Are there any API methods to start, stop and snapshot VM using pyvmomi?
I was able to get list of all VMs using provided sample code but there is no such sample code. Plus, i looked up method for same tasks, but documentation seems to be so complicated.
Assuming you're on vSphere 6.5 or newer and trying to use the REST API: https://vdc-download.vmware.com/vmwb-repository/dcr-public/423e512d-dda1-496f-9de3-851c28ca0814/0e3f6e0d-8d05-4f0c-887b-3d75d981bae5/VMware-vSphere-Automation-SDK-REST-6.7.0/docs/apidocs/operations/com/vmware/vcenter/vm/power.start-operation.html
POST https://{server}/rest/vcenter/vm/{vm}/power/start
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I am looking for a language / framework or a method by which I can build API / web application code such that it can run on Serverless compute's like aws lambda and the same code runs on a dedicated compute system like lightsail or EC2.
First I thought of using Docker to do this but AWS Lambda entry point is a specific function signature which is very different than Spring Controllers. Is there a solution available currently?
So basically when I run it in lambda - it will have cold start issue, later when the app is ready or get popular I would like to move it to a EC2 instance for better performance and higher traffic load.
I want to start right in this situation so that later it can be easy to port and resolve the performance issue's
I'd say; no this is not possible easily.
When you are building an api that you'd want to run on lambda's you most likely will be using an API Gateway which takes care of your routing to different lambda functions (best practice). So the moment you would me working on an api like this migrating to EC2 would be a nightmare as you would need to rebuild the whole application a more of a monolith application which could run on EC2.
I would honestly commit to either run it on EC2/Containers or run it on Lambda, if cold start is your main issue with Lambda's you might wanna look into Lambda Snapstart for Java or use another language like Typescript/Python.
After some correct keywords in google I finally got what I was looking for, checkout this blog and code library shared by AWS which helps you convert the request and response of the request as per the framework required http request
Running APIs Written in Java on AWS Lambda: https://aws.amazon.com/blogs/opensource/java-apis-aws-lambda/
Repo Code: https://github.com/awslabs/aws-serverless-java-container
Thanks Ricardo for your response - will do check out Lambda Snapstart for sure and try it as well. I have not tested out this completely but it looks promising to some extent.
I've been trying to Run cartography on my EC2 account for the last 2 days. I have no previous knowledge of Neo4j, But following their installation process doesn't work.
First I've tried to install Neo4j using rpm instructions for Neo4J website, no success acessing Neo4j on port 7474. Error: Connection refused.
Then I gave up trying to make Neo4J work on an EC2 installation, and used their MarketPlace AMi- Works Like a charm but I don't know what is being installed on that AMI. So I decided to install and run cartography on this instance.
My first problem was installing python, pip and java correctly. After everything working, I've discovered neo4j bolt port used my public IP, not my localhost. After thatI was able to finally execute Cartography, but Not it's giving me the following error:
neobolt.exceptions.ClientError: Supplied bookmark [FB:kcwQ40omSYgvSzKPpCQTXDOcCBSQ] does not conform to pattern neo4j:bookmark:v1:tx
Have Anyone really was able to use this?, every step along the way requires some specific libraries.
Thanks !
I maintain cartography and hope I can help (wish I saw this earlier though haha)
Few things to check:
Are you using Neo4j 4.x? cartography currently only supports 3.5.x.
To run for one AWS account,
AWS_PROFILE=profilename cartography --neo4j-uri <uri for your neo4j instance; usually bolt://localhost:7687>`
To run multiple accounts, set up an AWS config file and run
AWS_CONFIG_FILE=/path/to/your/aws/config cartography --neo4j-uri <uri for your neo4j instance; usually bolt://localhost:7687> --aws-sync-all-profiles
(see https://github.com/lyft/cartography/blob/master/docs/setup/install.md#cartography-installation)
If you have more questions feel free to open a GitHub issue or start a thread on our Slack (can talk about more specialized setups like if you're using containers or anything like that too)
I am running a Django App inside GCP. My idea was to call a python script from "view.py" for some machine learning algorithm and then display the result on the page.
But now I understand that running a machine learning library like Scikit-learn on GAE will not be possible (read Tim's answer here and this thread).
But suppose I need to still do this, I believe there are 2 ways possible, but I am not sure weather my guess is right or wrong
1) As the Google-Datalab provides the entire anaconda like distribution, if we have any datalab api which can be called from a python file in the Django app, I can achieve my goal ?
2) If I can install the scikit-learn library on any compute engine on GCP and somehow send it the request to run my code and then return the output back to the python file in the Django app ?
I am very new to client-server and cloud computing on the whole, so please provide examples (if possible) for any suggestion/ pointer for the help.
Regards,
I believe what you want is to use the App Engine Flex environment rather than the standard App Engine environment.
App Engine Flex uses a compute engine VM for running your code, so it does not have the library limitations that standard App Engine has.
Specifically, you'll need to add a 'requirements.txt' file to specify the version of scikit-learn that you want installed, and then add a 'vm: true' clause to your app.yaml file.
sklearn is now supported on ML Engine.
So, another alternative now is to use online prediction on Cloud ML Engine, and deploy your scikit-learn model as a web service.
Here is a fully worked out example of using fully-managed scikit-learn training, online prediction and hyperparameter tuning:
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/blogs/sklearn/babyweight_skl.ipynb
What should I do to profile worflows exposed as Windows Workflow services? Which tool did you use?
I have tried to use dotTrace (jetBrains): I can see data in the profiling snapshot, but it seems I cannot see methods called by workflows.
Depending on the information you want to get out of it you can use AppFabric. Once installed you can go into IIS and set monitoring to "Troubleshooting" and get back pretty much everything the workflow has done.
Let's say that I setup my own cloud using the open source cloud foundry implementation provided on cloudfoundry.org. Will each app that I deploy be run as a separate user? Or is there any of VMWare's virtualization technology in use here? E.g. would each app run in a separate virtual machine or anything like that? How can I configure the memory, cpu, and disk resource limits for each app?
I asked this on the mailing list. Here's the response I got:
If your DEA is configured to run in secure mode, then each app runs as its own user and process isolation is used to protect them. We are moving toward a model of using linux cgroups http://en.wikipedia.org/wiki/Cgroups when on linux, using the warden cgroup wrappers that are already in our source tree.
VM based isolation for a single app is pretty heavy weight, but we have long term plans to provide this for apps that need/desire it. (As opposed to the warden/cgroup work which is a near term project)
Since this is related to the open source for cloud foundry, you can try asking your question on https://groups.google.com/a/cloudfoundry.org/group/vcap-dev
You should get a quick response there!