• Onshape Onboarding

This contains documentation for getting started with using OnShape for CAD. OnShape cloud-hosted CAD software that lets you work with others like on a Google Doc, with the power and capabilities of any other software like Solidworks or Inventor.

• VS Code installation

Visual Studio Code, commonly known as VSCode, is a popular tool used by programmers worldwide. It serves as a text editor and an Integrated Development Environment (IDE) that supports a wide variety of programming languages. One of its key features is its extensive library of extensions. These extensions add on to the basic functionalities of VSCode, making coding more efficient and convenient. However, there's a catch. When these extensions are installed and used frequently, they generate a multitude of files. These files are typically stored in a folder named .vscode-extension within your home directory. On a cluster computing facility such as the FASTER and Grace clusters at Texas A&M University, there's a limitation on how many files you can have in your home directory. For instance, the file number limit could be 10000, while the .vscode-extension directory can hold around 4000 temporary files even with just a few extensions. Thus, if the number of files in your home directory surpasses this limit due to VSCode extensions, you might face some issues. This restriction can discourage users from taking full advantage of the extensive features and extensions offered by the VSCode editor. To overcome this, we can shift the .vscode-extension directory to the scratch space. The scratch space is another area in the cluster where you can store files and it usually has a much higher limit on the number of files compared to the home directory. We can perform this shift smoothly using a feature called symbolic links (or symlinks for short). Think of a symlink as a shortcut or a reference that points to another file or directory located somewhere else. Here's a step-by-step guide on how to move the .vscode-extension directory to the scratch space and create a symbolic link to it in your home directory: 1. Copy the .vscode-extension directory to the scratch space: Using the cp command, you can copy the .vscode-extension directory (along with all its contents) to the scratch space. Here's how: cp -r ~/.vscode-extension /scratch/user Don't forget to replace /scratch/user with the actual path to your scratch directory. 2. Remove the original .vscode-extension directory: Once you've confirmed that the directory has been copied successfully to the scratch space, you can remove the original directory from your home space. You can do this using the rm command: rm -r ~/.vscode-extension It's important to make sure that the directory has been copied to the scratch space successfully before deleting the original. 3. Create a symbolic link in the home directory: Lastly, you'll create a symbolic link in your home directory that points to the .vscode-extension directory in the scratch space. You can do this as follows: ln -s /scratch/user/.vscode-extension ~/.vscode-extension By following this process, all the files generated by VSCode extensions will be stored in the scratch space. This prevents your home directory from exceeding its file limit. Now, when you access ~/.vscode-extension, the system will automatically redirect you to the directory in the scratch space, thanks to the symlink. This method ensures that you can use VSCode and its various extensions without worrying about hitting the file limit in your home directory.

• Fine-tuning LLMs with PEFT and LoRA

As LLMs get larger fine-tuning to the full extent can become difficult to train on consumer hardware. Storing and deploying these tuned models can also be quite expensive and difficult to store. With PEFT (parameter -efficent fine tuning), it approaches fine-tune on a smaller scale of model parameters while freezing most parameters of the pretrained LLMs. Basically it is providing full performance that which is similar if not better than full fine tuning while only having a small number of trainable parameters. This source explains that as well as going over LORA diagrams and a code walk through.

• PyFR installation to local machine

These instructions were executed on the FASTER and Grace cluster computing facilities at Texas A&M University. However, the process can be applied to other clusters with similar environments. For local installation, please refer to the PyFR documentation. Please note that these instructions were valid at the time of writing. Depending on the time you're executing these, the versions of the modules may need to be updated. 1. Loading Modules The first step involves loading pre-installed software libraries required for PyFR. Execute the following commands in your terminal to load these modules: module load foss/2022b module load libffi/3.4.4 module load OpenSSL/1.1.1k module load METIS/5.1.0 module load HDF5/1.13.1 2. Python Installation from Source Choose a location for Python 3.11.1 installation, preferably in a .local directory. Navigate to the directory containing the Python 3.11.1 source code. Then configure and install Python: cd $INSTALL/Python-3.11.1/ ./configure --prefix=$LOCAL --enable-shared --with-system-ffi --with-openssl=/sw/eb/sw/OpenSSL/1.1.1k-GCCcore-11.2.0/ PKG_CONFIG_PATH=$LOCAL/pkgconfig LDFLAGS=/usr/lib64/libffi.so.6.0.2 make clean; make -j20; make install; 3. Virtual Environment Setup A virtual environment allows you to isolate Python packages for this project from others on your system. Create and activate a virtual environment using: pip3.11 install virtualenv python3.11 -m venv pyfr-venv . pyfr-venv/bin/activate 4. Install PyFR Dependencies Several Python packages are required for PyFR. Install these packages using the following commands: pip3 install --upgrade pip pip3 install --no-cache-dir wheel pip3 install --no-cache-dir botorch pandas matplotlib pyfr pip3 uninstall -y pyfr 5. Install PyFR from Source Finally, navigate to the directory containing the PyFR source code, and then install PyFR: cd /scratch/user/sambit98/github/PyFR/ python3 setup.py develop Congratulations! You've successfully set up PyFR on the FASTER and Grace cluster computing facilities. You should now be able to use PyFR for your computational fluid dynamics simulations.

• A “Hands-on” Introduction to OpenMP*

Open Multi-Processing, is an API designed to simplify the integration of parallelism in software development, particularly for applications running on multi-core processors and shared-memory systems. It is an important resource as it goes over what openMP and ways to work with it. It is especially important because it provides a straightforward way to express parallelism in code through pragma directives, making it easier to create parallel regions, parallelize loops, and define critical sections. The key benefit of OpenMP lies in its ease of use, automatic thread management, and portability across various compilers and platforms. For app development, especially in the context of mobile or desktop applications, OpenMP can enhance performance by leveraging the capabilities of modern multi-core processors. By parallelizing computationally intensive tasks, such as image processing, data analysis, or simulations, apps can run faster and more efficiently, providing a smoother user experience and taking full advantage of the available hardware resources. OpenMP's scalability allows apps to adapt to different hardware configurations, making it a valuable tool for developers aiming to optimize their software for a range of devices and platforms.

• TensorFlow Docs

TensorFlow is a powerful framework for Deep Learning, developed by google. This specifically is their python package, which is easy to use and can be used to train incredibly powerful models.