When registering a model into Azure Machine Learning, it keeps increments the version number. Can i set the version number to a specific number? Or even stop it from increments every time?
The version number is controlled by the system and automatically increments.
You can use tags to have custom tracking, for example
model.add_tags({"user-version":"alpha-0.1"})
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I am working on cosmos db change feed for a real time project. we are running our webjobs in azure app service with P3V2 specification. there are multiple webjobs running using change feed. So to monitor the processes we have used the change feed lag estimator for monitoring record lags. the implementation is according to following document.
https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/how-to-use-change-feed-estimator
For one of the webjob in the .net core code we have put a delay of 10 mins using await Task.delay() function. for that specific webjob we are getting estimation in millions even though the records which we are processing are not more than 100.
This is kind of uncertain behavior we are observing. can anyone help to find the exact reason?
Is the Estimator matching a processor that is currently running and processing documents? Normally what you describe matches a scenario where the Processor is not running/never ran or never completed a successful run on some of the leases.
You can use the detailed estimation to understand how the lag is distributed across leases: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/how-to-use-change-feed-estimator#as-an-on-demand-detailed-estimation
We have an API where we store the configuration in a container in Cosmos DB. We are considering using the Cosmos change feed to subscribe to configuration changes using a change feed processor in order to be able to remove the configurations from cache when they are changed. We have deployments in multiple Azure regions, thus our account is multiregion write account. Now, I read in the documentation that
Starting the change feed processor at a specific date and time is not supported in multi-region write accounts.
What does it mean in practice? Will the processor read and handle all changes from the beginning every time the API process is restarted? Is there any way to pass around this limitation?
Your Cosmos DB account either has 1 write region (with as many read region replicas as you want) or has all regions being both write and read regions). Reference: https://learn.microsoft.com/azure/cosmos-db/sql/how-to-multi-master
You can start a change feed processor with 3 different starting points:
Now
The beginning of the collection lifetime
Some particular point in time
This note means that if your account has multiple write regions (instead of 1 write region), you can only start a change feed from Now or the Beginning, you cannot start a Change Feed from a specific point in time.
Let's say a new CMS Component type needs to be defined. In order to do that, the standard steps are:
Define the new type into ${extensionName}-items.xml
Stop the server (If it is up and running)
Perform a build so that model and Jalo classes are being generated(for the new type) and compiled -> this takes approx 4 min
Start the server back again -> this takes approx 8 minutes
Perform "Update Running System" so that the DB will know about the newly defined type.
The above described steps take a lot of time.
Is it possible to define new types and load them into the Hybris type system without restarting the server in order to speed up the development process ?
Is it possible to define and load new types into the Hybris type
system without restarting the server?
The answer is yes, you can define new item type using backoffice/hmc types in order to use it on the fly. But the system will not generate all the classes.
Is it possible to define new types and load them into the Hybris type
system without restarting the server in order to speed up the
development process ?
The answer is no, for the new item type development you need the system generated classes in order to deal with the business logic, which only possible with the steps you have mentioned.
If your question is, how can we speed up the development process with SAP Hybris?
There are few products available in the market, which boost development speed by loading all your changes without restarting the server like Jrebel, DCEVM, fakereplace etc. Jrebel is paid but you will get ROI.
You can see a significant difference if you use a system having Linux OS, multi-core CPU, SSD hard disk, DB other than HSQL.
Below are some tricks & tips
speed up server start time
prevent Grunt from being executed
reducing compile time
question & answers
This can now be achieved(and saves approximately ten minutes used on build and server start) by using a custom ANT target and JRebel.
Please check the following explanation video which depicts how to define and load new types into the Hybris Type System without restarting the server using JRebel.
Please also check this documentation as well.
I've just learned about Terraform at the highest level and I'm wondering if it's something I should look into for the following purpose:
I'm developing a toy project and exploring the idea of a quick "back-up-and-share-your-own-data" type of feature. The user would mostly need to access their data when they open or save a document (more of a periodic sync than a pub-sub model). It would be great to allow users to capitalize on DigitalOcean's hourly rate.
Would it be possible to just use a tiny persistent volume on DigitalOcean, then programmatically...
Spin up a minimal droplet in response to a (client-side) application event
Hook up to a DigitalOcean floating IP address
Run for just the 1-2 minutes of operation time required
Then destroy the droplet?
This feels like cheating... is this a use-case for Terraform or would I basically be reinventing the wheel when I should just be using the provider API?
I deployed a node.js app as a learning tool and noticed that I'm getting billed for the project (around a $1/day). I know node.js on Google Cloud uses Compute Engine to run the vm's, but they say the flexible environment has all the advantages of the AppEngine platform, but it seems the instances don't auto stop and start to reduce billing when not in use.
I have java project that's been running on App Engine for years and I've never been billed anything, i'm guessing that's because the instances are shutdown automatically when not in use. So my questions are;
Is there a way to configure the flexible environment to mimic the standard environment to reduce the operating costs?
Am I miss-using something with the flexible environment?
According to Google App Engine Documentation,
Instances within the standard environment have access to a daily limit
of resource usage that is provided at no charge defined by a set of
quotas...
Instances within the flexible environment are charged the cost of the
underlying Google Compute Engine Virtual Machines.
According to this article,
Currently, the Flexible Environment needs at least one instance
running to serve traffic and there is no free tier.
This means that at any one time, you have at least one instance running, if you're using a Flexible VM. That should explain the billing.
Please note that by default appengine launches two g1-small instances. Depending on your application needs, this may be an over-kill. You should configure the compute resource settings in your app.yaml to the appropriate sizes of RAM, disk size and CPU, so as to save costs. You may also want to specify the min_num_instances as 1 in your service scaling settings.
I had the same problem. You can try to use Google's pricing calculator to figure out which configuration you need and how to minimize the cost of your application.
According to the calculator, the minimal cost for a flexible environment app is a little less than 40$ per month, There is nothing to do about it right now.
I eventually moved to Heruko because of that.