I have GCE instances and provide our own Web APIs on the VMs. I really want to use GCP Cloud Trace to check the latency. Can I use it on GCE? Some say that it works on GAE. As far as I've read docs, it does not seem that I can use it.
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I have created a Google Cloud Function to do Image Processing, I am using a Deep Learning model for one part of the process, but It uses GPU so I could unable GPU to change CPU and it is working well. After reading many links.
My question is: how can enable use of GPU for Cloud Functions? How could I send one image to be processed in a Compute Engine instance with GPU from Cloud Functions? Finally, I read something about Atheros but it looks expensiver more than 1k/month.
Thanks for your comments and ideas.
GPU are expensive, there is no real solution for that. You need to have a small VM with a small GPU to limit cost, but it's still expensive.
The communication between Cloud Functions and the VM is up to you. It can be through HTTP rest API, gRPC, custom protocol on custom port. If you use the VM private IP, you need to add a serverless VPC connector to your Cloud Functions to bridge the serverless world managed by Google with your own VPC where live your VM
I am new to GCE. I am trying to write a cron job to take some action based on current CPU utilization of some VMs on GCE. Is there a way I can get this info using 'gcloud' command?
I have tried gcloud compute instances describe <instance_name>. But that does not provide current CPU utilization info.
I found this other post that talks about getting this info from StackDriver - Understanding instance/cpu/utilization of Google Compute Engine
I am looking for this info using 'gcloud'.
Appreciate any help. Thank you in advance.
gcloud commands are used to create and manage Google Cloud resources. I checked the gcloud command references and it does't cover monitoring metric. To monitor performance of your VM instance, you need to use stackdriver monitoring
If you really want this feature in gcloud command, you can open a feature request here
I came across the article : Bringing the best of serverless to you
where I came to know about upcoming product called Serverless containers on Cloud Functions which is currently in Alpha.
As described in the article:
Today, we’re also introducing serverless containers, which allow you
to run container-based workloads in a fully managed environment and
still only pay for what you use.
and in GCP solutions page
Serverless containers on Cloud Functions enables you to run your own containerized workloads on
GCP with all the benefits of serverless. And you will still pay only
for what you use. If you are interested in learning more about
serverless containers, please sign up for the alpha.
So my question is how this serverless containers different from app engine flexible with custom runtime, which also use a docker file?
And it's my suspicion, since mentioned named is Serverless containers on Cloud Functions, the differentiation may include role of cloud functions. If so what is the role played by cloud functions in the serverless containers?
Please clarify.
What are Cloud Funtions?
From the official documentation:
Google Cloud Functions is a serverless execution environment for building and connecting cloud services. With Cloud Functions you write simple, single-purpose functions that are attached to events emitted from your cloud infrastructure and services. Your function is triggered when an event being watched is fired. Your code executes in a fully managed environment. There is no need to provision any infrastructure or worry about managing any servers.
In simple words, the Cloud Function is triggered by some event (HTTP request, PubSub message, Cloud Storage file insert...), runs the code of the function, returns a result and then the function dies.
Currently there are available four runtime environments:
Node.js 6
Node.js 8 (Beta)
Python (Beta)
Go (Beta)
With the Serverless containers on Cloud Functions product it is intended that you can provide your own custom runtime environment with a Docker Image. But the life cycle of the Cloud Function will be the same:
It is triggered > Runs > Outputs Result > Dies
App Engine Flex applications
Applications running in the App Engine flexible environment are deployed to virtual machines, i.e Google Cloud Compute Engine instances. You can choose the type of machine you want use and the resources (CPU, RAM, disk space). The App Engine flexible environment automatically scales your app up and down while balancing the load.
As well as in the case of the Cloud Functions there runtimes provided by Google but if you would like to use an alternative implementation of Python, Java, Node.js, Go, Ruby, PHP, .NET you can use Custom Runtimes. Or even you can work with another language like C++, Dart..., you just need to provide a Docker Image for your Application.
What are differences between Cloud Functions and App Engine Flex apps?
The main difference between them are its life cycle and the use case.
As commented above a Cloud Function has a defined life cycle and it dies when it task concludes. They should be used to do 1 thing and do it well.
On the other hand an Application running on the GAE Flex environment will always have at least 1 instance running. The typical case for this applications are to serve several endpoints where users can do REST API calls. But they provide more flexibility as you have full control over the Docker Image provided. You can do "almost" whatever you want there.
