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@ -104,7 +104,7 @@ task.connect(input_model)
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## Accessing Models
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### Querying Models
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Retrieve a list of model objects by querying the system by model names, projects, tags, and more, using the
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[`Model.query_models`](../references/sdk/model_model.md#modelquery_models) and / or
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[`Model.query_models`](../references/sdk/model_model.md#modelquery_models) and/or
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the [`InputModel.query_models`](../references/sdk/model_inputmodel.md#inputmodelquery_models) class methods. These
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methods return a list of model objects that match the queries. The list is ordered according to the models’ last update
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time.
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@ -127,7 +127,7 @@ Compatible with Docker versions 0.6.5 and above
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* `rand_string` - random lower-case letters string (up to 32 characters)
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* The resulting name must start with an alphanumeric character, while the rest of the name may contain alphanumeric characters,
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underscores (`_`), dots (`.`) and / or dashes (`-`)
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underscores (`_`), dots (`.`) and/or dashes (`-`)
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* For example: `clearml-id-{task_id}-{rand_string:.8}`
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@ -1294,7 +1294,7 @@ This feature is available under the ClearML Enterprise plan
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:::
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The ClearML Enterprise Server includes the configuration vault. Users can add configuration sections to the vault and, once
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the vault is enabled, the configurations will be merged into the ClearML and ClearML Agent configurations upon code execution and / or agent launch.
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the vault is enabled, the configurations will be merged into the ClearML and ClearML Agent configurations upon code execution and/or agent launch.
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These configurations override the configurations written in the configuration file.
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12
docs/faq.md
12
docs/faq.md
@ -46,7 +46,7 @@ title: FAQ
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* [Is there something ClearML can do about uncommitted code running?](#help-uncommitted-code)
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* [I read there is a feature for centralized model storage. How do I use it?](#centralized-model-storage)
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* [When using PyCharm to remotely debug a machine, the Git repo is not detected. Do you have a solution?](#pycharm-remote-debug-detect-git)
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* [Debug images and / or artifacts are not loading in the UI after I migrated ClearML Server to a new address. How do I fix this?](#migrate_server_debug)
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* [Debug images and/or artifacts are not loading in the UI after I migrated ClearML Server to a new address. How do I fix this?](#migrate_server_debug)
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**Remote Debugging (ClearML PyCharm Plugin)**
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@ -668,10 +668,10 @@ repository / commit ID. For detailed information about using the plugin, see the
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**Debug images and/or artifacts are not loading in the UI after I migrated ClearML Server to a new address. How do I fix this?** <a id="migrate_server_debug"></a>
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This can happen if your debug images and / or artifacts were uploaded to the ClearML file server, since the value
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This can happen if your debug images and/or artifacts were uploaded to the ClearML file server, since the value
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registered was their full URL at the time of registration (e.g. `https://files.<OLD_ADDRESS>/path/to/artifact`).
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To fix this, the registered URL of each debug image and / or artifact needs to be replaced with its current URL.
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To fix this, the registered URL of each debug image and/or artifact needs to be replaced with its current URL.
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* For **debug images**, use the following command. Make sure to insert the old address and the new address that will replace it
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```bash
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@ -946,7 +946,7 @@ try removing deprecated images:
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**Why is web login authentication not working?** <a className="tr_top_negative" id="port-conflict"></a>
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A port conflict between the ClearML Server MongoDB and / or Elastic instances, and other instances running on your system may prevent web login authentication from working correctly.
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A port conflict between the ClearML Server MongoDB and/or Elastic instances, and other instances running on your system may prevent web login authentication from working correctly.
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ClearML Server uses the following default ports which may be in conflict with other instances:
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@ -955,9 +955,9 @@ ClearML Server uses the following default ports which may be in conflict with ot
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You can check for port conflicts in the logs in `/opt/clearml/log`.
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If a port conflict occurs, change the MongoDB and / or Elastic ports in the `docker-compose.yml`, and then run the Docker compose commands to restart the ClearML Server instance.
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If a port conflict occurs, change the MongoDB and/or Elastic ports in the `docker-compose.yml`, and then run the Docker compose commands to restart the ClearML Server instance.
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To change the MongoDB and / or Elastic ports for your ClearML Server, do the following:
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To change the MongoDB and/or Elastic ports for your ClearML Server, do the following:
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1. Edit the `docker-compose.yml` file.
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1. Add the following environment variable(s) in the `services/trainsserver/environment` section:
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@ -61,7 +61,7 @@ new_dataset.tags = ['latest']
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The new dataset inherits the contents of the datasets specified in `Dataset.create`'s `parents` argument.
