Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
#!/bin/bash | |
### steps #### | |
# Verify the system has a cuda-capable gpu | |
# Download and install the nvidia cuda toolkit and cudnn | |
# Setup environmental variables | |
# Verify the installation | |
### | |
### to verify your gpu is cuda enable check |
Magic words:
psql -U postgres
Some interesting flags (to see all, use -h
or --help
depending on your psql version):
-E
: will describe the underlaying queries of the \
commands (cool for learning!)-l
: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)Press minus + shift + s
and return
to chop/fold long lines!
import pandas as pd | |
import pandas.io.sql as sqlio | |
import psycopg2 | |
conn = psycopg2.connect("host='{}' port={} dbname='{}' user={} password={}".format(host, port, dbname, username, pwd)) | |
sql = "select count(*) from table;" | |
dat = sqlio.read_sql_query(sql, conn) | |
conn = None |
You can use ssacli
(smart storage administrator command line interface) tool to manage any of supported HP Smart Array Controllers in your Proxmox host without need to reboot your server to access Smart Storage Administrator in BIOS. That means no host downtime when managing your storage.
CLI is not as convenient as GUI interface provided by BIOS or desktop utilities, but still allows you to fully manage your controller, physical disks and logical drives on the fly with no Proxmox host downtime.
ssacli
replaces older hpssacli
, but shares the same syntax and adds support for newer servers and controllers.
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli
. Please find it here: https://github.com/ericmjl/pyds-cli
Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.
Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.
Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!
Strongly inspired by https://gist.github.com/heymonkeyriot/9a2f429caff5c091d5429666fa080403.
On Ubuntu :
sudo apt install python3 python3-pip