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Created
January 8, 2025 07:23— forked from Qix-/coro.cpp
C++20 coroutines + LibUV sample, v2
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When we compare this code with Airflow, the strengths of your code lie in its simplicity, lightweight nature, and the ability to easily integrate with existing Python code:
Simplicity: This code provides a simple and straightforward way to model and work with DAGs without needing to go through the process of setting up and configuring a comprehensive system like Airflow. For smaller teams or projects with less complexity, this can be an advantage.
Lightweight and easy to incorporate: Your code is a compact, single-file solution that can be easily integrated into an existing Python project without having to set up an entire Airflow environment. When your primary focus is on creating task dependencies with parameter combinations, rather than scheduling and monitoring, your code is easier to incorporate.
Focused on task generation: Your code emphasizes creating a Cartesian product of tasks associated with nodes' parameters. It is geared towards tackling
Favorite nsight systems profiling commands for Pytorch scripts
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Benchmarking serialization/unserialization in python using json, pickle and cPickle
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Often, there's a misconception about the Global Interpreter Lock (GIL) in Python, where it's believed that the
Python interpreter only permits one thread to run at any given time. Infact, Python Interpreter allows threads
running in parallel when GIL is released in a function. For instance, functions like time.sleep leverage I/O
blocking within Py_BEGIN_ALLOW_THREADS and Py_END_ALLOW_THREADS, enabling the Python interpreter to handle
multiple threads simultaneously in such cases.
Installing CUDA 12.1.1 + PyTorch nightly + Python 3.10 on Ubuntu 22.10
Installing CUDA 12.1.1 + PyTorch nightly + Python 3.10 on Ubuntu 22.10
Should you keep your NVIDIA driver?
CUDA 12.1.1 toolkit is gonna offer to install Nvidia driver 530 for us. It's from New Feature branch. It's likely to be newer than the default Nvidia driver you would've installed via apt-get (apt would prefer to give you 525, i.e. Production Branch).
If you're confident that you already have a new enough Nvidia driver for CUDA 12.1.1, and you'd like to keep your driver: feel free to skip this "uninstall driver" step.
But if you're not sure, or you know your driver is too old: let's uninstall it. CUDA will install a new driver for us later.
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Example for Ansible git-module and ssh agent forwarding
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