For a couple of days, I have been trying to install the Zenko cloud server for development purposes on my ubuntu 20.04 machine. I am new to Docker and definitely not very much comfortable with Kubernetes and Helm. I am trying to follow this instruction. During the installation stage when I am trying to follow this link, I am getting this error
Error: validation failed: [unable to recognize "": no matches for kind "PodSecurityPolicy" in version "policy/v1beta1", unable to recognize "": no matches for kind "PodDisruptionBudget" in version "policy/v1beta1", unable to recognize "": no matches for kind "CronJob" in version "batch/v1beta1"]
while executing this command.
helm install
https://github.com/scality/Zenko/releases/download/1.2.5/zenko-1.2.5.tgz
I have also tried this link to install Zenko. I have successfully cloned Zenko from the git repository.
git clone https://github.com/scality/Zenko.git
But while executing cd ./zenko/charts, I am getting this error.
bash: cd: ./zenko/charts: No such file or directory
I have installed Minkube by following this link and also installed Helm2 by following this link. Also, I have tried to follow this Zenko documentation but did not quite understand it.
My current goal is to install the Zenko cloud server and upload files to Amazon S3 and also to my local directory where both can be managed via Zenko according to their documentation.
It will be very helpful if someone shows me some way to solve this issue. Thanks in advance.
I want to automate the process of pulling docker images from Azure container Registry to the Azure VM. I have already done the following:
Created an Azure container Registry.
Setup username and password in the Azure Container Registry.
Pushed the image from my local machine to the Container Registry.
I have setup up terraform code to automate the build out of Azure VM. I also want to include the docker pull and docker run commands so that those tasks are automated. Below are the commands I would like to automate into terraform:
sudo docker login --username xxx --password xxx xxx.azurecr.io
sudo docker pull xxx.azurecr.io/xx/xxx
sudo docker run --network=host xxx.azurecr.io/xxx/xxx
Any help would be much appreciated. Thank you folks!
As I know, if you want to execute the Docker CLI command in the VM, you should install the Docker engine first.
In addition, if you want to run the Docker CLI commands in the VM automated after creating the VM through Terraform, You can use VM extension in Terraform. Write a shell script with the commands and then run it in the VM extension. Here is the example that Using Terraform with Azure VM Extensions.
Does anyone have a fully compiled version of pandas that is compatible with AWS Lambda?
After searching around for a few hours, I cannot seem to find what I'm looking for and the documentation on this subject is non-existent.
I need access to the package in a lambda function however I have been unsuccessful at getting the package to compile properly for usage in a Lambda function.
In lieu of the compilation can anyone provide reproducible steps to create the binaries?
Unfortunately I have not been able to successfully reproduce any of the guides on the subjects as they mostly combine pandas with scipy which I don't need and adds an extra layer of burden.
I believe you should be able to use the recent pandas version (or likely, the one on your machine). You can create a lambda package with pandas by yourself like this,
First find where the pandas package is installed on your machine i.e. Open a python terminal and type
import pandas
pandas.__file__
That should print something like '/usr/local/lib/python3.4/site-packages/pandas/__init__.py'
Now copy the pandas folder from that location (in this case '/usr/local/lib/python3.4/site-packages/pandas) and place it in your repository.
Package your Lambda code with pandas like this:
zip -r9 my_lambda.zip pandas/
zip -9 my_lambda.zip my_lambda_function.py
You can also deploy your code to S3 and make your Lambda use the code from S3.
aws s3 cp my_lambda.zip s3://dev-code//projectx/lambda_packages/
Here's the repo that will get you started
After some tinkering around and lot's of googling I was able to make everything work and setup a repo that can just be cloned in the future.
Key takeaways:
All static packages have to be compiled on an ec2 amazon Linux instance
The python code needs to load the libraries in the lib/ folder before executing.
Github repo:
https://github.com/moesy/AWS-Lambda-ML-Microservice-Skeleton
The repo mthenw/awesome-layers lists several publicly available aws lambda layers.
In particular, keithrozario/Klayers has pandas+numpy and is up-to-date as of today with pandas 0.25.
Its ARN is arn:aws:lambda:us-east-1:113088814899:layer:Klayers-python37-pandas:1
I know the question was asked a couple years ago and Lambda was on a different stage back then.
I faced similar issues lately and I thought it would be a good idea to add the newest solution here for future users facing the same problem.
