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4
docs/clearml_agent/clearml_agent_ref.md
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@ -87,7 +87,7 @@ clearml-agent daemon [-h] [--foreground] [--queue QUEUES [QUEUES ...]] [--order-
|`--create-queue`| If the queue name provided with `--queue` does not exist in the server, create it on-the-fly and use it.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--detached`| Run agent in the background. The `clearml-agent` command returns immediately.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--docker`| Run in Docker mode. Execute the Task inside a Docker container. To specify the image name and optional arguments, either: <ul><li> Use `--docker <image_name> <args>` on the command line, or </li><li>Use `--docker` on the command line, and specify the default image name and arguments in the configuration file.</li></ul> Environment variable settings for Docker containers: <ul><li>`CLEARML_DOCKER_SKIP_GPUS_FLAG` - Ignore the `--gpus` flag inside the Docker container. This also lets you execute ClearML Agent using Docker versions earlier than 19.03.</li><li>`NVIDIA_VISIBLE_DEVICES` - Limit GPU visibility for the Docker container.</li><li> `CLEARML_AGENT_GIT_USER` and `CLEARML_AGENT_GIT_PASS` - Pass these credentials to the Docker container at execution.</li></ul>|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--downtime`| Specify downtime for clearml-agent in `<hours> <days>` format. For example, use `09-13 TUE` to set Tuesday's downtime to 09-13. <br/><br/>NOTE: <ul><li>This feature is available under the ClearML Enterprise plan</li><li>Make sure to have only one of uptime / downtime configuration and not both.</li></ul> |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--downtime`| Specify downtime for clearml-agent in `<hours> <days>` format. For example, use `09-13 TUE` to set Tuesday's downtime to 09-13. <br/><br/>NOTES: <ul><li>This feature is available under the ClearML Enterprise plan</li><li>Make sure to configure only `--uptime` or `--downtime`, but not both.</li></ul> |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--dynamic-gpus`| Allow to dynamically allocate GPUs based on queue properties, configure with `--queue <queue_name>=<num_gpus>`. For example: `--dynamic-gpus --queue dual_gpus=2 single_gpu=1` <br/><br/>NOTE: This feature is available under the ClearML Enterprise plan|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--force-current-version`| To use your current version of ClearML Agent when running in Docker mode (the `--docker` argument is specified), instead of the latest ClearML Agent version which is automatically installed, specify `force-current-version`. <br/><br/> For example, if your current ClearML Agent version is `0.13.1`, but the latest version of ClearML Agent is `0.13.3`, use `--force-current-version` and your Task will execute in the Docker container with ClearML Agent version `0.13.1`.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--foreground`| Pipe full log to stdout/stderr. Do not use this option if running in background.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
@ -103,7 +103,7 @@ clearml-agent daemon [-h] [--foreground] [--queue QUEUES [QUEUES ...]] [--order-
|`--standalone-mode`| Do not use any network connects. This assumes all requirements are pre-installed.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--status`| Print the worker's schedule (uptime properties, server's runtime properties and listening queues)|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--stop`| Terminate a running ClearML Agent, if other arguments are the same. If no additional arguments are provided, agents are terminated in lexicographical order.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--uptime`| Specify uptime for clearml-agent in `<hours> <days>` format. For example, use `17-20 TUE` to set Tuesday's uptime to 17-20. <br/><br/>NOTES<ul><li>This feature is available under the ClearML Enterprise plan </li><li>Make sure to have only one of uptime / downtime configuration and not both.</li></ul>|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--uptime`| Specify uptime for clearml-agent in `<hours> <days>` format. For example, use `17-20 TUE` to set Tuesday's uptime to 17-20. <br/><br/>NOTES:<ul><li>This feature is available under the ClearML Enterprise plan </li><li>Make sure to configure only `--uptime` or `--downtime`, but not both.</li></ul>|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
|`--use-owner-token`| Generate and use the task owner's token for the execution of the task.|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
## execute

2
docs/clearml_sdk/task_sdk.md
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@ -627,7 +627,7 @@ Notice that if one of the frameworks loads an existing weights file, the running
"Input Model", pointing directly to the original training task's model. This makes it easy to get the full lineage of
every trained and used model in our system!
Models loaded by the ML framework appear under the "Input Models" section, under the Artifacts tab in the ClearML UI.
Models loaded by the ML framework appear in an experiment's **Artifacts** tab under the "Input Models" section in the ClearML UI.
### Setting Upload Destination

