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 Trainers of Large AI models – A new jobrole

With the advent of AI, job roles will evolve, diversify and increase. Here is a new job role of “AI Trainer” that I came across.

AI models are generating contextual writings, writing codes, making pictures out of text, and also making videos out of text. The playfield has become infinite. We all know that.  There are models which do the needful and then the models need the people that feeding data, check the bugs, and retrain the models and simulations.

 

Some examples can be:

1. Rank a series of code responses that were produced by an AI model
2. Edit codes written by AI model to fix bugs and improve performance
3. Write a shorter code to the code developed by the AI model
4. Write pieces of codes to reasoning to show an AI model how to respond to a prompt.

The interesting part is the prerequisite is not any degree but your proficiency in any language like Python, Java, Javascript, SQL, C/C++/C#, HTML

Should you know other languages like Ruby, Rust, Swift, Go, Matlab, PHP, NET, and DART; better your chances to be selected.

Shortlisting has also been out of the pieces of code you have written in GitHub, Geeklist, Snipplr, Masterbranch, or Stackoverflow. Hence not only the understanding of languages but also the proof of the same at the correct platform is necessary.

 

In Large models, this bug fixing is done with tools like NVIDIA Computer Sanitizer.

 

Finding and fixing errors in code is essential when developing software, but it can be tough and time-consuming. When dealing with parallel programming using many threads, debugging becomes even more complicated.

One such set of tools is NVIDIA Compute Sanitizer is there to help developers with error fixing.  By using this tool, developers can save time and effort while making their CUDA applications more reliable and faster. You can find Compute Sanitizer bundled in the CUDA Toolkit.

 

Compute Sanitizer is a set of tools that can do various checks to ensure your code works correctly. One of the main challenges in debugging is figuring out what’s causing the problem. Once you find the cause, fixing it is usually easier. This is especially true in situations where many things are happening at the same time, like in parallel execution.

Compute Sanitizer is excellent at helping with this root-cause debugging. It checks your code for issues like memory mistakes, race conditions, using variables before they have values and synchronization errors. These issues might cause bugs, but their effects may not directly show the exact source of the problem in your code. 

 

Compute Sanitizer includes four main tools that serve different purposes:

1. Memcheck: This tool helps detect memory access errors and memory leaks in your code.
2. Racecheck: It is used to identify potential hazards related to shared memory data access.
3. Initcheck: This tool is responsible for detecting instances where a device’s global memory is accessed without proper initialization.
4. Synccheck: This tool helps in detecting hazards related to thread synchronization.

 

In addition to these tools, Compute Sanitizer also provides some extra features:

1. An API that allows developers to create custom sanitizing and tracing tools specifically designed for CUDA applications.
2. Integration with NVIDIA Tools Extension (NVTX) to enhance debugging capabilities.
3. Coredump support, which can be utilized in conjunction with cuda-gdb for better debugging assistance.

Preparing yourself continuously for the upcoming job roles in your domain is the essence of growth.

Keep an eye on the ever-moving goal, upgrade, and perform. That is the only way to survive when you are a novice and thrive as a veteran.