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Add Dataset alias explanation (#449)

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pollfly 2023-01-22 14:46:30 +02:00 committed by GitHub
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6
docs/clearml_data/best_practices.md
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@ -32,6 +32,12 @@ Organizing your datasets into projects by use-case makes it easier to access the
If only a project is specified when using [`Dataset.get`](../references/sdk/dataset.md#datasetget), the method returns the
most recent dataset in a project. The same is true with tags; if a tag is specified, the method will return the most recent dataset that is labeled with that tag.
In cases where you use a dataset in a task (e.g. consuming a dataset), you can easily track which dataset the task is
using by using `Dataset.get`'s `alias` parameter. Pass `alias=<dataset_alias_string>`, and the task using the dataset
will store the dataset’s ID in the `dataset_alias_string` parameter under the task's **CONFIGURATION > HYPERPARAMETERS >
Datasets` section.
## Document your Datasets
Attach informative metrics or debug samples to the Dataset itself. Use the [`get_logger`](../references/sdk/dataset.md#get_logger)

11
docs/clearml_data/data_management_examples/data_man_cifar_classification.md
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@ -85,7 +85,8 @@ from clearml import Dataset
dataset_path = Dataset.get(
dataset_name=dataset_name,
dataset_project=dataset_project
dataset_project=dataset_project,
alias="Cifar dataset"
).get_local_copy()
trainset = datasets.CIFAR10(
@ -95,7 +96,13 @@ trainset = datasets.CIFAR10(
transform=transform
)
```
In cases like this, where you use a dataset in a task, you can have the dataset's ID stored in the task’s
hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset’s ID in the
`dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way
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 we provide the path to PyTorch's dataset object.
The script then trains a neural network to classify images using the dataset created above.

10
docs/guides/datasets/data_man_cifar_classification.md
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@ -85,7 +85,9 @@ from clearml import Dataset
dataset_path = Dataset.get(
dataset_name=dataset_name,
dataset_project=dataset_project).get_local_copy()
dataset_project=dataset_project,
alias="Cifar dataset"
).get_local_copy()
trainset = datasets.CIFAR10(
root=dataset_path,
@ -94,6 +96,12 @@ trainset = datasets.CIFAR10(
transform=transform
)
```
In cases like this, where you use a dataset in a task, you can have the dataset's ID stored in the task’s
hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset’s ID in the
`dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way
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.