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16
docs/guides/advanced/execute_remotely.md
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@ -3,7 +3,7 @@ title: Remote Execution
---
The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py)
script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely/) method.
script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method.
:::note
Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue
@ -37,16 +37,19 @@ In the example script's `train` function, the following code explicitly reports
```python
Logger.current_logger().report_scalar(
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
)
```
In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
```python
Logger.current_logger().report_scalar(
"test", "loss", iteration=epoch, value=test_loss)
"test", "loss", iteration=epoch, value=test_loss
)
Logger.current_logger().report_scalar(
"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
)
```
These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's
@ -68,9 +71,8 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in
the info panel of the **MODELS** tab.
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
and any snapshots created using PyTorch.
![image](../../img/examples_remote_execution_artifacts.png)

17
docs/guides/frameworks/autokeras/autokeras_imdb_example.md
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@ -2,8 +2,14 @@
title: AutoKeras IMDB
---
The [autokeras_imdb_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/autokeras/autokeras_imdb_example.py) example
script demonstrates the integration of **ClearML** into code, which uses [autokeras](https://github.com/keras-team/autokeras).
It trains text classification networks on the Keras built-in [IMDB](https://keras.io/api/datasets/imdb/) dataset, using the autokeras [TextClassifier](https://autokeras.com/text_classifier/) class, and searches for the best model. It uses two TensorBoard callbacks, one for training and one for testing. **ClearML** automatically logs everything the code sends to TensorBoard. When the script runs, it creates an experiment named `autokeras imdb example with scalars`, which is associated with the `autokeras` project.
script demonstrates the integration of ClearML into code, which uses [autokeras](https://github.com/keras-team/autokeras).
The example does the following:
* Trains text classification networks on the Keras built-in [IMDB](https://keras.io/api/datasets/imdb/) dataset, using
the autokeras [TextClassifier](https://autokeras.com/text_classifier/) class, and searches for the best model.
* Uses two TensorBoard callbacks, one for training and one for testing.
* ClearML automatically logs everything the code sends to TensorBoard.
* Creates an experiment named `autokeras imdb example with scalars`, which is associated with the `autokeras` project.
## Scalars
@ -26,12 +32,11 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the model info panel in the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
![image](../../../img/examples_keras_18.png)
The model info panel contains the model details, including the model URL, framework, and snapshot locations.
Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
![image](../../../img/examples_keras_17.png)

3
docs/guides/frameworks/lightgbm/lightgbm_example.md
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@ -25,7 +25,8 @@ ClearML automatically logs the configurations applied to LightGBM. They appear i
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using LightGBM.
![LightGBM model](../../../img/examples_lightgbm_model.png)

27
docs/guides/frameworks/pytorch/pytorch_mnist.md
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@ -8,10 +8,9 @@ demonstrates the integration of **ClearML** into code that uses PyTorch.
The example script does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset.
* Uses **ClearML** automatic logging.
* Calls the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method to demonstrate explicit reporting,
which allows adding customized reporting to the code.
* Creates an experiment named `pytorch mnist train`, which is associated with the `examples` project.
* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch
* Additional metrics are logged by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method.
## Scalars
@ -19,16 +18,19 @@ In the example script's `train` function, the following code explicitly reports
```python
Logger.current_logger().report_scalar(
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
)
```
In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
```python
Logger.current_logger().report_scalar(
"test", "loss", iteration=epoch, value=test_loss)
"test", "loss", iteration=epoch, value=test_loss
)
Logger.current_logger().report_scalar(
"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
)
```
These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
@ -49,17 +51,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in
the info panel of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_mnist_02.png)
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
![image](../../../img/examples_pytorch_mnist_03.png)

