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41 lines
1.7 KiB
Markdown
41 lines
1.7 KiB
Markdown
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---
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title: AutoKeras Integration
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---
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Integrate **ClearML** into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a **ClearML**
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Task in a code, and **ClearML** automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly,
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and Seaborn, and all other automatic logging, and explicit reporting added to the code (see [Logging](../../../fundamentals/logger.md)).
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**ClearML** allows to:
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* Visualize experiment results in the **ClearML Web UI**.
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* Track and upload models.
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* Track model performance and create tracking leaderboards.
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* Rerun experiments, reproduce experiments on any target machine, and tune experiments.
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* Compare experiments.
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See the [AutoKeras](autokeras_imdb_example.md) example, which shows **ClearML** automatically logging:
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* Scalars
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* Hyperparameters
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* The console log
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* Models.
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Once these are logged, they can be visualized in the **ClearML Web UI**.
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:::note
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If you are not already using **ClearML**, see [Getting Started](/getting_started/ds/best_practices.md).
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:::
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## Adding ClearML to code
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Add two lines of code:
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```python
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from clearml import Task
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task = Task.init(project_name="myProject", task_name="myExperiment")
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```
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When the code runs, it initializes a Task in **ClearML Server**. A hyperlink to the experiment's log is output to the console.
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CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e
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CLEARML results page: https://app.clearml-master.hosted.allegro.ai/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log
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Later in the code, define callbacks using TensorBoard, and **ClearML** logs TensorBoard scalars, histograms, and images.
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