5
docs/getting_started/ds/ds_first_steps.md
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@ -104,7 +104,7 @@ At the beginning of your code, import the `clearml` package:
from clearml import Task
```
:::note Full Automatic Logging
:::tip Full Automatic Logging
To ensure full automatic logging, it is recommended to import the `clearml` package at the top of your entry script.
:::
@ -128,12 +128,11 @@ ClearML results page: https://app.clear.ml/projects/4043a1657f374e9298649c6ba72a
**That’s it!** You are done integrating ClearML with your code :)
Now, [command-line arguments](../../fundamentals/hyperparameters.md#tracking-hyperparameters), [console output](../../fundamentals/logger.md#types-of-logged-results) as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.
<br/>
Sit back, relax, and watch your models converge :) or continue to see what else can be done with ClearML [here](ds_second_steps.md).
## YouTube Playlist
Or watch the YouTube Getting Started Playlist on our YouTube Channel!
Or watch the Getting Started Playlist on our YouTube Channel!
[![Watch the video](https://img.youtube.com/vi/bjWwZAzDxTY/hqdefault.jpg)](https://www.youtube.com/watch?v=bjWwZAzDxTY&list=PLMdIlCuMqSTnoC45ME5_JnsJX0zWqDdlO&index=2)

14
docs/getting_started/ds/ds_second_steps.md
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@ -23,7 +23,7 @@ Once you have a Task object you can query the state of the Task, get its model,
## Log Hyperparameters
For full reproducibility, it's paramount to save Hyperparameters for each experiment. Since Hyperparameters can have substantial impact
For full reproducibility, it's paramount to save hyperparameters for each experiment. Since hyperparameters can have substantial impact
on Model performance, saving and comparing these between experiments is sometimes the key to understanding model behavior.
ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
@ -43,7 +43,7 @@ Check [this](../../fundamentals/hyperparameters.md) out for all Hyperparameter l
ClearML lets you easily store the output products of an experiment - Model snapshot / weights file, a preprocessing of your data, feature representation of data and more!
Essentially, artifacts are files (or python objects) uploaded from a script and are stored alongside the Task.
These Artifacts can be easily accessed by the web UI or programmatically.
These artifacts can be easily accessed by the web UI or programmatically.
Artifacts can be stored anywhere, either on the ClearML server, or any object storage solution or shared folder.
See all [storage capabilities](../../integrations/storage.md).
@ -73,9 +73,9 @@ Check out all [artifact logging](../../clearml_sdk/task_sdk.md#artifacts) option
### Using Artifacts
Logged Artifacts can be used by other Tasks, whether it's a pre-trained Model or processed data.
To use an Artifact, first we have to get an instance of the Task that originally created it,
then we either download it and get its path, or get the Artifact object directly.
Logged artifacts can be used by other Tasks, whether it's a pre-trained Model or processed data.
To use an artifact, first we have to get an instance of the Task that originally created it,
then we either download it and get its path, or get the artifact object directly.
For example, using a previously generated preprocessed data.
@ -84,7 +84,7 @@ preprocess_task = Task.get_task(task_id='preprocessing_task_id')
local_csv = preprocess_task.artifacts['data'].get_local_copy()
```
The `task.artifacts` is a dictionary where the keys are the Artifact names, and the returned object is the Artifact object.
The `task.artifacts` is a dictionary where the keys are the artifact names, and the returned object is the artifact object.
Calling `get_local_copy()` returns a local cached copy of the artifact. Therefore, next time we execute the code, we don't
need to download the artifact again.
Calling `get()` gets a deserialized pickled object.
@ -130,7 +130,7 @@ Like before we have to get the instance of the Task training the original weight
:::note
Using TensorFlow, the snapshots are stored in a folder, meaning the `local_weights_path` will point to a folder containing your requested snapshot.
:::
As with Artifacts, all models are cached, meaning the next time we run this code, no model needs to be downloaded.
As with artifacts, all models are cached, meaning the next time we run this code, no model needs to be downloaded.
Once one of the frameworks will load the weights file, the running Task will be automatically updated with “Input Model” pointing directly to the original training Task’s Model.
This feature lets you easily get a full genealogy of every trained and used model by your system!

2
docs/hyperdatasets/single_frames.md
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@ -276,7 +276,7 @@ frame = DatasetVersion.get_single_frame(
To access a SingleFrame, the following must be specified:
* `frame_id`, which can be found in the WebApp, in the frame's **FRAMEGROUP DETAILS**
* The frame's dataset - either with `dataset_name` or `dataset_id`
* The dataset version - either with `version_id` or `version_name`
* The dataset version - either with `version_id` or `version_name`
### Updating SingleFrames

4
docs/webapp/applications/apps_dashboard.md
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@ -6,7 +6,7 @@ title: Project Dashboard
The ClearML Project Dashboard App is available under the ClearML Pro plan
:::
The Project Dashboard Application provides an overview of a project or workspace’s progress. It presents an aggregated
The Project Dashboard Application provides an overview of a project or workspace’s progress. It presents an aggregated
view of task status and a chosen metric over time, as well as project GPU and worker usage. It also supports alerts/warnings
on completed/failed Tasks via Slack integration.
@ -27,7 +27,7 @@ of the chosen metric over time.
* Monitored Metric - Title - Metric title to track
* Monitored Metric - Series - Metric series (variant) to track
* Monitored Metric - Trend - Choose whether to track the monitored metric's highest or lowest values
* Slack Notification (optional) - Set up Slack integration for notifications of task failure. Select the
* Slack Notification (optional) - Set up Slack integration for notifications of task failure. Select the
`Alert on completed experiments` under `Additional options` to set up alerts for task completions.
* API Token - Slack workspace access token
* Channel Name - Slack channel to which task failure alerts will be posted