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This not only helps trace back dataset changes with full genealogy, but also makes the storage more efficient,
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since it only stores the changed and / or added files from the parent versions.
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since it only stores the changed and/or added files from the parent versions.
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When you access the Dataset, it automatically merges the files from all parent versions
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in a fully automatic and transparent process, as if the files were always part of the requested Dataset.
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@ -1,108 +0,0 @@
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---
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title: Dataset Management with CLI and SDK
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---
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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)
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class to ingest the data.
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## Creating the Dataset
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### Downloading the Data
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Before registering the CIFAR dataset with `clearml-data`, you need to obtain a local copy of it.
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Execute this python script to download the data
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```python
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from clearml import StorageManager
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manager = StorageManager()
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dataset_path = manager.get_local_copy(
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remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
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)
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# make sure to copy the printed value
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print("COPY THIS DATASET PATH: {}".format(dataset_path))
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```
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Expected response:
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```bash
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COPY THIS DATASET PATH: ~/.clearml/cache/storage_manager/global/f2751d3a22ccb78db0e07874912b5c43.cifar-10-python_artifacts_archive_None
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```
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The script prints the path to the downloaded data. It will be needed later on.
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### Creating the Dataset
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To create the dataset, execute the following command:
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```
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clearml-data create --project dataset_examples --name cifar_dataset
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```
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Expected response:
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```
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clearml-data - Dataset Management & Versioning CLI
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Creating a new dataset:
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New dataset created id=ee1c35f60f384e65bc800f42f0aca5ec
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```
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Where `ee1c35f60f384e65bc800f42f0aca5ec` is the dataset ID.
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## Adding Files
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Add the files that were just downloaded to the dataset:
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```
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clearml-data add --files <dataset_path>
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```
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where `dataset_path` is the path that was printed earlier, which denotes the location of the downloaded dataset.
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:::note
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There's no need to specify a `dataset_id`, since the `clearml-data` session stores it.
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:::
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## Finalizing the Dataset
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Run the [`close`](../../references/sdk/dataset.md#close) command to upload the files (it'll be uploaded to ClearML Server by default):<br/>
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```
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clearml-data close
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```
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This command sets the dataset task's status to *completed*, so it will no longer be modifiable. This ensures future
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reproducibility.
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Information about the dataset can be viewed in the WebApp, in the dataset's [details panel](../../webapp/datasets/webapp_dataset_viewing.md#version-details-panel).
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In the panel's **CONTENT** tab, you can see a table summarizing version contents, including file names, file sizes, and hashes.
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## Using the Dataset
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Now that a new dataset is registered, you can consume it.
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The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) example
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script demonstrates using the dataset within Python code.
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```python
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dataset_name = "cifar_dataset"
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dataset_project = "dataset_examples"
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from clearml import Dataset
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dataset_path = Dataset.get(
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dataset_name=dataset_name,
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dataset_project=dataset_project,
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alias="Cifar dataset"
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).get_local_copy()
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trainset = datasets.CIFAR10(
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root=dataset_path,
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train=True,
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download=False,
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transform=transform
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)
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```
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In cases like this, where you use a dataset in a task, you can have the dataset's ID stored in the task’s
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hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset’s ID in the
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`dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way
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you can easily track which dataset the task is using.
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The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method will return a path to the cached,
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downloaded dataset. Then the dataset path is input to PyTorch's `datasets` object.
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The script then trains a neural network to classify images using the dataset created above.
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@ -1,106 +0,0 @@
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---
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title: Data Management with Python
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---
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The [dataset_creation.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/dataset_creation.py) and
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[data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py)
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together demonstrate how to use ClearML's [`Dataset`](../../references/sdk/dataset.md) class to create a dataset and
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subsequently ingest the data.
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## Dataset Creation
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The [dataset_creation.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/dataset_creation.py) script
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demonstrates how to do the following:
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* Create a dataset and add files to it
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* Upload the dataset to the ClearML Server
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* Finalize the dataset
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### Downloading the Data
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You first need to obtain a local copy of the CIFAR dataset.
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```python
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from clearml import StorageManager
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manager = StorageManager()
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dataset_path = manager.get_local_copy(
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remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
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)
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```
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This script downloads the data and `dataset_path` contains the path to the downloaded data.
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### Creating the Dataset
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```python
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from clearml import Dataset
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dataset = Dataset.create(
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dataset_name="cifar_dataset",
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dataset_project="dataset examples"
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)
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```
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This creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
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can be viewed in the WebApp.
