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pollfly
2024-02-13 09:57:46 +02:00
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docs/getting_started/video_tutorials/agent_remote_execution_and_automation.md
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@@ -44,7 +44,7 @@ You can also run the agent in conda mode or poetry mode, which essentially do th
However, there’s also docker mode. In this case the agent will run every incoming task in its own docker container instead of just a virtual environment. This makes things much easier if your tasks have system package dependencies for example, or when not every task uses the same python version. For our example, we’ll be using docker mode.
Now that our configuration is ready, we can start our agent in docker mode by running the command `clearml-agent daemon –docker`
Now that our configuration is ready, we can start our agent in docker mode by running the command `clearml-agent daemon –docker`.
After running the command, we can see it pop up in our workers table. Now the agent will start listening for tasks in the `default` queue, and it’s ready to go!

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docs/getting_started/video_tutorials/experiment_manager_hands-on.md
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@@ -26,7 +26,7 @@ This is the experiment manager's UI, and every row you can see here, is a single
We’re currently in our project folder. As you can see, we have our very basic toy example here that we want to keep track of by using ClearML’s experiment manager.
The first thing to do is to install the `clearml` python package in our virtual environment. Installing the package itself, will add 3 commands for you. We’ll cover the `clearml-data` and `clearml-task` commands later. For now the one we need is `clearml-init`
The first thing to do is to install the `clearml` python package in our virtual environment. Installing the package itself, will add 3 commands for you. We’ll cover the `clearml-data` and `clearml-task` commands later. For now the one we need is `clearml-init`.
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.