Login: ps_navy Pass: RD!jrLP9zg
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Show around anypoint , explain
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Go and create SQS (explain what it is, save the following attributes: )
Queue URL:
Login: ps_navy Pass: RD!jrLP9zg
Show around anypoint , explain
Go and create SQS (explain what it is, save the following attributes: )
Queue URL:
Wether you arrive here by chance, or you need to install Python for our Python/ML classes @ DI; here is a solution for you. As in classes we use Jupyter notebooks, the easiest way to have all data packages + jupyter is downloading Anaconda Python, which is a mega dist with python and all the batteries included.
# Installation instructions | |
## Windows | |
### Core requirements: | |
**Azure CLI**: | |
- The main steps are from https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-windows?tabs=azure-cli |
import matplotlib.pyplot as plt | |
import numpy as np | |
import pandas as pd | |
import sklearn.model_selection | |
import sklearn.datasets | |
import sklearn.metrics | |
import autosklearn.classification | |
from smac.tae import StatusType |
version: "2" | |
services: | |
mongo-shard1: | |
image: mongo | |
command: mongod --shardsvr --replSet mongors1 --dbpath /data/db --port 27017 | |
mongo-shard2: | |
image: mongo | |
command: mongod --shardsvr --replSet mongors2 --dbpath /data/db --port 27017 | |
mongo-cfg1: | |
image: mongo |
// Start the MongoDB Container | |
docker run --name mongo -d mongo | |
docker exec -i mongo sh -c 'mongoimport -d oreilly -c students --drop --type json' < students.json | |
// Check that Mongodb adds a projection on non-used fields | |
db.students.explain("executionStats").aggregate( [ | |
{ $group: { _id: "$grade", frequency: { $sum: 1 } } }, | |
{ $match: { "frequency": { $gt : 10 } } } |