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2
docs/clearml_agent/clearml_agent_env_var.md
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@ -22,7 +22,7 @@ but can be overridden by command-line arguments.
|**CLEARML_CUDA_VERSION** | Sets the CUDA version to be used |
|**CLEARML_CUDNN_VERSION** | Sets the CUDNN version to be used |
|**CLEARML_CPU_ONLY** | Force CPU only mode |
|**CLEARML_DOCKER_SKIP_GPUS_FLAG** | Skips the GPUs flag (support for docker V18 |
|**CLEARML_DOCKER_SKIP_GPUS_FLAG** | Skips the GPUs flag (support for docker V18) |
|**CLEARML_AGENT_DOCKER_ARGS_HIDE_ENV** | Hide Docker environment variables containing secrets when printing out the Docker command. When printed, the variable values will be replaced by `********`. See [`agent.hide_docker_command_env_vars`](../configs/clearml_conf.md#hide_docker) |
|**CLEARML_AGENT_DISABLE_SSH_MOUNT** | Disables the auto `.ssh` mount into the docker |
|**CLEARML_AGENT_FORCE_CODE_DIR**| Allows overriding the remote execution code directory to bypass repository cloning and use a repo already available where the remote agent is running. |

2
docs/clearml_data/data_management_examples/data_man_cifar_classification.md
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@ -10,7 +10,7 @@ class to ingest the data.
### Downloading the Data
Before registering the CIFAR dataset with `clearml-data`, you need to obtain a local copy of it.
Execute this python script to download the data
Execute this python script to download the data:
```python
from clearml import StorageManager

4
docs/configs/clearml_conf.md
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@ -128,7 +128,7 @@ Use with care! This might introduce security risks by allowing access to keys/se
---
**`docker_args_extra_precedes_task`** (*bool*)
**`agent.docker_args_extra_precedes_task`** (*bool*)
* Allow the arguments specified in `agent.extra_docker_arguments` to override task level docker arguments, in the case that
the same argument is passed in both. If set to `False`, a task's docker arguments will override the `extra_docker_arguments`.
@ -1039,6 +1039,8 @@ and limitations on bucket naming.
---
<a id="log_env_var"/>
**`sdk.development.log_os_environments`** (*[string]*)
* Log specific environment variables. OS environments are listed in the UI, under an experiment's

2
docs/faq.md
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@ -855,7 +855,7 @@ export CLEARML_API_HOST="http://localhost:8008"
#### How can I track OS environment variables with experiments? <a id="track-env-vars"></a>
You can set environment variables to track in an experiment by specifying them in the `sdk.development.log_os_environments`
field of the [`clearml.conf`](configs/clearml_conf.md) file:
field of the [`clearml.conf`](configs/clearml_conf.md#log_env_var) file:
```editorconfig
log_os_environments: ["AWS_*", "CUDA_VERSION"]

2
docs/fundamentals/agents_and_queues.md
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@ -1,5 +1,5 @@
---
title: Workers & Queues
title: Workers and Queues
---
Two major components of MLOps/LLMOps are experiment reproducibility, and the ability to scale work to multiple machines. ClearML workers,

2
docs/fundamentals/hyperparameters.md
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@ -61,7 +61,7 @@ You can also specify environment variables using the `CLEARML_LOG_ENVIRONMENT` e
export CLEARML_LOG_ENVIRONMENT=PWD,PYTHONPATH
```
* No environment variables
* No environment variables:
```
export CLEARML_LOG_ENVIRONMENT=
```

1
docs/guides/frameworks/megengine/megengine_mnist.md
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@ -50,5 +50,6 @@ The model info panel contains the model details, including:
## Console
All console output during the script's execution appears in the experiment's **CONSOLE** page.
![Console tab](../../../img/examples_megengine_console.png)

2
docs/guides/frameworks/pytorch/pytorch_mnist.md
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@ -9,7 +9,7 @@ The example script does the following:
* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist)
dataset.
* Creates an experiment named `pytorch mnist train` in the `examples` project.
* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch
* ClearML automatically logs `argparse` command line options, and models (and their snapshots) created by PyTorch.
* Additional metrics are logged by calling [`Logger.report_scalar()`](../../../references/sdk/logger.md#report_scalar).
## Scalars

4
docs/guides/frameworks/tensorflow/tensorboard_pr_curve.md
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@ -11,8 +11,8 @@ The example script does the following:
label of each random color is associated with the normal distribution that generated it.
* Computes the probability that each color belongs to the class, using three other normal distributions.
* Generate PR curves using those probabilities.
* Creates a summary per class using [tensorboard.plugins.pr_curve.summary](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py),
* ClearML automatically captures TensorBoard output, TensorFlow Definitions, and output to the console
* Creates a summary per class using [tensorboard.plugins.pr_curve.summary](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/summary.py).
* ClearML automatically captures TensorBoard output, TensorFlow Definitions, and output to the console.
## Plots

2
docs/guides/ide/remote_jupyter_tutorial.md
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@ -85,7 +85,7 @@ Jupyter Lab URL: http://localhost:8878/?token=ff7e5e8b9e5493a01b1a72530d18181320
VSCode server available at http://localhost:8898/
```
Click on the JupyterLab link, which will open the remote session
Click on the JupyterLab link, which will open the remote session.
Now, let's execute some code in the remote session!

