OK it seems I got to my endgoal. Which was to get cert bot working with nginx. I ended up doing everything inside the docker container just because it turned out to be much easier.
Basically create a dockercompose yaml file. with 3 services in my case because I aldo had the next js frontend. The key seems to be setting up the volumes correctly.
services:
webserver:
image: nginx:latest
ports:
- 80:80
- 443:443
restart: always
volumes:
- ./nginx/conf/:/etc/nginx/conf.d/:ro
- ./certbot/www:/var/www/certbot/:ro
- ./certbot/conf/:/etc/nginx/ssl/:ro (For me etc/letencrypt is what worked for me here after the :'s)
certbot:
image: certbot/certbot:latest
volumes:
- ./certbot/www/:/var/www/certbot/:rw
- ./certbot/conf/:/etc/letsencrypt/:rw
Using this I just added the frontend container. I also had a nginx.conf file.
I didnt follow the tutorial exactly here so I had to have 2 diffrent configs. One only port 80 and acme challange. And the other one was port 80 and 443 and the acme challange in 443. (I am not sure if the acme should be in 443 I think if I had it in port 80s server block it would have worked with one file) Anyway I used the first config then created the keys using docker exec to control the certbot config. Then switched to the second config.
One thing I had in my docker file was a entrypoint that the tutorial doesnt mention. Namely /bin/sh -c 'trap exit TERM; while :; do certbot renew; sleep 12h & wait $${!}; done;'" this script in the entrypoint field under certbot. This should run renew every 12 hours to try and renew the cert but you can only renew when you have 30 days left on the 90 day cert so its not as wasteful as you might think. Still is wasteful but it was the easiest way IMO.
Also if someone else knows better please let me know if I should move the acme to port 80 instead of 443 even with this entrypoint because technically the cert should never run out.
Reasons:
Blacklisted phrase (1): this tutorial
Whitelisted phrase (-1): worked for me
RegEx Blacklisted phrase (2.5): please let me know
I also seem to be facing the same issue, but mine is not in prod, just locally, the index page renders for the first time, then after refresh it does not render to the UI, when i change the name from index.vue to home.vue and navigate to /home, the UI shows, the console shows no error as well as the network tab.
Pycharm is likely running in its own separate environment. When you run pip install pyinputplus into your command prompt it is installing the package locally.
Instead, try pip installing inside of the terminal in pycharm.
If you need to use DateTimePicker, you actually need to install the Extended.Wpf.Toolkit package through NuGet:
Then add the following namespace reference to your XAML file:
xmlns:xctk="http://schemas.xceed.com/wpf/xaml/toolkit"
Finally, use the DateTimePicker control in GridL, for example:
<xctk:DateTimePicker Name="dateTimePicker" Format="FullDateTime" />
Don`t put PWA and OneSignal service workers in the same "scope" (your site's root). Put OneSignal in a diferent "scope" (a subdir of your root) , and declare it in your OneSignal's account page, in the OneSignal's site. Init OneSignal inside "head" section of your pages. Works for me.
I was able to fix it by changing the url that was not working in these two files (when adding it only in node_modules/react-native, the expo-modules-core error appeared) new url:
"https://sourceforge.net/projects/boost/files/boost/1.76.0/boost_1_76_0.tar.gz/download"
When handling credential creation on the backend, there are couple of validation steps whether the given created credential is valid or not. When parsing the data on the backend for your data, the data should not have left over bytes which mean that returned data somehow malformed.
The reason why you get such error seems that you just assign random bytes to the public key. Note that the public key should be COSE encoded. So, your random bytes does not conform to the spec and it may throw an unexpected errors on the RP side.
The error continued to occur though the DB pool configs added.
Added QUARKUS_DATASOURCE_JDBC_ACQUISTION_TIMEOUT as 10s as an environment variable in my Kubernetes deployment though I have been trying various fixes for long time including upgrading Keycloak. In my case it was Keycloak version 25.0.6 and using Quarkus framework.
The earlier versions were using Jboss and this fix will not be applicable if the issue exists in older versions.
Added QUARKUS_DATASOURCE_JDBC_ACQUISTION_TIMEOUT as 10s as an environment variable in my Kubernetes deployment though I have been trying various fixes for long time. In my case it was Keycloak version 25.0.6 and using Quarkus framework.
