Edit links.yml and images (#168)

.github/workflows/links.yml

@@ -31,9 +31,13 @@ jobs:
         uses: lycheeverse/lychee-action@v1.2.0
         with:
           #format: json
-          args: --verbose --no-progress ${{ env.MD_FILES }} --github-token ${{secrets.GITHUB_TOKEN}} --exclude https://s3.* https://files.* https://demo.*  ^\w*#.* https://s3.* https://ex.*
-            https://files.* (https://app)(.community.clear.ml|https://app.clear.ml)?(/)?(dashboard|profile|projects.*)? (https://clear.ml)(/)?(blog|pricing|docs)(/)?
+          args: --verbose --no-progress ${{ env.MD_FILES }} --github-token ${{secrets.GITHUB_TOKEN}} --exclude https://s3.*
+            https://files.* https://demo.*  ^\w*#.* http(s)?://example.com.* (https://clear.ml)(/)?(blog|pricing|docs)(/)?(latest/docs/index.html)?
+            (https://app)(.community.clear.ml|.app.clear.ml)?(/)?(dashboard|profile|projects.*)?
             http://localhost.* `http.+` https://.{10,}\.git https://api.* http://[0-9].*
+            https://allegroai.jfrog.io/clearml/api/pypi/public/simple mailto:clearml@allegro.ai
+            https://allegroai.github.io/clearml-helm-charts https://gist.github.com https://twitter.com/clearmlapp
+
 
       #- name: Read Results
       #  if: ${{ steps.lychee.outputs.exit_code != 0 }}

docs/clearml_agent.md

@@ -581,7 +581,7 @@ Do not enqueue training or inference tasks into the services queue. They will pu
 ### Setting Server Credentials
 
 Self hosted [ClearML Server](deploying_clearml/clearml_server.md) comes by default with a services queue.
-By default, the server is open and does not require username and password, but it can be [password-protected](deploying_clearml/clearml_server_security#user-access-security).
+By default, the server is open and does not require username and password, but it can be [password-protected](deploying_clearml/clearml_server_security.md#user-access-security).
 In case it is password-protected, the services agent will need to be configured with server credentials (associated with a user).
 
 To do that, set these environment variables on  the ClearML Server machine with the appropriate credentials:

docs/fundamentals/pipelines.md

@@ -400,7 +400,7 @@ Example:
 ```python
 if __name__ == '__main__':
     PipelineDecorator.run_locally()
-    executing_pipeline(pickle_url='https://exaple.com/iris_dataset.pkl')
+    executing_pipeline(pickle_url='https://example.com/iris_dataset.pkl')
     print('pipeline completed')
 ```
 

docs/getting_started/ds/ds_first_steps.md

@@ -56,7 +56,7 @@ ClearML results page: https://app.community.clear.ml/projects/4043a1657f374e9298
 
 **That’s it!** You are done integrating ClearML with your code :)
 
-Now, [command-line arguments](../../fundamentals/hyperparameters.md#command-line-parsing), [console output](../../fundamentals/logger#types-of-logged-results) as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.
+Now, [command-line arguments](../../fundamentals/hyperparameters.md#command-line-parsing), [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).

docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md

@@ -65,6 +65,6 @@ ClearML automatically tracks images logged to TensorboardLogger. They appear in
 PyTorch Ignite also offers a dedicated `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model 
 checkpoints during training and validation.
 
-For more information, see the [PyTorch Ignite ClearMLLogger](pytorch_ignite_mnist)
+For more information, see the [PyTorch Ignite ClearMLLogger](pytorch_ignite_mnist.md)
 example.
 

docs/guides/reporting/clearml_logging_example.md

@@ -196,7 +196,7 @@ logger.report_media('audio', 'pink panther', iteration=1, local_path=audio_local
 ### 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://clear.ml/docs/latest/docs/index.html")
 ```
 
 ![HTML sample](../../img/colab_explicit_reporting_09.png)

docs/guides/reporting/html_reporting.md

@@ -21,7 +21,7 @@ See the example script's [report_html_url](https://github.com/allegroai/clearml/
 function, which reports the ClearML documentation's home page.
 
 ```python
-Logger.current_logger().report_media("html", "url_html", iteration=iteration, url="https://clear.ml/docs")
+Logger.current_logger().report_media("html", "url_html", iteration=iteration, url="https://clear.ml/docs/latest/docs/index.html")
 ```
 
 ## Reporting HTML Local Files

docs/webapp/webapp_exp_table.md

@@ -22,7 +22,7 @@ The experiments table default and customizable columns are described in the foll
 |---|---|---|
 | **TYPE** | Type of experiment. **ClearML** supports multiple [task types](../fundamentals/task.md#task-types) for experimentation, and a variety of workflows and use cases. | Default |
 | **NAME** | Experiment name.  |  Default |
-| **TAGS** | Descriptive, user-defined, color-coded tags assigned to experiments. Use tags to classify experiments, and filter the list. See [tagging experiments](webapp_exp_track_visual#tagging-experiments). | Default |
+| **TAGS** | Descriptive, user-defined, color-coded tags assigned to experiments. Use tags to classify experiments, and filter the list. See [tagging experiments](webapp_exp_track_visual.md#tagging-experiments). | Default |
 | **STATUS** | Experiment state (status). See a list of the [task states and state transitions](../fundamentals/task.md#task-states). |  Default |
 | **PROJECT** | Name of experiment's project.  |  Default |
 | **USER** | User who created or cloned the experiment.  |  Default (hidden) |