Notes from a coding dog
https://www.osgeo.org/projects/geonode/
Invalid HTTP_HOST header: 'my_geonode.net'. The domain name provided is not valid according to RFC 1034/1035.proxy_set_header Host $host in /etc/nginx/sites-enabled/geonode.conf on nginx container]https://gist.github.com/linuxkathirvel/3f88774d9a23ed73045223fc2ecab200import os
import time
import boto3
import numpy as np
import pandas as pd
import sagemaker
#from sagemaker import get_execution_role
def get_execution_role():
return "SageMaker-ExecutionRole-20241030T121452"
sess = boto3.Session()
sm = sess.client("sagemaker")
role = get_execution_role()
sagemaker_session = sagemaker.Session(boto_session=sess)
bucket = "shared-hs-mlops-bucket" #sagemaker_session.default_bucket()
region = boto3.Session().region_name
model_package_group_name = "PipelineModelPackageGroup"
prefix = "mas-pipeline-model-example"
pipeline_name = "mas-serial-inference-pipeline" # SageMaker Pipeline name
raw_dir = os.path.join(os.getcwd(), "data", "raw")
#data_dir = os.path.join(os.getcwd(), "data")
#os.makedirs(data_dir, exist_ok=True)
#raw_dir = os.path.join(os.getcwd(), "data/raw")
#os.makedirs(raw_dir, exist_ok=True)
#########################################################################
## Download Data
s3 = boto3.client("s3")
s3.download_file(
f"sagemaker-example-files-prod-{region}",
"datasets/tabular/california_housing/cal_housing.tgz",
"cal_housing.tgz",
)
#!tar -zxf cal_housing.tgz
columns = [
"longitude", "latitude", "housingMedianAge",
"totalRooms", "totalBedrooms","population",
"households", "medianIncome", "medianHouseValue",
]
cal_housing_df = pd.read_csv("CaliforniaHousing/cal_housing.data", names=columns, header=None)
cal_housing_df["medianHouseValue"] /= 500000 # Scaling target down to avoid overcomplicating the example
cal_housing_df.to_csv(f"./data/raw/raw_data_all.csv", header=True, index=False)
rawdata_s3_prefix = "{}/data/raw".format(prefix)
#########################################################################
## Upload to S3
raw_s3 = sagemaker_session.upload_data(
bucket = bucket,
path="./data/raw/",
key_prefix=rawdata_s3_prefix)
print(raw_s3) #s3://shared-hs-mlops-bucket/mas-pipeline-model-example/data/raw# method 1
curl -sL https://aws-amplify.github.io/amplify-cli/install-win -o install.cmd && install.cmd
# method 2
npm i -g @aws-amplify/clihttps://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html
https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html
pip install --platform manylinux2014_x86_64 --target=package --implementation cp --python-version 3.9 --only-binary=:all: --upgrade pandas
Using Lambda layer (https://docs.aws.amazon.com/lambda/latest/dg/chapter-layers.html)
Content before more comment is regarded as page excerpt.
This is the first project during cursus period. I will record all my processes for doing this project.
If you want to use specified python version, you should specify it in tester.sh file. such as, I have no python command in my computer, only python3 in my computer, So I modified the python in tester.sh to python3.
The test tool use python to excute test code. And need git library for python.
if still cannot work, please install the packages based on requirements.txt.
# The plugin only avaliable on vim
# If you machine has no vim editor, you'd better install it firstly
apt update
sudo apt install vim
sudo apt install python3-pip
# upgrade pip and setuptools
sudo python3 -m pip install --upgrade pip setuptools
# install norminette
sudo python3 -m pip install norminette
# update norminette
python3 -m pip install --upgrade norminette