Alir3z4

import os
import pickle
import warnings

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout

benkehoe

#!/bin/sh

# MIT No Attribution
#
# Copyright 2020 Ben Kehoe
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to

nhymxu

gunicorn run:app --workers=9
gunicorn run:app --workers=9 --worker-class=meinheld.gmeinheld.MeinheldWorker

Macbook Pro 2015 Python 3.7

Framework	Server	Req/s	Max latency	+/- Stdev

faizanbashir

FROM alpine:latest

LABEL MAINTAINER="Faizan Bashir <faizan.ibn.bashir@gmail.com>"

# Linking of locale.h as xlocale.h
# This is done to ensure successfull install of python numpy package
# see https://forum.alpinelinux.org/comment/690#comment-690 for more information.

WORKDIR /var/www/

kmhofmann

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht

Dev-Dipesh

Redis is Database whereas RabbitMQ was designed as a message router or message-orientated-middleware (mom), so I'm sure if you look for benchmarks, you'll find that RabbitMQ will outperform Redis when it comes to message routing.

RabbitMQ is written in Erlang which was specifically designed by the telecom industry to route messages, you get clustering out of the box due to it being written in Erlang which means in a clustered environment, RabbitMQ will outperform Redis even further.

Furthermore, you get guaranteed delivery of messages due to the AMQP protocol, in other words, if the network drops while consuming the message, the consumer won't be able to say thanks for the message, so the consumer will drop the message and Rabbit will requeue the message, if you publish a message and the queue didn't say thanks to the publisher due to network problems or timeouts, Rabbit will drop the message and the publisher will keep on trying to publish the message. You can have publish retries with backoff policies, so

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

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

chenjianjx

'''
A python script which starts celery worker and auto reload it when any code change happens.  
I did this because Celery worker's "--autoreload" option seems not working for a lot of people. 
'''

import time
from watchdog.observers import Observer  ##pip install watchdog
from watchdog.events import PatternMatchingEventHandler  
import psutil  ##pip install psutil
import os

angstwad

# Copyright 2016-2022 Paul Durivage
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,