magnetikonline

Add user ssh-agent as daemon to Ubuntu 18.04LTS server

Create a new systemd user unit, which starts ssh-agent upon login to server. Will remain resident until the final session for the user has logged out.

Steps

• Create /etc/systemd/user/ssh-agent.service.

• Run the following commands (under your user account, not root) to install the systemd unit and start:

9bitbear

https://linuxconfig.org/overclock-your-radeon-gpu-with-amdgpu

Install sensors:

sudo apt install lm-sensors

Edit device files

sudo nano /sys/class/drm/card2/device/pp_sclk_od

9bitbear

1. Download latest AMD APP SDK from this page: http://developer.amd.com/tools-and-sdks/opencl-zone/amd-accelerated-parallel-processing-app-sdk/
2. Open terminal and write:

cd
cd Downloads
tar xvf AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2
sudo sh ./AMD-APP-SDK-v3.0.130.136-GA-linux64.sh

It should look like this:

9bitbear

Some notes on how to setup various open-source XMR Miners on Ubuntu 16

Miners tested:

• xmr-stak
• xmrig
• cpu-miner-multi

Using Systemd

• setup

9bitbear

Running AMD Radeon GPUS on Ubuntu 16.04

Cards tested (so far):

Model	Released
Radeon RX 550	April 2017
Radeon RX 560	April 2017
Radeon HD 7800 Series	March 2012

Install 17.40.2712 Beta Linux Driver

talolard

import tensorflow as tf
def conv1d(input_, output_size, width, stride):
    '''
    :param input_: A tensor of embedded tokens with shape [batch_size,max_length,embedding_size]
    :param output_size: The number of feature maps we'd like to calculate
    :param width: The filter width
    :param stride: The stride
    :return: A tensor of the concolved input with shape [batch_size,max_length,output_size]
    '''
    inputSize = input_.get_shape()[-1] # How many channels on the input (The size of our embedding for instance)

shagunsodhani

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Introduction

• The paper introduces:
  • Sentiment Sentiment Treebank - A dataset containing 215,154 phrases with fine-grained sentiment labels (5 classes).
  • Recursive Neural Tensor Network - Model to learn these fine-grained sentiment labels.
• Link to the paper

Sentiment Sentiment Treebank

JosephDavidsonKSWH

import keras
from keras.models import Model
import keras.backend as K
from keras.engine.topology import Layer, InputLayer, Input, Node
from keras.layers import merge, Dense, TimeDistributed, LSTM
import theano.tensor as T
import theano

import numpy as np

herrfz

# Reuters-21578 dataset downloader and parser
#
# Author:  Eustache Diemert <eustache@diemert.fr> 
# http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html
#
# Modified by @herrfz, get pandas DataFrame from the orig SGML
# License: BSD 3 clause

from __future__ import print_function

sebald

\usepackage{listings}
\usepackage{color}
 
\definecolor{dkgreen}{rgb}{0,0.6,0}
\definecolor{gray}{rgb}{0.5,0.5,0.5}
\definecolor{mauve}{rgb}{0.58,0,0.82}
\definecolor{gray}{rgb}{0.4,0.4,0.4}
\definecolor{darkblue}{rgb}{0.0,0.0,0.6}
\definecolor{lightblue}{rgb}{0.0,0.0,0.9}
\definecolor{cyan}{rgb}{0.0,0.6,0.6}