Small edits (#886)

docs/clearml_agent/clearml_agent_env_caching.md

@@ -20,7 +20,7 @@ the same environment will be used.
 ClearML does not support environment reuse when using Poetry package manager
 :::
 
-To enable virutal environment reuse, modify the `clearml.conf` file and uncomment the `venvs_cache` section.
+To enable virtual environment reuse, modify the `clearml.conf` file and uncomment the `venvs_cache` section.
 ```
 venvs_cache: {
         # maximum number of cached venvs

docs/clearml_sdk/model_sdk.md

@@ -201,7 +201,6 @@ These methods can be used on `Model`, `InputModel`, and/or `OutputModel` objects
   * 3d plots 
     * Scatter plot - [`report_scatter3d`](../references/sdk/model_outputmodel.md#report_scatter3d) 
     * Surface plot - [`report_surface`](../references/sdk/model_outputmodel.md#report_surface)
-
   
 ## SDK Reference
 

docs/deploying_clearml/clearml_server_config.md

@@ -391,7 +391,7 @@ If the `apiserver.conf` file does not exist, create your own in ClearML Server's
 an alternate folder you configured), and input the modified configuration
 :::
 
-See the [Flask-Cors documentation](https://flask-cors.corydolphin.com/en/latest/api.html) for detailed initialization 
+See the [Flask-Cors documentation](https://flask-cors.readthedocs.io/en/latest/api.html) for detailed initialization 
 options.
 
 ### Custom UI Context Menu Actions

docs/guides/frameworks/pytorch_ignite/pytorch_ignite_mnist.md

@@ -3,7 +3,7 @@ title: PyTorch Ignite ClearMLLogger
 ---
 
 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). 
+example script that uses [ignite](https://github.com/pytorch/ignite) and integrates **ClearMLLogger** and its [helper handlers](https://pytorch.org/ignite/v0.5.0.post2/generated/ignite.handlers.clearml_logger.html). 
 
 PyTorch Ignite supports a `ClearMLLogger` handler to log metrics, text, model / optimizer parameters, plots, and model 
 checkpoints during training and validation. 

docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md

@@ -196,7 +196,7 @@ object, setting the following optimization parameters:
 
 ## Running as a Service
 
-The optimization can run as a service, if the `run_as_service` argument is set to `true`. For more information about 
+To run the optimization as a service, set the `run_as_service` argument to `true`. For more information about 
 running as a service, see [Services Mode](../../../clearml_agent/clearml_agent_services_mode.md).
 
 ```python
@@ -210,7 +210,7 @@ if args['run_as_service']:
 ## Optimize
 
 The optimizer is ready. Set the report period and [start](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md#start) 
-it, providing the callback method to report the best performance.
+it, providing the callback method to report the best performance:
 
 ```python
 # report every 12 seconds, this is way too often, but we are testing here J

docs/integrations/ignite.md

@@ -78,7 +78,6 @@ Integrate ClearML with the following steps:
    ```
 
 1. Attach the `ClearMLLogger` object to helper handlers to log experiment outputs. Ignite supports the following helper handlers for ClearML:
-
    * **ClearMLSaver** - Saves input snapshots as ClearML artifacts.
    * **GradsHistHandler** and **WeightsHistHandler** - Logs the model's gradients and weights respectively as histograms.
    * **GradsScalarHandler** and **WeightsScalarHandler** - Logs gradients and weights respectively as scalars.
@@ -122,7 +121,7 @@ Integrate ClearML with the following steps:
    
 Visualize all the captured information in the experiment's page in ClearML's [WebApp](#webapp).
 
-For more information, see the [ignite documentation](https://pytorch.org/ignite/generated/ignite.contrib.handlers.clearml_logger.html). 
+For more information, see the [ignite documentation](https://pytorch.org/ignite/v0.5.0.post2/generated/ignite.handlers.clearml_logger.html). 
 
 See code example [here](https://github.com/pytorch/ignite/blob/master/examples/mnist/mnist_with_clearml_logger.py).
 

docs/release_notes/ver_1_1.md

@@ -34,7 +34,7 @@ This release is not backwards compatible - see notes below on upgrading
 - Add `Task.force_store_standalone_script()` to force storing standalone script instead of a Git repository reference ([ClearML GitHub issue #340](https://github.com/allegroai/clearml/issues/340))
 - Add `Logger.set_default_debug_sample_history()` and `Logger.get_default_debug_sample_history()` to allow controlling 
   maximum debug samples programmatically
-- Add populate now stores function arg types as part of the hyperparemeters
+- Add populate now stores function arg types as part of the hyperparameters
 - Add `status_message` argument to `Task.mark_stopped()`
 - Change `HTTP` driver timeout and retry codes (connection timeout will now trigger a retry)
 

docs/webapp/applications/apps_dashboard.md

@@ -6,7 +6,7 @@ title: Project Dashboard
 The ClearML Project Dashboard App is available under the ClearML Pro plan
 :::
 
-The Project Dashboard Application provides an overview of a project or workspace's progress. It presents an aggregated 
+The Project Dashboard Application provides an overview of a project's or workspace's progress. It presents an aggregated 
 view of task status and a chosen metric over time, as well as project GPU and worker usage. It also supports alerts/warnings 
 on completed/failed Tasks via Slack integration.
 

src/pages/index.js

@@ -44,7 +44,7 @@ const features = [
     description: (
       <>
           <code>ClearML-Data</code> lets you <b>abstract the Data from your Code</b>.
-          CLI / programmatic interface easily create datasets from anywhere.
+          Easily create datasets from anywhere using the CLI or programmatic interface.
           ClearML-Data is a fully differentiable solution on top of object-storage / http / NAS layer.
           <b> We solve your data localization problem, so you can process it anywhere.</b>
       </>