0

Ssh Generate Key Pair Linux

Onone software crack for gta https://okalinka.ru/forum/?download=8780. Also, as @drichardson found below, there is an issue with passphrase protected private keys. Is there a way to force ssh to use the key without checking the permissions. The SSH client will automatically use the key based mechanism to login and as our key has the default name and location, hence it will automatically log you in using the private key. JIRA is built for every. This may be done using "ssh-keygen -K", which will download all available resident keys from the tokens attached to the host and write public/private key files for them.

OWASP Free Training - SF2014 - Keary and Manico

This scenario is often referred to as bring your own key, or BYOK. This is typically done with ssh-keygen. Crack any mac application s. Verify that you're using the correct user name for your AMI. If anyone else can access your private-key file, they can attempt to log in to the remote host computer pretending to be you. How to steam keys you can try these out.

1

Key generator 5 Steps: Setup VS Code for Remote Development via SSH from

Load the SSH agent, if you haven't done so. The easiest way is to invoke $ ssh-agent bash or $ ssh-agent tcsh (or another shell you use). Generally it should be as low permission as possible (Read only by your user only), at minimum on Windows you should be able to remove all other users permissions which will allow the key to be loaded. Now login as root user: $ sudo -i OR $ su -i Edit sshd_config file. Wow lich king 3.3. 5a patch. For added assurance, when you use Azure Key Vault, you can import or generate keys in hardware security modules (HSMs) that never leave the HSM boundary. If you had previously generated an SSH key pair, you may see the following prompt.

2
US7844572B2 - Remote feature activator ... - Google Patents
1 Electronic Notice Board Using Raspberry Pi and Android Phone 12%
2 How to establish passwordless ssh between two servers 100%
3 How to use SSH keys for authentication - Tutorial 81%
4 Stat 6.1 - System Administration Guide 25%
5 CS 419 Exam 3 Study Guide 64%

Digital NOTAM Event Specification

Is there any way i cann connect by ssh without providing the full path to the private key located on my computer? Copy keys to remote server. An SSH key pair consists of a public key and a private key. In the template, if I set "dissablePasswordAuthentication: false", then I can use my private key to ssh into the box (it doesn't ask for a password). Private key stays with the user (and only there), while the public key is sent to the server. Could you please try bypassing ssh-agent by implicitly specifying the path of the private key with -i.

3

Windows10 WSL ssh Permission denied public key on new session

Swapnil Kambli (Rank: Level 3) 2 years ago. Mark of chaos patch 1.72 skype. First we need to generate the public and private SSH key pair. A system running Windows 10; A user account with administrative privileges. You can yourself be a CA and issue your own certificates, these are called self signed certificates but for commercial purpose your self signed certificated will. Both servers are in CentOS I want to SSH from Server 1 to Server 2 using a private key I have (OpenSSH SSH-2 Private Key).

Newest 'openssh' Questions - Information Security Stack

You can use that [HOST] folder as the reference for permissions to reset [HOST] folder and files to. Enable SSH on a headless Raspberry Pi (add file to SD card on another machine) For headless setup, SSH can be enabled by placing a file named ssh, without any extension, onto the boot partition of the SD card from. The SSH key pair establishes trust between the client and server, thereby removing the need for a password during authentication. Skyrim uncut patch german https://okalinka.ru/forum/?download=8685. Next, let us copy the public key to the Server. Permission private key ssh.

4

How to copy ssh key Code Example

By default, 2020-bit RSA key pairs are used. Configuring SSH key auth for discovery. Enter your key passphrase if asked. Goodgame empire cheat hack tool 2020. But I can't figure out how to fix it, does anybody have an idea, or could share suggestions? When enabling SSH on a Pi that may be connected to the internet, you should change its default password to ensure that it remains secure.

Manual: System/SSH client - MikroTik Wiki

Using a text editor, create a file in which to store your private key. This access mode is recommended due to higher security and easier operation. Store the private key in a secure Pipelines environment variable. What should my permissions be on my id_rsa file to not have to type a password each time I use an app that uses it. Enter your private key passphrase. This section describes how you create a private certificate authority (CA) with an optional certificate revocation list (CRL) using ACM Private CA. You can use these procedures to create both root CAs and subordinate CAs, resulting in an auditable hierarchy of trust relationships that matches your organizational needs.

5

How to fix " Permission denied (publickey) " issue in Gitlab

After reaching peak frustration, I went to Twitter to ask what was going on. 2 Answers 2. In addition to that if some plugins are used like vagrant-hostmanager which are ssh-ing. My server is on CentOS 7.5. Define a passphrase to protect your private key, whenever possible. Authenticate securely using public and private keys. Sometimes it is necessary that we must have the SSH public key.

Let me tell you why I think GCP is better than AWS and you tell me where you agree or disagree.


