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Update slack link
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@ -6,7 +6,7 @@ title: Community Resources
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For feature requests or bug reports, see **ClearML** [GitHub Issues](https://github.com/allegroai/clearml/issues).
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If you have any questions, post on the **ClearML** [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A).
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If you have any questions, post on the **ClearML** [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw).
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Or, tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/clearml) with the **clearml** tag.
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@ -30,7 +30,7 @@ Contribution comes in many forms:
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* Reporting [issues](https://github.com/allegroai/clearml/issues) you've come upon
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* Participating in issue discussions in the [issue tracker](https://github.com/allegroai/clearml/issues) and the
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[ClearML community slack space](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A)
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[ClearML community slack space](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw)
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* Suggesting new features or enhancements
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* Implementing new features or fixing outstanding issues
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@ -86,7 +86,7 @@ Enhancement suggestions are tracked as GitHub issues. After you determine which
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Before you submit a new PR:
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* Verify that the work you plan to merge addresses an existing [issue](https://github.com/allegroai/clearml/issues) (If not, open a new one)
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* Check related discussions in the [ClearML slack community](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A)
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* Check related discussions in the [ClearML slack community](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw)
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(Or start your own discussion on the ``#clearml-dev`` channel)
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* Make sure your code conforms to the ClearML coding standards by running:
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@ -151,7 +151,7 @@ All console output during the execution of the migration script is saved to a lo
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If the migration script does not complete successfully, the migration script prints the error.
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:::important
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For help in resolving migration issues, check the **ClearML** [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A),
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For help in resolving migration issues, check the **ClearML** [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw),
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[GitHub Issues](https://github.com/allegroai/clearml-server/issues), and the **ClearML Server** sections of the [FAQ](../faq.md).
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:::
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@ -63,6 +63,6 @@ Talking of which, let’s say your wait times are very long because all data sci
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In the following video we’ll go a little deeper yet into this newly discovered automation thing we just saw and introduce things like automatic hyperparameter optimization and pipelines.
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But for now, feel free to start spinning up some agents on your own machines completely for free at [app.clear.ml](https://app.clear.ml) or by using our self-hosted server on GitHub, and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A) if you need any help.
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But for now, feel free to start spinning up some agents on your own machines completely for free at [app.clear.ml](https://app.clear.ml) or by using our self-hosted server on GitHub, and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw) if you need any help.
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</Collapsible>
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@ -86,6 +86,6 @@ If we now click on details again to look at the content, we can see that our cho
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In this video, we’ve covered the most important uses of ClearML Data, so hopefully you have a good intuition into what’s possible now and how valuable it can be. Building and updating your dataset versions from code is the best way to keep everything updated and make sure no data is ever lost. You’re highly encouraged to explore ways to automate as much of this process as possible, take a look at our documentation to find the full range of possibilities.
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So what are you waiting for? Start tracking your datasets with `clearml-data` and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A) if you need any help.
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So what are you waiting for? Start tracking your datasets with `clearml-data` and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw) if you need any help.
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</Collapsible>
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@ -66,6 +66,6 @@ And then we’re not even talking about all the ways to automate tasks using the
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For the next videos we’ll finally cover automation and orchestration as well as ClearML Data, our data versioning tool.
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Feel free to check out and test all of these features at [app.clear.ml](https://app.clear.ml), or using our self-hosted server on GitHub and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A) if you need any help.
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Feel free to check out and test all of these features at [app.clear.ml](https://app.clear.ml), or using our self-hosted server on GitHub and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw) if you need any help.
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</Collapsible>
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@ -68,5 +68,5 @@ Scalars such as loss or accuracy will be plotted on the same axes which makes co
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Finally, plots such as a confusion matrix and debug samples can be compared too. For those times when you just want to confirm that the new model is better with your own eyes.
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Now that you’re ready to start tracking and managing your experiments, we’ll cover some more advanced features and concepts of the experiment manager in the next video. But if you want to get started right now, head over to clear.ml and join our community [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A) if you need any help.
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Now that you’re ready to start tracking and managing your experiments, we’ll cover some more advanced features and concepts of the experiment manager in the next video. But if you want to get started right now, head over to clear.ml and join our community [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw) if you need any help.
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</Collapsible>
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@ -453,5 +453,5 @@ machine, on a remote machine, and it will give you a remote interactive Jupyter
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instance so that you can always code already on the remote machine. So that's also really, really cool. It's something
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we're going to cover soon, but I think the video is already long enough. So thank you very, very much for watching.
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Thank you very, very much for your attention. Let me know in the comments: if you want to see videos of these
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hyperparameters, and pipelines, and sessions, and don't forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A) if you need any help.
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hyperparameters, and pipelines, and sessions, and don't forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw) if you need any help.
