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pollfly
2023-01-25 13:25:29 +02:00
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31 changed files with 51 additions and 51 deletions
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docs/getting_started/ds/ds_first_steps.md
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@@ -32,7 +32,7 @@ pip install clearml
Please create new clearml credentials through the settings page in your `clearml-server` web app,
or create a free account at https://app.clear.ml/settings/webapp-configuration
In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".
In the settings > workspace page, press "Create new credentials", then press "Copy to clipboard".
Paste copied configuration here:
```

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docs/getting_started/ds/ds_second_steps.md
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@@ -40,7 +40,7 @@ Check [this](../../fundamentals/hyperparameters.md) out for all Hyperparameter l
## Log Artifacts
ClearML allows you to easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
ClearML allows you to easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
Essentially, artifacts are files (or python objects) uploaded from a script and are stored alongside the Task.
These Artifacts can be easily accessed by the web UI or programmatically.
@@ -157,7 +157,7 @@ The experiment table is a powerful tool for creating dashboards and views of you
### Creating Leaderboards
Customize the [experiments table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics and tags.
It's possible to filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
It's possible to filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.

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docs/getting_started/video_tutorials/pipelines_from_tasks.md
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@@ -45,7 +45,7 @@ The structure of your pipeline will be derived from looking at this `parents` ar
Now we do the same for the final step. However, remember the empty hyperparameters we saw before? We still have to overwrite these. We can use the `parameter_override` argument to do just that.
For example, we can tell the first step to use the global pipeline parameter raw data url like so. But we can also reference output artifacts from a previous step by using its name and we can of course also just overwrite a parameter with a normal value. Finally, we can even pass along the unique task ID of a previous step, so you can get the task object based on that ID and access anything and everything within that task.
For example, we can tell the first step to use the global pipeline parameter raw data url like so. But we can also reference output artifacts from a previous step by using its name, and we can of course also just overwrite a parameter with a normal value. Finally, we can even pass along the unique task ID of a previous step, so you can get the task object based on that ID and access anything and everything within that task.
And that’s it! We now have our first pipeline!