- CPU: Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
- GPU: NVIDIA V100
- Memory: 251GiB
- OS: Ubuntu 16.04.6 LTS (Xenial Xerus)
Docker Images:
- tensorflow/tensorflow:latest-gpu
- tensorflow/serving:latest-gpu
[Unit] | |
Description=Clash service | |
After=network.target | |
[Service] | |
[Service] | |
Type=simple | |
StandardError=journal | |
User=clash |
Read this first: http://akrl.sdf.org/gccemacs.html
For that you need to compile gcc (duh). I edited Homebrew's gcc formula:
Removing the last commit
To remove the last commit from git, you can simply run git reset --hard HEAD^
If you are removing multiple commits from the top, you can run git reset --hard HEAD~2 to remove the last two commits. You can increase the number to remove even more commits.
If you want to "uncommit" the commits, but keep the changes around for reworking, remove the "--hard": git reset HEAD^
which will evict the commits from the branch and from the index, but leave the working tree around.
If you want to save the commits on a new branch name, then run git branch newbranchname
before doing the git reset.
ORIGINAL did fork but search didn't helped me
Author: https://github.com/seanorama
Note: This was tested on HDP 3.1. It may not work with other Spark/YARN distributions.
#!/bin/sh /etc/rc.common | |
START=99 | |
STOP=15 | |
USE_PROCD=1 | |
start_service(){ | |
procd_open_instance clash | |
procd_set_param command /usr/bin/clash -d /etc/clash |
log4j.appender.kafka=org.apache.kafka.log4jappender.KafkaLog4jAppender | |
log4j.appender.kafka.brokerList=localhost:9092 | |
log4j.appender.kafka.topic=flink_logs | |
log4j.appender.kafka.layout=org.apache.log4j.PatternLayout | |
log4j.appender.kafka.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss} %-5p %c{1}:%L - %m%n | |
log4j.appender.kafka.level=INFO |
high-availability: zookeeper | |
high-availability.zookeeper.quorum: localhost:2181 | |
high-availability.storageDir: hdfs:///flink/recovery | |
high-availability.zookeeper.path.root: /flink | |
yarn.application-attempts: 10 |
import numpy as np | |
import faiss | |
def search_knn(xq, xb, k, distance_type=faiss.METRIC_L2): | |
""" wrapper around the faiss knn functions without index """ | |
nq, d = xq.shape | |
nb, d2 = xb.shape | |
assert d == d2 |