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7
docs/apps/clearml_task.md
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@ -23,9 +23,8 @@ Specify a docker container to run the code in by with the `--docker <docker_imag
The ClearML Agent will pull it from dockerhub or a docker artifactory automatically. The ClearML Agent will pull it from dockerhub or a docker artifactory automatically.
### Package Dependencies ### Package Dependencies
If the local script requires packages to be installed installed or the remote repository doesn't have a requirements.txt file, If the local script requires packages to be installed, or the remote repository doesn't have a requirements.txt file,
specify manually the required python packages using <br/> specify manually the required python packages using `--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`.
`--packages "<package_name>"`, for example `--packages "keras" "tensorflow>2.2"`.
### Queue ### Queue
Tasks are passed to ClearML Agents via [Queues](../fundamentals/agents_and_queues.md). Specify a queue to enqueue the task to. Tasks are passed to ClearML Agents via [Queues](../fundamentals/agents_and_queues.md). Specify a queue to enqueue the task to.
@ -33,7 +32,7 @@ If a queue isn't chosen in the `clearml-task` command, the task will not be exec
and can be enqueued at a later point. and can be enqueued at a later point.
### Branch and Working Directory ### Branch and Working Directory
A specific branch and commit ID, other than latest commit in master, to be executed can be specified by passing To specify a specific branch and commit ID to be executed, pass
`--branch <branch_name> --commit <commit_id>` flags. `--branch <branch_name> --commit <commit_id>` flags.
If unspecified, `clearml-task` will use the latest commit from the master branch. If unspecified, `clearml-task` will use the latest commit from the master branch.

2
docs/guides/frameworks/autokeras/autokeras_imdb_example.md
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@ -1,5 +1,5 @@
--- ---
title: AutoKeras Imdb title: AutoKeras IMDB
--- ---
The [autokeras_imdb_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/autokeras/autokeras_imdb_example.py) example The [autokeras_imdb_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/autokeras/autokeras_imdb_example.py) example
script demonstrates the integration of **ClearML** into code, which uses [autokeras](https://github.com/keras-team/autokeras). script demonstrates the integration of **ClearML** into code, which uses [autokeras](https://github.com/keras-team/autokeras).

2
docs/guides/ide/google_colab.md
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@ -5,7 +5,7 @@ title: ClearML Agent on Google Colab
[Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as [Google Colab](https://colab.research.google.com) is a common development environment for data scientists. It offers a convenient IDE as well as
compute provided by google. compute provided by google.
Users can transform a Google Colab instance into an available resource in ClearML using [Clearml Agent](../../clearml_agent.md). Users can transform a Google Colab instance into an available resource in ClearML using [ClearML Agent](../../clearml_agent.md).
In this tutorial, we will go over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up In this tutorial, we will go over how to create a ClearML worker node in a Google Colab notebook. Once the worker is up
and running, users can send Tasks to be executed on the Google Colab's HW. and running, users can send Tasks to be executed on the Google Colab's HW.

58
docs/guides/reporting/clearml_logging_example.md
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@ -1,9 +1,9 @@
--- ---
title: Explicit Reporting - Jupyter Notebook title: Using Logger - Jupyter Notebook
--- ---
The [jupyter_logging_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/reporting/jupyter_logging_example.ipynb) The [jupyter_logging_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/reporting/jupyter_logging_example.ipynb)
script demonstrates the integration of **ClearML** explicit reporting running in a Jupyter Notebook. All **ClearML** script demonstrates the integration of ClearML's explicit reporting module, `Logger`, in a Jupyter Notebook. All ClearML
explicit reporting works with Jupyter Notebook. explicit reporting works with Jupyter Notebook.
This example includes several types of explicit reporting, including: This example includes several types of explicit reporting, including:
@ -20,19 +20,23 @@ In the ``clearml`` GitHub repository, this example includes a clickable icon to
To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar) To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar)
method. The scalar plots appear in the **web UI** in **RESULTS** **>** **SCALARS**. method. The scalar plots appear in the **web UI** in **RESULTS** **>** **SCALARS**.
