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Merge script and ipynb example docs (#101)
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@ -45,8 +45,7 @@ Check out some of ClearML's automatic reporting examples for supported packages:
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* [TensorBoardX](../guides/frameworks/tensorboardx/tensorboardx.md) - logging TensorBoardX scalars, debug
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samples, and text in code using PyTorch
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* Matplotlib
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* [Matplotlib Script Example](../guides/frameworks/matplotlib/matplotlib_example.md) and [Jupyter Notebook](../guides/frameworks/matplotlib/allegro_clearml_matplotlib_example.md) -
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logging scatter diagrams plotted with Matplotlib
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* [Matplotlib](../guides/frameworks/matplotlib/matplotlib_example.md) - logging scatter diagrams plotted with Matplotlib
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* [Matplotlib with PyTorch](../guides/frameworks/pytorch/pytorch_matplotlib.md) - logging debug images shown
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by Matplotlib
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@ -1,47 +0,0 @@
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---
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title: Keras with TensorBoard - Jupyter Notebook
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---
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The [ClearML_keras_TB_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb)
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example demonstrates **ClearML** automatically logging code, which is running in Jupyter Notebook and is using Keras and TensorBoard.
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The example script does the following:
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1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function)
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dataset.
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1. Builds a sequential model using a categorical crossentropy loss objective function.
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1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
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1. During script execution, creates an experiment named `Keras with TensorBoard example` which is associated with the `Colab notebooks` project.
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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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The loss and accuracy metric scalar plots appear in **RESULTS** **>** **SCALARS**, along with the resource utilization plots,
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which are titled **:monitor: machine**.
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## Histograms
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Histograms for layer density appear in **RESULTS** **>** **PLOTS**.
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## Hyperparameters
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**ClearML** automatically logs TensorFlow Definitions, which appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
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## Log
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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## Configuration Objects
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The configuration appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS** **>** **General**.
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@ -8,7 +8,7 @@ demonstrates **ClearML**'s automatic logging of code running in a Jupyter Notebo
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The example does the following:
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1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function)
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dataset.
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1. Builds a sequential model using a categorical crossentropy loss objective function.
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1. Builds a sequential model using a categorical cross entropy loss objective function.
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1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
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@ -2,23 +2,28 @@
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title: Keras with TensorBoard
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---
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The [keras_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
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example demonstrates the integration of **ClearML** into code which uses Keras and TensorBoard.
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The example below demonstrates the integration of **ClearML** into code which uses Keras and TensorBoard.
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View it in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
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or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb)
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The example does the following:
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:::note
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The example in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb)
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includes a clickable icon to open the notebook in Google Colab.
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:::
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The example script does the following:
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1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function)
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dataset.
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1. Builds a sequential model using a categorical crossentropy loss objective function.
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1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
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1. During script execution, it creates an experiment named `Keras with TensorBoard example` which is associated with the
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`examples` project.
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1. Builds a sequential model using a categorical cross entropy loss objective function.
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1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
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1. During script execution, creates an experiment named `Keras with TensorBoard example`, which is associated with the
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`examples` project (in script) or the `Colab notebooks` project (in Jupyter Notebook) .
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## Scalars
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The loss and accuracy metric scalar plots appear in the **RESULTS** **>** **SCALARS**, along with the resource utilization
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plots, which are titled **:monitor: machine**.
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The loss and accuracy metric scalar plots appear in **RESULTS** **>** **SCALARS**, along with the resource utilization plots,
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which are titled **:monitor: machine**.
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@ -44,16 +49,17 @@ TensorFlow Definitions appear in **TF_DEFINE**.
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Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**.
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## Configuration Objects
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In the experiment code, a configuration dictionary is connected to the Task by calling the [Task.connect](../../../references/sdk/task.md#connect)
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method.
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```python
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task.connect_configuration({'test': 1337, 'nested': {'key': 'value', 'number': 1}})
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```
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It appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS**.
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It appears in **CONFIGURATIONS** **>** **CONFIGURATION OBJECTS** **>** **General**.
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@ -1,32 +0,0 @@
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---
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title: Matplotlib - Jupyter Notebook
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---
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The [jupyter_matplotlib_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb)
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example demonstrates the integration of **ClearML** into code running in Jupyter Notebook, which uses `matplotlib` to plot
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scatter diagrams, and show images. **ClearML** automatically logs the diagrams and images. When the script runs, ClearML
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creates an experiment named `Matplotlib example` which is associated with the `Colab notebooks` project.
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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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## Plots
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The scatter plots appear in the **ClearML Web UI**, in **RESULTS** **>** **PLOTS**.
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## Debug Samples
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The images appear in **RESULTS** **>** **DEBUG SAMPLES**. Each debug sample image is associated with a metric.
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View the debug sample in the image viewer.
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@ -2,10 +2,21 @@
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title: Matplotlib
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---
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The [matplotlib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/matplotlib_example.py)
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example demonstrates integrating **ClearML** into code that uses `matplotlib` to plot scatter diagrams, and show images.
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**ClearML** automatically logs the diagrams and images. When the script runs, it creates an experiment named `Matplotlib example`,
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which is associated with the `examples` project.
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The example below demonstrates integrating **ClearML** into code that uses `matplotlib` to plot scatter diagrams, and
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show images. **ClearML** automatically logs the diagrams and images.
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View the example in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/matplotlib_example.py)
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or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb).
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:::note
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The example in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb),
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includes a clickable icon to open the notebook in Google Colab.
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:::
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When the example runs, it creates an experiment named `Matplotlib example`,
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which is associated with the `examples` project (in script) or the `Colab notebooks` project (in Jupyter Notebook).
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## Plots
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@ -67,10 +67,10 @@ module.exports = {
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{'Autokeras': ['guides/frameworks/autokeras/integration_autokeras', 'guides/frameworks/autokeras/autokeras_imdb_example']},
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{'FastAI': ['guides/frameworks/fastai/fastai_with_tensorboard']},
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{
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'Keras': ['guides/frameworks/keras/allegro_clearml_keras_tb_example', 'guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']
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'Keras': ['guides/frameworks/keras/jupyter', 'guides/frameworks/keras/keras_tensorboard']
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},
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{'LightGBM': ['guides/frameworks/lightgbm/lightgbm_example']},
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{'Matplotlib': ['guides/frameworks/matplotlib/allegro_clearml_matplotlib_example', 'guides/frameworks/matplotlib/matplotlib_example']},
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{'Matplotlib': ['guides/frameworks/matplotlib/matplotlib_example']},
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{'Pytorch':
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['guides/frameworks/pytorch/pytorch_distributed_example', 'guides/frameworks/pytorch/pytorch_matplotlib',
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'guides/frameworks/pytorch/pytorch_mnist', 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/pytorch/pytorch_tensorboardx',
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