The earlier versions were using Jboss and this fix will not be applicable if the issue exists in older versions.
My previous answer has been deleted by other. If this post just seems like spam, it's because you haven't had the same problem as me.
Please leave this article alone to help others who are having the same problem as me.
possible.
Although this post is 5 years old, I am leaving a link and sample image of the package I created for the questioner and for those who come across this post after experiencing the same problem as me.
Like the questioner, I also started with matplotlib, and it was very slow.
I thought about trying to modify mplfinance, but it was difficult to do.
I eventually had to move on to pyqt and try pyqtgraph and finplot.
In the case of plotly, Bokeh, and Seaborn, they were excluded from the options because they did not seem to be able to connect with other GUIs.
pyqt was fast. However, it was only slightly faster than maplotlib.
The same problem of slowdown occurred as data increased.
I was wondering whether to try a language other than Python, but I went back to matplotlib and tried something new.
And it was successful.
I was able to create candlestick charts at high speed.
And this doesn't use pyqt.
By making it that way, it can now be used by connecting to a GUI other than pyqt.
Even when connected to tkinter, it operates smoothly.
It seems to work comfortably up to 10,000 pieces of data.
However, when the number of data is around 40,000, it is not comfortable. I had to use a little trick to make it even smoother.
I'll solve this problem someday.
What works for me after changing a bit of the code from @Danial
int lineCount = LogTextBox.LineCount;
if (lineCount > 300)
{
int excessLines = lineCount - 300;
var text = LogTextBox.Text;
int removeIndex = 0;
for (int i = 0; i < excessLines; i++)
{
removeIndex = text.IndexOf(Environment.NewLine, removeIndex) +
Environment.NewLine.Length;
}
LogTextBox.Text = text.Substring(removeIndex);
}
Without seeing code for smoke generation and for the smoke, it is hard to give a great answer, I suggest checking to see how many smoke entities you are creating. Are you creating 1 per game tick? Creating a bunch of entities needlessly will wear on frame rate. Also double check the code on your smoke entities is just a simple timer, and not anything too complex.
Please share a code snippet so someone can review and see if there's another issue causing the frame dip.
Reasons:
RegEx Blacklisted phrase (2.5): Please share a code
Yes, a custom WordPress implementation can easily handle tens of thousands of posts. We've seen some with millions of posts and large volume of visits, for a similar purpose. In that case it was powered by an EC2 t3.xlarge instance backed by an RDS db.t3.large Mariadb.
Hello everyone if come across a special expert who help me in recovering my lost funds in crypto if you need to recover any previous old funds you can easily reach out to him at recoveryexpert326 at Gmail dot com
Was facing the same issue irrespective of the name I give. Issue from my end was that time on my laptop was not in sync, once I synced it from date and time settings in windows, I was able to create the s3 bucket
This happened because Systems, Privacy and Security, Local Network allows application to access the local network and you need to turn it on for Docker.
I have been using conda for the last ~5 years, building many conda envs that were used by thousands of folks from many different teams. So far, from the discussion, I am not seeing a compelling need to use poetry in conjunction with conda. From what I can read and gather, poetry is solving the issue with pip, rather than conda. But let me summarize from historical timeline, and highlighted the key differences between conda/pip, ...:
First there is a pip to do package management. It does not have a concept of separate env yet, so on the same machine, if you want to run one version of torch for one app, and another version of torch on another app, it becomes a headache.
Then comes conda from Anaconda Inc. It comes out with a novel solution to solve the mix and match package requirement problem, by creating concept of conda env. Basically each conda env is mapped to a different physical directories, and therefore env1 can have a different torch version from env2.
As part of env management, it also have to solve what and how to install packages into each env. So conda is also a package manager.
As a package manager, one can specify conda packages and their versions (say -torch>=2.4, torchvision>=2.4). One can specify the variants and their dependency packages as well (such as torch with gpu for cuda 12.4), or let conda figure out the dependency for you (and transitive dependency also, which pip cannot handle).
The nice thing about conda as a package manager is that once it figure out all the dependency, it will list the packages to be updated and prompt you. This is very useful as you want to inspect all the proposed changes before going ahead. (Many times, I aborted the operations pending further analysis.
