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pollfly
2023-01-12 16:57:08 +02:00
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2
docs/getting_started/ds/ds_second_steps.md
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@@ -178,7 +178,7 @@ or check these pages out:
- Scale you work and deploy [ClearML Agents](../../clearml_agent.md)
- Develop on remote machines with [ClearML Session](../../apps/clearml_session.md)
- Structure your work and put it into [Pipelines](../../pipelines/pipelines.md)
- Improve your experiments with [HyperParameter Optimization](../../fundamentals/hpo.md)
- Improve your experiments with [Hyperparameter Optimization](../../fundamentals/hpo.md)
- Check out ClearML's integrations to [external libraries](../../integrations/libraries.md).
## YouTube Playlist

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docs/getting_started/mlops/mlops_second_steps.md
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@@ -16,7 +16,7 @@ The sections below describe the following scenarios:
## Building Tasks
### Dataset Creation
Let's assume we have some code that extracts data from a production Database into a local folder.
Let's assume we have some code that extracts data from a production database into a local folder.
Our goal is to create an immutable copy of the data to be used by further steps:
```bash
@@ -24,7 +24,7 @@ clearml-data create --project data --name dataset
clearml-data sync --folder ./from_production
```
We could also add a Tag `latest` to the Dataset, marking it as the latest version.
We could also add a tag `latest` to the Dataset, marking it as the latest version.
### Preprocessing Data
The second step is to preprocess the date. First we need to access it, then we want to modify it,

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docs/getting_started/video_tutorials/hyperparameter_optimization.md
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@@ -20,7 +20,7 @@ keywords: [mlops, components, hyperparameter optimization, hyperparameter]
<details className="cml-expansion-panel info">
<summary className="cml-expansion-panel-summary">Read the transcript</summary>
<div className="cml-expansion-panel-content">
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.
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.