Carlo Moro cnmoro

Manage Docker as a non-root user

The Docker daemon binds to a Unix socket instead of a TCP port. By default that Unix socket is owned by the user root and other users can only access it using sudo. The Docker daemon always runs as the root user.

If you don’t want to preface the docker command with sudo, create a Unix group called docker and add users to it. When the Docker daemon starts, it creates a Unix socket accessible by members of the docker group.

Warning: The docker group grants privileges equivalent to the root user. For details on how this impacts security in your system, see Docker Daemon Attack Surface.

Note: To run Docker without root privileges, see Run the Docker daemon as a non-root user (Rootless mode) .

zRam is a virtual memory compression using block devices named /dev/zram using a fast compression algorithm (LZ4) that compress the least recently used (LRU) or inactive space in the memory allows the GNU/Linux kernel to free up more memory with less performance hit.

zRam is greatly increased the available amount of memory by compressing memory without swap disks/partition. It is recommended for the user to use zRam instead of not use/disable the swap to prevent out of memory (OOM) killer. Create a zRam block devices Load the zRam modules to the kernel using modprobe:

sudo modprobe zram

Set the zRam extremely fast compression algorithm using lz4:

Reference: https://help.ubuntu.com/community/Oracle%20Instant%20Client

Tested on: Ubuntu 18.04, 20.04

Decide which version of the Oracle client to install
- Each version of the client supports certain versions of the database; for example, Oracle client 12.1 supports Oracle database 10.2 - 19c
- See here for more information: http://support.oracle.com/epmos/faces/DocumentDisplay?id=207303.1
Download the Oracle client packages
- Go here and download the desired RPM package(s): https://www.oracle.com/database/technologies/instant-client/downloads.html

	from sklearn.feature_extraction.text import CountVectorizer
	from sklearn.decomposition import LatentDirichletAllocation
	from minivectordb.embedding_model import EmbeddingModel
	from sklearn.metrics.pairwise import cosine_similarity
	import tiktoken, nltk, numpy as np, fasttext, base64
	from nltk.tokenize import sent_tokenize
	from nltk.corpus import stopwords

	nltk.download('punkt')
	nltk.download('stopwords')

	import tiktoken

	gpt_encoding = tiktoken.encoding_for_model("gpt-3.5-turbo-16k")

	def chunk_text(full_text, tokens_per_chunk=300, chunk_overlap=20):
	chunks = []
	current_chunk = []
	current_chunk_length = 0
	tokens = gpt_encoding.encode(full_text)
	for i, token in enumerate(tokens):

	import uvicorn, asyncio
	import motor.motor_asyncio
	from fastapi import FastAPI
	import pandas as pd

	app = FastAPI()
	client = motor.motor_asyncio.AsyncIOMotorClient()
	db = client['MYDB']

	def process_data(data):

	import category_encoders as ce

	categoric_features = ['a', 'b', 'c']

	woe_encoder = ce.WOEEncoder(cols=categoric_features)
	woe_encoder.fit(X[categorics], y)

	def get_woe_value(feature):
	for map in woe_encoder.ordinal_encoder.mapping:
	if map['col'] == feature:

	# CLUSTERING

	# Davies Bouldin Index -> Menor Melhor para escolha do K

	# Descrição das estatísticas das features
	df.groupby("cluster").describe()

	centroids = kmeans.cluster_centers_
	max = centroids[0]

	import shap
	import numpy as np
	import pandas as pd

	categoric_features = tuple(['FEATURE1', 'FEATURE2', 'ETC'])

	def avaliar_importancias_features(modelo_treinado, X):
	explainer = shap.Explainer(modelo_treinado)
	shap_values = explainer.shap_values(X)

	SERVER-SIDE

	$ sudo apt install wireguard
	$ sudo -i
	$ cd /etc/wireguard/
	$ umask 077; wg genkey \| tee privatekey \| wg pubkey > publickey
	$ cat privatekey
	( Save the key )
	$ cat publickey
	( Save the key )