While conda as a package manager works well when the number of packages in your conda env is small. It slows down significantly when the number of packages are big, or when the number of versions of the packages are big. This is when mamba comes in. Somehow the dependency resolver works a lot faster than conda. (I had the experience that conda took 20 hours, while mamba took less than 10 minutes. The conda env has like 600 packages though).
While most python packages are available for installation in both conda and pip, some packages are still only in pip. So conda/mamba does have a section for pip install. The recommendation is install all conda packages first, then pip. Once you have a env that is good, you can generate a conda env export with packages and their version, and optionally the package build id for quick replication of conda env.
Subsequently, pip then comes out with a solution to do virtualenv for pip packages only. So works similar to a separate conda env (that virtualenv maps to different directories).
Now since pip does not do transitive dependency, so dependency resolution runs a lot faster than conda (but the result is not 100% guaranteed).
Another issue with pip install is often a compilation is required, and users often got stuck in this step (not having the right versions of header file, etc...) Conda packages on the other hand, usually do just the download the bz2 zip and unzip. No compilation needed, so quicker and more reliable and less error-prone.
So poetry looks like to trying to solve the issue with package management issue pip virtual env. With conda env, it is less compelling to use poetry, although I can see one scenario: it is to keep track of the key package required for a particular conda env. For example, in build a conda env say llm-torch, I only need/want to specify the following: conda create -p /opt/conda/envs/llm-torch python=3.11 pytorch=2.5.1 cudnn cudatoolkit=11.8 transformers=4.46.3 pandas numpy jupyterlab and the resultant env may have pulled in over 100+ packages. This recipe of python, pytorch, cudatoolkit, need to be captured somewhere. Normally, you checkin to your git source code repo, but the poetry yaml file could also provide a way to document/specify this step.
BTW, in using conda/mamba, I found a utility called conda-tree. Input a package, it can list what packages it is depends on, and also what other packages in your conda env are using this package. It can also dump out the whole dependency tree. (Unfortunately, this works only for conda packages but not for pip)/
Did you ever get this? I am working on it as well so I'll post. I do have very nice formatted receipts printing with escpos codes. I'll be using OPOS. I also need to get MICR read and check endorsement and maybe check front printing.
Reasons:
RegEx Blacklisted phrase (3): Did you ever get this
#include <iostream>
#include <regex>
std::string line = "{10}{20}Hello World!";
// Regular expression, that contains 3 match groups: int, int, and anything
std::regex matcher("\\{(\\d+)\\}\\{(\\d+)\\}(.+)");
std::smatch match;
if (!std::regex_search(line, match, matcher)) {
throw std::runtime_error("Failed to match expected format.");
}
int start = std::stoi(match[1]);
int end = std::stoi(match[2]);
std::string text = match[3];
I do not have an answer, but I am fighting with a "similar" problem on an Ubuntu 24.04.1 LTS.
Here are some notes that may help.
My problem is related to nvim replacing vim and breaking traditional vim functionalities. Specifically if I install nvim with apt, then it overrides vim and its configs, because of aggressive configurations. specifically, I would like to invoke nvim using "nvim" and vim using "vim". I am fine with vi to be associated with whatever the user prefenvimrs.
on this ubuntu, default neovim is currently 0.9.5-6ubuntu2 (normal apt install, as seen from dpkg --list)
If i uninstall nvim with apt uninstall neovim, the system puts back vim in charge, and apparently everything is fine.
I see that nvim installed via snap behaves more correctly than nvim installed via apt. The snap version is nvim 0.10.3
I also experienced weird behaviors (apparently unrelated to nvim) of tmux. when i operate on the last line of the window, it behaves erratically.
Strangely, I have this problem on a machine where i installed Ubuntu on the bare metal. I do not have it on several ubuntu 24.04 WSL. I am quite sure that in the malfunctioning installation, I was installing tmux, vim and later neovim, all with apt install.
uninstalling nvim with apt remove neovim, all the alternatives were reverted to vim.basic. Reinstalling neovim later with apt install neovim did not recreate the alternatives...
After fighting this for a while, I found a solution to my problem. My website has many SVGs, and these SVGs use a custom font that I serve. When I serve these SVGs in an <img> tag, they can't access my custom font (unless I include it in the nested style, which would increase the file size too much for my liking). In order to get around this, I redownloaded each SVG again on DOMContentLoaded, and I replaced the <img> tags with inline versions of the SVGs:
After disabling this functionality, I saw that caching was working. I don't know the precise reason why, but fetching via JavaScript right away after the images loading via the <img> tag somehow messed with the cache. I found two solutions:
If I didn't use <img> tags for the SVGs initially and I just
downloaded them via JavaScript on DOMContentLoaded, the cache would
work across refreshes of the website.
