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#!/usr/bin/env ruby | |
# List all keys stored in memcache. | |
# Credit to Graham King at http://www.darkcoding.net/software/memcached-list-all-keys/ for the original article on how to get the data from memcache in the first place. | |
require 'net/telnet' | |
headings = %w(id expires bytes cache_key) | |
rows = [] |
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Producer | |
Setup | |
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test-rep-one --partitions 6 --replication-factor 1 | |
bin/kafka-topics.sh --zookeeper esv4-hcl197.grid.linkedin.com:2181 --create --topic test --partitions 6 --replication-factor 3 | |
Single thread, no replication | |
bin/kafka-run-class.sh org.apache.kafka.clients.tools.ProducerPerformance test7 50000000 100 -1 acks=1 bootstrap.servers=esv4-hcl198.grid.linkedin.com:9092 buffer.memory=67108864 batch.size=8196 |
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-- Table information like sortkeys, unsorted percentage | |
-- see http://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html | |
SELECT * FROM svv_table_info; | |
-- Table sizes in GB | |
SELECT t.name, COUNT(tbl) / 1000.0 AS gb | |
FROM ( | |
SELECT DISTINCT datname, id, name | |
FROM stv_tbl_perm | |
JOIN pg_database ON pg_database.oid = db_id |
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#!/usr/bin/env bash | |
# Author: Sasha Nikiforov | |
# source of inspiration | |
# https://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions | |
# Detect platform | |
if [ "$(uname)" == "Darwin" ]; then | |
# MacOS |
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<annotation> | |
<folder>GeneratedData_Train</folder> | |
<filename>000001.png</filename> | |
<path>/my/path/GeneratedData_Train/000001.png</path> | |
<source> | |
<database>Unknown</database> | |
</source> | |
<size> | |
<width>224</width> | |
<height>224</height> |
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import tensorflow as tf | |
from object_detection.anchor_generators.multiple_grid_anchor_generator import create_ssd_anchors | |
from object_detection.models.ssd_mobilenet_v2_feature_extractor_test import SsdMobilenetV2FeatureExtractorTest | |
import matplotlib.pyplot as plt | |
from matplotlib import patches | |
import numpy as np | |
def get_feature_map_shapes(image_height, image_width): | |
""" |