It turns out that amazon released the concept of layers in the re:Invent 2018. It is a great feature. This post in medium describes it much better than I could here: Creating New AWS Lambda Layer For Python Pandas Library
The easiest way to get pandas working in a Lambda function is to utilize Lambda Layers and AWS Data Wrangler. A Lambda Layer is a zip archive that contains libraries or dependencies. According to the AWS documentation, using layers keeps your deployment package small, making development easier.
The AWS Data Wrangler is an open source package that extends the power of pandas to AWS services.
Follow the instructions (under AWS Lambda Layer) here.
Another option is to download the pre-compiled wheel files as discussed on this post: https://aws.amazon.com/premiumsupport/knowledge-center/lambda-python-package-compatible/
Essentially, you need to go to the project page on https://pypi.org and download the files named like the following:
For Python 2.7: module-name-version-cp27-cp27mu-manylinux1_x86_64.whl
For Python 3.6: module-name-version-cp36-cp36m-manylinux1_x86_64.whl
Then unzip the .whl files to your project directory and re-zip the contents together with your lambda code.
NOTE: The main Python function file(s) must be in the root folder of the resulting deployment package .zip file. Other Python modules and dependencies can be in sub-folders. Something like:
my_lambda_deployment_package.zip
├───lambda_function.py
├───numpy
│ ├───[subfolders...]
├───pandas
│ ├───[subfolders...]
└───[additional package folders...]
#ashtonium's answer actually works and is most likely the easiest, however, a few additional steps are required. Also, Pandas requires Pytz (mentioned in the link provided by #b3rt0) so that package is needed as well.
Download the whl-files from PyPI (the Pandas file ends with ...manylinux1_x86_64.whl, there is only one Pytz file of relevance)
Unzip the whl-files using terminal command, e.g. unzip filename.whl (Linux/MacOS)
Create a new folder structure python/lib/python3.7/site-packages/ (swap 3.7 for version of your choice)
Move folders from step 2 to site-packages folder in step 3
Zip root folder in new structure, i.e. python
Create a new layer in AWS management console where you upload the zip-file
This is a very common question, I hope my solution helps.
Update on Aug 19, 2020:
Wheel-files aren't available for all packages. In these cases you can skip to step 3, go into the site-packages folder and install the package in there with pip3 install PACKAGE_NAME -t . (no venv required). Some packages are easier than others, some are trickier. Psycopg2 for example, requires you to move only one of the two (as of this writing) package folders.
/Cheers
There are some precompiled packages on github by ryfeus.
My solution has been to maintain 2 requirements.txt style files of packages that go in my layer, one named provided_packages.txt and one named provided_linux_installs.txt
Before deployment (if the packages are not already installed) I run:
pip install -r provided_packages.txt -t layer_name/python/lib/python3.8/site-packages/.
pip download -r provided_linux_installs.txt --platform manylinux1_x86_64 --no-deps -d layer_name/python/lib/python3.8/site-packages
cd layer_name/python/lib/python3.8/site-packages
unzip \*.whl
rm *.whl
Then deploy normally (I am using cdk synth & cdk deploy \* --profile profile_name)
In case helpful, my provided_linux_installs.txt looks like this:
pandas==1.1.0
numpy==1.19.1
pytz==2020.1
python-dateutil==2.8.1
I have started to maintain a GitHub repo for easy and quick access to layers. https://github.com/kuharan/Lambda-Layers
I have been using these for my open-source projects and stuff.
I managed to deploy a pandas code in aws lambda using python3.6 runtime . this is the step that i follow :
Add required libraries into requirements.txt
Build project in a docker container (using aws sam cli : sam build --use-container)
Run code (sam local invoke --event test.json)
this is a helper : https://github.com/ysfmag/aws-lambda-py-pandas-template
# all the step are done in AWS EC2 Linux Free tier so that all the Libraries are compatible with the Lambda environment
# install the required packages
mkdir packages
pip3 install -t . pandas
pip3 install -t . numpy --upgrade
pip3 install -t . wikipedia --upgrade
pip3 install -t . sklearn --upgrade
pip3 install -t . pickle-mixin --upgrade
pip3 install -t . fuzzywuzzy --upgrade
# Now remove all unnecessary files
sudo rm -r *.whl *.dist-info __pycache__
# Now make a DIR so that lambda function can reconginzes
sudo mkdir -p build/python/lib/python3.6/site-packages
# Now move all the files from packages folder to site-packages folder
sudo mv /home/ec2-user/packages/* build/python/lib/python3.6/site-packages/
# Now move to the build packages
cd build
# Now zip all the files starting from python folder to site-packages
sudo zip -r python.zip .