```python
# report two scalar series on two different graphs # report two scalar series on two different graphs
for i in range(10): for i in range(10):
logger.report_scalar("graph A", "series A", iteration=i, value=1./(i+1)) logger.report_scalar("graph A", "series A", iteration=i, value=1./(i+1))
logger.report_scalar("graph B", "series B", iteration=i, value=10./(i+1)) logger.report_scalar("graph B", "series B", iteration=i, value=10./(i+1))
```
![image](../../img/colab_explicit_reporting_01.png) ![Separate scalar plots](../../img/colab_explicit_reporting_01.png)
```python
# report two scalar series on the same graph # report two scalar series on the same graph
for i in range(10): for i in range(10):
logger.report_scalar("unified graph", "series A", iteration=i, value=1./(i+1)) logger.report_scalar("unified graph", "series A", iteration=i, value=1./(i+1))
logger.report_scalar("unified graph", "series B", iteration=i, value=10./(i+1)) logger.report_scalar("unified graph", "series B", iteration=i, value=10./(i+1))
```
![image](../../img/colab_explicit_reporting_02.png) ![Unified scalar plots](../../img/colab_explicit_reporting_02.png)
## Plots ## Plots
@ -43,6 +47,7 @@ Plots appear in **RESULTS** **>** **PLOTS**.
Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) method. Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) method.
Use the `mode` parameter to plot data points as markers, or both lines and markers. Use the `mode` parameter to plot data points as markers, or both lines and markers.
```python
scatter2d = np.hstack( scatter2d = np.hstack(
(np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))) (np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))
) )
@ -56,13 +61,15 @@ Use the `mode` parameter to plot data points as markers, or both lines and marke
yaxis="title y", yaxis="title y",
mode='lines+markers' mode='lines+markers'
) )
```
![image](../../img/colab_explicit_reporting_04.png) ![2d scatter plot](../../img/colab_explicit_reporting_04.png)
### 3D Plots ### 3D Plots
To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method. To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
```python
# report 3d scatter plot # report 3d scatter plot
scatter3d = np.random.randint(10, size=(10, 3)) scatter3d = np.random.randint(10, size=(10, 3))
logger.report_scatter3d( logger.report_scatter3d(
@ -74,12 +81,14 @@ To plot a series as a 3-dimensional scatter plot, use the [Logger.report_scatter
yaxis="title y", yaxis="title y",
zaxis="title z", zaxis="title z",
) )
```
![image](../../img/colab_explicit_reporting_05.png) ![3d scatter plot](../../img/colab_explicit_reporting_05.png)
To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface) To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface)
method. method.
```python
# report 3d surface # report 3d surface
surface = np.random.randint(10, size=(10, 10)) surface = np.random.randint(10, size=(10, 10))
logger.report_surface( logger.report_surface(
@ -91,14 +100,16 @@ method.
yaxis="title Y", yaxis="title Y",
zaxis="title Z", zaxis="title Z",
) )
```
![image](../../img/colab_explicit_reporting_06.png) ![3d surface plot](../../img/colab_explicit_reporting_06.png)
### Confusion Matrices ### Confusion Matrices
Report confusion matrices by calling the [Logger.report_matrix](../../references/sdk/logger.md#report_matrix) Report confusion matrices by calling the [Logger.report_matrix](../../references/sdk/logger.md#report_matrix)
method. method.
```python
# report confusion matrix # report confusion matrix
confusion = np.random.randint(10, size=(10, 10)) confusion = np.random.randint(10, size=(10, 10))
logger.report_matrix( logger.report_matrix(
@ -109,14 +120,16 @@ method.
xaxis="title X", xaxis="title X",
yaxis="title Y", yaxis="title Y",
) )
```
![image](../../img/colab_explicit_reporting_03.png) ![Confusion matrix](../../img/colab_explicit_reporting_03.png)
### Histograms ### Histograms
Report histograms by calling the [Logger.report_histogram](../../references/sdk/logger.md#report_histogram) Report histograms by calling the [Logger.report_histogram](../../references/sdk/logger.md#report_histogram)
method. To report more than one series on the same plot, use the same `title` argument. method. To report more than one series on the same plot, use the same `title` argument.
