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200 lines
5.9 KiB
Markdown
200 lines
5.9 KiB
Markdown
---
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title: Explicit Reporting - Jupyter Notebook
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---
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The [jupyter_logging_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/reporting/jupyter_logging_example.ipynb)
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script demonstrates the integration of **ClearML** explicit reporting running in a Jupyter Notebook. All **ClearML**
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explicit reporting works with Jupyter Notebook.
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This example includes several types of explicit reporting, including:
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* Scalars
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* Some plots
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* Media.
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:::note
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In the ``clearml`` GitHub repository, this example includes a clickable icon to open the notebook in Google Colab.
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:::
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## Scalars
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To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar)
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method. The scalar plots appear in the **web UI** in **RESULTS** **>** **SCALARS**.
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# report two scalar series on two different graphs
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for i in range(10):
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logger.report_scalar("graph A", "series A", iteration=i, value=1./(i+1))
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logger.report_scalar("graph B", "series B", iteration=i, value=10./(i+1))
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# report two scalar series on the same graph
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for i in range(10):
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logger.report_scalar("unified graph", "series A", iteration=i, value=1./(i+1))
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logger.report_scalar("unified graph", "series B", iteration=i, value=10./(i+1))
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## Plots
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Plots appear in **RESULTS** **>** **PLOTS**.
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### 2D Plots
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Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) method.
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Use the `mode` parameter to plot data points as markers, or both lines and markers.
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scatter2d = np.hstack(
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(np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))
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)
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# report 2d scatter plot with markers
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logger.report_scatter2d(
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"example_scatter",
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"series_lines+markers",
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iteration=iteration,
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scatter=scatter2d,
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xaxis="title x",
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yaxis="title y",
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mode='lines+markers'
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)
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### 3D Plots
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To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
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# report 3d scatter plot
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scatter3d = np.random.randint(10, size=(10, 3))
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logger.report_scatter3d(
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"example_scatter_3d",
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"series_xyz",
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iteration=iteration,
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scatter=scatter3d,
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xaxis="title x",
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yaxis="title y",
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zaxis="title z",
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)
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To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface)
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method.
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# report 3d surface
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surface = np.random.randint(10, size=(10, 10))
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logger.report_surface(
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"example_surface",
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"series1",
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iteration=iteration,
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matrix=surface,
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xaxis="title X",
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yaxis="title Y",
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zaxis="title Z",
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)
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### Confusion Matrices
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Report confusion matrices by calling the [Logger.report_matrix](../../references/sdk/logger.md#report_matrix)
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method.
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# report confusion matrix
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confusion = np.random.randint(10, size=(10, 10))
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logger.report_matrix(
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"example_confusion",
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"ignored",
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iteration=iteration,
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matrix=confusion,
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xaxis="title X",
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yaxis="title Y",
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)
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### Histograms
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Report histograms by calling the [Logger.report_histogram](../../references/sdk/logger.md#report_histogram)
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method. To report more than one series on the same plot, use the same `title` argument.
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# report a single histogram
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histogram = np.random.randint(10, size=10)
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logger.report_histogram(
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"single_histogram",
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"random histogram",
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iteration=iteration,
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values=histogram,
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xaxis="title x",
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yaxis="title y",
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)
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# report a two histograms on the same plot
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histogram1 = np.random.randint(13, size=10)
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histogram2 = histogram * 0.75
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logger.report_histogram(
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"two_histogram",
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"series 1",
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iteration=iteration,
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values=histogram1,
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xaxis="title x",
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yaxis="title y",
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)
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logger.report_histogram(
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"two_histogram",
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"series 2",
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iteration=iteration,
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values=histogram2,
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xaxis="title x",
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yaxis="title y",
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)
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## Media
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Report audio, HTML, image, and video by calling the [Logger.report_media](../../references/sdk/logger.md#report_media)
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method using the `local_path` parameter. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
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The media for these examples is downloaded using the [StorageManager.get_local_copy](../../references/sdk/storage.md#storagemanagerget_local_copy)
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method.
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For example, to download an image:
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image_local_copy = StorageManager.get_local_copy(
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remote_url="https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg",
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name="picasso.jpg"
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)
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### Audio
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logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local_copy)
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### HTML
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logger.report_media("html", "url_html", iteration=1, url="https://allegro.ai/docs/index.html")
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### Images
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logger.report_image("image", "image from url", iteration=100, local_path=image_local_copy)
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### Video
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logger.report_media('video', 'big bunny', iteration=1, local_path=video_local_copy)
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## Text
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Report text messages by calling the [Logger.report_text](../../references/sdk/logger.md#report_text).
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logger.report_text("hello, this is plain text")
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