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Small edits (#136)
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@ -23,9 +23,8 @@ Specify a docker container to run the code in by with the `--docker <docker_imag
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The ClearML Agent will pull it from dockerhub or a docker artifactory automatically.
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### Package Dependencies
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If the local script requires packages to be installed installed or the remote repository doesn't have a requirements.txt file,
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specify manually the required python packages using <br/>
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`--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`.
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If the local script requires packages to be installed, or the remote repository doesn't have a requirements.txt file,
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specify manually the required python packages using `--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`.
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### Queue
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Tasks are passed to ClearML Agents via [Queues](../fundamentals/agents_and_queues.md). Specify a queue to enqueue the task to.
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@ -33,7 +32,7 @@ If a queue isn't chosen in the `clearml-task` command, the task will not be exec
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and can be enqueued at a later point.
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### Branch and Working Directory
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A specific branch and commit ID, other than latest commit in master, to be executed can be specified by passing
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To specify a specific branch and commit ID to be executed, pass
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`--branch <branch_name> --commit <commit_id>` flags.
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If unspecified, `clearml-task` will use the latest commit from the master branch.
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@ -1,5 +1,5 @@
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---
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title: AutoKeras Imdb
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title: AutoKeras IMDB
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---
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The [autokeras_imdb_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/autokeras/autokeras_imdb_example.py) example
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script demonstrates the integration of **ClearML** into code, which uses [autokeras](https://github.com/keras-team/autokeras).
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@ -5,7 +5,7 @@ title: ClearML Agent on Google Colab
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[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as
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compute provided by google.
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Users can transform a Google Colab instance into an available resource in ClearML using [Clearml Agent](../../clearml_agent.md).
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Users can transform a Google Colab instance into an available resource in ClearML using [ClearML Agent](../../clearml_agent.md).
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In this tutorial, we will go over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up
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and running, users can send Tasks to be executed on the Google Colab's HW.
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@ -1,9 +1,9 @@
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---
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title: Explicit Reporting - Jupyter Notebook
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title: Using Logger - 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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script demonstrates the integration of ClearML's explicit reporting module, `Logger`, 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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@ -20,19 +20,23 @@ In the ``clearml`` GitHub repository, this example includes a clickable icon to
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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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```python
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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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```
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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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```python
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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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```
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## Plots
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@ -43,114 +47,126 @@ Plots appear in **RESULTS** **>** **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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```python
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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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```
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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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```python
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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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```
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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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```python
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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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```
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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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```python
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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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```
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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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```python
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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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```
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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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```python
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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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```
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## Media
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@ -162,39 +178,51 @@ 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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```python
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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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```
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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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```python
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logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local_copy)
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```
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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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```python
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logger.report_media("html", "url_html", iteration=1, url="https://allegro.ai/docs/index.html")
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```
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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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```python
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logger.report_image("image", "image from url", iteration=100, local_path=image_local_copy)
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```
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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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```python
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logger.report_media('video', 'big bunny', iteration=1, local_path=video_local_copy)
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```
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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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```python
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logger.report_text("hello, this is plain text")
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```
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@ -1,15 +1,15 @@
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---
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title: Explicit Reporting
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title: Explicit Reporting Tutorial
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---
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In this tutorial, learn how to extend **ClearML** automagical capturing of inputs and outputs with explicit reporting.
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In this tutorial, learn how to extend ClearML automagical capturing of inputs and outputs with explicit reporting.
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In this example, we will add the following to the [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
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example script from ClearML's GitHub repo:
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* Setting an output destination for model checkpoints (snapshots).
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* Explicitly logging a scalar, other (non-scalar) data, and logging text.
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* Registering an artifact, which is uploaded to **ClearML Server**, and **ClearML** logs changes to it.
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* Registering an artifact, which is uploaded to **ClearML Server**, and ClearML logs changes to it.
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* Uploading an artifact, which is uploaded, but changes to it are not logged.
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## Prerequisites
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@ -19,10 +19,9 @@ example script from ClearML's GitHub repo:
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## Before Starting
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Make a copy of `pytorch_mnist.py` in order to add explicit reporting to it.
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Make a copy of [`pytorch_mnist.py`](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
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in order to add explicit reporting to it.
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* In the local **ClearML** repository, `example` directory.
