ArrayServer | Serves Numpy Arrays Persistently in Memory Mapped Files | Data Manipulation library
kandi X-RAY | ArrayServer Summary
kandi X-RAY | ArrayServer Summary
Ever wanted to store large numpy arrays persistently, but using something faster than disk? Well here you go. The server process holds a handle to a memory mapped file containing a binary representation of a numpy array. When the client requests the array, the server returns the filename and the client can access the memory mapped array without having to read from disk.
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Top functions reviewed by kandi - BETA
- Store a new array
- Return a numpy . memmap .
- Sets the value of an array .
- Delete a given array
- Get array metadata .
- Remove an item from the cache .
- Initialize connection parameters .
- Construct the URL for a given name .
ArrayServer Key Features
ArrayServer Examples and Code Snippets
Community Discussions
Trending Discussions on ArrayServer
QUESTION
I am running a script with powershell to create virtual machines, for example I have a list of 10 virtual machines and when I run the script they are only created from 5 to 5, does anyone know what can be due?
The structuring of the script is a workflow function that is responsible for creating the NIC and everything related to the virtual machine. This function is called every time you want to create a new machine through a loop in the following simplified way:
...ANSWER
Answered 2018-Aug-20 at 15:30Powershell workflows by default only allow 5 parallel streams running.
There's supposed to be a -throttlelimit
parameter to change that behavior.
Also, you probably want ARM Template to that do that for you. They handle dependencies and concurrency and dont have that limit
check this thread:
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Install ArrayServer
You can use ArrayServer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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