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Upgrading a PC was more of a learning experience than I expected

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Some people buy computing power for self-training or self-edification projects. Others rent computing power. I like owning the gear I work on. I purchased a desktop to be used as a gaming machine, a development system, a containerized workload machine, a Data Science machine, and a Machine Learning platform. I didn't understand that it would turn into a series of hardware upgrades bound by PC architecture constraints. This was a great learning experience but not the best raw dollars investment from a pure cost/capabilities point of view. The 3 years of upgrades cost $1200. I saved some money purchasing previous-generation hardware. Buying current-generation hardware upgrades would cost $1900.  I could have stopped anywhere on the path. Apple wasn't a player in the gaming, M/L, or GPU market when I made my purchase. Apple has caught up for most of my use cases with its large shared memory architecture and performant CPUs. A Macbook might be the simplest approach for someone want
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My Windows software development ecosystem is complicated

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Modern development is surprisingly complicated.  My personal software development environment is suffering a severe case of urban sprawl.  While working on some Container IAC scripting for ML and the cloud, I had to pop into a Linux environment. This environment map made me realize that I have a crazy set of different specialized sandboxes. Software systems continue to grow and become more complicated. Software Engineering platforms growing right alongside them. YouTube https://youtu.be/67i43rBkk1c Why this page exists This page exists as a link root for the video and to make it easy to refer to this diagram. Revision History Created 2024 08
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Manually validating compatibility and running NVIDIA (NIM) container images

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NVIDIA NIMs are ready to run pre-packaged containerized models.  The NIMs and their included models are available in a variety of profiles supporting different compute hardware configurations.  You can run the NIMs in an interrogatory mode that will tell you which models are compatible with your GPU hardware. You can then run the NIM with the associated profile.   Sometimes there are still problems and we have to add additional tuning parameters to fit in memory or change data types. In my case, the data type change is because of some bug in the NIM startup detection code.   This article requires additional polish.  It has more than a few rough edges.   NVIDIA NIMs are semi-opaque. You cannot build your own NIM.  NIM construction details are not described by NVIDIA.  Examining NVidia Model Container Images The first step is to select models we think can fit and run on our NVIDIA GPU hardware. The first step is to investigate models of the different types by visiting the appropriate NVI
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