24
docs/guides/frameworks/pytorch/pytorch_tensorboard.md
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@ -3,16 +3,14 @@ title: PyTorch with TensorBoard
---
The [pytorch_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_tensorboard.py)
example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoard.
example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoard.
The example does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset.
* Creates a TensorBoard `SummaryWriter` object to log:
* Scalars during training.
* Scalars and debug samples during testing.
* Test text message to the console (a test message to demonstrate **ClearML**'s automatic logging).
* Creates an experiment named `pytorch with tensorboard`, which is associated with the `examples` project.
* ClearML automatically captures scalars and text logged using the TensorBoard `SummaryWriter` object, and
the model created by PyTorch.
## Scalars
@ -23,13 +21,13 @@ These scalars, along with the resource utilization plots, which are titled **:mo
## Debug Samples
**ClearML** automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
ClearML automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
![image](../../../img/examples_pytorch_tensorboard_08.png)
## Hyperparameters
**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
![image](../../../img/examples_pytorch_tensorboard_01.png)
@ -41,16 +39,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboard_02.png)
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
Clicking on a model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
![image](../../../img/examples_pytorch_tensorboard_03.png)

28
docs/guides/frameworks/pytorch/pytorch_tensorboardx.md
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@ -3,28 +3,26 @@ title: PyTorch TensorBoardX
---
The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py)
example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX.
example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
The example does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset.
* Creates a TensorBoardX `SummaryWriter` object to log:
* Scalars during training
* Scalars and debug samples during testing
* A test text message to the console (a test message to demonstrate **ClearML** automatic logging).
* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project in the **ClearML Web UI**.
* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project.
* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and
the model created by PyTorch.
## Scalars
The loss and accuracy metric scalar plots, along with the resource utilization plots, which are titled **:monitor: machine**,
appear in the experiment's page in the **web UI**, under **RESULTS** **>** **SCALARS**.
.
appear in the experiment's page in the [web UI](../../../webapp/webapp_overview.md), under **RESULTS** **>** **SCALARS**.
![image](../../../img/examples_pytorch_tensorboardx_03.png)
## Hyperparameters
**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
**HYPER PARAMETERS** **>** **Args**.
![image](../../../img/examples_pytorch_tensorboardx_01.png)
@ -37,16 +35,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboardx_04.png)
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
![image](../../../img/examples_pytorch_tensorboardx_05.png)

5
docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md
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@ -28,10 +28,13 @@ ClearML automatically logs command line options defined with argparse and Tensor
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
![PyTorch Lightning model](../../../img/examples_pytorch_lightning_model.png)
Clicking on a model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
## Console
All other console output appears in **RESULTS > CONSOLE**.

21
docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md
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@ -3,28 +3,25 @@ title: scikit-learn with Joblib
---
The [sklearn_joblib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py)
demonstrates the integration of **ClearML** into code that uses `scikit-learn` and `joblib` to store a model and model snapshots,
and `matplotlib` to create a scatter diagram. When the script runs, it creates an experiment named `scikit-learn joblib examplescikit-learn joblib example`, which is associated with the `examples` project.
demonstrates the integration of ClearML into code that uses `scikit-learn` and `joblib` to store a model and model snapshots,
and `matplotlib` to create a scatter diagram. When the script runs, it creates an experiment named
`scikit-learn joblib examplescikit-learn joblib example`, which is associated with the `examples` project.
## Plots
**ClearML** automatically logs the scatter plot, which appears in the experiment's page in the **ClearML web UI**, under
**RESULTS** **>** **PLOTS**.
ClearML automatically logs the scatter plot, which appears in the [experiment's page](../../../webapp/webapp_exp_track_visual.md)
in the ClearML web UI, under **RESULTS** **>** **PLOTS**.
![image](../../../img/examples_sklearn_joblib_example_06.png)
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
![image](../../../img/examples_sklearn_joblib_example_01.png)
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
![image](../../../img/examples_sklearn_joblib_example_02.png)

