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pollfly
2023-08-09 13:28:25 +03:00
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docs/guides/reporting/3d_plots_reporting.md
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@@ -33,7 +33,7 @@ Visualize the reported surface plot in **PLOTS**.
## 3D Scatter Plot
To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d)
To plot a series as a 3D scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d)
method.
```python
# report 3d scatter plot

2
docs/guides/reporting/clearml_logging_example.md
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@@ -67,7 +67,7 @@ logger.report_scatter2d(
### 3D Plots
To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
To plot a series as a 3D scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
```python
# report 3d scatter plot

10
docs/guides/reporting/explicit_reporting.md
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@@ -4,7 +4,7 @@ title: Explicit Reporting Tutorial
In this tutorial, learn how to extend ClearML automagical capturing of inputs and outputs with explicit reporting.
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)
In this example, you will add the following to the [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
example script from ClearML's GitHub repo:
* Setting an output destination for model checkpoints (snapshots).
@@ -38,7 +38,7 @@ experiment runs. Some possible destinations include:
* Azure Storage.
Specify the output location in the `output_uri` parameter of the [`Task.init`](../../references/sdk/task.md#taskinit) method.
In this tutorial, we specify a local folder destination.
In this tutorial, specify a local folder destination.
In `pytorch_mnist_tutorial.py`, change the code from:
@@ -135,7 +135,7 @@ def train(args, model, device, train_loader, optimizer, epoch):
### Plot Other (Not Scalar) Data
The script contains a function named `test`, which determines loss and correct for the trained model. We add a histogram
The script contains a function named `test`, which determines loss and correct for the trained model. Add a histogram
and confusion matrix to log them.
```python
@@ -187,7 +187,7 @@ def test(args, model, device, test_loader):
### 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. Use the [Logger.report_text](../../references/sdk/logger.md#report_text)
method and its argument `level` to report a debugging message.
```python
@@ -259,7 +259,7 @@ Supported artifacts include:
* Dictionaries - stored as JSONs
* Numpy arrays - stored as NPZ files
In the tutorial script, we upload the loss data as an artifact using the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact)
In the tutorial script, upload the loss data as an artifact using the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact)
method with metadata specified in the `metadata` parameter.
```python