svc | Common packages for Luther Systems services

Import GensimWord2VecVectorizer in your Flask Web app python file.

According to dataproc batches docs, the subnetwork URI needs to be specified using argument --subnet.

Try:

record_path is the path to the record, so you should specify the full path

It depends on the cluster setup. If you are using a GKE cluster and it is setup as VPC native, then you will be able to reach the clusterIP service from a host in the same VPC.

At least three reasons why you need to re-create it properly (2 technical and advice):

1. ExternalName is used for accessing external services or services in other namespaces. The way it works is when looking up the service's name happens, CNAME will be returned. So in other words it works for egress connections when requests should be directed somewhere else.

   See service - externalname type and use cases Kubernetes Tips - Part 1 blog post from Alen Komljen.

   Your use case is different. You want to get requests from outside the kubernetes cluster to exposed loadbalancer and then direct traffic from it to another service within the cluster. It's not possible by built-in kubernetes terms, because service can be either LoadBalancer or ExternalName. You can see in your last manifest there are two types which will not work at all. See service types.

2. Avoid unnecessary complexity. It will be hard to keep track of everything since there will be more and more services and other parts.

3. Based on documentation it's generally possible to have issues using ExternalName with some protocols:

   Warning: You may have trouble using ExternalName for some common protocols, including HTTP and HTTPS. If you use ExternalName then the hostname used by clients inside your cluster is different from the name that the ExternalName references.

   For protocols that use hostnames this difference may lead to errors or unexpected responses. HTTP requests will have a Host: header that the origin server does not recognize; TLS servers will not be able to provide a certificate matching the hostname that the client connected to.

   Reference - Warning

If you do

After a few days of research I finally solved the problem, the problem was with IIS Web Server which I had enabled when I was working on a project in ASP.NET core, I uninstalled it and the problem was solved.

How to uninstall IIS from Windows 10:

• Go to Control Panel > Programs and Features
• Click Turn Windows features on or off
• Scroll down to Internet Information Services
• Click on the square next to Internet Information Services so it becomes empty
• Click OK and restart the PC (required).

Though very late, I'll try to give my answer. According to the doc, here's the considered primal optimization problem for LinearSVC: ,phi being the Identity matrix, given that LinearSVC only solves linear problems.

Effectively, this is just one of the possible problems that LinearSVC admits (it is the L2-regularized, L1-loss in the terms of the LIBLINEAR paper) and not the default one (which is the L2-regularized, L2-loss). The LIBLINEAR paper gives a more general formulation for what concerns what's referred to as loss in Chapter 2, then it further elaborates also on what's referred to as penalty within the Appendix (A2+A4).

Basically, it states that LIBLINEAR is meant to solve the following unconstrained optimization pb with different loss functions xi(w;x,y) (which are hinge and squared_hinge); the default setting of the model in LIBLINEAR does not consider the bias term, that's why you won't see any reference to b from now on (there are many posts on SO on this).

• , hinge or L1-loss
• , squared_hinge or L2-loss.

For what concerns the penalty, basically this represents the norm of the vector w used. The appendix elaborates on the different problems:

• L2-regularized, L1-loss (penalty='l2', loss='hinge'):
• L2-regularized, L2-loss (penalty='l2', loss='squared_hinge'), default in LinearSVC:
• L1-regularized, L2-loss (penalty='l1', loss='squared_hinge'):

Instead, as stated within the documentation, LinearSVC does not support the combination of penalty='l1' and loss='hinge'. As far as I see the paper does not specify why, but I found a possible answer here (within the answer by Arun Iyer).

Eventually, effectively the combination of penalty='l2', loss='hinge', dual=False is not supported as specified in here (it is just not implemented in LIBLINEAR) or here; not sure whether that's the case, but within the LIBLINEAR paper from Appendix B onwards it is specified the optimization pb that's solved (which in the case of L2-regularized, L1-loss seems to be the dual).

For a theoretical discussion on SVC pbs in general, I found that chapter really useful; it shows how the minimization of the norm of w relates to the idea of the maximum-margin.

You may try this sed: