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pollfly
2023-10-09 15:48:19 +03:00
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parent 5dad105950
commit 3a4b10e43b
31 changed files with 95 additions and 97 deletions
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2
docs/clearml_data/best_practices.md
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@@ -34,7 +34,7 @@ most recent dataset in a project. The same is true with tags; if a tag is specif
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 >
will store the dataset's ID in the `dataset_alias_string` parameter under the task's **CONFIGURATION > HYPERPARAMETERS >
Datasets** section.

2
docs/clearml_data/clearml_data.md
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@@ -20,7 +20,7 @@ ClearML Data Management solves two important challenges:
Moreover, it can be difficult and inefficient to find on a git tree the commit associated with a certain version of a dataset.
Use ClearML Data to create, manage, and version your datasets. Store your files in any storage location of your choice
(S3 / GS / Azure / Network Storage) by setting the dataset’s upload destination (see [`--storage`](clearml_data_cli.md#upload)
(S3 / GS / Azure / Network Storage) by setting the dataset's upload destination (see [`--storage`](clearml_data_cli.md#upload)
CLI option or [`output_url`](clearml_data_sdk.md#uploading-files) parameter).
Datasets can be set up to inherit from other datasets, so data lineages can be created, and users can track when and how

2
docs/clearml_data/clearml_data_sdk.md
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@@ -8,7 +8,7 @@ See [Hyper-Datasets](../hyperdatasets/overview.md) for ClearML's advanced querya
:::
Datasets can be created, modified, and managed with ClearML Data's python interface. You can upload your dataset to any
storage service of your choice (S3 / GS / Azure / Network Storage) by setting the dataset’s upload destination (see
storage service of your choice (S3 / GS / Azure / Network Storage) by setting the dataset's upload destination (see
[`output_url`](#uploading-files) parameter of `Dataset.upload()`). Once you have uploaded your dataset, you can access
it from any machine.

4
docs/clearml_data/data_management_examples/data_man_cifar_classification.md
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@@ -97,8 +97,8 @@ trainset = datasets.CIFAR10(
)
```
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
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.

2
docs/clearml_data/data_management_examples/data_man_simple.md
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@@ -118,7 +118,7 @@ You'll need to input the Dataset ID you received when created the dataset above
```bash
clearml-data add --files new_data.txt
```
Which should return this output:
The console should display this output:
```console
clearml-data - Dataset Management & Versioning CLI