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### Adding Files
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```python
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dataset.add_files(path=dataset_path)
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```
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This adds the downloaded files to the current dataset.
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### Uploading the Files
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```python
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dataset.upload()
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```
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This uploads the dataset to the ClearML Server by default. The dataset's destination can be changed by specifying the
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target storage with the `output_url` parameter of the [`upload`](../../references/sdk/dataset.md#upload) method.
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### Finalizing the Dataset
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Run the [`finalize`](../../references/sdk/dataset.md#finalize) command to close the dataset and set that dataset's tasks
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status to *completed*. The dataset can only be finalized if it doesn't have any pending uploads.
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```python
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dataset.finalize()
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```
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After a dataset has been closed, it can no longer be modified. This ensures future reproducibility.
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Information about the dataset can be viewed in the WebApp, in the dataset's [details panel](../../webapp/datasets/webapp_dataset_viewing.md#version-details-panel).
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In the panel's **CONTENT** tab, you can see a table summarizing version contents, including file names, file sizes, and hashes.
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## Data Ingestion
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Now that a new dataset is registered, you can consume it!
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The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script
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demonstrates data ingestion using the dataset created in the first script.
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```python
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dataset_name = "cifar_dataset"
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dataset_project = "dataset_examples"
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dataset_path = Dataset.get(
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dataset_name=dataset_name,
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dataset_project=dataset_project
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).get_local_copy()
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```
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The script above gets the dataset and uses the [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy)
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method to return a path to the cached, read-only local dataset.
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If you need a modifiable copy of the dataset, use the following:
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```python
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Dataset.get(dataset_name, dataset_project).get_mutable_local_copy("path/to/download")
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```
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The script then creates a neural network to train a model to classify images from the dataset that was
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created above.
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@ -55,7 +55,7 @@ The following are the `ClearMLLogger` parameters:
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* `histogram_granularity` - Histogram sampling granularity. Default is 50.
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### Logging
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To log scalars, ignite engine's output and / or metrics, use the `OutputHandler`.
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To log scalars, ignite engine's output and/or metrics, use the `OutputHandler`.
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* Log training loss at each iteration:
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```python
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@ -72,7 +72,7 @@ Customize the columns on the tracking leaderboard by hiding any of the default c
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## Step 4: Show Metrics or Hyperparameters
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The leaderboard can contain any combination of metrics and hyperparameters. For each metric, choose whether to view the last (most
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recent), minimum, and / or maximum values.
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recent), minimum, and/or maximum values.
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**To select metrics or hyperparameters:**
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@ -9,7 +9,7 @@ and functionality for the following purposes:
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* Integrating the powerful features of [Dataviews](dataviews.md) with an experiment
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* [Annotating](webapp/webapp_datasets_frames.md#annotations) images and videos
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Datasets consist of versions with SingleFrames and / or FrameGroups. Each Dataset can contain multiple versions, which
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Datasets consist of versions with SingleFrames and/or FrameGroups. Each Dataset can contain multiple versions, which
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can have multiple children that inherit their parent's contents.
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Mask-labels can be defined globally, for a DatasetVersion. When defined this way, they will be applied to all masks in
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@ -158,7 +158,7 @@ versions are locked for further changes and which can be modified. See details [
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Dataset versions can have either *Draft* or *Published* state.
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A *Draft* version is editable, so frames can be added to and deleted and / or modified.
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A *Draft* version is editable, so frames can be added to and deleted and/or modified.
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A *Published* version is read-only, which ensures reproducible experiments and preserves the Dataset version contents.
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Child versions can only be created from *Published* versions, as they inherit their predecessor version contents.
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@ -236,7 +236,7 @@ myDataset = DatasetVersion.create_snapshot(
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#### Adding Metadata and Comments
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Add a metadata dictionary and / or comment to a snapshot.
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Add a metadata dictionary and/or comment to a snapshot.
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For example:
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@ -35,7 +35,7 @@ A frame filter contains the following criteria:
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* Any combination of the following rules:
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* ROI rule - Include or exclude frames containing at least one ROI with any combination of labels in the Dataset version.
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Optionally, limit the number of matching ROIs (instances) per frame, and / or limit the confidence level of the label.
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Optionally, limit the number of matching ROIs (instances) per frame, and/or limit the confidence level of the label.
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For example: include frames containing two to four ROIs labeled `cat` and `dog`, with a confidence level from `0.8` to `1.0`.
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* Frame rule - Filter by frame metadata key-value pairs, or ROI labels.
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For example: if some frames contain the metadata
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@ -150,7 +150,7 @@ myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=
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### Adding Queries
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To add a query to a DataView, use the [`DataView.add_query`](../references/hyperdataset/dataview.md#add_query) method
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and specify Dataset versions, ROI and / or frame queries, and other criteria.
|
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and specify Dataset versions, ROI and/or frame queries, and other criteria.
|
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|
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The `dataset_name` and `version_name` arguments specify the Dataset Version. The `roi_query` and `frame_query` arguments
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specify the queries.
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@ -158,7 +158,7 @@ specify the queries.
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* `frame_query` must be assigned a Lucene query.
|
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Multiple queries can be added to the same or different Dataset versions, each query with the same or different ROI
|
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and / or frame queries.
|
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and/or frame queries.
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|
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You can retrieve the Dataview frames using [`DataView.to_list`](../references/hyperdataset/dataview.md#to_list),
|
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[`DataView.to_dict`](../references/hyperdataset/dataview.md#to_dict), or [`DataView.get_iterator`](../references/hyperdataset/dataview.md#get_iterator)
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@ -286,7 +286,7 @@ list_of_frames = myDataView.to_list()
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#### Frame Queries
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||||
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Use frame queries to filter frames by ROI labels and / or frame metadata key-value pairs that a frame must include or
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Use frame queries to filter frames by ROI labels and/or frame metadata key-value pairs that a frame must include or
|
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exclude for the Dataview to return the frame.
|
||||
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**Frame queries** match frame meta key-value pairs, ROI labels, or both.
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@ -39,11 +39,11 @@ Customize the table using any of the following:
|
||||
|
||||
* Dynamic column order - Drag a column title to a different position.
|
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* Resize columns - Drag the column separator to change the width of that column. Double-click the column separator for automatic fit.
|
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* Filter by user and / or status - When a filter is applied to a column, its filter icon will appear with a highlighted
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||||
* Filter by user and/or status - When a filter is applied to a column, its filter icon will appear with a highlighted
|
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dot on its top right (<img src="/docs/latest/icons/ico-filter-on.svg" alt="Filter on" className="icon size-md" /> ). To
|
||||
clear all active filters, click <img src="/docs/latest/icons/ico-filter-reset.svg" alt="Clear filters" className="icon size-md" />
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in the top right corner of the table.
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||||
* Sort columns - By experiment name and / or elapsed time since creation.
|
||||
* Sort columns - By experiment name and/or elapsed time since creation.
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||||
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||||
:::note
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||||
The following Dataviews-table customizations are saved on a **per-project** basis:
|
||||
|
||||
@ -34,7 +34,7 @@ enables modifying [Dataviews](webapp_dataviews.md), including:
|
||||
select **Import to current dataview** or **Import as aux dataview**.
|
||||
|
||||
:::note
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||||
After importing a Dataview, it can be renamed and / or removed.
|
||||
After importing a Dataview, it can be renamed and/or removed.
|
||||
:::
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||||
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||||
### Selecting Dataset Versions
|
||||
|
||||
@ -31,7 +31,7 @@ The **FILTERING** section lists the SingleFrame filters iterated by a Dataview,
|
||||
|
||||
Each frame filter is composed of:
|
||||
* A Dataset version to input from
|
||||
* ROI Rules for SingleFrames to include and / or exclude certain criteria.
|
||||
* ROI Rules for SingleFrames to include and/or exclude certain criteria.
|
||||
* Weights for debiasing input data.
|
||||
|
||||
Combinations of frame filters can implement complex querying.
|
||||
|
||||
@ -75,7 +75,7 @@ allowing the pipeline logic to reuse the step outputs.
|
||||
|
||||
### Callbacks
|
||||
|
||||
Callbacks can be utilized to control pipeline execution flow. A callback can be defined to be called before and / or after
|
||||
Callbacks can be utilized to control pipeline execution flow. A callback can be defined to be called before and/or after
|
||||
the execution of every task in a pipeline. Additionally, you can create customized, step-specific callbacks.
|
||||
|
||||
### Pipeline Reusing
|
||||
|
||||
@ -28,7 +28,7 @@ The models table contains the following columns:
|
||||
| **STARTED** | Elapsed time since the run started. To view the date and time of start, hover over the elapsed time. | Date-time |
|
||||
| **UPDATED** | Elapsed time since the last update to the run. To view the date and time of update, hover over the elapsed time. | Date-time |
|
||||
| **RUN TIME** | The current / total running time of the run. | Time |
|
||||
| **_Metrics_** | Add metrics column (last, minimum, and / or maximum values). Available options depend upon the runs in the table. | Varies according to runs in table |
|
||||
| **_Metrics_** | Add metrics column (last, minimum, and/or maximum values). Available options depend upon the runs in the table. | Varies according to runs in table |
|
||||
| **_Hyperparameters_** | Add hyperparameters. Available options depend upon the runs in the table. | Varies according to runs in table |
|
||||
|
||||
## Customizing the Runs Table
|
||||
@ -57,8 +57,8 @@ on a column, and the relevant filter appears.
|
||||
|
||||
There are a few types of filters:
|
||||
* Value set - Choose which values to include from a list of all values in the column
|
||||
* Numerical ranges - Insert minimum and / or maximum value
|
||||
* Date ranges - Insert starting and / or ending date and time
|
||||
* Numerical ranges - Insert minimum and/or maximum value
|
||||
* Date ranges - Insert starting and/or ending date and time
|
||||
* Tags - Choose which tags to filter by from a list of all tags used in the column.
|
||||
* Filter by multiple tag values using the **ANY** or **ALL** options, which correspond to the logical "AND" and "OR" respectively. These
|
||||
options appear on the top of the tag list.
|
||||
|
||||
@ -236,7 +236,7 @@ To assist in experiment analysis, the comparison page supports:
|
||||
experiment table with the currently compared experiments at the top.
|
||||
1. Find the experiments to add by sorting and [filtering](webapp_exp_table.md#filtering-columns) the experiments with
|
||||
the appropriate column header controls. Alternatively, use the search bar to find experiments by name.
|
||||
1. Select experiments to include in the comparison (and / or clear the selection of any experiment you wish to remove).
|
||||
1. Select experiments to include in the comparison (and/or clear the selection of any experiment you wish to remove).
|
||||
1. Click **APPLY**.
|
||||
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@ The experiments table default and customizable columns are described in the foll
|
||||
| **ITERATION** | Last or most recent iteration of the experiment. | Default |
|
||||
| **DESCRIPTION** | A description of the experiment. For cloned experiments, the description indicates it was auto generated with a timestamp. | Default (hidden) |
|
||||
| **RUN TIME** | The current / total running time of the experiment. | Default (hidden) |
|
||||
| **_Metrics_** | Add metrics column (last, minimum, and / or maximum values). The metrics depend upon the experiments in the table. See [adding metrics](#to-add-metrics). | Customizable |
|
||||
| **_Metrics_** | Add metrics column (last, minimum, and/or maximum values). The metrics depend upon the experiments in the table. See [adding metrics](#to-add-metrics). | Customizable |
|
||||
| **_Hyperparameters_** | Add hyperparameters. The hyperparameters depend upon the experiments in the table. See [adding hyperparameters](#to-add-hyperparameters). | Customizable |
|
||||
|
||||
|
||||
@ -61,7 +61,7 @@ Use experiments table customization for various use cases, including:
|
||||
* Creating a [leaderboard](#creating-an-experiment-leaderboard) that will update in real time with experiment
|
||||
performance, which can be shared and stored.
|
||||
* Sorting models by metrics - Models are associated with the experiments that created them. For each metric, use the last
|
||||
value, the minimal value, and / or the maximal value.
|
||||
value, the minimal value, and/or the maximal value.
|
||||
* Tracking hyperparameters - Track hyperparameters by adding them as columns, and applying filters and sorting.
|
||||
|
||||
Changes are persistent (cached in the browser), and represented in the URL so customized settings can be saved in a browser
|
||||
@ -81,17 +81,17 @@ all the experiments in the project. The customizations of these two views are sa
|
||||
|
||||
|
||||
|
||||
### Adding Metrics and / or Hyperparameters
|
||||
### Adding Metrics and/or Hyperparameters
|
||||
|
||||
|
||||
|
||||
Add metrics and / or hyperparameters columns to the experiments table. The metrics and hyperparameters depend upon the
|
||||
Add metrics and/or hyperparameters columns to the experiments table. The metrics and hyperparameters depend upon the
|
||||
experiments in the table.
|
||||
|
||||
#### To Add Metrics:
|
||||
|
||||
* Click <img src="/docs/latest/icons/ico-settings.svg" alt="Setting Gear" className="icon size-md" /> **>** **+ METRICS** **>** Expand a metric **>** Select the **LAST** (value),
|
||||
**MIN** (minimal value), and / or **MAX** (maximal value) checkboxes.
|
||||
**MIN** (minimal value), and/or **MAX** (maximal value) checkboxes.
|
||||
|
||||
#### To Add Hyperparameters:
|
||||
|
||||
@ -112,8 +112,8 @@ on a column, and the relevant filter appears.
|
||||
|
||||
There are a few types of filters:
|
||||
* Value set - Choose which values to include from a list of all values in the column
|
||||
* Numerical ranges - Insert minimum and / or maximum value
|
||||
* Date ranges - Insert starting and / or ending date and time
|
||||
* Numerical ranges - Insert minimum and/or maximum value
|
||||
* Date ranges - Insert starting and/or ending date and time
|
||||
* Tags - Choose which tags to filter by from a list of all tags used in the column.
|
||||
* Filter by multiple tag values using the **ANY** or **ALL** options, which correspond to the logical "AND" and "OR" respectively. These
|
||||
options appear on the top of the tag list.
|
||||
|
||||
@ -60,7 +60,7 @@ User parameters are editable in any experiment, except experiments whose status
|
||||
Select source code by changing any of the following:
|
||||
|
||||
* Repository, commit (select by ID, tag name, or choose the last commit in the branch), script, and /or working directory.
|
||||
* Installed Python packages and / or versions - Edit or clear (remove) them all.
|
||||
* Installed Python packages and/or versions - Edit or clear (remove) them all.
|
||||
* Uncommitted changes - Edit or clear (remove) them all.
|
||||
|
||||
**To select different source code:**
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
title: Model Details
|
||||
---
|
||||
|
||||
In the models table, double-click on a model to view and / or modify the following:
|
||||
In the models table, double-click on a model to view and/or modify the following:
|
||||
* General model information
|
||||
* Model configuration
|
||||
* Model label enumeration
|
||||
|
||||
@ -223,7 +223,7 @@ The user group table lists all the active user groups. Each row includes a group
|
||||
#### To edit a user group:
|
||||
1. Hover over the user group's row on the table
|
||||
1. Click the <img src="/docs/latest/icons/ico-edit.svg" alt="Edit Pencil" className="icon size-md" /> button
|
||||
1. Edit the group's name and / or description
|
||||
1. Edit the group's name and/or description
|
||||
1. Edit group members (see details [here](#to-create-a-user-group))
|
||||
1. Click **Save**
|
||||
|
||||
@ -241,7 +241,7 @@ This feature is available under the ClearML Enterprise plan
|
||||
:::
|
||||
|
||||
Workspace administrators can use the **Access Rules** page to manage workspace permissions, by specifying which users
|
||||
and / or user groups have access permissions to the following workspace resources:
|
||||
and/or user groups have access permissions to the following workspace resources:
|
||||
|
||||
* [Projects](../fundamentals/projects.md)
|
||||
* [Tasks](../fundamentals/task.md)
|
||||
@ -260,7 +260,7 @@ Access privileges can be viewed, defined, and edited in the **Access Rules** tab
|
||||
specific project or task), click the input box, and select the object from the list that appears. Filter the
|
||||
list by typing part of the desired object name
|
||||
1. Select the permission type - **Read Only** or **Read & Modify**
|
||||
1. Assign users and / or [user groups](#user-groups) to be given access. Click the desired input box, and select the
|
||||
1. Assign users and/or [user groups](#user-groups) to be given access. Click the desired input box, and select the
|
||||
users / groups from the list that appears. Filter the list by typing part of the desired object name. To revoke
|
||||
access, hover over a user's or group's row and click the <img src="/docs/latest/icons/ico-trash.svg" alt="Trash can" className="icon size-md" />
|
||||
button
|
||||
|
||||
@ -128,7 +128,7 @@ module.exports = {
|
||||
{'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']},
|
||||
{'ClearML Task': ['guides/clearml-task/clearml_task_tutorial']},
|
||||
{'ClearML Agent': ['guides/clearml_agent/executable_exp_containers', 'guides/clearml_agent/exp_environment_containers']},
|
||||
{'Datasets': ['guides/datasets/data_man_cifar_classification', 'guides/datasets/data_man_python']},
|
||||
{'Datasets': ['clearml_data/data_management_examples/data_man_cifar_classification', 'clearml_data/data_management_examples/data_man_python']},
|
||||
{'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']},
|
||||
{'Docker': ['guides/docker/extra_docker_shell_script']},
|
||||
{'Frameworks': [
|
||||
|
||||
Loading…
Reference in New Issue
Block a user