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Example edits (#158)
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pollfly
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@ -3,7 +3,7 @@ title: Remote Execution
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---
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The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/examples/advanced/execute_remotely_example.py)
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script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely/) method.
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script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method.
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:::note
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Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue
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@ -37,16 +37,19 @@ In the example script's `train` function, the following code explicitly reports
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```python
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Logger.current_logger().report_scalar(
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"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
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"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
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)
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```
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In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
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```python
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Logger.current_logger().report_scalar(
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"test", "loss", iteration=epoch, value=test_loss)
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"test", "loss", iteration=epoch, value=test_loss
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)
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Logger.current_logger().report_scalar(
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"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
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"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
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)
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```
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These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's
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@ -68,9 +71,8 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in
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the info panel of the **MODELS** tab.
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
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and any snapshots created using PyTorch.
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@ -2,8 +2,14 @@
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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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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.
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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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The example does the following:
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* Trains text classification networks on the Keras built-in [IMDB](https://keras.io/api/datasets/imdb/) dataset, using
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the autokeras [TextClassifier](https://autokeras.com/text_classifier/) class, and searches for the best model.
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* Uses two TensorBoard callbacks, one for training and one for testing.
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* ClearML automatically logs everything the code sends to TensorBoard.
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* Creates an experiment named `autokeras imdb example with scalars`, which is associated with the `autokeras` project.
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## Scalars
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@ -26,12 +32,11 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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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.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
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The model info panel contains the model details, including the model URL, framework, and snapshot locations.
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Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
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the model’s details and access the model.
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@ -25,7 +25,8 @@ ClearML automatically logs the configurations applied to LightGBM. They appear i
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
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models and any snapshots created using LightGBM.
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@ -8,10 +8,9 @@ demonstrates the integration of **ClearML** into code that uses PyTorch.
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The example script does the following:
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* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
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dataset.
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* Uses **ClearML** automatic logging.
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* Calls the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method to demonstrate explicit reporting,
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which allows adding customized reporting to the code.
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* Creates an experiment named `pytorch mnist train`, which is associated with the `examples` project.
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* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch
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* Additional metrics are logged by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) method.
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## Scalars
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@ -19,16 +18,19 @@ In the example script's `train` function, the following code explicitly reports
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```python
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Logger.current_logger().report_scalar(
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"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item())
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"train", "loss", iteration=(epoch * len(train_loader) + batch_idx), value=loss.item()
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)
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```
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In the `test` method, the code explicitly reports `loss` and `accuracy` scalars.
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```python
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Logger.current_logger().report_scalar(
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"test", "loss", iteration=epoch, value=test_loss)
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"test", "loss", iteration=epoch, value=test_loss
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)
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Logger.current_logger().report_scalar(
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"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset)))
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"test", "accuracy", iteration=epoch, value=(correct / len(test_loader.dataset))
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)
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```
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These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
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@ -49,17 +51,12 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in
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the info panel of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
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and any snapshots created using PyTorch.
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The model info panel contains the model details, including:
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* Model URL
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* Framework
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* Snapshot locations.
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Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
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the model’s details and access the model.
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@ -3,16 +3,14 @@ title: PyTorch with TensorBoard
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---
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The [pytorch_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_tensorboard.py)
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example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoard.
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example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoard.
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The example does the following:
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* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
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dataset.
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* Creates a TensorBoard `SummaryWriter` object to log:
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* Scalars during training.
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* Scalars and debug samples during testing.
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* Test text message to the console (a test message to demonstrate **ClearML**'s automatic logging).
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* Creates an experiment named `pytorch with tensorboard`, which is associated with the `examples` project.
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* ClearML automatically captures scalars and text logged using the TensorBoard `SummaryWriter` object, and
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the model created by PyTorch.
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## Scalars
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@ -23,13 +21,13 @@ These scalars, along with the resource utilization plots, which are titled **:mo
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## Debug Samples
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**ClearML** automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
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ClearML automatically tracks images and text output to TensorFlow. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
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## Hyperparameters
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**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
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ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**.
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@ -41,16 +39,12 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
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models and any snapshots created using PyTorch.
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The model info panel contains the model details, including:
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* Model URL
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* Framework
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* Snapshot locations.
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Clicking on a model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
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the model’s details and access the model.
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@ -3,28 +3,26 @@ title: PyTorch TensorBoardX
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---
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The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py)
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example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX.
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example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
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The example does the following:
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* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
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dataset.
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* Creates a TensorBoardX `SummaryWriter` object to log:
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* Scalars during training
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* Scalars and debug samples during testing
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* A test text message to the console (a test message to demonstrate **ClearML** automatic logging).
