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2
docs/clearml_agent.md
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@ -61,7 +61,7 @@ Install ClearML Agent as a system Python package and not in a Python virtual env
clearml-agent init
```
The setup wizard prompts for **ClearML** credentials (see [here](webapp/webapp_profile.md#creating-clearml-credentials) about obtaining credentials).
The setup wizard prompts for **ClearML** credentials (see [here](webapp/webapp_profile.md#clearml-credentials) about obtaining credentials).
CLEARML-AGENT setup process

12
docs/deploying_clearml/clearml_config_for_clearml_server.md
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@ -6,13 +6,13 @@ title: Configuring ClearML for Your ClearML Server
This documentation page applies to deploying your own open source ClearML Server. It does not apply to ClearML Hosted Service users.
:::
The **ClearML** configuration file that will be initialized will contain the host URLs of the **ClearML Server**, and
**ClearML** credentials, allowing the code to integrate with the server. Later, **ClearML** can be tailored to fit requirements
The ClearML **configuration file** that will be initialized will contain the host URLs of the **ClearML Server**, and
ClearML credentials, allowing the code to integrate with the server. Later, ClearML can be tailored to fit requirements
by setting [configuration options](../configs/clearml_conf.md).
**To configure ClearML for your ClearML Server:**
1. If not installed already, install `clearml` (see [install](../getting_started/ds/ds_first_steps.md))
1. In a terminal session, run the **ClearML** setup wizard.
1. In a terminal session, run the ClearML setup wizard.
```
clearml-init
```
@ -21,7 +21,7 @@ by setting [configuration options](../configs/clearml_conf.md).
<summary className="cml-expansion-panel-summary">Learn about creating multiple ClearML configuration files</summary>
<div className="cml-expansion-panel-content">
Additional **ClearML** configuration files can be created, for example, to use inside Docker containers when executing
Additional ClearML configuration files can be created, for example, to use inside Docker containers when executing
a Task.
Use the `--file` option for `clearml-init`.
@ -42,7 +42,7 @@ by setting [configuration options](../configs/clearml_conf.md).
If the setup wizard's response indicates that a configuration file already exists, follow the instructions in
[here](#add-clearml-to-a-configuration-file). The wizard does not edit or overwrite existing configuration files.
1. The setup wizard prompts for **ClearML** credentials.
1. The setup wizard prompts for ClearML credentials.
ClearML SDK setup process
@ -53,7 +53,7 @@ by setting [configuration options](../configs/clearml_conf.md).
Paste copied configuration here:
1. Get **ClearML** credentials. Open the **ClearML Web UI** in a browser. On the **PROFILE** page, click
1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the **SETTINGS > WORKSPACE** page, click
**Create new credentials** **>** **Copy to clipboard**.
1. At the command prompt `Paste copied configuration here:`, copy and paste the **ClearML** credentials.

2
docs/deploying_clearml/clearml_server_config.md
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@ -9,7 +9,7 @@ This documentation page applies to deploying your own open source ClearML Server
This page describes the **ClearML Server** [deployment](#clearml-server-deployment-configuration) and [feature](#clearml-server-feature-configurations) configurations. Namely, it contains instructions on how to configure **ClearML Server** for:
* [Sub-domains and load balancers](#sub-domains-and-load-balancers) - An AWS load balancing example
* [Opening Elasticsearch, MongoDB, and Redis for External Access](#opening-elasticsearch-mongodb-and-redis-for-external-access).
* [Opening Elasticsearch, MongoDB, and Redis for External Access](#opening-elasticsearch-mongodb-and-redis-for-external-access)
* [Web login authentication](#web-login-authentication) - Create and manage users and passwords
* [Using hashed passwords](#using-hashed-passwords) - Option to use hashed passwords instead of plain-text passwords
* [Non-responsive Task watchdog](#non-responsive-task-watchdog) - For inactive experiments

8
docs/faq.md
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@ -128,7 +128,7 @@ When a new ClearML Server version is available, the notification is:
**How do I find out ClearML version information?** <a id="versions"></a>
ClearML server version information is available in the ClearML webapp profile page. On the bottom right of the page,
ClearML server version information is available in the ClearML webapp Settings page. On the bottom right of the page,
it says **Version**, followed by three numbers: the web application version, the API server version, and the API version.
![Server version information](img/faq_server_versions.png)
@ -162,7 +162,7 @@ that metric column.
**Can I store more information on the models?** <a id="store-more-model-info"></a>
Yes! For example, you can use the [Task.set_model_label_enumeration](references/sdk/task.md#set_model_label_enumerationenumerationnone)
Yes! For example, you can use the [Task.set_model_label_enumeration](references/sdk/task.md#set_model_label_enumeration)
method to store label enumeration:
```python
@ -176,7 +176,7 @@ For more information about `Task` class methods, see the [Task Class](fundamenta
**Can I store the model configuration file as well?** <a id="store-model-configuration"></a>
Yes! Use the [Task.set_model_config](references/sdk/task.md#set_model_configconfig_textnone-config_dictnone)
Yes! Use the [Task.set_model_config](references/sdk/task.md#set_model_config)
method:
```python
@ -708,7 +708,7 @@ Yes! You can run ClearML in Jupyter Notebooks using either of the following:
**Option 2: Install ClearML in your Jupyter Notebook**
1. In the ClearML Web UI, Profile page, create credentials and copy your access key and secret key. These are required in the Step 3.
1. In the ClearML Web UI > **Settings > Workspace** page, create credentials and copy your access key and secret key. These are required in the Step 3.
1. Install the ClearML Python Package.

7
docs/guides/frameworks/pytorch/pytorch_mnist.md
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@ -33,13 +33,14 @@ Logger.current_logger().report_scalar(
)
```
These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
page **>** **RESULTS** **>** **SCALARS**.
These scalars can be visualized in plots, which appear in the ClearML [web UI](../../../webapp/webapp_overview.md),
in the experiment's page **>** **RESULTS** **>** **SCALARS**.
![image](../../../img/examples_pytorch_mnist_07.png)
## Hyperparameters
**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
![image](../../../img/examples_pytorch_mnist_01.png)