This post is not just a rant about my predilection for GCP. I genuinely want to read your opinions, especially those of you who have used both platforms like me (I have about two years of experience with each platform). And if you feel I have said something that you think it's wrong or not factually accurate, also please let me know, I am happy to be educated or informed in a civiland constructive manner. I have no affiliation with Google whatsoever, this is entirely my opinion based on my perceptions of the merits of both platforms. I am also conscious that there are more tools and services that I could ever expect to use in a lifetime so it is entirely plausible I am missing areas where AWS by far outshines GCP due to my limited experience or missing even more areas where GCP is better.

  • Where do you agree?
  • Where do you disagree?
  • Can you provide examples of your own where AWS or GCP was better than the other?
EDIT: Thank you all for your feedback! I have learned a lot and it was very helpful for me to expand and modify some parts of this post and publish on medium.

Ikea for Cars
If AWS and GCP were both car companies and you wanted to purchase a car, AWS would give you the wheel, a chunky verbose manual and the keys and then tell you to go to twenty different shops they also own to get the rest of the components and ask you to put them together yourself the best you can. Sure, maybe you can hire a service and get tools to automate this part, but it still falls on you to assemble these components together and maintain the automation.
The experience of GCP on the other hand is more like collecting the car keys and driving off from the parking lot, with the option of dismantling and customising the car if you wish, but the default is a fully built functioning car so you can achieve your objectives, which is driving around, not assemble the car.
My first experience working with AWS, before I had much to compare it to, was brief and I didn’t like it; I felt the interface and the way tools and settings were organised was counter-intuitive and weird.
For example assigning a static ip to a server was just bizarre, I kept looking for ways to assign the static ip without knowing that it was meant to be called elastic ip and hidden away in a separate set of menus. Then these elastic ips were part of a different pool of ips than the ones that were assigned dynamically. To my dismay I had to stop a production server to change the ip and also change the DNS pointing to that new ip, this was because my predecessor hadn’t assigned an static ip to the server, my bet is that he probably gave up after ten minutes trying to figure out that it was called an elastic ip.
My second experience working with AWS was after a year and a half working with GCP and now by comparison I really couldn’t stand AWS, it took me a few months to get accustomed back to use it and I remember that in my first few weeks I actually considered quitting and just accepting roles with GCP.
It’s not that AWS is harder to use than GCP, it’s that it is needlessly hard; a disjointed, sprawl of infrastructure primitives with poor cohesion between them. A challenge is nice, a confusing mess is not, and the problem with AWS is that a large part of your working hours will be spent untangling their documentation and weeding through features and products to find what you want, rather than focusing on cool interesting challenges.
Let’s just go over a few of the things that make AWS such a pain to use and how it compares with GCP.
Accounts vs Projects
One of the first differences that strikes you when going from GCP to AWS is accounts vs projects. In GCP you have one master account/project that you can use to manage the rest of your projects, people log in with the google account and then you can set permissions to any project however you want. So you can have a dev project, a production project, etc. All of this works out of the box and there is absolutely nothing additional for you to do.
In AWS you have accounts, and each account has a separate set of users. There are ways to connect these accounts so your user has permissions on other accounts. One way of doing this is creating a master users account and then adding roles that can be assumed in all other accounts by this master account.
This is not only a pain to set up, it’s very painful to use as well. For example when using terraform scripts you need to coordinate multiple roles across several modules if you need to work across multiple accounts.
Command Line Interface Tools (CLI tools)
Let’s just compare what you have to do in order to use GCP cli compared to AWS provided we are using 2FA and a couple of different projects/accounts.
In GCP after you install the Google SDK, all you need to do is run gcloud init, which redirects you in the browser to a Google login page. Here you can login with your two factor authentication (which if you have an android phone is as easy as unlocking the phone and pressing okay) and you are done. Your login session is attached to your Google session so when you kill this session you are logged out— very simple.
In AWS you need to create a token that you can use to login with your CLI, simple enough, right? But now we want to use two factor auth, and this is where the fun begins.
After you login with your token you then need to create a script to give you a 12 hour session, and you need to do this every day, because there is no way to extend this.
Okay, but that’s not a big deal, you say, after all it’s just a code that you need to input once a day and you can get on with your day after that.
But wait, there is more! If you need to assume roles in another account, you need to create yet another script that creates another profile for you to use.
That’s one step plus two scripts, plus many steps in between. And sure, you can automate much of this or use someone else’s tools you find online (that you most likely will need to tweak), but why? Why do we have to do so much work to use AWS? Why can’t AWS abstract away this pain away from you in the way that Google has done?
Web User Interface
If using the CLI is too painful for you, you can always log in to the portal and use their user interface, although I don’t recommend you do this for everything, in fact I recommend you use it the least possible and only for reference and to check status of your services.
AWS interface looks like it was designed by a lonesome alien living in an asteroid who once saw a documentary about humans clicking with a mouse. It is confusing, counterintuitive, messy and extremely overcrowded.
I can’t even count the times I’ve gotten lost or stumped in the AWS console, sometimes over the most stupid details, like missing that there was a next button hidden on a weird corner. Or trying to use search bars that can only search prefixes (WTF?)
But the biggest frustration I have from the AWS console is how you are always overwhelmed with scores of settings and options you need to fill in before actually provisioning anything.
One example that comes to mind is when someone at work said we should use codebuild/codedeploy to replace Jenkins for ECS deployments. The first engineer tried, he got stuck, the second engineer tried, he got stuck, I tried for hours and I got stuck… in the end I just gave up for lack of wanting to spend any more time on a tool that doesn’t seem to be that popular for CI/CD that I thought was meant to make life easier.