26
docs/guides/frameworks/tensorboardx/tensorboardx.md
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@ -3,15 +3,13 @@ title: TensorBoardX with PyTorch
---
The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py)
example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX.
example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
The script does the following:
1. Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
1. Creates a TensorBoardX `SummaryWriter` object to log:
* Scalars during training
* Scalars and debug samples during testing
* A test text message to the console (a test message to demonstrate **ClearML**).
1. Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project in the **ClearML Web UI**.
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
* Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project
* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and
the model created by PyTorch.
## Scalars
@ -22,7 +20,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
## Hyperparameters
**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
**HYPER PARAMETERS** **>** **Args**.
![image](../../../img/examples_pytorch_tensorboardx_01.png)
@ -35,17 +33,13 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboardx_04.png)
The model info panel contains the model details, including:
* Model URL
* Framework
* Snapshot locations.
Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
![image](../../../img/examples_pytorch_tensorboardx_05.png)

19
docs/guides/frameworks/tensorflow/tensorflow_mnist.md
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@ -3,7 +3,7 @@ title: TensorFlow MNIST
---
The [tensorflow_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py)
example demonstrates the integration of **ClearML** into code that uses TensorFlow and Keras to train a neural network on
example demonstrates the integration of ClearML into code that uses TensorFlow and Keras to train a neural network on
the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digits dataset.
The script builds a TensorFlow Keras model, and trains and tests it with the following:
@ -24,7 +24,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
## Hyperparameters
**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**
ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**
**>** **TF_DEFINE**.
![image](../../../img/examples_tensorflow_mnist_01.png)
@ -37,18 +37,13 @@ All console output appears in **RESULTS** **>** **CONSOLE**.
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using TensorFlow.
![image](../../../img/examples_tensorflow_mnist_03.png)
The model info panel contains the model details, including:
* Model design
* Label enumeration
* Model URL
* Framework
* Snapshot locations.
Clicking on a model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
![image](../../../img/examples_tensorflow_mnist_10.png)

40
docs/guides/frameworks/xgboost/xgboost_sample.md
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@ -3,26 +3,15 @@ title: XGBoost and scikit-learn
---
The [xgboost_sample.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/xgboost/xgboost_sample.py)
example demonstrates integrating ClearML into code that trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris)
classification dataset, using XGBoost to do the following:
example demonstrates integrating ClearML into code that uses [XGBoost](https://xgboost.readthedocs.io/en/stable/).
* Load a model ([xgboost.Booster.load_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.load_model))
* Save a model ([xgboost.Booster.save_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.save_model))
* Dump a model to JSON or text file ([xgboost.Booster.dump_model](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.dump_model))
* Plot feature importance ([xgboost.plot_importance](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_importance))
* Plot a tree ([xgboost.plot_tree](https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.plot_tree))
And using scikit-learn to score accuracy ([sklearn.metrics.accuracy_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)).
ClearML automatically logs:
* Input model
* Output model
* Model checkpoints (snapshots)
* Feature importance plot
* Tree plot
* Output to console.
When the script runs, it creates an experiment named `XGBoost simple example`, which is associated with the `examples` project.
The example does the following:
* Trains a network on the scikit-learn [iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris)
classification dataset using XGBoost
* Scores accuracy using scikit-learn
* ClearML automatically logs the input model registered by XGBoost, and the output model (and its checkpoints),
feature importance plot, and tree plot created with XGBoost.
* Creates an experiment named `XGBoost simple example`, which is associated with the `examples` project.
## Plots
@ -39,17 +28,12 @@ All other console output appear in **RESULTS** **>** **CONSOLE**.
## Artifacts
Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
of the **MODELS** tab.
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using XGBoost.
![image](../../../img/examples_xgboost_sample_10.png)
The model info panel contains the model details, including:
* Model design
* Label enumeration
* Model URL
* Framework.
Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
![image](../../../img/examples_xgboost_sample_03.png)

4
docs/webapp/webapp_exp_comparing.md
View File

@ -40,8 +40,8 @@ The **DETAILS** tab includes deep comparisons of the following:
### Artifacts
* Input model and model design.
* Output model and model design.
* Input model and model configuration.
* Output model and model configuration.
* Other artifacts, if any.
### Execution Details