Small edits (#663)

docs/guides/frameworks/autokeras/autokeras_imdb_example.md

@@ -9,7 +9,7 @@ The example does the following:
 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.
+* Creates an experiment named `autokeras imdb example with scalars` in the `autokeras` project.
 
 ## Scalars
 

docs/guides/frameworks/fastai/fastai_with_tensorboard.md

@@ -12,7 +12,7 @@ The ClearML repository also includes [examples using FastAI v2](https://github.c
 The example code does the following:
 1. Trains a simple deep neural network on the fastai built-in MNIST dataset (see the [fast.ai](https://fastai1.fast.ai) documentation).
 1. Uses the fastai `LearnerTensorboardWriter` callback, and ClearML automatically logs TensorBoard through the callback. 
-1. During script execution, creates an experiment named `fastai with tensorboard callback`, which is associated with the `examples` project.
+1. During script execution, creates an experiment named `fastai with tensorboard callback` in the `examples` project.
 
 ## Scalars
 

docs/guides/frameworks/keras/jupyter.md

@@ -12,7 +12,7 @@ The example does the following:
    
 1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback. 
    
-1. During script execution, creates an experiment named `notebook example` which is associated with the `examples` project.
+1. During script execution, creates an experiment named `notebook example` in the `examples` project.
 
 ## Scalars
 

docs/guides/frameworks/keras/keras_tensorboard.md

@@ -16,7 +16,7 @@ The example script does the following:
    dataset.
 1. Builds a sequential model using a categorical cross entropy loss objective function.
 1. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
-1. During script execution, creates an experiment named `Keras with TensorBoard example`, which is associated with the 
+1. During script execution, creates an experiment named `Keras with TensorBoard example` in the 
    `examples` project (in script) or the `Colab notebooks` project (in Jupyter Notebook).
 
 

docs/guides/frameworks/lightgbm/lightgbm_example.md

@@ -9,7 +9,7 @@ The example script does the following:
 * Creates a dataset for LightGBM to train a model
 * Specifies configuration which are automatically captured by ClearML
 * Saves model which ClearML automatically captures
-* Creates an experiment named `LightGBM`, which is associated with the `examples` project.
+* Creates an experiment named `LightGBM` in the `examples` project.
 
 ## Scalars
 

docs/guides/frameworks/matplotlib/matplotlib_example.md

@@ -13,8 +13,8 @@ The example in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/mast
 includes a clickable icon to open the notebook in Google Colab.
 :::
 
-When the example runs, it creates an experiment named `Matplotlib example`, 
-which is associated with the `examples` project (in script) or the `Colab notebooks` project (in Jupyter Notebook).
+When the example runs, it creates an experiment named `Matplotlib example`
+in the `examples` project (in script) or the `Colab notebooks` project (in Jupyter Notebook).
 
 
 

docs/guides/frameworks/megengine/megengine_mnist.md

@@ -10,7 +10,7 @@ The example script does the following:
 * Trains a simple deep neural network on MegEngine's built-in [MNIST](https://megengine.org.cn/doc/stable/en/reference/api/megengine.data.dataset.MNIST.html)
   dataset.
 * Creates a TensorBoardX `SummaryWriter` object to log scalars during training.  
-* Creates a ClearML experiment named `megengine mnist train`, which is associated with the `examples` project.
+* Creates a ClearML experiment named `megengine mnist train` in the `examples` project.
 
 ## Hyperparameters
 

docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md

@@ -2,7 +2,7 @@
 title: Audio Classification - Jupyter Notebooks
 ---
 
-The [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) example script demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classification UrbanSound8K` which is associated with the `Audio Example` project.
+The [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) example script demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. The example also demonstrates connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classification UrbanSound8K` in the `Audio Example` project.
 
 ## Scalars
 

docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md

@@ -3,7 +3,7 @@ title: Audio Preprocessing - Jupyter Notebook
 ---
 
 The example [audio_preprocessing_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.ipynb) 
-demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. ClearML automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing`, which is associated with the `Audio Example` project.
+demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. ClearML automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. The example also demonstrates connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing` in the `Audio Example` project.
 
 ## Plots
 

docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md

@@ -6,8 +6,8 @@ The example [image_classification_CIFAR10.ipynb](https://github.com/allegroai/cl
 demonstrates integrating ClearML into a Jupyter Notebook, which uses PyTorch, TensorBoard, and TorchVision to train a 
 neural network on the CIFAR10 dataset for image classification. ClearML automatically logs the example script's 
 calls to TensorBoard methods in training and testing which report scalars and image debug samples, as well as the model 
-and console log. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, 
-it creates an experiment named `image_classification_CIFAR10` which is associated with the `Image Example` project.
+and console log. The example also demonstrates connecting parameters to a Task and logging them. When the script runs, 
+it creates an experiment named `image_classification_CIFAR10` in the `Image Example` project.
 
 Another example optimizes the hyperparameters for this image classification example (see the [Hyperparameter Optimization - Jupyter Notebook](hyperparameter_search.md) documentation page). This image classification example must run before the hyperparameter optimization example.
 

docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md

@@ -2,7 +2,7 @@
 title: Tabular Data Downloading and Preprocessing - Jupyter Notebook
 ---
 
-The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates ClearML storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` which is associated with the `Table Example` project.
+The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates ClearML storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` in the `Table Example` project.
 
 This tabular data is prepared for another script, [train_tabular_predictor.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/train_tabular_predictor.ipynb), which trains a network with it. 
 

docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md

@@ -4,7 +4,7 @@ title: Text Classification - Jupyter Notebook
 
 The example [text_classification_AG_NEWS.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.ipynb) 
 demonstrates using Jupyter Notebook for ClearML, and the integration of ClearML into code which trains a network 
-to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. ClearML automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named `text classifier` which is associated with the `Text Example` project.
+to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. ClearML automatically logs the scalar and console output by calling TensorBoard methods. The example code explicitly logs parameters to the Task. When the script runs, it creates an experiment named `text classifier` in the `Text Example` project.
 
 ## Scalars
 

docs/guides/frameworks/pytorch/pytorch_abseil.md

@@ -8,7 +8,7 @@ example demonstrates the integration of ClearML into code that uses PyTorch and
 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
-* Creates an experiment named `pytorch mnist train with abseil`, which is associated with the `examples` project
+* Creates an experiment named `pytorch mnist train with abseil` in the `examples` project
 * ClearML automatically logs the absl.flags, and the models (and their snapshots) created by PyTorch 
 * Additional metrics are logged by calling the [Logger.report_scalar](../../../references/sdk/logger.md#report_scalar) 
   method

docs/guides/frameworks/pytorch/pytorch_distributed_example.md

@@ -19,7 +19,7 @@ The script does the following:
   Each Task in a subprocess references the main Task by calling [Task.current_task](../../../references/sdk/task.md#taskcurrent_task), 
     which always returns the main Task.
 
-1. When the script runs, it creates an experiment named `test torch distributed` which is associated with the `examples` project in the **ClearML Web UI**.
+1. When the script runs, it creates an experiment named `test torch distributed` in the `examples` project in the **ClearML Web UI**.
 
 ### Artifacts
 

docs/guides/frameworks/pytorch/pytorch_mnist.md

@@ -8,7 +8,7 @@ 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.
-* Creates an experiment named `pytorch mnist train`, which is associated with the `examples` project.
+* Creates an experiment named `pytorch mnist train` in 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.
 

docs/guides/frameworks/pytorch/pytorch_tensorboard.md

@@ -8,7 +8,7 @@ example demonstrates the integration of ClearML into code that uses PyTorch and
 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 an experiment named `pytorch with tensorboard`, which is associated with the `examples` project.
+* Creates an experiment named `pytorch with tensorboard` in the `examples` project.
 * ClearML automatically captures scalars and text logged using the TensorBoard `SummaryWriter` object, and 
   the model created by PyTorch. 
 

docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md

@@ -7,7 +7,7 @@ script integrates ClearML into code that uses [PyTorch Ignite](https://github.co
 
 The example script does the following:
 * Trains a neural network on the CIFAR10 dataset for image classification.
-* Creates a [ClearML Task](../../../fundamentals/task.md) named `image classification CIFAR10`, which is associated with 
+* Creates a [ClearML Task](../../../fundamentals/task.md) named `image classification CIFAR10` in
   the `examples` project.
 * Calls the [`Task.connect`](../../../references/sdk/task.md#connect) method to track experiment configuration.
 * Uses `ignite`'s `TensorboardLogger` and attaches handlers to it. See [`TensorboardLogger`](https://github.com/pytorch/ignite/blob/master/ignite/contrib/handlers/tensorboard_logger.py). 

docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md

@@ -10,7 +10,7 @@ checkpoints during training and validation.
 
 The example script does the following:
 * Trains a model to classify images from the MNIST dataset. 
-* Creates a [ClearML Task](../../../fundamentals/task.md) named `ignite`, which is associated with the `examples` 
+* Creates a [ClearML Task](../../../fundamentals/task.md) named `ignite` in the `examples` 
   project. ClearMLLogger connects to ClearML so everything which is logged through it and its handlers 
   is automatically captured by ClearML. 
 * Uses the following ClearMLLogger helper handlers:

docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md

@@ -9,7 +9,7 @@ script demonstrates the integration of ClearML into code that uses [PyTorch Ligh
 The example script does the following:
 * Trains a simple deep neural network on the PyTorch built-in MNIST dataset
 * Defines Argparse command line options, which are automatically captured by ClearML
-* Creates an experiment named `pytorch lightning mnist example`, which is associated with the `examples` project.
+* Creates an experiment named `pytorch lightning mnist example` in the `examples` project.
 
 ## Scalars
 

docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md

@@ -5,7 +5,7 @@ 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 example`, which is associated with the `examples` project.
+`scikit-learn joblib example` in the `examples` project.
 
 ## Plots
 

docs/guides/frameworks/tensorboardx/tensorboardx.md

@@ -7,7 +7,7 @@ example demonstrates the integration of ClearML into code that uses PyTorch and
 
 The 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
-* Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project
+* Creates an experiment named `pytorch with tensorboardX` in the `examples` project
 * ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and 
   the model created by PyTorch 
 

docs/guides/frameworks/tensorboardx/video_tensorboardx.md

@@ -6,7 +6,7 @@ The [moveiepy_tensorboardx.py](https://github.com/allegroai/clearml/blob/master/
 example demonstrates the integration of ClearML into code, which creates a TensorBoardX `SummaryWriter` object to log 
 video data. 
 
-When the script runs, it creates an experiment named `pytorch with video tensorboardX`, which is associated with 
+When the script runs, it creates an experiment named `pytorch with video tensorboardX` in 
 the `examples` project. 
 
 ## Debug Samples

docs/guides/frameworks/xgboost/xgboost_metrics.md

@@ -6,7 +6,7 @@ The [xgboost_metrics.py](https://github.com/allegroai/clearml/blob/master/exampl
 example demonstrates the integration of ClearML into code that uses XGBoost to train 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. ClearML automatically captures models and scalars logged with XGBoost.
 
-When the script runs, it creates a ClearML experiment named `xgboost metric auto reporting`, which is associated with 
+When the script runs, it creates a ClearML experiment named `xgboost metric auto reporting` in 
 the `examples` project.
 
 ## Scalars

docs/guides/frameworks/xgboost/xgboost_sample.md

@@ -11,7 +11,7 @@ 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.
+* Creates an experiment named `XGBoost simple example` in the `examples` project.
 
 ## Plots