From 5ea532ebd8c0fd3ba6a6e2ac9c90dffab8768083 Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Thu, 13 Jan 2022 09:42:36 +0200 Subject: [PATCH] Example edits (#158) --- docs/guides/advanced/execute_remotely.md | 16 ++++---- .../autokeras/autokeras_imdb_example.md | 17 +++++--- .../frameworks/lightgbm/lightgbm_example.md | 3 +- .../frameworks/pytorch/pytorch_mnist.md | 27 ++++++------- .../frameworks/pytorch/pytorch_tensorboard.md | 24 +++++------ .../pytorch/pytorch_tensorboardx.md | 28 +++++-------- .../pytorch_lightning_example.md | 5 ++- .../scikit-learn/sklearn_joblib_example.md | 21 +++++----- .../frameworks/tensorboardx/tensorboardx.md | 26 +++++------- .../frameworks/tensorflow/tensorflow_mnist.md | 19 ++++----- .../frameworks/xgboost/xgboost_sample.md | 40 ++++++------------- docs/webapp/webapp_exp_comparing.md | 4 +- 12 files changed, 98 insertions(+), 132 deletions(-) diff --git a/docs/guides/advanced/execute_remotely.md b/docs/guides/advanced/execute_remotely.md index d9051461..fda6d585 100644 --- a/docs/guides/advanced/execute_remotely.md +++ b/docs/guides/advanced/execute_remotely.md @@ -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) diff --git a/docs/guides/frameworks/autokeras/autokeras_imdb_example.md b/docs/guides/frameworks/autokeras/autokeras_imdb_example.md index 19b8b8cf..dada22c3 100644 --- a/docs/guides/frameworks/autokeras/autokeras_imdb_example.md +++ b/docs/guides/frameworks/autokeras/autokeras_imdb_example.md @@ -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) \ No newline at end of file diff --git a/docs/guides/frameworks/lightgbm/lightgbm_example.md b/docs/guides/frameworks/lightgbm/lightgbm_example.md index 94ed89ae..0cd571db 100644 --- a/docs/guides/frameworks/lightgbm/lightgbm_example.md +++ b/docs/guides/frameworks/lightgbm/lightgbm_example.md @@ -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) diff --git a/docs/guides/frameworks/pytorch/pytorch_mnist.md b/docs/guides/frameworks/pytorch/pytorch_mnist.md index 91a4968e..55f1d257 100644 --- a/docs/guides/frameworks/pytorch/pytorch_mnist.md +++ b/docs/guides/frameworks/pytorch/pytorch_mnist.md @@ -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) \ No newline at end of file diff --git a/docs/guides/frameworks/pytorch/pytorch_tensorboard.md b/docs/guides/frameworks/pytorch/pytorch_tensorboard.md index 5314c6c6..84488153 100644 --- a/docs/guides/frameworks/pytorch/pytorch_tensorboard.md +++ b/docs/guides/frameworks/pytorch/pytorch_tensorboard.md @@ -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) \ No newline at end of file diff --git a/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md b/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md index 02dfa73d..ff01f8b0 100644 --- a/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md +++ b/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md @@ -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) diff --git a/docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md b/docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md index 59559787..880c7b39 100644 --- a/docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md +++ b/docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md @@ -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**. diff --git a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md index 3bad6642..41810377 100644 --- a/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md +++ b/docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md @@ -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) \ No newline at end of file diff --git a/docs/guides/frameworks/tensorboardx/tensorboardx.md b/docs/guides/frameworks/tensorboardx/tensorboardx.md index 5f8504d3..b0cbfddc 100644 --- a/docs/guides/frameworks/tensorboardx/tensorboardx.md +++ b/docs/guides/frameworks/tensorboardx/tensorboardx.md @@ -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) diff --git a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md index afebf687..f8cc4317 100644 --- a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md +++ b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md @@ -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) \ No newline at end of file diff --git a/docs/guides/frameworks/xgboost/xgboost_sample.md b/docs/guides/frameworks/xgboost/xgboost_sample.md index 9ad40840..c7418912 100644 --- a/docs/guides/frameworks/xgboost/xgboost_sample.md +++ b/docs/guides/frameworks/xgboost/xgboost_sample.md @@ -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) \ No newline at end of file diff --git a/docs/webapp/webapp_exp_comparing.md b/docs/webapp/webapp_exp_comparing.md index 7a076aec..5da7f39a 100644 --- a/docs/webapp/webapp_exp_comparing.md +++ b/docs/webapp/webapp_exp_comparing.md @@ -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