```python
# report a single histogram # report a single histogram
histogram = np.random.randint(10, size=10) histogram = np.random.randint(10, size=10)
logger.report_histogram( logger.report_histogram(
@ -127,9 +140,11 @@ method. To report more than one series on the same plot, use the same `title` ar
xaxis="title x", xaxis="title x",
yaxis="title y", yaxis="title y",
) )
```
![image](../../img/colab_explicit_reporting_12.png) ![Histogram](../../img/colab_explicit_reporting_12.png)
```python
# report a two histograms on the same plot # report a two histograms on the same plot
histogram1 = np.random.randint(13, size=10) histogram1 = np.random.randint(13, size=10)
histogram2 = histogram * 0.75 histogram2 = histogram * 0.75
@ -149,8 +164,9 @@ method. To report more than one series on the same plot, use the same `title` ar
xaxis="title x", xaxis="title x",
yaxis="title y", yaxis="title y",
) )
```
![image](../../img/colab_explicit_reporting_07.png) ![Two histograms in one plot](../../img/colab_explicit_reporting_07.png)
## Media ## Media
@ -162,39 +178,51 @@ method.
For example, to download an image: For example, to download an image:
```python
image_local_copy = StorageManager.get_local_copy( image_local_copy = StorageManager.get_local_copy(
remote_url="https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg", remote_url="https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg",
name="picasso.jpg" name="picasso.jpg"
) )
```
### Audio ### Audio
```python
logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local_copy) logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local_copy)
```
![image](../../img/colab_explicit_reporting_08.png) ![Audio sample](../../img/colab_explicit_reporting_08.png)
### HTML ### HTML
```python
logger.report_media("html", "url_html", iteration=1, url="https://allegro.ai/docs/index.html") logger.report_media("html", "url_html", iteration=1, url="https://allegro.ai/docs/index.html")
```
![image](../../img/colab_explicit_reporting_09.png) ![HTML sample](../../img/colab_explicit_reporting_09.png)
### Images ### Images
```python
logger.report_image("image", "image from url", iteration=100, local_path=image_local_copy) logger.report_image("image", "image from url", iteration=100, local_path=image_local_copy)
```
![image](../../img/colab_explicit_reporting_10.png) ![Image sample](../../img/colab_explicit_reporting_10.png)
### Video ### Video
```python
logger.report_media('video', 'big bunny', iteration=1, local_path=video_local_copy) logger.report_media('video', 'big bunny', iteration=1, local_path=video_local_copy)
```
![image](../../img/colab_explicit_reporting_11.png) ![Video sample](../../img/colab_explicit_reporting_11.png)
## Text ## Text
Report text messages by calling the [Logger.report_text](../../references/sdk/logger.md#report_text). Report text messages by calling the [Logger.report_text](../../references/sdk/logger.md#report_text).
```python
logger.report_text("hello, this is plain text") logger.report_text("hello, this is plain text")
```
![image](../../img/colab_explicit_reporting_13.png) ![Text report to console](../../img/colab_explicit_reporting_13.png)

23
docs/guides/reporting/explicit_reporting.md
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@ -1,15 +1,15 @@
--- ---
title: Explicit Reporting title: Explicit Reporting Tutorial
--- ---
In this tutorial, learn how to extend **ClearML** automagical capturing of inputs and outputs with explicit reporting. In this tutorial, learn how to extend ClearML automagical capturing of inputs and outputs with explicit reporting.
In this example, we will add the following to the [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py) In this example, we will add the following to the [pytorch_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
example script from ClearML's GitHub repo: example script from ClearML's GitHub repo:
* Setting an output destination for model checkpoints (snapshots). * Setting an output destination for model checkpoints (snapshots).
* Explicitly logging a scalar, other (non-scalar) data, and logging text. * Explicitly logging a scalar, other (non-scalar) data, and logging text.
* Registering an artifact, which is uploaded to **ClearML Server**, and **ClearML** logs changes to it. * Registering an artifact, which is uploaded to **ClearML Server**, and ClearML logs changes to it.
* Uploading an artifact, which is uploaded, but changes to it are not logged. * Uploading an artifact, which is uploaded, but changes to it are not logged.
## Prerequisites ## Prerequisites
@ -19,9 +19,8 @@ example script from ClearML's GitHub repo:
## Before Starting ## Before Starting
Make a copy of `pytorch_mnist.py` in order to add explicit reporting to it. Make a copy of [`pytorch_mnist.py`](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py)
in order to add explicit reporting to it.
* In the local **ClearML** repository, `example` directory.
```bash ```bash
cp pytorch_mnist.py pytorch_mnist_tutorial.py cp pytorch_mnist.py pytorch_mnist_tutorial.py
@ -59,7 +58,7 @@ task = Task.init(project_name='examples',
output_uri=model_snapshots_path) output_uri=model_snapshots_path)
``` ```
When the script runs, **ClearML** creates the following directory structure: When the script runs, ClearML creates the following directory structure:
+ - <output destination name> + - <output destination name>
| +-- <project name> | +-- <project name>
@ -79,7 +78,7 @@ For example, if the Task ID is `9ed78536b91a44fbb3cc7a006128c1b0`, then the dire
## Step 2: Logger Class Reporting Methods ## Step 2: Logger Class Reporting Methods
In addition to **ClearML** automagical logging, the **ClearML** Python In addition to ClearML automagical logging, the `clearml` Python
package contains methods for explicit reporting of plots, log text, media, and tables. These methods include: package contains methods for explicit reporting of plots, log text, media, and tables. These methods include:
* [Logger.report_histogram](../../references/sdk/logger.md#report_histogram) * [Logger.report_histogram](../../references/sdk/logger.md#report_histogram)
@ -99,6 +98,7 @@ package contains methods for explicit reporting of plots, log text, media, and t
First, create a logger for the Task using the [Task.get_logger](../../references/sdk/task.md#get_logger) First, create a logger for the Task using the [Task.get_logger](../../references/sdk/task.md#get_logger)
method. method.
```python ```python
logger = task.get_logger logger = task.get_logger
``` ```
@ -187,7 +187,7 @@ def test(args, model, device, test_loader):
### Log Text ### Log Text
Extend **ClearML** by explicitly logging text, including errors, warnings, and debugging statements. We use the [Logger.report_text](../../references/sdk/logger.md#report_text) Extend ClearML by explicitly logging text, including errors, warnings, and debugging statements. We use the [Logger.report_text](../../references/sdk/logger.md#report_text)
method and its argument `level` to report a debugging message. method and its argument `level` to report a debugging message.
```python ```python
@ -203,7 +203,7 @@ logger.report_text(
## Step 3: Registering Artifacts ## Step 3: Registering Artifacts
Registering an artifact uploads it to **ClearML Server**, and if it changes, the change is logged in **ClearML Server**. Registering an artifact uploads it to **ClearML Server**, and if it changes, the change is logged in **ClearML Server**.
Currently, **ClearML** supports Pandas DataFrames as registered artifacts. Currently, ClearML supports Pandas DataFrames as registered artifacts.
### Register the Artifact ### Register the Artifact
@ -245,7 +245,6 @@ sample = Task.current_task().get_registered_artifacts()['Test_Loss_Correct'].sam
replace=True, replace=True,
random_state=1 random_state=1
) )
``` ```
## Step 4: Uploading Artifacts ## Step 4: Uploading Artifacts
@ -280,7 +279,9 @@ task.upload_artifact(
After extending the Python experiment script, run it and view the results in the **ClearML Web UI**. After extending the Python experiment script, run it and view the results in the **ClearML Web UI**.