If I use the attribute loading="lazy" in the <img> tags, it somehow didn't cause this strange behavior and the cache would still work across refreshes of the website. For those that have the same issue, keep in mind you may not want this lazy loading behavior, so this might not work for you.
As I mentioned, I don't know why "eager" downloading of the SVGs immediately with <img> tags without the attribute and then pulling the SVGs via JavaScript messed with the cache, but lazy loading resolved the issue, so I went with option #2.
Either way, both of my solutions end up doing some sort of lazy loading. If I didn't want this, I think I would have been forced to include font styling inside the SVGs, or I would have had to switch the format of the images from SVG to something else.
Lastly, I would like to confirm that I only originally experienced this issue in Safari on MacOS and all browsers on iOS, and the above two solutions solved the issue.
I have the same problem and I can't solve it with your hints. As far as I can see the versions of gradle and Java are incompatible. What I don't know is, how to check and administer both versions in my flutter-project.
Could you please give me a further hint?
Thx in advance
Reasons:
Blacklisted phrase (1): Thx
Blacklisted phrase (1): I have the same problem
RegEx Blacklisted phrase (2.5): Could you please give me
"Cannot compare left expression of type '...' with right expression of type '...'
I understand the source of the problem (Hibernate 6.x expects the same data type on join statements) and I know how to fix" can you please elaborate on the solution to this error, I am currently running into the same issue.
Reasons:
RegEx Blacklisted phrase (2.5): can you please elaborate
you are putting this in php, or in which, in case you already have a server, but in JavaScript, how would it connect to html, this without using php or another language only with JavaScript
How would it be done?
I need some help. I am trying to set a Solana wallet secret key but always gives me the following ERROR - Invalid SOLANA_WALLET_SECRET_KEY format: Cleaned key length is 32, expected 64. Check your key format.
Your problem comes from your prediction.
As input, you have an array of vectors and in your first prediction you give an array to your model.
Your model was trained on an array of vectors.
To fix the problem, add a [ ].
This gives:
Not sure if this is still relevant or not but you seem to have a misunderstanding of the paradigm of k8s as it related to pods. Pods are seen as basically throw away. Pod died, start a new one. Pod evicted, start a new one elsewhere, etc. The only times that k8s really keeps them around is some small cases where there's a small hickup actually starting the pod, like resources or such.
In order to get something that will stay alive you want to use something like a Deployment, DaemonSet, StatefulSet, etc. You provide a template spec to these objects and they manage the creation of the Pods for you.
Task managing on Android in that way because more of a performance hindering goal around Android 4 I believe..maybe 5. I forgot Google's URL or I'd look it up real quick but it's not critical info 🤖
I don't even think it's entirely possible with the way android is designed.. now it just sleeps things when it should & wakes if an activity/service/receiver is triggered.. yeah? Yeah.
If you know that why are you so adamant about constantly ending it? I'm curious, I used to go deep on Android kernels & task manager optimization in days of 5 hour batteries, unlocked bootloader's & HTC excellence lol
I had the same problem.
At my case the solutions above didn't solve my problem.
My issue was,I'm using proxmox hypervisor. my vm servers are connected vm bridge/network which is using MTU 1400.
I have to add MTU 1400 to my /etc/network/interfaces at each postgre vms
iface ens18 inet static
address xxxxxx
gateway xxx
# dns-* options are implemented by the resolvconf package, if installed
dns-nameservers xxx
dns-nameservers yyy
dns-search xxxx
mtu 1400
this is my first answer, I tried to translate teh formulas from PT-BR to EN-US.
Put both Tables formatted as tables. Table2 refers to the right one.
Then, paste this on cell A3:
=REPLACE(IF([@Type]="BOS",JOINTEXT("/",1;UNIQUE(XLOOKUP([Concept],Table2[Concept Name],Table2[Acct]))),XLOOKUP([@Concept],Tablet2[Concept Name],Table2[Acct])),"0/","").
It worked, I still cannot post images, sorry.