16
docs/guides/reporting/media_reporting.md
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@ -3,15 +3,16 @@ title: Media Reporting
---
The [media_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/media_reporting.py) example
demonstrates reporting (uploading) images, audio, and video. Use the [Logger.report_media](../../references/sdk/logger.md#report_media)
method to upload from:
demonstrates reporting (uploading) images, audio, and video. Use [`Logger.report_media()`](../../references/sdk/logger.md#report_media)
to upload from:
* Local path
* BytesIO stream
* URL of media already uploaded to some storage
ClearML uploads media to the bucket specified in the ClearML configuration file or ClearML can be configured for image storage, see [Logger.set_default_upload_destination](../../references/sdk/logger.md#set_default_upload_destination)
(storage for [artifacts](../../clearml_sdk/task_sdk.md#setting-upload-destination) is different). Set credentials for storage in the ClearML
[configuration file](../../configs/clearml_conf.md).
ClearML uploads media to the bucket specified in the ClearML configuration file. You can configure ClearML for image
storage using [`Logger.set_default_upload_destination()`](../../references/sdk/logger.md#set_default_upload_destination)
(note that [artifact storage](../../clearml_sdk/task_sdk.md#setting-upload-destination) is handled differently).
Set the storage credentials in the [clearml.conf file](../../configs/clearml_conf.md#sdk-section).
ClearML reports media in the **ClearML Web UI** **>** experiment details **>** **DEBUG SAMPLES**
tab.
@ -21,8 +22,7 @@ project.
## Reporting (Uploading) Media from a Source by URL
Report by calling the [Logger.report_media](../../references/sdk/logger.md#report_media)
method using the `url` parameter.
Report by using the `url` parameter of [`Logger.report_media()`](../../references/sdk/logger.md#report_media):
```python
# report video, an already uploaded video media (url)
@ -45,7 +45,7 @@ The reported audio can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail
## Reporting (Uploading) Media from a Local File
Use the `local_path` parameter.
Report by using the `local_path` parameter of [`Logger.report_media()`](../../references/sdk/logger.md#report_media):
```python
# report audio, report local media audio file

11
docs/guides/reporting/text_reporting.md
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@ -3,14 +3,15 @@ title: Text Reporting
---
The [text_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/text_reporting.py) script
demonstrates reporting explicit text, by calling the [Logger.report_text](../../references/sdk/logger.md#report_text)
method.
demonstrates reporting explicit text by calling [`Logger.report_text()`](../../references/sdk/logger.md#report_text).
ClearML reports these tables in the **ClearML Web UI**, experiment details, **CONSOLE** tab.
ClearML reports the text in the **ClearML Web UI**, in the experiment's **CONSOLE** tab.
When the script runs, it creates an experiment named `text reporting` in the `examples` project.
# report text
Logger.current_logger().report_text("hello, this is plain text")
```python
# report text
Logger.current_logger().report_text("hello, this is plain text")
```
![image](../../img/examples_reporting_text.png)

16
docs/guides/ui/tuning_exp.md
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@ -16,7 +16,9 @@ example script.
In the `examples/frameworks/pytorch` directory, run the experiment script:
python pytorch_mnist.py
```commandline
python pytorch_mnist.py
```
## Step 2: Clone the Experiment
@ -42,15 +44,12 @@ To demonstrate tuning, change two hyperparameter values.
## Step 4: Run a Worker Daemon Listening to a Queue
To execute the cloned experiment, use a worker that can run a worker daemon listening to a queue.
To execute the cloned experiment, use a [ClearML Agent](../../fundamentals/agents_and_queues.md).
:::note
For more information about workers, worker daemons, and queues, see [Agents and queues](../../fundamentals/agents_and_queues.md).
:::
Run the worker daemon on the local development machine.
Run the agent on the local development machine:
1. Open a terminal session.
1. Run the following `clearml-agent` command which runs a worker daemon listening to the `default` queue:
```
clearml-agent daemon --queue default
```
@ -119,5 +118,4 @@ To compare the original and tuned experiments:
## Next Steps
* For more information about editing experiments, see [modify experiments](../../webapp/webapp_exp_tuning.md#modifying-experiments)
in the User Interface section.
* For more information about editing experiments, see [modifying experiments](../../webapp/webapp_exp_tuning.md#modifying-experiments).

2
docs/webapp/webapp_exp_track_visual.md
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@ -132,7 +132,7 @@ parameter's line, and the type, description, and default value appear, if they w
#### Environment Variables
If environment variables were listed in the `CLEARML_LOG_ENVIRONMENT` environment variable or the `sdk.development.log_os_environments`
If environment variables were listed in the `CLEARML_LOG_ENVIRONMENT` environment variable or the [`sdk.development.log_os_environments`](../configs/clearml_conf.md#log_env_var)
field of the `clearml.conf` file, the **Environment** group displays the listed environment variables (see [this FAQ](../faq.md#track-env-vars)).
:::note

4
docs/webapp/webapp_exp_tuning.md
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@ -127,7 +127,7 @@ Add, change, or delete hyperparameters, which are organized in the **ClearML Web
* **General** - Parameter dictionaries (from code, connected to the Task by calling [`Task.connect()`](../references/sdk/task.md#connect)).
* Environment variables - Tracked if variables were listed in the `CLEARML_LOG_ENVIRONMENT` environment variable
or the `sdk.development.log_os_environments` field of the `clearml.conf` file (see this [FAQ](../faq.md#track-env-vars)).
or the [`sdk.development.log_os_environments`](../configs/clearml_conf.md#log_env_var) field of the `clearml.conf` file (see this [FAQ](../faq.md#track-env-vars)).
* Custom named parameter groups (see the `name` parameter in [`Task.connect`](../references/sdk/task.md#connect)).
@ -178,7 +178,7 @@ model in the **MODELS** tab.
**To edit a model's configuration or label enumeration:**
1. Click the model name hyperlink. The model details appear in the **MODELS** tab.
1. Edit the model configuration or label enumeration.
1. Edit the model configuration or label enumeration:
* Model configuration - In the **NETWORK** tab **>** Hover and click **EDIT**. **>** CLick **EDIT** or **CLEAR** (to
remove the configuration).