```bash
python pytorch_mnist_tutorial.py python pytorch_mnist_tutorial.py
```
**To view the experiment results, do the following:** **To view the experiment results, do the following:**

2
docs/references/clearml_agent_ref.md
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@ -446,7 +446,7 @@ clearml-agent execute [-h] --id TASK_ID [--log-file LOG_FILE] [--disable-monitor
**`gpus`** **`gpus`**
* Specify active GPUs for the daemon to use (docker / virtual environment), Equivalent to setting * Specify active GPUs for the daemon to use (docker / virtual environment), Equivalent to setting
NVIDIA_VISIBLE_DEVICES Examples: `--gpus 0` or `--gpu 0,1,2` or `--gpus all` `NVIDIA_VISIBLE_DEVICES`. Examples: `--gpus 0` or `--gpu 0,1,2` or `--gpus all`
--- ---

6
docs/webapp/webapp_exp_tuning.md
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@ -130,11 +130,11 @@ Add, change, or delete hyperparameters, which are organized in the **ClearML Web
* **TF_DEFINE** - TensorFlow definitions (from code, TF_DEFINEs automatic logging). * **TF_DEFINE** - TensorFlow definitions (from code, TF_DEFINEs automatic logging).
* **General** - Parameter dictionaries (from code, connected to the Task by calling the [Task.connect](../references/sdk/task.md#connect) * **General** - Parameter dictionaries (from code, connected to the Task by calling the [Task.connect](../references/sdk/task.md#connect)
method. method).
* Environment variables - Tracked if the `CLEARML_LOG_ENVIRONMENT` environment variable was set (see this [FAQ](../faq#track-env-vars)). * Environment variables - Tracked if the `CLEARML_LOG_ENVIRONMENT` environment variable was set (see this [FAQ](../faq#track-env-vars)).
* Custom named parameter groups - see the `name` parameter in [Task.connect](../references/sdk/task.md#connectmutable-namenone). * Custom named parameter groups (see the `name` parameter in [Task.connect](../references/sdk/task.md#connectmutable-namenone)).
**To add, change, or delete hyperparameters:** **To add, change, or delete hyperparameters:**
@ -191,7 +191,7 @@ model 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 * Model configuration - In the **NETWORK** tab **>** Hover and click **EDIT**. **>** CLick **EDIT** or **CLEAR** (to
remove the configuration remove the configuration).
Users can also search for the configuration (hover over the configuration textbox, the search box appears) and copy the Users can also search for the configuration (hover over the configuration textbox, the search box appears) and copy the
configuration to the clipboard (hover and click <img src="/docs/latest/icons/ico-clipboard.svg" alt="Copy Clipboard" className="icon size-md" />). configuration to the clipboard (hover and click <img src="/docs/latest/icons/ico-clipboard.svg" alt="Copy Clipboard" className="icon size-md" />).

2
docs/webapp/webapp_model_table.md
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@ -58,7 +58,7 @@ allow each feature. Model states are *Draft* (editable) and *Published* (read-on
| ClearML Action | Description | States Valid for the Action | | ClearML Action | Description | States Valid for the Action |
|---|---|--| |---|---|--|
| View details | Model details include general information, the model configuration, and label enumeration. Click a model and the info panel slides open. | Any state | | View details | Model details include general information, the model configuration, and label enumeration. Click a model, and the info panel slides open. | Any state |
| Publish | Publish a model to prevent changes to it. *Published* models are read-only. If a model is Published, its experiment also becomes Published (read-only). | *Draft* | | Publish | Publish a model to prevent changes to it. *Published* models are read-only. If a model is Published, its experiment also becomes Published (read-only). | *Draft* |
| Archive | To more easily work with active models, move a model to the archive. See [Archiving](webapp_archiving). | Any state | | Archive | To more easily work with active models, move a model to the archive. See [Archiving](webapp_archiving). | Any state |
| Tags | Tag models with color-coded labels to assist in organizing work. See [tagging models](#tagging-models). | Any state | | Tags | Tag models with color-coded labels to assist in organizing work. See [tagging models](#tagging-models). | Any state |

12
docs/webapp/webapp_overview.md
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@ -2,19 +2,19 @@
title: Overview title: Overview
--- ---
The **ClearML Web UI** is the graphical user interface for the **ClearML** platform, which includes: The **ClearML Web UI** is the graphical user interface for the ClearML platform, which includes:
* Experiment management * Experiment management
* Browsing * Browsing
* Resource utilization monitoring * Resource utilization monitoring
* Profile management * Profile management
* Direct access to the **ClearML** community (Slack Channel, Youtube, and GitHub). * Direct access to the ClearML community (Slack Channel, Youtube, and GitHub).
