tun | efficient reverse proxy that expose a local server | Proxy library

Q : " What is going wrong? "

A :
There is not a single thing that we can say that it "goes wrong", the code-execution eco-system is so multi-layered, that it is not as trivial as we might wish to enjoy & there are several (different, some hidden) places, where configurations decide, how many CPU-cores will actually bear the overall processing-load.

Situation is also version-dependent & configuration-specific ( both Scikit, Numpy, Scipy have mutual dependencies & underlying dependencies on respective compilation options for numerical packages used )

Given a documented feature of interpretation of negative numbers in top-level n_jobs parameter in RandomizedSearchCV(...) methods, submit the very same task, yet configured so that it has got explicit amount of permitted (top-level) n_jobs = CPU_cores_allowed_to_load and observe, when & how many cores do actually get loaded during the whole flow of processing.

Results:
if and only if that very number of "permitted" CPU-cores was loaded, the top-level call did correctly "propagate" the parameter settings to each & every method or procedure used alongside the flow of processing

In case your observation proves the settings were not "obeyed", we can only review the whole scope of all source-code verticals to decide, who is to be blamed for such dis-obedience of not keeping the work compliant with the top-level set ceiling for the n_jobs. While O/S tools for CPU-core affinity mappings may give us some chances to "externally" restrict the number of such cores used, some other adverse effects ( the add-on management costs being the least performance-punishing ones ) will arise - thermal-management introduced CPU-core "hopping", being the disallowed by affinity maps, will on contemporary processors cause a more and more reduced clock-frequency (as cores get indeed hot in numerically intensive processing), thus prolonging the overall task processing times, as there are "cooler" (thus faster) CPU-cores in the system (those, that were prevented from being used by the affinity-mapping), yet these are very the same CPU-cores, that the affinity-mappings disallowed from being used for temporally placing our task processing (while the hot ones, from which the flow of the processing was reallocated due to reached thermal-ceilings, got some time to cold down and re-gain the chances to run at not decreased CPU-clock-rates)

Top-level call might have set an n_jobs-parameter, yet any lower-level component might have "obeyed" that one value ( without knowing, how many other, concurrently working peers did the same - as in joblib.Parallel() and similar constructors do, not mentioning the other, inherently deployed, GIL-evading multithreading libraries - as that happen to lack any mutual coordination so as to keep the top-level set n_jobs-ceiling )

The OpenMP 5.2 specification states:

The target construct generates a target task. The generated task region encloses the target region. If a depend clause is present, it is associated with the target task. [...]. If the nowait clause is present, execution of the target task may be deferred. If the nowait clause is not present, the target task is an included task.

This means that your code is executed in a task with a possibly deferred execution (with nowait). Thus, it can be executed at the end of the parallel in the worst case, but always before all the dependent tasks and taskwait directives waiting for the target task (or the ones including a similar behaviour like taskgroup). Because of that, you need not to modify the working arrays (nor release them) during this time span. If you do, the behaviour is undefined.

You should especially pay attention to the correctness of synchronization points and task dependencies in your code (it is impossible for us to check that with the current incomplete provided code).

Find out the answer -- It's due to the btrfs... Once using ext3/ext4 to contain the DB device file, IO performance is good.

The real problem is that dff.values create a typed matrix whom can't countained None values for int or float. But in reality the executemany can insert None values.

The fastest solution I've found is to correct the list of lists given to executemany instead of correcting the dataframe content before creating the list of lists.

My inserted data aren't dff.values.tolist() anymore but:

You are looking to track the validation performances like using validation_data or validation_split when you fit your Keras model (see here for a reference).

However GridSearchCV (from sklearn) is not so clever to understand that the validation set (created during CV splitting) must be used with KerasClassifier as validation_data in order to track scores/losses for each epoch.

In other words, you can't track the performances of each validation set (created during CV splitting) using GridSearchCV

This is a possible solution.

This is because of your dependent variable. You chose make. Did you inspect this field? You have training and testing; where do you put an outcome with only one observation, like make = "mercury"? How can you train with that? How could you test for it if you didn't train for it?

You're assigning the "content" of addr to ifr_addr before setting the address, for the ip like the netmask. Thus you're sending NULL to ioctl for the IP and then NULL for the MASK. The inet_pton touch only addr, which does not then change ifr.ifr_addr.

Here is the corrected code :

In order to have an answer to the question.

OP is trying to create a SYNACK packet to answer incomming SYN packet. TCP SYN packet is generated from an OS stack, and uses Options.

Wireshark complains that header is too short. It can be seen, that header length in the tcp header is set to 40 bytes, while the actual header present is only 20 bytes (the whole packet is 40 bytes: 20 bytes IP header and 20 bytes tcp header).

The issue is that the field tcp->doff, which is tcp header, length is copied from the incomming SYN packet. Although not shown, incomming SYN packet presumably has TCP options in it, and thus its header is 40 bytes, not 20 bytes. Thus copying tcp->doff leads to the error message in quesition.

For the reference, tcp header length field is tcp header length in multiples of 32 bits, or 4 bytes. Minimal tcp header is 20 bytes or 5 tcp header units. Alternatively tcp->doff = sizeof(struct tcphdr)/4 should work too.

Here is a minimal example of a pod with OpenVPN client. I used kylemanna/openvpn as a server and to generate a basic client config. I only added two routes to the generated config to make it working. See below: