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docs/clearml_data/data_management_examples/data_man_simple.md
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@ -7,7 +7,7 @@ In this example we'll create a simple dataset and demonstrate basic actions on i
## Prerequisites ## Prerequisites
1. First, make sure that you have cloned the [clearml](https://github.com/allegroai/clearml) repository. It contains all 1. First, make sure that you have cloned the [clearml](https://github.com/allegroai/clearml) repository. It contains all
the needed files. the needed files.
1. Open terminal and change directory to the cloned repository's examples folder 1. Open terminal and change directory to the cloned repository's examples folder:
``` ```
cd clearml/examples/reporting cd clearml/examples/reporting
@ -140,7 +140,7 @@ Using ClearML Data, you can create child datasets that inherit the content of ot
1 files removed 1 files removed
``` ```
1. Close and finalize the dataset 1. Close and finalize the dataset:
```bash ```bash
clearml-data close clearml-data close

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docs/faq.md
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@ -241,7 +241,8 @@ To replace the URL of each model, execute the following commands:
sudo docker exec -it clearml-mongo /bin/bash sudo docker exec -it clearml-mongo /bin/bash
``` ```
1. Create the following script inside the Docker shell (as well as the URL protocol if you aren't using `s3`): 1. Create the following script inside the Docker shell (as well as the URL protocol if you aren't using `s3`).
Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`.
```bash ```bash
cat <<EOT >> script.js cat <<EOT >> script.js
@ -250,7 +251,6 @@ To replace the URL of each model, execute the following commands:
db.model.save(e);}); db.model.save(e);});
EOT EOT
``` ```
Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`.
1. Run the script against the backend DB: 1. Run the script against the backend DB:
@ -273,7 +273,7 @@ To fix this, the registered URL of each model needs to be replaced with its curr
sudo docker exec -it clearml-mongo /bin/bash sudo docker exec -it clearml-mongo /bin/bash
``` ```
1. Create the following script inside the Docker shell: 1. Create the following script inside the Docker shell (Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`, as well as the URL protocol prefixes if you aren't using S3):
```bash ```bash
cat <<EOT >> script.js cat <<EOT >> script.js
db.model.find({uri:{$regex:/^s3/}}).forEach(function(e,i) { db.model.find({uri:{$regex:/^s3/}}).forEach(function(e,i) {
@ -281,7 +281,6 @@ To fix this, the registered URL of each model needs to be replaced with its curr
db.model.save(e);}); db.model.save(e);});
EOT EOT
``` ```
Make sure to replace `<old-bucket-name>` and `<new-bucket-name>`, as well as the URL protocol prefixes if you aren't using S3.
1. Run the script against the backend DB: 1. Run the script against the backend DB:
```bash ```bash

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docs/hyperdatasets/dataset.md
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@ -94,37 +94,37 @@ myDataset = DatasetVersion.get_current(dataset_name='myDataset')
### Deleting Datasets ### Deleting Datasets
Use the [`Dataset.delete`](../references/hyperdataset/hyperdataset.md#datasetdelete) method to delete a Dataset. Use the [`Dataset.delete`](../references/hyperdataset/hyperdataset.md#datasetdelete) class method to delete a Dataset:
Delete an empty Dataset (no versions). * Delete an empty Dataset (no versions):
```python ```python
Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False) Dataset.delete(dataset_name='MyDataset', delete_all_versions=False, force=False)
``` ```
Delete a Dataset containing only versions whose status is *Draft*. * Delete a Dataset containing only versions whose status is *Draft*:
```python ```python
Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=False) Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=False)
``` ```
Delete a Dataset even if it contains versions whose status is *Published*. * Delete a Dataset even if it contains versions whose status is *Published*:
```python ```python
Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=True) Dataset.delete(dataset_name='MyDataset', delete_all_versions=True, force=True)
``` ```
Delete a Dataset and the sources associated with its deleted frames: * Delete a Dataset and the sources associated with its deleted frames:
```python ```python
Dataset.delete( Dataset.delete(
dataset_name='MyDataset', delete_all_versions=True, force=True, delete_sources=True dataset_name='MyDataset', delete_all_versions=True, force=True, delete_sources=True
) )
``` ```
This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources` This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources`
parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process' progress by passing parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process' progress by passing
`show_progress=True` (`tqdm` required). `show_progress=True` (`tqdm` required).
### Tagging Datasets ### Tagging Datasets

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docs/integrations/ignite.md
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@ -38,22 +38,22 @@ during training and validation.
Integrate ClearML with the following steps: Integrate ClearML with the following steps:
1. Create a `ClearMLLogger` object: 1. Create a `ClearMLLogger` object:
```python ```python
from ignite.contrib.handlers.clearml_logger import * from ignite.contrib.handlers.clearml_logger import *
clearml_logger = ClearMLLogger(task_name="ignite", project_name="examples") clearml_logger = ClearMLLogger(task_name="ignite", project_name="examples")
``` ```
This creates a [ClearML Task](../fundamentals/task.md) called `ignite` in the `examples` project, which captures your This creates a [ClearML Task](../fundamentals/task.md) called `ignite` in the `examples` project, which captures your
script's information, including Git details, uncommitted code, python environment. script's information, including Git details, uncommitted code, python environment.
You can also pass the following parameters to the `ClearMLLogger` object: You can also pass the following parameters to the `ClearMLLogger` object:
* `task_type` – The type of experiment (see [task types](../fundamentals/task.md#task-types)). * `task_type` – The type of experiment (see [task types](../fundamentals/task.md#task-types)).
* `report_freq` – The histogram processing frequency (handles histogram values every X calls to the handler). Affects * `report_freq` – The histogram processing frequency (handles histogram values every X calls to the handler). Affects
`GradsHistHandler` and `WeightsHistHandler` (default: 100). `GradsHistHandler` and `WeightsHistHandler` (default: 100).
* `histogram_update_freq_multiplier` – The histogram report frequency (report first X histograms and once every X * `histogram_update_freq_multiplier` – The histogram report frequency (report first X histograms and once every X
reports afterwards) (default: 10). reports afterwards) (default: 10).
* `histogram_granularity` - Histogram sampling granularity (default: 50). * `histogram_granularity` - Histogram sampling granularity (default: 50).
1. Attach the `ClearMLLogger` to output handlers to log metrics: 1. Attach the `ClearMLLogger` to output handlers to log metrics: