coltonbh

GPU Support For Docker Swarm

Docker compose has nice support for GPUs, K8s has moved their cluster-wide GPU scheduler from experimental to stable status. Docker swarm has yet to support the device option used in docker compose so the mechanisms for supporting GPUs on swarm are a bit more open-ended.

Basic documentation

• NVIDIA container runtime for docker. The runtime is no longer required to run GPU support with the docker cli or compose; however, it appears necessary so that one can set Default Runtime: nvidia for swarm mode.
• docker compose GPU support
• Good GitHub Gist Reference for an overview on Swarm with GPUs. It is a bit dated, but has good links and conversation.
• [Miscellaneous Opt

dergigi

Moved to nostr-resources.com

aewhite

# Solution based on https://stackoverflow.com/a/51785735/278836
import tensorflow as tf

def extract_patches(images):
    return tf.image.extract_patches(
        images,
        (1, 3, 3, 1),
        (1, 1, 1, 1),
        (1, 1, 1, 1),
        padding="VALID")

gjreasoner

# Resize the file system in UI, under VM -> Hardware -> Click on the disk to resize, click "Resize disk" button

# Confirm increase in disk space (1TB in my case)
$ lsblk
NAME                      MAJ:MIN RM  SIZE RO TYPE MOUNTPOINT
sda                         8:0    0    1T  0 disk
├─sda1                      8:1    0    1M  0 part
├─sda2                      8:2    0    1G  0 part /boot
└─sda3                      8:3    0    1T  0 part

RafaelWO

Setting up a Docker Swarm with GPUs

Installing Docker

Official instructions.

Add yourself to the docker group to be able to run containers as non-root (see Post-install steps for Linux).

jsoma

{
  "Version":"2012-10-17",
  "Statement":[
    {
      "Sid":"PublicRead",
      "Effect":"Allow",
      "Principal": "*",
      "Action":["s3:GetObject","s3:GetObjectVersion"],
      "Resource":["arn:aws:s3:::YOUR_BUCKET_NAME_GOES_HERE/*"]
    }

ih2502mk

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

kissgyorgy

import asyncio
import psycopg2

# dbname should be the same for the notifying process
conn = psycopg2.connect(host="localhost", dbname="example", user="example", password="example")
conn.set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)

cursor = conn.cursor()
cursor.execute(f"LISTEN match_updates;")

alexlib

Python version of the MATLAB code in this Stack Overflow post: http://stackoverflow.com/a/18648210/97160

The example shows how to determine the best-fit plane/surface (1st or higher order polynomial) over a set of three-dimensional points.

Implemented in Python + NumPy + SciPy + matplotlib.

hungyiwu

#!/bin/bash
#SBATCH --job-name=installSE
#SBATCH -c 4                # Number of cores
#SBATCH -t 0-03             # Runtime in D-HH:MM, minimum of 10 minutes
#SBATCH -p short            # Partition to submit to, whichever is faster
#SBATCH --mem-per-cpu=4G    # Memory (see also --mem)
#SBATCH -o joboutput.out    # job output
#SBATCH -e joberrors.err    # job error log

# load modules