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@@ -9,7 +9,7 @@ This tutorial assumes that you've already [signed up](https://app.clear.ml) to C
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ClearML provides tools for **automation**, **orchestration**, and **tracking**, all key in performing effective MLOps and LLMOps.
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Effective MLOps and LLMOps rely on the ability to scale work beyond one's own computer. Moving from your own machine can be time-consuming.
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Even assuming that you have all the drivers and applications installed, you still need to manage multiple python environments
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Even assuming that you have all the drivers and applications installed, you still need to manage multiple Python environments
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for different packages / package versions, or worse - manage different Dockers for different package versions.
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Not to mention, when working on remote machines, executing experiments, tracking what's running where, and making sure machines
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@@ -21,7 +21,7 @@ ClearML Agent was designed to deal with such issues and more! It is a tool respo
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machine of choice through the ClearML WebApp with no need for additional code.
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The agent will set up the environment for a specific Task's execution (inside a Docker, or bare-metal), install the
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required python packages, and execute and monitor the process.
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required Python packages, and execute and monitor the process.
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## Set up an Agent
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@@ -72,7 +72,7 @@ Cloning a task duplicates the task's configuration, but not its outputs.
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1. Click **CLONE** in the window.
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The newly cloned task will appear and its info panel will slide open. The cloned task is in draft mode, so
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it can be modified. You can edit the Git / code references, control the python packages to be installed, specify the
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it can be modified. You can edit the Git / code references, control the Python packages to be installed, specify the
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Docker container image to be used, or change the hyperparameters and configuration files. See [Modifying Tasks](../../webapp/webapp_exp_tuning.md#modifying-tasks) for more information about editing tasks in the UI.
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## Enqueue a Task
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