allanbatista

fasttext skipgram \
         -thread $(grep ^cpu\\scores /proc/cpuinfo | uniq |  awk '{print $4}' )\
         -minCount 2 \
         -wordNgrams 1 \
         -minn 2 \
         -maxn 7 \
         -epoch 10 \
         -dim 64 \
         -input train.txt \
         -output $(date +'model-%Y-%m-%dT%H-%M-%S')

allanbatista

# https://hub.docker.com/r/d34dc3n73r/netdata-glibc/
docker run -d --name=netdata \
  -p 19999:19999 \
  --restart=unless-stopped \
  -v /proc:/host/proc:ro \
  -v /sys:/host/sys:ro \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  --gpus all \
  -e NVIDIA_VISIBLE_DEVICES=all \
  --cap-add SYS_PTRACE \

allanbatista

import tensorflow as tf
import numpy as np
import random as rn
from tensorflow.keras import backend as K


np.random.seed(0)
rn.seed(0)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
tf.set_random_seed(0)

allanbatista

Use boto3 to access GCS as S3

Enable Interoperability and generate a Access Key and Secret Key.

install requirements

pip3 install boto3

allanbatista

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity


def __batch_cosine_similarity_internal__(x_pred, x_true, batch_size=1024):
    x1_len = x_pred.shape[0]
    idx = np.array([])
    val = np.array([])

    for i in range(0, len(x_true), batch_size):

allanbatista

#!/bin/bash

ffmpeg -i $1 -c:v libx264 -profile:v baseline -level 3.0 -pix_fmt yuv420p wp-$1

allanbatista

references by https://forum.odroid.com/viewtopic.php?t=34769 power by odroid

Download and install

apt update && apt upgrade && apt dist-upgrade
apt install git bc dkms
git clone --depth 1 https://github.com/whitebatman2/rtl8821CU.git
cd rtl8821CU/

allanbatista

#!/bin/bash
# find last version in https://cmake.org/download/
wget https://github.com/Kitware/CMake/releases/download/v3.15.2/cmake-3.15.2-Linux-x86_64.sh && \
  chmod +x cmake-3.15.2-Linux-x86_64.sh \
  sudo sh cmake-3.15.2-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir

allanbatista

import xlrd
import csv
import glob, os

for file in glob.glob("*.xlsx"):
    print("{} start".format(file))
    wb = xlrd.open_workbook(file)
    sh = wb.sheet_by_name('Plan1')
	
    with open("{}.csv".format(file.split(".")[0]), 'w+') as f:

allanbatista

import numpy as np
from sklearn.metrics import roc_curve

def calculate_thresholds(n_classes, y_true, y_pred):
    """
    n_classes => 123
    y_true => [[1, 0], ...] # one hot encode lavels
    y_pred => np.array([[0.9, 0.3]]) # with probabilities
    """
    fpr = dict()