What is a Serverless Container?
As stated on the official blog post (search for Serverless Containerss), it's basically a Cloud Function running inside a custom environment defined by the Dockerfile.
It is stated on the official blog post:
With serverless containers, we are providing the same underlying
infrastructure that powers Cloud Functions, but you’ll be able to
simply provide a Docker image as input.
So, instead of deploying your code on the CF, you could also just deploy the Docker image with the runtime and the code to execute.
What's the difference between this Cloud Functions with custom runtimes vs App Engine Flexible?
There are 5 basic differences:
Network: On GAE Flexible you can customize the network the instances run. This let's you add firewalls rules to restrict egress and ingress traffic, block specific ports or specify the SSL you wish to run.
Time-Out: Cloud Functions can run for a maximum of 9 minutes, Flexible on the other hand, can run indefinitely.
Ready only environment: Cloud Functions environment is read-only while Flexible could be written (this is only intended to store spontaneous information as once the Flexible instance is restarted or terminated, all the stored data is lost).
Cold Boot: Cloud Functions are fast to deploy and fast to start compared to Flexible. This is because Flexible runs inside a VM, thus this extra time is taken in order for the VM to start.
How they work: Cloud functions are event driven (ex: upload of photo to cloud storage executing a function) on the other hand flexible is request driven.(ex: handling a request coming from a browser)
As you can see, been able to deploy a small amount of code without having to take care of all the things listed above is a feature.
Also, take into account that Serverless Containers are still in Alpha, thus, many things could change in the future and there is still not a lot of documentation explaining in-depth it's behavior.
I have an application which uses objectify which i want to deploy in a Google Compute engine to access google datastore. I have been able to test this application in local development server using Objectify. I am also able to access the cloud datastore from the compute engine by following the documentation in https://cloud.google.com/datastore/docs/getstarted/start_java/.
But when I deploy my application in the google compute engine I am not able to communicate with the google cloud datastore and am getting the following exception:
No API environment is registered for this thread.
I should be missing something. Kindly help me out.
As far as I can tell, Objectify currently only works with Datastore if you are running in Google App Engine (GAE), not for Datastore Access via Google Compute Engine (GCE). There is an open issue https://github.com/objectify/objectify/issues/203
The reason it does not work on Google Compute Engine is apparently because the API for Datastore access in GCE is apparently different from the one used for GAE.
I am trying evaluate Google Compute Engine (GCE) for a cloud project in our company. We have some experience in working with Amazon Web Services but would like to know if GCE is a better alternative for our project.
I have following questions. Our choice for the project will be based on the answers for the questions so please help me with these queries.
Is there an equivalent of AWS Route53 and Elastic Load Balancer on Google cloud? If they are not available then how do we load balance GCE instances?
Is there a concept like regions? (such as us-east-coast-1, us-west-coast-1, etc…). Helpful in making sure that the service is not affected during natural calamities.
Is there an equivalent of Cloud Watch to help us auto scale compute engine instances based on load?
Can we setup a private cloud on Google cloud platform?
Can we get persistent public IP addresses for GCE instances?
Are there any advantages (in terms of tighter integration OR pricing) when using Google services such as Google Analytics, YouTube, DoubleClick, etc?
Load Balancing
Google Cloud Platform's Compute Engine (GCE) recently added a Load Balancing feature. It's lower level than ELB (it only supports UDP / TCP, not HTTP(S)).
Regions
GCE has feature parity. AWS Regions correspond to GCE Regions, and AWS Availability Zones to GCE Zones
Autoscaling (CloudWatch)
Google Compute Engine does not have autoscaling, but Google App Engine does. Third party tools such as Scalr or RightScale are however compatible with Google Compute Engine
Disclaimer: I do work at Scalr.
Private Cloud
Did you mean dedicated instances? Those are not available in GCE.
If you meant VPC, then you can use GCE networks to achieve isolation. You'll also wish to disable ephemeral external IP addresses for the instances you want to isolate.
Persistent IPs
GCE has persistent IPs, they are called "Reserved Addresses"
Integration with other services
You will likely get better latency to Google services you use in your backend (I recall a couple presentations at Google I/O talking about Google App Engine + BigQuery).
For frontend services (Google Analytics), you'll likely see not benefit, since this depends on your users, not your servers.