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Edit video tutorial docs (#452)

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pollfly
2023-01-24 11:43:07 +02:00
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docs/getting_started/video_tutorials/experiment_manager_hands-on.md
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@@ -17,12 +17,7 @@ keywords: [mlops, components, Experiment Manager]
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<details className="cml-expansion-panel info">
<summary className="cml-expansion-panel-summary">Read the transcript</summary>
<div className="cml-expansion-panel-content">
### Video Transcript
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.
@@ -34,7 +29,7 @@ The first thing to do is to install the `clearml` python package in our virtual
If you paid attention in the first video of this series, you’d remember that we need to connect to a ClearML Server to save all our tracked data. The server is where we saw the list of experiments earlier. This connection is what `clearml-init` will set up for us. When running the command it’ll ask for your server API credentials.
To get those, go to your ClearML server webpage. If you’re using our hosted service, this will be at app.clear.ml. if you’re hosting your own, browse to your server's address at port 8080. Go to your settings on the top right and, under workspace, create new credentials. This will pop up a window with your API info, and you can just copy paste it into the `clearml-init` prompt.
To get those, go to your ClearML server webpage. If you’re using our hosted service, this will be at [app.clear.ml](https://app.clear.ml). if you’re hosting your own, browse to your server's address at port 8080. Go to your settings on the top right and, under workspace, create new credentials. This will pop up a window with your API info, and you can just copy paste it into the `clearml-init` prompt.
The prompt will suggest the server URLs that were in your copied snippet. If they are correct just press Enter, otherwise you can change them here.
@@ -72,6 +67,4 @@ Scalars such as loss or accuracy will be plotted on the same axes which makes co
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.
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 if you need any help.
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</details>
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.