Amazon seems to be particularly terrible at interfaces in almost all of their products though. For example in my Smart TV the Netflix app works flawlessly and is intuitive to use whereas the Amazon Prime app is an abomination, you are constantly accidentally pressing the wrong button or getting lost or the subtitles are often out of sync.
In a rant that a Google engineer who had worked at Amazon wrote a while back he explained the issue with Amazon and Bezos not understanding interfaces (or is it human interaction?)
Jeff Bezos is an infamous micro-manager. He micro-manages every single pixel of Amazon’s retail site. He hired Larry Tesler, Apple’s Chief Scientist and probably the very most famous and respected human-computer interaction expert in the entire world, and then ignored every goddamn thing Larry said for three years until Larry finally — wisely — left the company. Larry would do these big usability studies and demonstrate beyond any shred of doubt that nobody can understand that frigging website, but Bezos just couldn’t let go of those pixels, all those millions of semantics-packed pixels on the landing page. They were like millions of his own precious children. So they’re all still there, and Larry is not.
GCP’s user interface is on the other hand very intuitive to use and whenever you want to provision anything you are given sane defaults so you can deploy anything in a couple of clicks, I have never gotten lost using GCP or needed to consult a million pages of documentation to find out what I needed to do.
This however does not mean that GCP is taking away from you the power to configure things to an intricate detail, it just means they are giving you an example of a working configuration that you can then tweak to your purposes.
There are also other things you can do from the UI in GCP that either work really badly in AWS or are non-existent. For example you can easily open a terminal and ssh into any instance you have spun (provided you set permissions for it) and it works really well.
Another feature you have in GCP that I absolutely LOVE is the ability to view the CLI command that would do whatever settings you have in the console. That makes learning the cli so much easier, it’s far better than scouring the net for examples on how to do anything or trying to make sense of AWS’s gorgeous documentation…
Documentation
You can forgive the documentation in AWS being a nightmare to navigate for being a mere reflection of the confusing mess that is trying to describe. Whenever you are trying to solve a simple problem it is far too often you end up drowning in reference pages, the experience is like asking for a glass of water and being hosed down with a fire hydrant.
Great documentation is contextual, not referential. If you wanted to learn how to cook a dish, you don’t want someone to point you to a list of ingredients, you want a recipe describing how to use them, and this is where AWS documentation too often fails; it exhaustively describes everything that they have, but they don’t always do a good job at putting the documentation into context.
To be perfectly fair to whoever is tasked to document anything in AWS, it is a lot harder to document something that’s confusing and messy than something that’s simple to use. Extensive and overly verbose documentation is often a sign of complicated and over convoluted software or processes, so in this sense Google Cloud already has an advantage to begin with.
The documentation in GCP is generally clear and concise, and while it may not always be perfect I generally found it useful and to the point. If you want other good examples of great documentation look at DigitalOcean — they are great.
GKE vs EKS
If your intent is to use Kubernetes, don’t even bother with AWS, their implementation is so bad I can’t even comprehend how they have the gall to call it managed, especially when compared with GCP
In GCP if you want to spin a cluster, no problem, just a couple of clicks and you are there. The defaults are easy and sane and the entire product feels very cohesive with all the ugly, tedious bits abstracted away from your experience.
With GKE you don’t need to join the nodes, you don’t need to plan for an upgrade of these nodes either, it’s done automatically or with a couple of easy clicks, and this does not mean you are sacrificing complexity. You can customise a lot, but when presented with sane, simple defaults, it’s a lot easier to understand a product that when being overwhelmed with a barrage of options and trying to figure out how everything fits together as it’s the case with EKS.
Spinning an EKS cluster gives you essentially a brick. You have to spin your own nodes on the side and make sure they connect with the master, which a lot of work for you to do on top of the promise of “managed”
And yes, I know that there are official terraform modules that take care of most of this work for you and make the job a lot easier and there is also a tool called eksctl developed by weave works which is great, but these aim to simplify a complex solution that should have been abstracted away by AWS by design, not rely on others to make sense of the mess with complex scripts and tools.
Even if you use those tools to create your automation on top of AWS, the fact remains that there are a lot of moving parts underneath that you will always be responsible to orchestrate and make sure that are working and up to date. eksctl for example uses cloudformation templates in the background.
Product Overload
At the time of writing this, there are 169 AWS products compared to 90 in GCP. AWS has been around for longer and therefore they have more offering, and in good Amazon spirits, they constantly and aggressively are expanding this offering to give you more of what you may need (and a lot of what you don’t need)
This sounds like a good thing, until you start seeing the amount of half cooked products or near duplicates they have. One good example is Parameter Store and Secret Manager, the latter offers almost identical functionality except for a couple of extra features that you need to pay extra for, which begs the question, why not consolidate these products to avoid confusion and time wasted by the users investigating which one they should be using?
GCP on the other hand has fewer products but the ones they have (at least in my experience) feel more complete and well integrated with the rest of the ecosystem, and choosing one product over other doesn’t become an agonising choice that requires extensive research (okay, you still need to research, but not nearly as much)
I used to mock Apple in the past for how limiting they were and how very few features they had compared to Windows and Linux Distros until I started using a Macbook, it was then that it became so clear to me that having an opinionated approach on a few products and tighter integration of the various components often yields a far superior and more stable experience which is similar to the experience that I am having with GCP vs AWS. GCP gives you less, but what it gives you is far better integrated, simple to use and works better than its AWS counterpart — so unless you have a very compelling feature in AWS that you are missing in GCP, you should seriously consider picking GCP.
