I want to use Google Cloud as IaaS, mainly because they accept debt card not credit card,I'm trying to automate some tasks but mainly on windows because my apps crash with wine.
Would you guide me with the below?
What is the user provided OS on google cloud IaaS?
Where can I find tutorial about it or how to do it?
Can I provide a windows DVD?
Kindly check the link below.
https://cloud.google.com/products/calculator/
Compute Engine does not currently support uploading custom Windows images.
Related
Need to launch windows machine with Microsoft Edge browser in the cloud.
You should be able to do create a Windows 10 VM in most cloud, like Azure or AWS. You also have the option to do create virtual desktops. If sounds like you want to do browser testing, if that is the case, then I would encourage you to consider something like https://www.browserstack.com/.
If this doesn't answer you question, can you please add additional details.
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
Just trying to understand what's the difference between bitnami apps and google 'click-to-deploy' options on Google Cloud Engine?
For example: There is a 'Cassandra' click-to-deploy and there is a Bitnami version of 'Cassandra'
Can anyone tell me how do they compare and what are the differences?
- is one restrictive compared to the other?
- does bitnami version lock you in somehow?
- is there any performance difference (other than obvious performance difference that the hardware change would bring)
Thanks.
Bitnami makes application stacks that run on several cloud platforms including Google Cloud Platform, AWS, Azure and a few others. The Bitnami images you see on Google Cloud Launcher are created by employees of Bitnami and are mostly standard across cloud.
Click to Deploy images are usually created by Google Cloud Platform employees working in conjunction with application vendors.
There are differences in versions here and there related to maintenance, but there isn't any difference in the way they are intended to be used. Some Click to Deploy images will incur higher use charges due to licensing (ie. the Click to Deploy image contains the "Pro" version of a vendor's software), but these are called out during the selection process.
Neither version is intended to lock you into a particular platform, Google or Bitnami, it's just that there is duplication among the applications provided.
Google Cloud Dataflow has been released in June 2014 (more information in this blog post), but I can't find any technical documentation on the developers section of the cloud.google.com website: https://cloud.google.com/developers/
Does someone knows where I can find more information, technical documentation about this product?
I'm really interested about how works topology, is it static or dynamic?.. etc..
Google Cloud Dataflow is now in Alpha stage. The documentation is now publicly available here: https://cloud.google.com/dataflow/. Follow the documentation link.
Please note that in Alpha - access to the managed service is limited to invite only. You can request access via the link above. Use the "Apply for Alpha" button.
The Cloud Dataflow SDK for Java has also been made public & open sourced on GitHub here: https://github.com/GoogleCloudPlatform/DataflowJavaSDK. Please note that you can download the SDK and run your Dataflow programs locally without having to execute them on the managed service. Local pipeline execution is a great way to get a feel for the programming model, but understand that the local execution is not parallelized.
We are also moving support over to StackOverflow. Please use the tag: google-cloud-dataflow.
Cheers - Eric
Google Cloud Dataflow is currently in private beta. You can apply here. Documentation is provided upon approval.
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!