CHANG-NING TSAI crazyguitar

Ordinary launch commands (no profiling):

Single-process:

python main_amp.py -a resnet50 --b 224 --deterministic --workers 4 --opt-level O1 ./bare_metal_train_val/

Multi-process:

python -m torch.distributed.launch  --nproc_per_node=2 main_amp.py -a resnet50 --b 224 --deterministic --workers 4 --opt-level O1 ./bare_metal_train_val/

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.

reference:

pysleep

The provided reference demonstrates how Python allows sleep operations to run in parallel:

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.

	// Thank you to the folks at the C++ slack channel,
	// along with @lewissbaker for the excellent literature
	// (even though it took me a few days to be convinced
	// it really was so).

	#include <uv.h>

	#include <iostream>
	#include <experimental/coroutine>

	import networkx as nx
	from itertools import product
	"""
	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

	# This isn't supposed to run as a bash script, i named it with ".sh" for syntax highlighting.

	# https://developer.nvidia.com/nsight-systems
	# https://docs.nvidia.com/nsight-systems/profiling/index.html

	# My preferred nsys (command line executable used to create profiles) commands
	#
	# In your script, write
	# torch.cuda.nvtx.range_push("region name")
	# ...

	import cPickle
	import pickle
	import json
	import random
	from time import time
	from hashlib import md5

	test_runs = 1000

	def float_list():

	#include <rdma/fabric.h>
	#include <rdma/fabric.h>
	#include <rdma/fi_endpoint.h>
	#include <rdma/fi_cm.h>
	#include <rdma/fi_errno.h>
	#include <rdma/fi_rma.h>

	#include <pthread.h>
	#include <stdio.h>
	#include <stdlib.h>

	# files/env:
	Defaults env_keep += "SSH_AUTH_SOCK"

	# tasks/main.yml
	- name: ensure sudo keeps SSH_AUTH_SOCK in environment
	copy: src=env
	dest=/etc/sudoers.d/env
	mode=0440
	owner=root
	group=root