Put in a repo and make bootstrap scripts for mac and linux.
First, install zsh
using package manager (e.g. apt install zsh
). Then set as default (password prompt):
$ chsh -s /bin/zsh
Then oh my zsh
and zsh-autosuggestions
:
By default, PyTorch team doesn't release packages for ARM architecture. Luckily, someone is supplying unofficial builds (hats off to this person!).
Fast.ai also has a problem: it depends on spaCy, which also doesn't want to build on ARM by default. I'm not going to do NLP, so will install fast.ai without spaCy.
I initially tried using ARM builds from here:
$ docker pull postgres
$ docker run --name ss-pg -e POSTGRES_PASSWORD=secret -d -p 5432:5432 postgres
This pulls the Postgres image and runs it with name ss-pg
. You'll probably want to pick a better password than just secret
. This is the password for the postgres
admin user. The option -p 5432:5432
makes the Postgres instance available on localhost on port 5432, which will be convenient later when we fill it with data.
import java.util.Map; | |
import org.apache.storm.Config; | |
import org.apache.storm.ILocalCluster; | |
import org.apache.storm.Testing; | |
import org.apache.storm.generated.StormTopology; | |
import org.apache.storm.spout.SpoutOutputCollector; | |
import org.apache.storm.task.OutputCollector; | |
import org.apache.storm.task.TopologyContext; | |
import org.apache.storm.testing.CompleteTopologyParam; | |
import org.apache.storm.testing.MkClusterParam; |
#!/usr/bin/env python | |
import messagebird | |
class SmsException(Exception): pass | |
class SmsConnector: | |
api_key = 'xxx' | |
def __init__(self): | |
self.client = messagebird.Client(self.api_key) |
#!/usr/bin/python | |
import random | |
wintersporters = ['jan', 'jori', 'sander', 'erik', 'faab', 'gossie', 'maarten', 'justin'] | |
room_one = random.sample(wintersporters, len(wintersporters)//2) | |
room_two = filter(lambda x: x not in room_one, wintersporters) | |
print('Kamer 1: %s ' % room_one) | |
print('Kamer 2: %s ' % room_two) |
[jorivanlier@gate-001 Hadoop_ReturnVisit]$ ./ReturnVisitAnalysis.py | |
[DEBUG] ApplicationId: kpmgRestaurant | |
measurementTimestamp lastSeenTimestamp tDiffSec tDiffDays | |
count 5.060000e+02 5.060000e+02 5.060000e+02 506.000000 | |
mean 1.420460e+12 1.419093e+12 1.367332e+09 15.825605 | |
std 6.961196e+06 5.102638e+08 5.088012e+08 5.888903 | |
min 1.420443e+12 1.417697e+12 7.191615e+07 0.832363 | |
25% 1.420456e+12 1.418813e+12 1.118531e+09 12.945955 | |
50% 1.420458e+12 1.418989e+12 1.468340e+09 16.994682 | |
75% 1.420460e+12 1.419338e+12 1.651124e+09 19.110230 |