AWS is a lot more Expensive
AWS charges substantially more for their services than GCP does, but it also has another hidden cost attached to it; expertise and manpower.
With GCP, a relatively inexperienced engineer in platforming tools can pick it up and get his work done in a relatively short time because most of the tedious tasks of piecing all the parts together have been done by Google already. Also using GCP is substantially more intuitive so even if you have no previous experience on cloud platforms you will pick it up very quickly.
A task that may take you a day or less to do in GCP, you may spend a week to do the same thing in AWS. One example I can give here is Endpoints. I was working with a terraform cluster installation and I wanted to restrict outbound traffic to the internet. The problem is that if you do this then you are also cutting off traffic to AWS, in order to address this problem you need to set up endpoints. Endpoints essentially allow you to connect to AWS via the AWS intranet as opposed to the internet (Don’t ask me why cloud providers don’t do this by default, it makes no sense to me).
So simple enough, I just add these endpoints and then my job is done. Problem is that I was working with a cluster provisioner in terraform with a lot of moving parts and using multiple AWS services and you cannot set up an endpoint that applies to all AWS services, you can only do one endpoint per service and I had to do a lot of digging trying to figure out exactly all the services that the provisioner was using and add endpoints for each one of them, every time I added an endpoint, I found out I had to add another endpoint, I ended up adding about five of them and then I found out that a couple of the services that I was using didn’t have endpoints for them, so in the end I just had to allow outgoing traffic via a NAT.
Out of curiosity I investigated how to do this in Google Cloud because I had never done it before, just to compare how difficult it would be in comparison to AWS and I wasn’t surprised to find out that you can accomplish the same thing just by clicking on a checkbox or activating a setting and it applies to all services Also, doing this in GCP is free whereas in AWS you have to pay for each endpoint
The above is just one example, but I have found that generally any task that I want to do in AWS requires far more energy and effort to do than GCP, meaning you are probably going to need to hire far more engineers and need more time if you are using AWS than if you are using GCP.
The Cost of interrupted Flow
Another significant cost to your organisation if you decide to use AWS is the continuous interrupted flow. Flow is the state where you ideally want your engineers to be a good portion of their time at your company, not only they will be much happier, they will also be a lot more productive.
The problem with using AWS is that because everything is so confusing and complicated to use you will (at least at the beginning or when you need to apply a significant change or embark on another project) have to spend a lot of time reading documentation and testing to figure out how things work, and the irritating thing is that it won’t be fun experimentation, it will be tedious and trivial issues that should not exist, like the endpoint issue I described above.
Performance
I am not going to do extensive testing in both platforms and post benchmarks for this article since it’s a lot more work than I want to spend on this but I’ll just say that in my experience I felt that performance was almost always better in GCP, for example copying from instances to buckets in GCP is INSANELY fast, I remember being shocked by this because in a previous job I had to do a lot of hourly backups to buckets of large chunks of data in AWS and I always felt the copying was slow, but this was not the case at all for GCP.
This is a bit old but some articles by this guy may still apply today.
So what’s better about AWS?
As I mentioned I think that AWS certainly offers a lot more features and products than GCP does, and you may benefit from some of them. You can certainly do more with AWS, there is no contest here. If for example you need a truck with a server inside or a computer sent over to your office so you can dump your data inside and return it to Amazon, then AWS is for you. AWS also has more flexibility in terms of location of your data centres. Other than that… I would chose GCP any day, and I think GCP will cover the vast majority of your cases.
But wait, there are lots of third party tools to automate AWS
Yes, like the aforementioned eksctl, some of them do an amazing job at this but they are still third party tools. I firmly believe AWS needs to work a lot on their abstraction of needless complexity.
So if GCP is so much better, why so many more people use AWS?
AWS has been around a lot longer. Also the amount of engineers who are certified in AWS is substantially larger as well. When people start looking around to get into DevOps everyone is shouting AWS as must have on your CV so everyone is scurrying to get certified.
Imagine if you are an engineer with 5 years of experience in AWS with lots of money and effort spent on AWS certifications and you are tasked to do infrastructure at greenfield project, what are you going to chose? Not GCP.
On the flipside it seems a lot more companies are making the switch to GCP. I’ve been hearing from previous colleagues who told me that applied for some roles who were migrating to GCP and when they mentioned they didn’t have any experience in AWS their reply was “No one has” So it may just be a good time to learn GCP for a change.
So based on that account, and given that everyone is racing to get AWS certifications, you may just be better off doing the opposite and take on GCP because you may against less competition.
But this AWS complexity is creating so many jobs for us!
Yes, and it does sound like I am shooting myself on the foot by posting this because this is my job but I do have to admit that AWS does create a lot more jobs for DevOps, SRE, Sysadmin, Platform Engineers etc. due to this complexity and the lack of desire for the developers themselves wanting to tackle this complexity.
But then again I embrace change, if I have to learn other skills so be it, and I still think there is a lot of room to grow a career with GCP, it’s just that your work will hinge more on the interesting stuff, with tedious cloud platform bits abstracted away.
So I should pick GCP then?
No, you should pick whatever fits your needs. If you are a small company or an independent developer I totally recommend giving these two a miss and go with DigitalOcean or Linode or some of the smaller companies which are even easier to use and will cover your most basic needs for less money.
AWS is still my second choice as an enterprise cloud provider after GCP. I know there is also Azure, but many years of using Microsoft crap products (with the exception of VScode) really has put me off using anything they make, so I always to try to give Azure jobs a miss if I can help it, so I have no opinion on their platform other than a healthy dislike for the company behind it ;-).
submitted by DevsyOpsy to devops

