Python IDLE vs. Scale Computing Platform

Scenarios where python IDLE is well suited 1-Quick scripting and prototyping 2-Education and training 3-small projects utilities 4-exploring python libraries and modules Scenarios where python is less appropriate 1 large scale projects 2 complex debugging and profiling 3 multi language development 4 Advanced code analysis and inspection

Scale is best suited to environments that do not have excessive external or proprietary peripherals. Integrating with tape drive backups or robot tape libraries can be problematic. The most effective use of Scale systems is for companies running multiple instances of the same operating system. The hypervisor's code/file-sharing nature does an excellent job managing new instances while keeping the increase in storage to a minimum.

• Firstly, I would say Python IDLE interface is user friendly.
• Easy to learn for the beginners.
• Syntax highlighting is nice features.
• Smart indent helps a lot.

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• Simplicity - I do not have to create storage buckets or micromanage anything.
• Efficiency - Lower power use since moving to a hyperconverged environment.
• Scalability - Adding additional hardware is easy, and increasing things like the amount of RAM or number of cores is easy.

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• Too simplistic
• Could not find source revision management integration support
• Only basic debugging is available
• Does not have data-science-specific notebooks (but can be installed separately)

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• Dedicated Lights-Out Solution for low-end systems
• Ability to replace smaller storage drives with larger ones, rather than requiring the addition of an entire node for storage increases
• Different UI views for VMs: ability to minimize certain groups and have that persist through log-ins

Since I have had no issues with downtime; easier management of my cluster and the ability to lower the number of devices in my Infrastructure, I will gladly renew my support contract with Scale Computing HC3 and upgrade my equipment with them when it comes time for it.

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The IDE Python IDLE is a good place to start as it helps you become familiar with the way Python works and understand its syntax.

This IDE allows you to configure the environment, font, size, colors, .....

It also looks like any simple text editor for any operating system, I work with Windows or Linux interchangeably, and you don't have to learn to use the IDE before programming.

Once the IDE is executed you can start programming directly in it.

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Everything you need to do is point-and-click easy. If you are the kind of admin who wants to edit every config file and endlessly customize your environment, then Scale may not be for you. On the other hand, if you just want it to work really well, and do what they told you it will do, then Scale is the ideal system.

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Never had an outage or an error. Once a hard drive failed, but Scale kept on working flawlessly without interruption.

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Performance has been just as outstanding as reliability. I have never experienced delays in any aspect of usage.

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Python IDLE support is what the community can give you. As it is free software, it does not have support provided by the manufacturer or by third-parties.

In any case, for most of the problems that normal users can find, the solution, or alternatives, can be found quickly online.

As this IDE is made in Python, the support is the same group of Python developers.

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They are very knowledgeable about their own products and hardware addressing my concerns or issues very quickly and on the first contact. Calls concerning VMware migrations and Acronis backup took a little more time for more complicated issues, but the Scale Computing Platform techs were diligent to stay on top of issues until they were resolved. Most of my issues have been with the initial setup/migration.

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I did not do in-person training.

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Because Scale is so simple, the training is just as quick and easy.

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The implementation was very easy. We had Scale support on standby and they were ready and eager to help if needed. The process went so fast the employees in the organization did not even know it was done.

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It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop

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We previously used Microsoft Hyper V and VMWare and, before that, a room for single-purpose servers. My satisfaction with Scale is because it is a more straightforward product to install and use; it has incredible speed and reliability. In the past, getting support from Microsoft was labor intensive, and with VMWare, there was a language accent barrier.

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HC3 is one of the best products I have purchased for our district. It is unbelievably reliable to the point that they shoot themselves in the foot on support contracts.

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• In a short time, we were able to develop several ML models for various teams to make accurate decisions.
• Beginners can easily understand and adapt to GUI.
• We could automate several manual validation tasks and so could reduce human intervention.

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• With the high cost of the leading competitor the savings starts day one and saves every year after.
• Scale can be administered by a tier one technician so cost of labor is reduced.
• If there is a problem like a cyber attack, the savings kick in with the least amount of downtime.

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info