tps | Thin-Plate Spline implementation for npm

Jersey Client is thread-safe for multiple request executions. Documentation

Methods to create instances of WebResource are thread-safe. Methods that modify configuration and or filters are not guaranteed to be thread-safe.

The creation of a Client instance is an expensive operation and the instance may make use of and retain many resources. It is therefore recommended that a Client instance is reused for the creation of WebResource instances that require the same configuration settings.

It is recommended that the same instance of Client class is reused for multiple request executions. Changing Client configuration is not thread save and must be handled appropriately.

Do I need to create a connection pool for the Client ?
Short answer Yes.
By default Jersey Client use BasicHttpClientConnectionManager. Documentation

It is a simple connection manager that maintains only one connection at a time. Even though this class is thread-safe it ought to be used by one execution thread only. BasicHttpClientConnectionManager will make an effort to reuse the connection for subsequent requests with the same route. It will, however, close the existing connection and re-open it for the given route, if the route of the persistent connection does not match that of the connection request. If the connection has been already been allocated, then java.lang.IllegalStateException is thrown.

For multithread application, you need to override the default value with PoolingHttpClientConnectionManager.

It is a more complex implementation that manages a pool of client connections and is able to service connection requests from multiple execution threads. Connections are pooled on a per route basis. A request for a route for which the manager already has a persistent connection available in the pool will be serviced by leasing a connection from the pool rather than creating a brand new connection.

PoolingHttpClientConnectionManager maintains a maximum limit of connections on a per route basis and in total. Per default this implementation will create no more than 2 concurrent connections per given route and no more 20 connections in total. For many real-world applications these limits may prove too constraining, especially if they use HTTP as a transport protocol for their services.

For details Apache Connection Management

Configuration
The general recommendation is to avoid the infinity timeouts, which Jersey set by default. It can produce threads stuck in case of problems. See Best practices for web service timeouts choose the correct value. There are no specific values, they should be set based on services and environment performance. The correct timeouts and connection size will come thru the time after performance testing or real-time usage.
Just implement it flexible, add the ability to adjust settings on the fly.

Read Timeout

Read timeout interval property, in milliseconds. The value MUST be an instance of Integer. If the property is absent then the default value is an interval of infinity. A value of zero 0 is equivalent to an interval of infinity

You can set 1 minute like the initial value. Also, you can override timeout per request in case of exceptional cases.

Connection Timeout

Connect timeout interval property, in milliseconds. The value MUST be an instance of Integer. If the property is absent then the default value is an interval of infinity. A value of 0 is equivalent to an interval of infinity

Set 500 - 1000 milliseconds like the initial value.

MaxConnectionPerHost
Set 20 connections like initial value.

MaxConnections
Set 200 connections like initial value.

A ROC curve is based on soft predictions, i.e. it uses the predicted probability of an instance to belong to the positive class rather than the predicted class. For example with sklearn one can obtain the probabilities with predict_proba instead of predict (for the classifiers which provide it, example).

Note: OP used the tag multiclass-classification, but it's important to note that ROC curves can only be applied to binary classification problems.

One can find a short explanation of ROC curves here.

If I correctly understood your issue, you want to be able to open a "parent" without opening its children.

But then, why did you write your code to do so ?

See the culprit in the code bellow:

The issue lies in your squeeze. You don't need to specify the index when using squeeze. squeeze flattens the array into 1D. If you pick [:,0,:], it's only 1 entry and hence the error.

Simply do

You are mixing the formatting. Remove the f and try """ [setT]:{{[br]:"{{status:[{0}]}}" """.format(33)

If you evaluate

Disclaimer:
I am not an expert in this field and it's my first use of this API in this way. The specs and the current implementation don't seem to match, and it's very likely that things will change in the near future. So take this answer with all the salt you can, it is only backed by trial.

There are a few things that seems wrong in your implementation:

• duration and timestamp are set in micro-seconds, that's 1/1,000,000s. Your 500 duration is then only half a millisecond, that would be something like 2000FPS and your three images would get all displayed in 1.5ms. You will want to change that.
• In current Chrome's implementation, you need to specify the displayWidth and displayHeight members of the VideoFrameInit dictionary (though if I read the specs correctly that should have defaulted to the source image's width and height).

Then there is something I'm less sure about, but it seems that you can't batch-write many frames. It seems that the timestamp field is kind of useless in this case (even though it's required to be there, even with nonsensical values). Once again, specs have changed so it's hard to know if it's an implementation bug, or if it's supposed to work like that, but anyway it is how it is (unless I too missed something).

So to workaround that limitation you'll need to write periodically to the stream and append the frames when you want them to appear.

Here is one example of this, trying to keep it close to your own implementation by writing a new frame to the WritableStream when we want it to be presented.

Yes, DynamoDB supports some of the highest-scale sites on the internet. Amazon published some metrics about Prime Day load.

https://aws.amazon.com/blogs/aws/amazon-prime-day-2020-powered-by-aws/

Amazon DynamoDB powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of the 66-hour Prime Day, these sources made 16.4 trillion calls to the DynamoDB API, peaking at 80.1 million requests per second.

If you have a million RPS and each request is for 20 KB and you want them strongly consistent then you'd need to provision 5,000,000 RCU. If you're curious, you can calculate the cost based on us-east-1 pricing https://aws.amazon.com/dynamodb/pricing/provisioned/ which is $0.00013 per RCU-hour provisioned, so that's $650 per hour.

Write capacity and data storage have separate pricing.

price_without_taxes is a string, not a number.
In order to multiply it by float you have to convert it to a number.
python is "clever" enough to multiple string containing a number only by int, however it doesn't know how to multiply a string containing a number by float.
This should work better:

Clusters are for Aurora, not for RDS. So you have to make sure you choose Aurora when you try to create your Database in AWS Console: