The fast, feature-rich, GPU based terminal emulator. It's capable, scriptable, composable, cross-platform, and innovative.
The article details the author's investigation into slow Python tool startup times. They used the `python -X importtime` feature to identify import bottlenecks and visualized the resulting data using Kevin Michel's `python-importtime-graph` tool, revealing a dense treemap of import times.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
Explores the performance benefits of running Python without the Global Interpreter Lock (GIL), using the new experimental Python 3.13.0b4 pre-release with the --disable-gil flag. Discusses how this change can lead to faster execution times for CPU-intensive tasks in data science and machine learning.
A discussion post on Reddit's LocalLLaMA subreddit about logging the output of running models and monitoring performance, specifically for debugging errors, warnings, and performance analysis. The post also mentions the need for flags to output logs as flat files, GPU metrics (GPU utilization, RAM usage, TensorCore usage, etc.) for troubleshooting and analytics.
