keskival

import pandas as pd
import seaborn as sns
import numpy as np
from numpy.linalg import norm
import matplotlib.pyplot as plt

data_df = pd.read_csv('data/embedded_1k_reviews.csv')
queries_df = pd.read_csv('data/queries.csv')

data_df['ada_embedding'] = data_df.ada_embedding.apply(eval).apply(np.array)

keskival

#!/usr/bin/python

import openai
import json
import os
from datetime import datetime
from PIL import Image
import time
import pandas as pd

keskival

#!/bin/bash
PGPASSWORD="YOURPGPASSWORDHERE"
MASTODON_SYSTEM_DIR=/opt/mastodon/public/system
BACKUPS_DIR=/mnt/sda1/backups
POSTGRES_DUMP_FILENAME=pg_dump_mastodon_backup.sqlc
POSTGRES_DUMP_PATH=/bitnami/postgresql/${POSTGRES_DUMP_FILENAME}

microk8s kubectl exec -it -n mastodon mastodon-postgresql-0 -- env PGPASSWORD="${PGPASSWORD}" pg_dump -U mastodon -d mastodon_production --format=c --file=${POSTGRES_DUM>
microk8s kubectl cp -n mastodon mastodon-postgresql-0:${POSTGRES_DUMP_PATH} ${BACKUPS_DIR}/${POSTGRES_DUMP_FILENAME}   
PODNAME=$(microk8s kubectl get pod -n mastodon -l app.kubernetes.io/component=web -o name)

keskival

def mu_law(x, mu):
    return np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)

# value shape is [batches, width, D]
# filters shape is [width, D, C]
def causal_atrous_conv1d(value, filters, rate, padding):
    # Using height in 2-D as the 1-D.
    value_2d = tf.expand_dims(value, 2)
    filters_2d = tf.expand_dims(filters, 1)
    # Note that for filters using 'SAME' padding, padding zeros are added to the end of the input.

keskival

tero@Curie:~$ cat /proc/cpuinfo
processor	: 0
vendor_id	: GenuineIntel
cpu family	: 6
model		: 60
model name	: Intel(R) Core(TM) i5-4690 CPU @ 3.50GHz
stepping	: 3
microcode	: 0x19
cpu MHz		: 898.515
cache size	: 6144 KB