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docs/getting_started/video_tutorials/hyperparameter_optimization.md
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@@ -20,17 +20,17 @@ keywords: [mlops, components, hyperparameter optimization, hyperparameter]
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<Collapsible type="info" title="Video Transcript">
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).
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).
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.
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.
Soooo… Can we just clone a task like 100 times, inject different hyperparameters in every clone, run the clones on 10 agents and then sort the results based on a specific scalar?
Yeah, yeah we can, it's called hyperparameter optimization. And we can do all of this automatically too! No way you were going to clone and edit those 100 tasks yourself, right?
If you don’t know what Hyperparameter Optimization is yet, you can find a link to our blog post on the topic in the description below. But in its most basic form, hyperparameter optimization tries to optimize a certain output by changing a set of inputs.
If you don't know what Hyperparameter Optimization is yet, you can find a link to our blog post on the topic in the description below. But in its most basic form, hyperparameter optimization tries to optimize a certain output by changing a set of inputs.
Let’s say we’ve been working on this model here, and we were tracking our experiments with it anyway. We can see we have some hyperparameters to work with in the **Hyperparameters** tab of the web UI. They are logged by using the `Task.connect` function in our code. These are our inputs. We also have a scaler called `validation/epoch_accuracy`, that we want to get as high as possible. This is our output. We could also select to minimize the `epoch_loss` for example, that is something you can decide yourself.
Let's say we've been working on this model here, and we were tracking our experiments with it anyway. We can see we have some hyperparameters to work with in the **Hyperparameters** tab of the web UI. They are logged by using the `Task.connect` function in our code. These are our inputs. We also have a scaler called `validation/epoch_accuracy`, that we want to get as high as possible. This is our output. We could also select to minimize the `epoch_loss` for example, that is something you can decide yourself.
We can see that no code was used to log the scalar. It's done automatically because we are using TensorBoard.