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Insert video transcripts into collapsible panels (#488)
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@ -17,8 +17,9 @@ keywords: [mlops, components, ClearML agent]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Welcome to ClearML. In this video we’ll take a look at the ClearML Agent, which will allow you to run your tasks remotely and open the door for automating your workflows.
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Remember our overview from the previous video? We talked about the pip package that allows us to run experiments and data management as well as the server, which stores everything we track. Today we add a third component: the ClearML Agent.
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@ -63,3 +64,5 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg) if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, ClearML data]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video we’ll take a look at both the command line and python interfaces of our data versioning tool called `clearml-data`.
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In the world of machine learning, you are very likely dealing with large amounts of data that you need to put into a dataset. ClearML Data solves 2 important challenges that occur in this situation:
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@ -86,3 +87,5 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg) if you need any help.
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</Collapsible>
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@ -16,8 +16,9 @@ keywords: [mlops, components]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Welcome to ClearML! This video will serve as an overview of the complete ClearML stack. We’ll introduce you to the most important concepts and show you how everything fits together, so you can deep dive into the next videos, which will cover the ClearML functionality in more detail.
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ClearML is designed to get you up and running in less than 10 minutes and 2 magic lines of code. But if you start digging, you’ll quickly find out that it has a lot of functionality to offer. So let’s break it down, shall we?
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@ -50,3 +51,4 @@ As a final example of how you could use the agent's functionality, ClearML provi
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As you can see ClearML is a large toolbox, stuffed with the most useful components for both data scientists and MLOps engineers. We’re diving deeper into each component in the following videos if you need more details, but feel free to get started now at clear.ml.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, Experiment Manager]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Welcome to ClearML. In this video, we’ll go deeper into some of the best practices and advanced tricks you can use while working with ClearML experiment management.
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The first thing to know is that the Task object is the central pillar of both the experiment manager and the orchestration and automation components. This means that if you manage the task well in the experiment phase, it will be much easier to scale to production later down the line.
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@ -67,3 +68,4 @@ For the next videos we’ll finally cover automation and orchestration as well a
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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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg) if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, Experiment Manager]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Welcome to ClearML! In this video, you’ll learn how to quickly get started with the experiment manager by adding 2 simple lines of Python code to your existing project.
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This is the experiment manager's UI, and every row you can see here, is a single run of your code. So let’s set everything up in the code first, and then we’ll come back to this UI later in the video.
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@ -68,3 +69,4 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg) if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, machine learning, data scientist]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Welcome to ClearML! In this video, I'll try to walk you through a day in my life where I try to optimize a model, and
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I'll be teaching you how I used to do it before I was working for ClearML, and then now that I'm using ClearML all the
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time, what kind of problems it solved and what, how it made my life easier. So let's get started here.
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@ -318,3 +319,4 @@ So I hope this kind of inspired you a little bit to try out ClearML. It's free t
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or you can even host your own open source server with the interface that you can see right now. So why not have a go at
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it? And thank you for watching.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, machine learning, mlops engineer]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello again and welcome to ClearML. In this video we'll be going over a workflow of a potential MLOps Engineer. Now an
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MLOps Engineer is a vague term. This might be a specific person in your team that is doing only the Ops part of
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machine learning. So the infrastructure and all of the workers and whatnot. Or it could be you as a data scientist. It
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@ -453,3 +454,4 @@ instance so that you can always code already on the remote machine. So that's al
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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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg) if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, GitHub Actions, CI/CD]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello, welcome back to ClearML my name is Victor and in this video I'll be going through some CI/CD tips and tricks you
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can do with ClearML. For this video, I'm going to assume that you already know about ClearML and CI/CD.
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In general, the CI/CD stuff will be relatively easy to understand but if this is your first time working with ClearML,
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@ -282,3 +283,4 @@ these kinds of things and I hope you learned something valuable today. All of th
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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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), 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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@ -17,8 +17,9 @@ keywords: [mlops, components, hyperdatasets]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video, we're taking a closer look at Hyper-Datasets, a supercharged version of ClearML Data.
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Hyper-Datasets is a data management system that’s designed for unstructured data like text, audio, or visual data. It is part of the ClearML paid offering, which means it brings along quite a bit of upgrades over the open source `clearml-data`.
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@ -73,3 +74,4 @@ If you’ve been following along with the other Getting Started videos, you shou
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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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), if you need any help!
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, hyperparameter optimization, hyperparameter]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video we’ll take a look at one cool way of using the agent other than rerunning a task remotely: hyperparameter optimization (HPO).
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By now, we know that ClearML can easily capture our hyperparameters and scalars as part of the experiment tracking. We also know we can clone any task and change its hyperparameters, so they’ll be injected into the original code at runtime. In the last video, we learnt how to make a remote machine execute this task automatically by using the agent.
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@ -66,3 +67,4 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, automation, orchestration, pipeline]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video we’ll take a look at how pipelines can be used as a way to easily automate and orchestrate multiple tasks.
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Essentially, pipelines are a way to automate and orchestrate the execution of multiple tasks in a scalable way. Each task in the context of a ClearML pipeline is called a step or component, and it doesn’t necessarily have to be an existing ClearML *task*, it can be any code.
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@ -62,3 +63,4 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, automation, orchestration, pipeline]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video we’ll take a look at how pipelines can be created from tasks instead of from code like we saw in the last video.
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The tasks themselves are already in the system by using the experiment manager. What’s important to note here though is that hyperparameters, scalars, and artifacts should be reported correctly because the pipeline will consider them to be the inputs and outputs of each step. In that way, a step can easily access for example the artifacts from a previous step.
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@ -60,3 +61,4 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), if you need any help.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, features, ClearML]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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ClearML is an open source MLOPS platform.
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It's essentially a toolbox stuffed with everything you'll need to go from experimentation to production:
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@ -33,3 +34,4 @@ Doesn't matter if you're starting small or already in production, there's always
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Start for free at [app.clear.ml](https://app.clear.ml) or host your own server from our GitHub page.
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</Collapsible>
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@ -17,8 +17,9 @@ keywords: [mlops, components, Autoscaler]
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</iframe>
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</div>
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### Video Transcript
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<br/>
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<Collapsible type="info" title="Video Transcript">
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Hello and welcome to ClearML. In this video we’ll go a little more advanced and introduce autoscalers, the easiest way to build your very own flock of ClearML Agents.
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Data science is inherently very inconsistent in its demand for compute resources. One moment you’re just researching papers and need no compute at all, another moment you’re making 16 GPUs scream and wishing you had more. Especially when running Hyperparameter Optimization or Pipelines, it can be very handy to have some extra hardware for a short time.
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@ -66,3 +67,4 @@ 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-1kvcxu5hf-SRH_rmmHdLL7l2WadRJTQg), and we’ll help you out.
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</Collapsible>
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