6

changing the remote tmp path in ansible.cfg (Windows client)

Good day everyone,
I am having some issues any task on a Windows machine

i ran the following:
 ansible -m ping Windows -u ansible -kK -vvv 

And i received the following results:


```
192.168.140.147 | UNREACHABLE! => { "changed": false, "msg": "Authentication or permission failure. In some cases, you may have been able to authenticate and did not have permissions on the target directory. Consider changing the remote tmp path in ansible.cfg to a path rooted in \"/tmp\". Failed command was: ( umask 77 && mkdir -p \"` echo ~/.ansible/tmp/ansible-tmp-1602653442.59-46527212445528 `\" && echo ansible-tmp-1602653442.59-46527212445528=\"` echo ~/.ansible/tmp/ansible-tmp-1602653442.59-46527212445528 `\" ), exited with result 1, stderr output: OpenSSH_7.6p1 Ubuntu-4ubuntu0.3, OpenSSL 1.0.2n 7 Dec 2017\r\ndebug1: Reading configuration data /etc/ssh/ssh_config\r\ndebug1: /etc/ssh/ssh_config line 19: Applying options for *\r\ndebug1: auto-mux: Trying existing master\r\ndebug2: fd 3 setting O_NONBLOCK\r\ndebug2: mux_client_hello_exchange: master version 4\r\ndebug3: mux_client_forwards: request forwardings: 0 local, 0 remote\r\ndebug3: mux_client_request_session: entering\r\ndebug3: mux_client_request_alive: entering\r\ndebug3: mux_client_request_alive: done pid = 7535\r\ndebug3: mux_client_request_session: session request sent\r\ndebug1: mux_client_request_session: master session id: 2\r\nThe system cannot find the path specified.\r\ndebug3: mux_client_read_packet: read header failed: Broken pipe\r\ndebug2: Received exit status from master 1\r\n", "unreachable": true } 



Has anyone face this before? i am not too sure what am i supposed to change, i have no issues excuting playbooks/tasks on linux
I also have no issues establishing a ssh connection to the Windows client. I did try changing the path mentioned in the error, but the error was still the same..

Any idea how to fix this?
Host machine: Ubuthu 18.0
Client : Window 7
Ansible version: 2.5.1

Hostfile:
[Windows] 192.168.140.147 



Config file:
[defaults] # some basic default values... #inventory = /etc/ansible/hosts #library = /usshare/my_modules/ #module_utils = /usshare/my_module_utils/ #remote_tmp = ~/.ansible/tmp #local_tmp = ~/.ansible/tmp #plugin_filters_cfg = /etc/ansible/plugin_filters.yml #forks = 5 #poll_interval = 15 #sudo_user = root #ask_sudo_pass = True #ask_pass = True #transport = smart #remote_port = 22 #module_lang = C #module_set_locale = False # plays will gather facts by default, which contain information about # the remote system. # # smart - gather by default, but don't regather if already gathered # implicit - gather by default, turn off with gather_facts: False # explicit - do not gather by default, must say gather_facts: True #gathering = implicit # This only affects the gathering done by a play's gather_facts directive, # by default gathering retrieves all facts subsets # all - gather all subsets # network - gather min and network facts # hardware - gather hardware facts (longest facts to retrieve) # virtual - gather min and virtual facts # facter - import facts from facter # ohai - import facts from ohai # You can combine them using comma (ex: network,virtual) # You can negate them using ! (ex: !hardware,!facter,!ohai) # A minimal set of facts is always gathered. #gather_subset = all # some hardware related facts are collected # with a maximum timeout of 10 seconds. This # option lets you increase or decrease that # timeout to something more suitable for the # environment. # gather_timeout = 10 # additional paths to search for roles in, colon separated #roles_path = /etc/ansible/roles # uncomment this to disable SSH key host checking #host_key_checking = False # change the default callback, you can only have one 'stdout' type enabled at a time. #stdout_callback = skippy ## Ansible ships with some plugins that require whitelisting, ## this is done to avoid running all of a type by default. ## These setting lists those that you want enabled for your system. ## Custom plugins should not need this unless plugin author specifies it. # enable callback plugins, they can output to stdout but cannot be 'stdout' type. #callback_whitelist = timer, mail # Determine whether includes in tasks and handlers are "static" by # default. As of 2.0, includes are dynamic by default. Setting these # values to True will make includes behave more like they did in the # 1.x versions. #task_includes_static = False #handler_includes_static = False # Controls if a missing handler for a notification event is an error or a warning #error_on_missing_handler = True # change this for alternative sudo implementations #sudo_exe = sudo # What flags to pass to sudo # WARNING: leaving out the defaults might create unexpected behaviours #sudo_flags = -H -S -n # SSH timeout #timeout = 10 # default user to use for playbooks if user is not specified # (/usbin/ansible will use current user as default) #remote_user = root # logging is off by default unless this path is defined # if so defined, consider logrotate #log_path = /valog/ansible.log # default module name for /usbin/ansible #module_name = command # use this shell for commands executed under sudo # you may need to change this to bin/bash in rare instances # if sudo is constrained #executable = /bin/sh # if inventory variables overlap, does the higher precedence one win # or are hash values merged together? The default is 'replace' but # this can also be set to 'merge'. #hash_behaviour = replace # by default, variables from roles will be visible in the global variable # scope. To prevent this, the following option can be enabled, and only # tasks and handlers within the role will see the variables there #private_role_vars = yes # list any Jinja2 extensions to enable here: #jinja2_extensions = jinja2.ext.do,jinja2.ext.i18n # if set, always use this private key file for authentication, same as # if passing --private-key to ansible or ansible-playbook #private_key_file = /path/to/file # If set, configures the path to the Vault password file as an alternative to # specifying --vault-password-file on the command line. #vault_password_file = /path/to/vault_password_file # format of string {{ ansible_managed }} available within Jinja2 # templates indicates to users editing templates files will be replaced. # replacing {file}, {host} and {uid} and strftime codes with proper values. #ansible_managed = Ansible managed: {file} modified on %Y-%m-%d %H:%M:%S by {uid} on {host} # {file}, {host}, {uid}, and the timestamp can all interfere with idempotence # in some situations so the default is a static string: #ansible_managed = Ansible managed # by default, ansible-playbook will display "Skipping [host]" if it determines a task # should not be run on a host. Set this to "False" if you don't want to see these "Skipping" # messages. NOTE: the task header will still be shown regardless of whether or not the # task is skipped. #display_skipped_hosts = True # by default, if a task in a playbook does not include a name: field then # ansible-playbook will construct a header that includes the task's action but # not the task's args. This is a security feature because ansible cannot know # if the *module* considers an argument to be no_log at the time that the # header is printed. If your environment doesn't have a problem securing # stdout from ansible-playbook (or you have manually specified no_log in your # playbook on all of the tasks where you have secret information) then you can # safely set this to True to get more informative messages. #display_args_to_stdout = False # by default (as of 1.3), Ansible will raise errors when attempting to dereference # Jinja2 variables that are not set in templates or action lines. Uncomment this line # to revert the behavior to pre-1.3. #error_on_undefined_vars = False # by default (as of 1.6), Ansible may display warnings based on the configuration of the # system running ansible itself. This may include warnings about 3rd party packages or # other conditions that should be resolved if possible. # to disable these warnings, set the following value to False: #system_warnings = True # by default (as of 1.4), Ansible may display deprecation warnings for language # features that should no longer be used and will be removed in future versions. # to disable these warnings, set the following value to False: #deprecation_warnings = True # (as of 1.8), Ansible can optionally warn when usage of the shell and # command module appear to be simplified by using a default Ansible module # instead. These warnings can be silenced by adjusting the following # setting or adding warn=yes or warn=no to the end of the command line # parameter string. This will for example suggest using the git module # instead of shelling out to the git command. # command_warnings = False # set plugin path directories here, separate with colons #action_plugins = /usshare/ansible/plugins/action #cache_plugins = /usshare/ansible/plugins/cache #callback_plugins = /usshare/ansible/plugins/callback #connection_plugins = /usshare/ansible/plugins/connection #lookup_plugins = /usshare/ansible/plugins/lookup #inventory_plugins = /usshare/ansible/plugins/inventory #vars_plugins = /usshare/ansible/plugins/vars #filter_plugins = /usshare/ansible/plugins/filter #test_plugins = /usshare/ansible/plugins/test #terminal_plugins = /usshare/ansible/plugins/terminal #strategy_plugins = /usshare/ansible/plugins/strategy # by default, ansible will use the 'linear' strategy but you may want to try # another one #strategy = free # by default callbacks are not loaded for /bin/ansible, enable this if you # want, for example, a notification or logging callback to also apply to # /bin/ansible runs #bin_ansible_callbacks = False # don't like cows? that's unfortunate. # set to 1 if you don't want cowsay support or export ANSIBLE_NOCOWS=1 #nocows = 1 # set which cowsay stencil you'd like to use by default. When set to 'random', # a random stencil will be selected for each task. The selection will be filtered # against the `cow_whitelist` option below. #cow_selection = default #cow_selection = random # when using the 'random' option for cowsay, stencils will be restricted to this list. # it should be formatted as a comma-separated list with no spaces between names. # NOTE: line continuations