Answer Screenshot
import matplotlib.pyplot as plt import numpy as np # Dados da tabela umidade_teorica = [0, 2, 4, 6, 8, 10, 12] coef_inchamento = [0, 10, 20, 30, 25, 15, 5] # Plotar os dados originais plt.figure(figsize=(10, 6)) plt.plot(umidade_teorica, coef_inchamento, 'o-', label='Coeficiente de Inchamento') # Ajustar a curva (exemplo simples com polinômio de grau 2) z = np.polyfit(umidade_teorica, coef_inchamento, 2) p = np.poly1d(z) plt.plot(umidade_teorica, p(umidade_teorica), "r--", label='Curva Ajustada') # Traçar a tangente paralela ao eixo da umidade umidade_tangente = 5.95 # valor exemplo, ajuste conforme necessário coef_tangente = 25.10 # valor exemplo, ajuste conforme necessário plt.axhline(y=coef_tangente, color='g', linestyle='--', label='Tangente Paralela') plt.scatter(umidade_tangente, coef_tangente, color='red', label='Ponto Tangente (5.95, 25.10)') # Traçar a corda unindo o ponto de origem ao ponto tangente plt.plot([0, umidade_tangente], [0, coef_tangente], color='blue', linestyle='-', linewidth=2, label='Corda de Origem ao Ponto Tangente') # Personalizar o gráfico plt.xlabel('Umidade Teórica (%)') plt.ylabel('Coeficiente de Inchamento (%)') plt.title('Curva de Inchamento com Tangente e Corda') plt.legend() plt.grid(True
I tried this again from my workstation desktop environment, and come to find out that helm was displaying a window to ask for my credentials to access the credentials store.
Since I was logged in via ssh, I never got a window popup.
Of course, SSDP uses only UDP, not TCP, that's why I hope, that I can use only UDP golang functions to achieve my effect.
Now I am trying with something like:
yes , this kind of memory usage is completely normal as far as I have seen.
I had a project on a comparitive study between a few custom made models over transfer learning models. There every time I would run each of the models (or worse , all of them on a single notebook in a sequential manner) , it would completely use up all the vram available on the p100 in kaggle and the memory usage shot up to over 22gb the moment model fitting starts.
I would suggest using a lower batch size during fitting in order to reduce the resouce usage slighty , though i am no expert and pretty much a novice.
I would also suggest you to try different learning rates and optimziers as well and reduce the number of epochs, as in my experience after about 40 - 50 epochs , there's barely any noticeable difference.
There is a file called ListViewWidget.scss, but Guidewire in Guidewire Studio has it in a folder that they reserve as non-editable. If you do edit the file, you can adjust the css for the ListViewWidget.
Eg. something like this
results in the paging controls being shifted to the left and no longer right-justified
This could be something you could play with, but I dont think it would be Guidewire approved.
@ECHO OFF
SETLOCAL
set "var1=Hello world"
Echo var1 before: %var1%
call:myDosFunc var1
Echo var1 after : %var1%
goto :eof
:myDosFunc -- passing a variable by reference, and changing value
echo Notice that a variable passed by reference does NOT echo the value, and it echos the variable name instead.
CALL SET "arg2=%%%~1%%"
echo %arg2%
echo Arg1 variable name = '%~1' and Arg2 value = '%~2'
set "%~1=Good bye world!!!"
goto :eof
Reasons:
RegEx Blacklisted phrase (1.5): solve your problem?
I believe the save(...) method is marked with @Transactional meaning that a transaction is started and committed each time the save method is called in the for loop.
I'd first start by following the advice in this blog to enable SQL logging (which will you better understand the SQL that is generated by JPA).
Then I'd follow the bulk insert advice in this StackOverflow post and see if you get better performance.
Lastly, if you're going to be using JPA for persistence, I can't recommend Vlad Mihalcea's blog enough. You'll find a TON of valuable insights.
Utilizing the Full Text Search capability appears to provide the solution based on this post.
On the MongoDB create a text index, on the Foo collection, using a wildcard to index all document content. In my API I can then perform the search using that index:
var filter = Builders<Foo>.Filter.Text(request.Term);
await _foo.Find(filter).ToListAsync()
I was running into the same thing and the version I installed was v5.0.0
I downgraded to v4.2.1 and it is rendering now. I have an older version of react so I'm wondering if this had something to do with it, but I'm thinking this could help you as well.