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```bash
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cp pytorch_mnist.py pytorch_mnist_tutorial.py
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```
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@ -59,7 +58,7 @@ task = Task.init(project_name='examples',
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output_uri=model_snapshots_path)
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```
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When the script runs, **ClearML** creates the following directory structure:
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When the script runs, ClearML creates the following directory structure:
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|
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+ - <output destination name>
|
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| +-- <project name>
|
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@ -79,7 +78,7 @@ For example, if the Task ID is `9ed78536b91a44fbb3cc7a006128c1b0`, then the dire
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## Step 2: Logger Class Reporting Methods
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|
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In addition to **ClearML** automagical logging, the **ClearML** Python
|
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In addition to ClearML automagical logging, the `clearml` Python
|
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package contains methods for explicit reporting of plots, log text, media, and tables. These methods include:
|
||||
|
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* [Logger.report_histogram](../../references/sdk/logger.md#report_histogram)
|
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@ -99,6 +98,7 @@ package contains methods for explicit reporting of plots, log text, media, and t
|
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|
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First, create a logger for the Task using the [Task.get_logger](../../references/sdk/task.md#get_logger)
|
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method.
|
||||
|
||||
```python
|
||||
logger = task.get_logger
|
||||
```
|
||||
@ -187,7 +187,7 @@ def test(args, model, device, test_loader):
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|
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### Log Text
|
||||
|
||||
Extend **ClearML** by explicitly logging text, including errors, warnings, and debugging statements. We use the [Logger.report_text](../../references/sdk/logger.md#report_text)
|
||||
Extend ClearML by explicitly logging text, including errors, warnings, and debugging statements. We use the [Logger.report_text](../../references/sdk/logger.md#report_text)
|
||||
method and its argument `level` to report a debugging message.
|
||||
|
||||
```python
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||||
@ -203,7 +203,7 @@ logger.report_text(
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## Step 3: Registering Artifacts
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||||
|
||||
Registering an artifact uploads it to **ClearML Server**, and if it changes, the change is logged in **ClearML Server**.
|
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Currently, **ClearML** supports Pandas DataFrames as registered artifacts.
|
||||
Currently, ClearML supports Pandas DataFrames as registered artifacts.
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||||
|
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### Register the Artifact
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||||
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@ -245,7 +245,6 @@ sample = Task.current_task().get_registered_artifacts()['Test_Loss_Correct'].sam
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replace=True,
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random_state=1
|
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)
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||||
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||||
```
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||||
## Step 4: Uploading Artifacts
|
||||
@ -280,7 +279,9 @@ task.upload_artifact(
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||||
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||||
After extending the Python experiment script, run it and view the results in the **ClearML Web UI**.
|
||||
|
||||
python pytorch_mnist_tutorial.py
|
||||
```bash
|
||||
python pytorch_mnist_tutorial.py
|
||||
```
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||||
|
||||
**To view the experiment results, do the following:**
|
||||
|
||||
|
||||
@ -446,7 +446,7 @@ clearml-agent execute [-h] --id TASK_ID [--log-file LOG_FILE] [--disable-monitor
|
||||
|
||||
**`gpus`**
|
||||
* Specify active GPUs for the daemon to use (docker / virtual environment), Equivalent to setting
|
||||
NVIDIA_VISIBLE_DEVICES Examples: `--gpus 0` or `--gpu 0,1,2` or `--gpus all`
|
||||
`NVIDIA_VISIBLE_DEVICES`. Examples: `--gpus 0` or `--gpu 0,1,2` or `--gpus all`
|
||||
|
||||
|
||||
---
|
||||
|
||||
@ -130,11 +130,11 @@ Add, change, or delete hyperparameters, which are organized in the **ClearML Web
|
||||
* **TF_DEFINE** - TensorFlow definitions (from code, TF_DEFINEs automatic logging).
|
||||
|
||||
* **General** - Parameter dictionaries (from code, connected to the Task by calling the [Task.connect](../references/sdk/task.md#connect)
|
||||
method.
|
||||
method).
|
||||
|
||||
* Environment variables - Tracked if the `CLEARML_LOG_ENVIRONMENT` environment variable was set (see this [FAQ](../faq#track-env-vars)).
|
||||
|
||||
* Custom named parameter groups - see the `name` parameter in [Task.connect](../references/sdk/task.md#connectmutable-namenone).
|
||||
* Custom named parameter groups (see the `name` parameter in [Task.connect](../references/sdk/task.md#connectmutable-namenone)).
|
||||
|
||||
**To add, change, or delete hyperparameters:**
|
||||
|
||||
@ -191,7 +191,7 @@ model in the **MODELS** tab.
|
||||
1. Edit the model configuration or label enumeration.
|
||||
|
||||
* Model configuration - In the **NETWORK** tab **>** Hover and click **EDIT**. **>** CLick **EDIT** or **CLEAR** (to
|
||||
remove the configuration
|
||||
remove the configuration).