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docs/guides/frameworks/pytorch/pytorch_tensorboard.md
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@ -15,7 +15,8 @@ The example does the following:
## Scalars
In the example script, the `train` and `test` functions call the TensorBoard `SummaryWriter.add_scalar` method to log loss.
These scalars, along with the resource utilization plots, which are titled **:monitor: machine**, appear in the experiment's page in the **ClearML web UI** under **RESULTS** **>** **SCALARS**,
These scalars, along with the resource utilization plots, which are titled **:monitor: machine**, appear in the experiment's
page in the [ClearML web UI](../../../webapp/webapp_overview.md) under **RESULTS** **>** **SCALARS**.
![image](../../../img/examples_pytorch_tensorboard_07.png)

4
docs/guides/frameworks/tensorboardx/tensorboardx.md
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@ -6,10 +6,10 @@ The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/e
example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX.
The 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.
* 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 with tensorboardX` which is associated with the `examples` project
* ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and
the model created by PyTorch.
the model created by PyTorch
## Scalars

2
docs/hyperdatasets/webapp/webapp_dataviews.md
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@ -1,5 +1,5 @@
---
title: Dataviews Table
title: The Dataviews Table
---
[Dataviews](../dataviews.md) appear in the same Project as the experiment that stored the Dataview in the ClearML Enterprise platform,

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docs/webapp/webapp_exp_comparing.md
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@ -9,7 +9,7 @@ The **ClearML Web UI** provides a deep experiment comparison, allowing to locate
- [Artifacts](#artifacts) - Input model, output model, and model design.
- [Execution](#execution-details) - Installed packages and source code.
- [Configuration](#configuration) - Configuration objects used by the experiment.
* [Hyper Parameters](#hyperparameters)
* [Hyperparameters](#hyperparameters)
- [Values (table) view](#values-mode) - Key/value of all the arguments used by the experiments.
- [Parallel coordinates view](#parallel-coordinates-mode) - Impact of each argument on a selected metric
the experiments reported (see [task.connect_configuration](../references/sdk/task.md#connect_configuration)).