here are for formatting purposes only, as the INI parser # in python does not support them. #cow_whitelist=bud-frogs,bunny,cheese,daemon,default,dragon,elephant-in-snake,elephant,eyes,\ # hellokitty,kitty,luke-koala,meow,milk,moofasa,moose,ren,sheep,small,stegosaurus,\ # stimpy,supermilker,three-eyes,turkey,turtle,tux,udder,vader-koala,vader,www # don't like colors either? # set to 1 if you don't want colors, or export ANSIBLE_NOCOLOR=1 #nocolor = 1 # if set to a persistent type (not 'memory', for example 'redis') fact values # from previous runs in Ansible will be stored. This may be useful when # wanting to use, for example, IP information from one group of servers # without having to talk to them in the same playbook run to get their # current IP information. #fact_caching = memory # retry files # When a playbook fails by default a .retry file will be created in ~/ # You can disable this feature by setting retry_files_enabled to False # and you can change the location of the files by setting retry_files_save_path #retry_files_enabled = False #retry_files_save_path = ~/.ansible-retry # squash actions # Ansible can optimise actions that call modules with list parameters # when looping. Instead of calling the module once per with_ item, the # module is called once with all items at once. Currently this only works # under limited circumstances, and only with parameters named 'name'. #squash_actions = apk,apt,dnf,homebrew,pacman,pkgng,yum,zypper # prevents logging of task data, off by default #no_log = False # prevents logging of tasks, but only on the targets, data is still logged on the mastecontroller #no_target_syslog = False # controls whether Ansible will raise an error or warning if a task has no # choice but to create world readable temporary files to execute a module on # the remote machine. This option is False by default for security. Users may # turn this on to have behaviour more like Ansible prior to 2.1.x. See # https://docs.ansible.com/ansible/become.html#becoming-an-unprivileged-user # for more secure ways to fix this than enabling this option. #allow_world_readable_tmpfiles = False # controls the compression level of variables sent to # worker processes. At the default of 0, no compression # is used. This value must be an integer from 0 to 9. #var_compression_level = 9 # controls what compression method is used for new-style ansible modules when # they are sent to the remote system. The compression types depend on having # support compiled into both the controller's python and the client's python. # The names should match with the python Zipfile compression types: # * ZIP_STORED (no compression. available everywhere) # * ZIP_DEFLATED (uses zlib, the default) # These values may be set per host via the ansible_module_compression inventory # variable #module_compression = 'ZIP_DEFLATED' # This controls the cutoff point (in bytes) on --diff for files # set to 0 for unlimited (RAM may suffer!). #max_diff_size = 1048576 # This controls how ansible handles multiple --tags and --skip-tags arguments # on the CLI. If this is True then multiple arguments are merged together. If # it is False, then the last specified argument is used and the others are ignored. # This option will be removed in 2.8. #merge_multiple_cli_flags = True # Controls showing custom stats at the end, off by default #show_custom_stats = True # Controls which files to ignore when using a directory as inventory with # possibly multiple sources (both static and dynamic) #inventory_ignore_extensions = ~, .orig, .bak, .ini, .cfg, .retry, .pyc, .pyo # This family of modules use an alternative execution path optimized for network appliances # only update this setting if you know how this works, otherwise it can break module execution #network_group_modules=eos, nxos, ios, iosxr, junos, vyos # When enabled, this option allows lookups (via variables like {{lookup('foo')}} or when used as # a loop with `with_foo`) to return data that is not marked "unsafe". This means the data may contain # jinja2 templating language which will be run through the templating engine. # ENABLING THIS COULD BE A SECURITY RISK #allow_unsafe_lookups = False # set default errors for all plays #any_errors_fatal = False [inventory] # enable inventory plugins, default: 'host_list', 'script', 'yaml', 'ini' #enable_plugins = host_list, virtualbox, yaml, constructed # ignore these extensions when parsing a directory as inventory source #ignore_extensions = .pyc, .pyo, .swp, .bak, ~, .rpm, .md, .txt, ~, .orig, .ini, .cfg, .retry # ignore files matching these patterns when parsing a directory as inventory source #ignore_patterns= # If 'true' unparsed inventory sources become fatal errors, they are warnings otherwise. #unparsed_is_failed=False [privilege_escalation] #become=True #become_method=sudo #become_user=root #become_ask_pass=False [paramiko_connection] # uncomment this line to cause the paramiko connection plugin to not record new host # keys encountered. Increases performance on new host additions. Setting works independently of the # host key checking setting above. #record_host_keys=False # by default, Ansible requests a pseudo-terminal for commands