![image](../img/webapp_screenshots.gif) ![image](../img/webapp_screenshots.gif)
The **ClearML Web UI** is composed of the following pages: The **ClearML Web UI** is composed of the following pages:
* The [Home](webapp_home.md) Page - The dashboard for recent activity, and quick access to experiments and projects. * The [Home](webapp_home.md) Page - The dashboard for recent activity, and quick access to experiments and projects.
* The [Projects Page](webapp_projects_page.md) - The main experimentation page. It is a main projects page where specific projects can be opened. * The [Projects Page](webapp_projects_page.md) - The main experimentation page, where specific projects can be opened.
Each project page contains customizable [experiments](webapp_exp_table.md) and [models](webapp_model_table.md) tables Each project page contains customizable [experiments](webapp_exp_table.md) and [models](webapp_model_table.md) tables
with the following options: with the following options:
@ -27,12 +27,12 @@ The **ClearML Web UI** is composed of the following pages:
* [View](webapp_model_viewing.md) and [modify](webapp_model_modifying.md) models * [View](webapp_model_viewing.md) and [modify](webapp_model_modifying.md) models
* The [Workers and Queues](webapp_workers_queues.md) Page - The resource monitoring and queues management page. * The [Workers and Queues](webapp_workers_queues.md) Page - The resource monitoring and queues management page.
* The [Profile Page](webapp_profile.md) - Manage a **ClearML** user account: * The [Profile Page](webapp_profile.md) - Manage a ClearML user account:
* Create **ClearML** credentials * Create ClearML credentials
* Provide Cloud Storage Access credentials for the **ClearML Web UI** * Provide Cloud Storage Access credentials for the **ClearML Web UI**
* If using the **ClearML Hosted Service**, invite users and switch workspaces * If using the **ClearML Hosted Service**, invite users and switch workspaces
In addition, from the **ClearML Web UI**, use these buttons to access the **ClearML** community: In addition, from the **ClearML Web UI**, use these buttons to access the ClearML community:
* The **ClearML** <img src="/docs/latest/icons/ico-slack-c.svg" alt="Slack Channel" className="icon size-md" /> Slack channel. Ask questions about **ClearML**. * The **ClearML** <img src="/docs/latest/icons/ico-slack-c.svg" alt="Slack Channel" className="icon size-md" /> Slack channel. Ask questions about **ClearML**.
* The **ClearML** <img src="/docs/latest/icons/ico-youtube.svg" alt="YouTube" className="icon size-md" /> YouTube Channel. View our tutorials, presentations, and discussions. * The **ClearML** <img src="/docs/latest/icons/ico-youtube.svg" alt="YouTube" className="icon size-md" /> YouTube Channel. View our tutorials, presentations, and discussions.

2
sidebars.js
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@ -61,7 +61,7 @@ module.exports = {
'guides/guidemain', 'guides/guidemain',
{'Advanced': ['guides/advanced/execute_remotely', 'guides/advanced/multiple_tasks_single_process']}, {'Advanced': ['guides/advanced/execute_remotely', 'guides/advanced/multiple_tasks_single_process']},
{'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']}, {'Automation': ['guides/automation/manual_random_param_search_example', 'guides/automation/task_piping']},
{'Clearml Task': ['guides/clearml-task/clearml_task_tutorial']}, {'ClearML Task': ['guides/clearml-task/clearml_task_tutorial']},
{'Datasets': ['guides/datasets/data_man_cifar_classification', 'guides/datasets/data_man_python']}, {'Datasets': ['guides/datasets/data_man_cifar_classification', 'guides/datasets/data_man_python']},
{'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']}, {'Distributed': ['guides/distributed/distributed_pytorch_example', 'guides/distributed/subprocess_example']},
{'Docker': ['guides/docker/extra_docker_shell_script']}, {'Docker': ['guides/docker/extra_docker_shell_script']},