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10
docs/guides/datasets/data_man_cifar_classification.md
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@@ -2,13 +2,13 @@
title: Dataset Management with CLI and SDK
---
In this tutorial, we are going to manage the CIFAR dataset with `clearml-data` CLI, and then use ClearML's [`Dataset`](../../references/sdk/dataset.md)
In this tutorial, you are going to manage the CIFAR dataset with `clearml-data` CLI, and then use ClearML's [`Dataset`](../../references/sdk/dataset.md)
class to ingest the data.
## Creating the Dataset
### Downloading the Data
Before we can register the CIFAR dataset with `clearml-data`, we need to obtain a local copy of it.
Before registering the CIFAR dataset with `clearml-data`, you need to obtain a local copy of it.
Execute this python script to download the data
```python
@@ -43,7 +43,7 @@ New dataset created id=ee1c35f60f384e65bc800f42f0aca5ec
Where `ee1c35f60f384e65bc800f42f0aca5ec` is the dataset ID.
## Adding Files
Add the files we just downloaded to the dataset:
Add the files that were just downloaded to the dataset:
```
clearml-data add --files <dataset_path>
@@ -72,7 +72,7 @@ In the panel's **CONTENT** tab, you can see a table summarizing version contents
## Using the Dataset
Now that we have a new dataset registered, we can consume it.
Now that a new dataset is registered, you can consume it.
The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) example
script demonstrates using the dataset within Python code.
@@ -103,6 +103,6 @@ hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset’s I
you can easily track which dataset the task is using.
The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method will return a path to the cached,
downloaded dataset. Then we provide the path to PyTorch's dataset object.
downloaded dataset. Then the dataset path is input to PyTorch's `datasets` object.
The script then trains a neural network to classify images using the dataset created above.

4
docs/guides/datasets/data_man_python.md
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@@ -18,7 +18,7 @@ demonstrates how to do the following:
### Downloading the Data
We first need to obtain a local copy of the CIFAR dataset.
You first need to obtain a local copy of the CIFAR dataset.
```python
from clearml import StorageManager
@@ -79,7 +79,7 @@ In the panel's **CONTENT** tab, you can see a table summarizing version contents
## Data Ingestion
Now that we have a new dataset registered, we can consume it!
Now that a new dataset is registered, you can consume it!
The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script
demonstrates data ingestion using the dataset created in the first script.

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docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md
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@@ -62,7 +62,7 @@ ClearML automatically tracks images logged to TensorboardLogger. They appear in
## Ignite ClearMLLogger
PyTorch Ignite also offers a dedicated `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model
PyTorch Ignite also supports a dedicated `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model
checkpoints during training and validation.
For more information, see the [PyTorch Ignite ClearMLLogger](pytorch_ignite_mnist.md)

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docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md
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@@ -5,7 +5,7 @@ title: PyTorch Ignite ClearMLLogger
The `ignite` repository contains the [mnist_with_clearml_logger.py](https://github.com/pytorch/ignite/blob/master/examples/contrib/mnist/mnist_with_clearml_logger.py)
example script that uses [ignite](https://github.com/pytorch/ignite) and integrates **ClearMLLogger** and its [helper handlers](https://pytorch.org/ignite/generated/ignite.contrib.handlers.clearml_logger.html).
PyTorch Ignite offers a `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model
PyTorch Ignite supports a `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model
checkpoints during training and validation.
The example script does the following:

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docs/guides/ide/google_colab.md
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@@ -2,7 +2,7 @@
title: ClearML Agent on Google Colab
---
[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as
[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It supports a convenient IDE as well as
compute provided by google.
Users can transform a Google Colab instance into an available resource in ClearML using [ClearML Agent](../../clearml_agent.md).

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docs/guides/reporting/artifacts.md
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@@ -51,7 +51,7 @@ task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69
```
By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts)
method to retrieve it, we can see that ClearML tracked the change.
method to retrieve it, you can see that ClearML tracked the change.
```python
# change the artifact object