|
||||
|
||||
Users can also search for the configuration (hover over the configuration textbox, the search box appears) and copy the
|
||||
configuration to the clipboard (hover and click <img src="/docs/latest/icons/ico-clipboard.svg" alt="Copy Clipboard" className="icon size-md" />).
|
||||
|
||||
@ -58,7 +58,7 @@ allow each feature. Model states are *Draft* (editable) and *Published* (read-on
|
||||
|
||||
| ClearML Action | Description | States Valid for the Action |
|
||||
|---|---|--|
|
||||
| View details | Model details include general information, the model configuration, and label enumeration. Click a model and the info panel slides open. | Any state |
|
||||
| View details | Model details include general information, the model configuration, and label enumeration. Click a model, and the info panel slides open. | Any state |
|
||||
| Publish | Publish a model to prevent changes to it. *Published* models are read-only. If a model is Published, its experiment also becomes Published (read-only). | *Draft* |
|
||||
| Archive | To more easily work with active models, move a model to the archive. See [Archiving](webapp_archiving). | Any state |
|
||||
| Tags | Tag models with color-coded labels to assist in organizing work. See [tagging models](#tagging-models). | Any state |
|
||||
|
||||
@ -2,19 +2,19 @@
|
||||
title: Overview
|
||||
---
|
||||
|
||||
The **ClearML Web UI** is the graphical user interface for the **ClearML** platform, which includes:
|
||||
The **ClearML Web UI** is the graphical user interface for the ClearML platform, which includes:
|
||||
* Experiment management
|
||||
* Browsing
|
||||
* Resource utilization monitoring
|
||||
* Profile management
|
||||
* Direct access to the **ClearML** community (Slack Channel, Youtube, and GitHub).
|
||||
* Direct access to the ClearML community (Slack Channel, Youtube, and GitHub).
|
||||
|
||||
|
||||
|
||||
|
||||
The **ClearML Web UI** is composed of the following pages:
|
||||
* The [Home](webapp_home.md) Page - The dashboard for recent activity, and quick access to experiments and projects.
|
||||
* The [Projects Page](webapp_projects_page.md) - The main experimentation page. It is a main projects page where specific projects can be opened.
|
||||
* The [Projects Page](webapp_projects_page.md) - The main experimentation page, where specific projects can be opened.
|
||||
|
||||
Each project page contains customizable [experiments](webapp_exp_table.md) and [models](webapp_model_table.md) tables
|
||||
with the following options:
|
||||
@ -27,12 +27,12 @@ The **ClearML Web UI** is composed of the following pages:
|
||||
* [View](webapp_model_viewing.md) and [modify](webapp_model_modifying.md) models
|
||||
|
||||
* The [Workers and Queues](webapp_workers_queues.md) Page - The resource monitoring and queues management page.
|
||||
* The [Profile Page](webapp_profile.md) - Manage a **ClearML** user account:
|
||||
* Create **ClearML** credentials
|
||||
* The [Profile Page](webapp_profile.md) - Manage a ClearML user account:
|
||||
* Create ClearML credentials
|
||||
* Provide Cloud Storage Access credentials for the **ClearML Web UI**
|
||||
* If using the **ClearML Hosted Service**, invite users and switch workspaces
|
||||
|
||||
In addition, from the **ClearML Web UI**, use these buttons to access the **ClearML** community:
|
||||
In addition, from the **ClearML Web UI**, use these buttons to access the ClearML community:
|
||||
|
||||
* The **ClearML** <img src="/docs/latest/icons/ico-slack-c.svg" alt="Slack Channel" className="icon size-md" /> Slack channel. Ask questions about **ClearML**.
|
||||
* The **ClearML** <img src="/docs/latest/icons/ico-youtube.svg" alt="YouTube" className="icon size-md" /> YouTube Channel. View our tutorials, presentations, and discussions.
|
||||
|
||||
@ -61,7 +61,7 @@ module.exports = {
|
||||
'guides/guidemain',
|
||||
{'Advanced': ['guides/advanced/execute_remotely', 'guides/advanced/multiple_tasks_single_process']},
|
||||
{'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']},
|
||||
{'Clearml Task': ['guides/clearml-task/clearml_task_tutorial']},
|
||||
{'ClearML Task': ['guides/clearml-task/clearml_task_tutorial']},
|
||||
{'Datasets': ['guides/datasets/data_man_cifar_classification', 'guides/datasets/data_man_python']},
|
||||
{'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']},
|
||||
{'Docker': ['guides/docker/extra_docker_shell_script']},
|
||||
|
||||
Loading…
Reference in New Issue
Block a user