybreaches, we execute all user supplied code in a RestrictedPython trustedenvironment. Consequently, this environment blocksyour scripts from importing additional Python modules, unlessthey are explicitly safelisted during the start-up of NEST Server.

To mark modules as safe for execution within NEST Server and make themavailable to code from user supplied scripts that run through the /execroute, a comma separated list of Python module names can be assignedto the environment variable NEST_SERVER_MODULES prior to startingthe NEST Server.

If in the process of working through the tutorial, you stopped thesession and started a new one along the way, this mayhappen. Why?Under the hood,we usegit-lfsto save large files, and these files may not be fetched when a newenvironment is started. We try to retrieve them automatically when neededfor a renku command, but that may not always work.

The function def run_inference_on_image(image) (src classifies an image but the linear algebra operations used to classify the image do not appear to be viewable. Is possible to execute the model using the various matrix transformations that I assume are taking place ' under the hood' in function run_inference_on_image ? 041b061a72


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