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* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project in the **ClearML Web UI**.
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* Creates an experiment named `pytorch with tensorboardX`, which is associated with the `examples` project.
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* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and
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the model created by PyTorch.
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## Scalars
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The loss and accuracy metric scalar plots, along with the resource utilization plots, which are titled **:monitor: machine**,
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appear in the experiment's page in the **web UI**, under **RESULTS** **>** **SCALARS**.
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.
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appear in the experiment's page in the [web UI](../../../webapp/webapp_overview.md), under **RESULTS** **>** **SCALARS**.
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## Hyperparameters
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**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
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ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
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**HYPER PARAMETERS** **>** **Args**.
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@ -37,16 +35,12 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
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models and any snapshots created using PyTorch.
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The model info panel contains the model details, including:
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* Model URL
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* Framework
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* Snapshot locations.
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Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
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the model’s details and access the model.
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@ -28,10 +28,13 @@ ClearML automatically logs command line options defined with argparse and Tensor
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel of the **MODELS** tab.
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
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Clicking on a model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
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the model’s details and access the model.
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## Console
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All other console output appears in **RESULTS > CONSOLE**.
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@ -3,28 +3,25 @@ title: scikit-learn with Joblib
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---
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The [sklearn_joblib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py)
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demonstrates the integration of **ClearML** into code that uses `scikit-learn` and `joblib` to store a model and model snapshots,
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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.
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demonstrates the integration of ClearML into code that uses `scikit-learn` and `joblib` to store a model and model snapshots,
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and `matplotlib` to create a scatter diagram. When the script runs, it creates an experiment named
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`scikit-learn joblib examplescikit-learn joblib example`, which is associated with the `examples` project.
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## Plots
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**ClearML** automatically logs the scatter plot, which appears in the experiment's page in the **ClearML web UI**, under
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**RESULTS** **>** **PLOTS**.
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ClearML automatically logs the scatter plot, which appears in the [experiment's page](../../../webapp/webapp_exp_track_visual.md)
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in the ClearML web UI, under **RESULTS** **>** **PLOTS**.
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## Artifacts
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Model artifacts associated with the experiment appear in the info panel of the **EXPERIMENTS** tab and in the info panel
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of the **MODELS** tab.
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The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
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Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
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The model info panel contains the model details, including:
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* Model URL
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* Framework
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* Snapshot locations.
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Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
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view the model’s details and access the model.
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@ -3,15 +3,13 @@ title: TensorBoardX with PyTorch
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---
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The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorboardx/pytorch_tensorboardX.py)
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example demonstrates the integration of **ClearML** into code that uses PyTorch and TensorBoardX.
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example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
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The script does the following:
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1. Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
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1. Creates a TensorBoardX `SummaryWriter` object to log:
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* Scalars during training
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* Scalars and debug samples during testing
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* A test text message to the console (a test message to demonstrate **ClearML**).
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1. Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project in the **ClearML Web UI**.
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* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
|
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* Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project
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* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and
|
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the model created by PyTorch.
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## Scalars
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@ -22,7 +20,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
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## Hyperparameters
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**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
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ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>**
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**HYPER PARAMETERS** **>** **Args**.
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@ -35,17 +33,13 @@ Text printed to the console for training progress, as well as all other console
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## Artifacts
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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.
|
||||
|
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|
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|
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The model info panel contains the model details, including:
|
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* Model URL
|
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* Framework
|
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* Snapshot locations.
|
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Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
|
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view the model’s details and access the model.
|
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@ -3,7 +3,7 @@ title: TensorFlow MNIST
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---
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The [tensorflow_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py)
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example demonstrates the integration of **ClearML** into code that uses TensorFlow and Keras to train a neural network on
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example demonstrates the integration of ClearML into code that uses TensorFlow and Keras to train a neural network on
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the Keras built-in [MNIST](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist) handwritten digits dataset.
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The script builds a TensorFlow Keras model, and trains and tests it with the following:
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@ -24,7 +24,7 @@ The loss and accuracy metric scalar plots appear in the experiment's page in the
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## Hyperparameters
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**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**
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ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**
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**>** **TF_DEFINE**.
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@ -37,18 +37,13 @@ All console output appears in **RESULTS** **>** **CONSOLE**.
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## Artifacts
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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
|
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models and any snapshots created using TensorFlow.
|
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|
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|
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|
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The model info panel contains the model details, including:
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* Model design
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* Label enumeration
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* Model URL
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* Framework
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* 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.
|
||||
|
||||
|
||||
|
||||
@ -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.
|
||||
|
||||
|
||||
|
||||
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.
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
Loading…
Reference in New Issue
Block a user