executed under sudo. Uncomment this # line to disable this behaviour. #pty=False # paramiko will default to looking for SSH keys initially when trying to # authenticate to remote devices. This is a problem for some network devices # that close the connection after a key failure. Uncomment this line to # disable the Paramiko look for keys function #look_for_keys = False # When using persistent connections with Paramiko, the connection runs in a # background process. If the host doesn't already have a valid SSH key, by # default Ansible will prompt to add the host key. This will cause connections # running in background processes to fail. Uncomment this line to have # Paramiko automatically add host keys. #host_key_auto_add = True [ssh_connection] # ssh arguments to use # Leaving off ControlPersist will result in poor performance, so use # paramiko on older platforms rather than removing it, -C controls compression use #ssh_args = -C -o ControlMaster=auto -o ControlPersist=60s # The base directory for the ControlPath sockets. # This is the "%(directory)s" in the control_path option # # Example: # control_path_dir = /tmp/.ansible/cp #control_path_dir = ~/.ansible/cp # The path to use for the ControlPath sockets. This defaults to a hashed string of the hostname, # port and username (empty string in the config). The hash mitigates a common problem users # found with long hostames and the conventional %(directory)s/ansible-ssh-%%h-%%p-%%r format. # In those cases, a "too long for Unix domain socket" ssh error would occur. # # Example: # control_path = %(directory)s/%%h-%%r #control_path = # Enabling pipelining reduces the number of SSH operations required to # execute a module on the remote server. This can result in a significant # performance improvement when enabled, however when using "sudo:" you must # first disable 'requiretty' in /etc/sudoers # # By default, this option is disabled to preserve compatibility with # sudoers configurations that have requiretty (the default on many distros). # #pipelining = False # Control the mechanism for transferring files (old) # * smart = try sftp and then try scp [default] # * True = use scp only # * False = use sftp only #scp_if_ssh = smart # Control the mechanism for transferring files (new) # If set, this will override the scp_if_ssh option # * sftp = use sftp to transfer files # * scp = use scp to transfer files # * piped = use 'dd' over SSH to transfer files # * smart = try sftp, scp, and piped, in that order [default] #transfer_method = smart # if False, sftp will not use batch mode to transfer files. This may cause some # types of file transfer failures impossible to catch however, and should # only be disabled if your sftp version has problems with batch mode #sftp_batch_mode = False # The -tt argument is passed to ssh when pipelining is not enabled because sudo # requires a tty by default. #use_tty = True [persistent_connection] # Configures the persistent connection timeout value in seconds. This value is # how long the persistent connection will remain idle before it is destroyed. # If the connection doesn't receive a request before the timeout value # expires, the connection is shutdown. The default value is 30 seconds. #connect_timeout = 30 # Configures the persistent connection retry timeout. This value configures the # the retry timeout that ansible-connection will wait to connect # to the local domain socket. This value must be larger than the # ssh timeout (timeout) and less than persistent connection idle timeout (connect_timeout). # The default value is 15 seconds. #connect_retry_timeout = 15 # The command timeout value defines the amount of time to wait for a command # or RPC call before timing out. The value for the command timeout must # be less than the value of the persistent connection idle timeout (connect_timeout) # The default value is 10 second. #command_timeout = 10 [accelerate] #accelerate_port = 5099 #accelerate_timeout = 30 #accelerate_connect_timeout = 5.0 # The daemon timeout is measured in minutes. This time is measured # from the last activity to the accelerate daemon. #accelerate_daemon_timeout = 30 # If set to yes, accelerate_multi_key will allow multiple # private keys to be uploaded to it, though each user must # have access to the system via SSH to add a new key. The default # is "no". #accelerate_multi_key = yes [selinux] # file systems that require special treatment when dealing with security context # the default behaviour that copies the existing context or uses the user default # needs to be changed to use the file system dependent context. #special_context_filesystems=nfs,vboxsf,fuse,ramfs,9p # Set this to yes to allow libvirt_lxc connections to work without SELinux. #libvirt_lxc_noseclabel = yes [colors] #highlight = white #verbose = blue #warn = bright purple #error = red #debug = dark gray #deprecate = purple #skip = cyan #unreachable = red #ok = green #changed = yellow #diff_add = green #diff_remove = red #diff_lines = cyan [diff] # Always print diff when running ( same as always running with -D/--diff ) # always = no # Set how many context lines to show in diff # context = 3 
submitted by ansible_2020 to ansible