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</Collapsible>
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@ -280,7 +280,7 @@ or out-of-the-box allow you to run on GPU workers.
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So it's just one of the very many ways that you can use ClearML to do
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these kinds of things and I hope you learned something valuable today. All of the code that you saw in this example
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will be available in the link in the description, and if you need any help, join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), we're always there,
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will be available in the link in the description, and if you need any help, join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), we're always there,
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always happy to help and thank you for watching.
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</Collapsible>
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@ -72,6 +72,6 @@ After the remote machine has executed the experiment on the new dataview, we can
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If you’ve been following along with the other Getting Started videos, you should already start to see the potential this approach can have. For example: we could now run hyperparameter optimization on the data itself, because all of the filters and settings previously shown are just parameters on a task. The whole process could be running in parallel on a cloud autoscaler for example. Imagine finding the best training data confidence threshold for each class to optimize the model performance.
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If you’re interested in using Hyper-Datasets for your team, then contact us using our website, and we’ll get you going in no time. In the meantime, you can enjoy the power of the open source components at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), if you need any help!
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If you’re interested in using Hyper-Datasets for your team, then contact us using our website, and we’ll get you going in no time. In the meantime, you can enjoy the power of the open source components at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), if you need any help!
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</Collapsible>
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@ -66,5 +66,5 @@ As we saw earlier, if you’re a ClearML pro user, you can even launch your opti
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And don’t forget about autoscaling! You can run it for free using code of course, but with ClearML Pro you can set it up in the UI as well. Which means that, starting from scratch, you can have an autoscaling cluster of cloud VMs running hyperparameter optimization on your experiment tasks in just a few minutes. How cool is that?
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In the next video, we’ll take a look at another example of automation goodness: pipelines. In the meantime, why not try and optimize one of your existing models for free at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), if you need any help.
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In the next video, we’ll take a look at another example of automation goodness: pipelines. In the meantime, why not try and optimize one of your existing models for free at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), if you need any help.
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</Collapsible>
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@ -62,5 +62,5 @@ If we select a step from our pipeline, we can see much of the same details, but
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But now comes the most powerful feature of all. Again, a pipeline controller is a task like any other, so… we can clone it like any other. Pressing the **+ New Run** button will allow us to do that from the UI! We can even change our global pipeline parameters here and, just like normal tasks, these will be injected into the original task and overwrite the original parameters. In this way, you can very quickly run many pipelines each with different parameters.
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In the next video of this Getting Started series, we’ll get a long-overdue look at ClearML Data, our data versioning tool. In the meantime, slap some pipeline decorators on your own functions for free at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), if you need any help.
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In the next video of this Getting Started series, we’ll get a long-overdue look at ClearML Data, our data versioning tool. In the meantime, slap some pipeline decorators on your own functions for free at [app.clear.ml](https://app.clear.ml), and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), if you need any help.
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</Collapsible>
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@ -60,5 +60,5 @@ When we select a specific step, we can see its inputs and outputs as well as its
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Finally, we can also clone the whole pipeline and change its parameters by clicking on the **+ New Run** button. This is the most powerful feature of all, as it allows us to really quickly rerun the whole pipeline with different parameters from the UI. The agents will take care of the rest!
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In the next video of this Getting Started series, we’ll take a look at ClearML Data, for real this time. In the meantime, spin up some pipeline controllers yourself for free at [app.clear.ml](https://app.clear.ml) and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), if you need any help.
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In the next video of this Getting Started series, we’ll take a look at ClearML Data, for real this time. In the meantime, spin up some pipeline controllers yourself for free at [app.clear.ml](https://app.clear.ml) and don’t forget to join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), if you need any help.
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</Collapsible>
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@ -66,5 +66,5 @@ Finally, when everything is done and the remote machines are idle, they will be
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You can see that this functionality is very powerful when combined with for example hyperparameter optimization or pipelines that launch a lot of tasks at once. Obviously, it can be used as the primary way to get access to remote compute, but it can even be used as an extra layer on top of the machines you already have on-premise to spillover in case of large demand spikes for example. You don’t pay when you don’t use it, so there isn’t really a good reason not to have one running at all times.
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Get started right now for free at [app.clear.ml](https://app.clear.ml) and start spinning up remote machines with ClearML Pro if you want to save some money and effort by automating the boring stuff. If you run into any issues along the way, join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1rp61f0cg-Bu_7UlETQrvHHjw~hEBh5A), and we’ll help you out.
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Get started right now for free at [app.clear.ml](https://app.clear.ml) and start spinning up remote machines with ClearML Pro if you want to save some money and effort by automating the boring stuff. If you run into any issues along the way, join our [Slack Channel](https://join.slack.com/t/clearml/shared_invite/zt-1v74jzwkn-~XsuWB0btXOlfFQCh8DJQw), and we’ll help you out.
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</Collapsible>
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