Fix links and small edits (#686)

docs/clearml_agent.md

@@ -477,7 +477,7 @@ clearml-agent daemon --dynamic-gpus --gpus 0-7 --queue quad_gpu=4 dual_gpu=2
 ``` 
 
 The agent can now spin multiple Tasks from the different queues based on the number of GPUs configured to the queue.
-The agent will pick a Task from the `quad_gpu` queue, use GPUs 0-3 and spin it. Then it will pick a Task from `dual_gpu`
+The agent will pick a Task from the `quad_gpu` queue, use GPUs 0-3 and spin it. Then it will pick a Task from the `dual_gpu`
 queue, look for available GPUs again and spin on GPUs 4-5.
 
 Another option for allocating GPUs:

docs/deploying_clearml/clearml_server_linux_mac.md

@@ -8,7 +8,7 @@ For ClearML docker images, including previous versions, see [https://hub.docker.
 However, pulling the ClearML Docker image directly is not required. ClearML provides a docker-compose YAML file that does this. 
 The docker-compose file is included in the instructions on this page.
 
-For information about upgrading ClearML Server in Linux or macOS, see [here](upgrade_server_linux_mac.md)
+For information about upgrading ClearML Server in Linux or macOS, see [here](upgrade_server_linux_mac.md).
 
 :::important
 If ClearML Server is being reinstalled, clearing browser cookies for ClearML Server is recommended. For example, 

docs/faq.md

@@ -454,7 +454,7 @@ You can disable the deterministic behavior entirely by passing `Task.set_random_
 #### In the Web UI, I can't access files that my experiment stored. Why not?  
 
 ClearML stores file locations. The machine running your browser must have access to the location where the machine 
-that ran the Task stored the file. This applies to debug samples and artifacts. If, for example, the machine running the browser does not have access, you may see "Unable to load image", instead of the image.
+that ran the Task stored the file. This applies to debug samples and artifacts. If, for example, the machine running the browser does not have access, you may see `Unable to load image`, instead of the image.
 
 <a id="resource_monitoring"></a>
 
@@ -479,7 +479,7 @@ After thirty minutes, it remains unchanged.
 #### Can I control what ClearML automatically logs?   <a id="controlling_logging"></a>
 
 Yes! ClearML lets you control automatic logging for `stdout`, `stderr`, and frameworks when initializing a Task
-by calling the [`Task.init`](references/sdk/task.md#taskinit) method. 
+by calling [`Task.init()`](references/sdk/task.md#taskinit). 
 
 To control a Task's framework logging, use the `auto_connect_frameworks` parameter. Turn off all automatic logging by setting the 
 parameter to `False`. For finer grained control of logged frameworks, input a dictionary, with framework-boolean pairs. 
@@ -546,8 +546,10 @@ See [Storing Task Data Offline](guides/set_offline.md).
 
 Due to speed/optimization issues, the console displays only the last several hundred log lines.
 
-You can always download the full log as a file using the ClearML Web UI. In the ClearML Web UI > experiment 
-info panel > CONSOLE tab, use the *Download full log* feature.
+You can always download the full log as a file using the ClearML Web UI. In the **ClearML Web UI >** experiment's **CONSOLE** 
+tab, click `Download full log`.
+
+![Download console log](img/faq_download_console_log.png)
 
 <br/>
 
@@ -562,7 +564,7 @@ in relation to a specific metric (e.g. accuracy) in a parallel coordinates plot.
 The image below shows a parallel coordinates plot which displays the values of selected hyperparameters (`base_lr`, 
 `batch_size`, and `number_of_epochs`) and a performance metric (`accuracy`) of three experiments. 
 
-![Parallel Coordinates](img/clearml_faq_screenshots/compare_parallel_coordinates.png)
+![Parallel Coordinates](img/compare_parallel_coordinates.png)
 
 You can also visualize the differences in a scatter plot. In each experiment whose values will be compared, report a plot 
 with a single point, x-axis for the hyperparameter value, and Y-axis for the accuracy. 
@@ -585,7 +587,7 @@ Task.current_task().get_logger().report_scatter2d(
 When these experiments are compared in the UI's experiment comparison, all the reported `performance/accuracy` values 
 are displayed in a single plot.
 
-![Comparison plot](img/clearml_faq_screenshots/compare_plots.png)
+![Comparison plot](img/compare_plots.png)
 
 Another option is a histogram chart:
 
@@ -602,7 +604,7 @@ Task.current_task().get_logger().report_vector(
 )
 ```
 
-![Comparison plot histogram](img/clearml_faq_screenshots/compare_plots_hist.png)
+![Comparison plot histogram](img/compare_plots_hist.png)
 
 <br/>
 
@@ -647,8 +649,8 @@ logger.report_scatter2d(
 
 #### Is there something ClearML can do about uncommitted code running?    <a id="help-uncommitted-code"></a>
 
-Yes! ClearML stores the git diff as part of the experiment's information. You can view the git diff in the ClearML Web UI, 
-experiment info panel > EXECUTION tab.
+Yes! ClearML stores the git diff as part of the experiment's information. You can view the git diff in the **ClearML Web UI >** 
+experiment' **EXECUTION** tab.
 
 <br/>
 

docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md

@@ -2,7 +2,7 @@
 title: PyTorch Ignite ClearMLLogger
 ---
 
-The `ignite` repository contains the [mnist_with_clearml_logger.py](https://github.com/pytorch/ignite/blob/master/examples/contrib/mnist/mnist_with_clearml_logger.py)
+The `ignite` repository contains the [mnist_with_clearml_logger.py](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_clearml_logger.py)
 example script that uses [ignite](https://github.com/pytorch/ignite) and integrates **ClearMLLogger** and its [helper handlers](https://pytorch.org/ignite/generated/ignite.contrib.handlers.clearml_logger.html). 
 
 PyTorch Ignite supports a `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model 

docs/integrations/ignite.md

@@ -123,7 +123,7 @@ Visualize all the captured information in the experiment's page in ClearML's [We
 
 For more information, see the [ignite documentation](https://pytorch.org/ignite/generated/ignite.contrib.handlers.clearml_logger.html). 
 
-See code example [here](https://github.com/pytorch/ignite/blob/master/examples/contrib/mnist/mnist_with_clearml_logger.py)
+See code example [here](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_clearml_logger.py).
 
 ## WebApp
 

docs/webapp/applications/apps_overview.md

@@ -21,7 +21,7 @@ ClearML provides the following applications:
 * **Nvidia Clara** - Train models using Nvidia's Clara framework
 * [**Project Dashboard**](apps_dashboard.md) - High-level project monitoring with Slack alerts
 * [**Task Scheduler**](apps_task_scheduler.md) - Schedule tasks for one-shot and/or periodic execution at specified times (available under ClearML Enterprise Plan)
-* [**Trigger Manager**](apps_trigger_manager) - Define tasks to be run when predefined events occur (available under ClearML Enterprise Plan)
+* [**Trigger Manager**](apps_trigger_manager.md) - Define tasks to be run when predefined events occur (available under ClearML Enterprise Plan)
 
 ## App Pages Layout
 Each application's page is split into two sections: