Think Different

  Photo by SevenStorm JUHASZIMRUS by pexel Differences Between CPU-based Multi-threading and Multi-processing CPU-based Multi-threading : - Concept: Uses multiple threads within a single process. - Shared Memory: Threads share the same memory space. - I/O Bound Tasks: Effective for tasks that spend a lot of time waiting for I/O operations. - Global Interpreter Lock (GIL): In Python, the GIL can be a limiting factor for CPU-bound tasks since it allows only one thread to execute Python bytecode at a time. CPU-based Multi-processing : - Concept: Uses multiple processes, each with its own memory space. - Separate Memory: Processes do not share memory, leading to more isolation. - CPU Bound Tasks: Effective for tasks that require significant CPU computation since each process can run on a different CPU core. - No GIL: Each process has its own Python interpreter and memory space, so the GIL is not an issue. CUDA with PyTorch : - Concept: Utilizes the GPU for parallel computation. - Massi...

  Photo by MART PRODUCTION at pexel If you’re interested in customizing an agent for a specific task, one way to do this is to fine-tune the models on your dataset.  For preparing dataset you can see this article . 1. Curate the Dataset - Using NeMo Curator:   - Install NVIDIA NeMo: `pip install nemo_toolkit`   - Use NeMo Curator to prepare your dataset according to your specific requirements. 2. Fine-Tune the Model - Using NeMo Framework:   1. Setup NeMo:      ```python      import nemo      import nemo.collections.nlp as nemo_nlp      ```   2. Prepare the Data:      ```python      # Example to prepare dataset      from nemo.collections.nlp.data.text_to_text import TextToTextDataset      dataset = TextToTextDataset(file_path="path_to_your_dataset")      ```   3. Fine-Tune the Model:      ```python ...

  You might have used GPU for faster processing of your Machine Learning code with Pytorch. However, do you know that you can use that with Tensorflow as well? Here are the steps on how to enable GPU acceleration for TensorFlow to achieve faster performance: 1. Verify GPU Compatibility: Check for CUDA Support: Ensure your GPU has a compute capability of 3. 5 or higher (check NVIDIA's website). Install CUDA Toolkit and cuDNN: Download and install the appropriate CUDA Toolkit and cuDNN versions compatible with your TensorFlow version and GPU from NVIDIA's website. 2. Install GPU-Enabled TensorFlow: Use pip : If you haven't installed TensorFlow yet, use the following command to install the GPU version: Bash pip install tensorflow-gpu Upgrade Existing Installation: If you already have TensorFlow installed, upgrade it to the GPU version: Bash pip install --upgrade tensorflow-gpu 3. Verify GPU Detection: Run a TensorFlow script: Create a simple TensorFlow ...