upload the zip file to lambda layers
python 3.8 windows 10 lambda aws pandas
You need to do the following steps on a linux machine and python 3.8:
sudo mkdir python
sudo pip3 install --target python pandas
sudo zip -r pandas.zip python
create a public s3 bucket, upload pandas.zip, grab the public URL.
create new lambda layer using s3 URL from above.
add layer to lambda function and import pandas as pd like you normally would
No linux machine? Launch an Ubuntu EC2 instance or container:
sudo apt install python3.8 zip unzip python3-pip
run 1-3 above
Now you need to copy the zip to your local machine. Open a command terminal and change directory to the folder containing your EC2 instance's pem file and run: scp -i yourPemFile.pem ubuntu#'EC2.Instance.IP.Here':/home/path/to/pandas.zip C:\Users\YourUser\Desktop
run steps 4-6 from above
*for number 3 above: you need to grab your EC2 IP and insert it. You may get an error about the permissions on the pem file, if you do then right click the pem file > properties > security > advanced > disable inheritance and make sure only your user is in the "permission entries." Lastly, fix the paths to point to where the pandas.zip file is on the EC2 instance and where you want the file to end up locally.
**pay attention to the python runtime of the lambda function. Make sure it matches the version of python you're using to do the pip stuff (which should be 3.8).
***the original folder name "python" is named that for a reason as per AWS documentation.
After lots of googling on this and messing around, the concept of layers are great and seem to work for me.
This github repo from keithrozario has loads of pre-build layers you can simply add to your lambda via the arn which has some great stuff in there like pandas, requests and sqlalchemy.
I've create a template to compile and upload a layer (containing python dependencies) to lambda using the AWS CLI which you can find in my Gitlab repo here.
I'm running this on an Amazon Linux EC2, using a virtual environment (venv) to install libraries from a requirements.txt file and then load the zipped files to lambda using the AWS CLI.
Note the folder structure my_zip_file/python/binaries which is required for lambda.
Note: Pandas is quite a large library. Your zipped layer file must be below 70mb.
You may also encounter the horrible "OpenBLAS WARNING - could not determine the L2 cache size on this system" error message. I had to increase the memory from the default 128mb in order to the lambda to successfully run.
After searching around for a few hours, I cannot seem to find what I’m looking for and the documentation on this subject is non-existent.
So i decided to build the libraries myself to support the Amazon Linux 2 arch.
Read full blog here https://khanakia.medium.com/add-pandas-and-numpy-python-to-aws-lambda-layers-python-3-7-3-8-694db42f6119
I am trying to use the sync command from my file system to S3 on a Windows 2008 R2 server.
I have previously had no problem running this command on multiple local machines:
AWS S3 SYNC 'File system Name' S3://'S3 file directory name'
However when I try to run it from this box I get this error:
[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:581)
Every forum I see is using python scripts but I am just using the simple CLI commands.
Any idea why I am getting this error?
If you are running aws cli commands on Windows, above given commands i.e (sudo pip) will not work.
1) TO avoid "[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:581)" error
on cli you can use the format like :
AWS [aws-service-name] --no-verify-ssl [functions]
2) Then your cli command for S3 Sync becomes:
AWS S3 --no-verify-ssl SYNC 'File system Name' S3://'S3 file directory name'
This worked around the issue for me on ubuntu 14.04. I cannot confirm if it is an ideal/complete solution:
sudo pip uninstall certifi
sudo pip install certifi==2015.04.28
From here: https://github.com/aws/aws-cli/issues/1499
After setting up s3cmd and my S3 bucket, when I try this command
sudo s3cmd sync --recursive --preserve /srv s3://MyS3Bucket
I get this error:
ERROR: S3 error: 400 (InvalidRequest): The authorization mechanism you have provided is not supported. Please use AWS4-HMAC-SHA256.
My s3cmd version is 1.0.0 which is installed by default after following their "deb" installation guide for by Ubuntu 12.04
These days, it is recommended to use the AWS Command-Line Interface (CLI), which also provides a sync capability.
s3cmd version 1.5.2 is necessary for working with regions such as eu-central-1 (Frankfurt) or cn-north-1 (Bejing). debs for such are available in Debian experimental and unstable, and Ubuntu Wily universe. Or you can install from source from https://github.com/s3tools/s3cmd.