andrebrait

Here is the best setup (I think so :D) for K-series Keychron keyboards on Linux.

Note: many newer Keychron keyboards use QMK as firmware and most tips here do not apply to them. Maybe the ones related to Bluetooth can be useful, but everything related to Apple's keyboard module (hid_apple) on Linux, won't work. As far as I know, all QMK-based boards use the hid_generic module instead. Examples of QMK-based boards are: Q, Q-Pro, V, K-Pro, etc.

Most of these commands have been tested on Ubuntu 20.04 and should also work on most Debian-based distributions. If a command happens not to work for you, take a look in the comment section.

Make Fn + F-keys work (NOT FOR QMK-BASED BOARDS)

Older Keychron keyboards (those not based on QMK) use the hid_apple driver on Linux, even in the Windows/Android mode, both in Bluetooth and Wired modes.

varqox

How to record multiple applications and microphone into one audio file on Linux

Step 0. Terminology

Sinks are for output, sources are for input. To stream source to sink a loopback must be created. More shall you find there.

Step 1. Create output sink that will be recorded

Our output sink will be named recording.

pacmd load-module module-null-sink sink_name=recording sink_properties=device.description=recording

MatrixManAtYrService

from locust import HttpLocust, TaskSet, task
import json

class UserBehavior(TaskSet):

    def on_start(self):
        # this retrieves a coookie from the server and stores in in a RequestsCookieJar:  self.locust.client.cookes
        # that is, it stores the cookie on an instance of this class, not the class itself (as your code does)
        # your server may have different requirements for how the login request should look, but this worked for me
	self.client.post("/login",

kylehounslow

from __future__ import print_function
import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'

embanner

dusenberrymw

Keras Tips & Tricks

fit_generator

• Can using either threading or multiprocessing for concurrent and parallel processing, respectively, of the data generator.
• In the threading approach (model.fit_generator(..., pickle_safe=False)), the generator can be run concurrently (but not parallel) in multiple threads, with each thread pulling the next available batch based on the shared state of the generator and placing it in a shared queue. However, the generator must be threadsafe (i.e. use locks at synchronization points).
• Due to the Python global interpreter lock (GIL), the threading option generally does not benefit from >1 worker (i.e. model.fit_generator(..., nb_worker=1) is best). One possible use case in which >1 threads could be beneficial is the presence of exceptionally long IO times, during which the GIL will be released to enable concurrency. Note also that TensorFlow's `session.run(

cgarciae

nvidia-docker with docker-compose

Setup

1. Install nvidia-docker-plugin
2. Reboot (for some reason)
3. On the folder with this docker-compose.yml file do

docker-compose up

fchollet

from keras.models import Sequential
from keras.layers import Dense

x, y = ...
x_val, y_val = ...

# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')

JonCole

Some of the Redis best practices content has moved

This content from this markdown file has moved a new, happier home where it can serve more people. Please check it out : https://docs.microsoft.com/azure/azure-cache-for-redis/cache-best-practices.

NOTE: Client specific guidance listed below is still valid and should still be considered. I will update this document once all content has been moved.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats