diff --git a/README.md b/README.md index 57ece633..735b6445 100644 --- a/README.md +++ b/README.md @@ -13,11 +13,11 @@ The [ClearML documentation website](https://clear.ml/docs/latest/docs) is built using [Docusaurus 2](https://v2.docusaurus.io/), a modern static website generator. -## Contributing (yes please!) +## Contributing (yes, please!) **PRs are always welcomed** :heart: -Good PR examples +Good PR examples: * If you see something that is inaccurate or missing * A topic that interests you is not addressed * You feel that a guide would have made your life easier diff --git a/docs/faq.md b/docs/faq.md index f64099e3..de14c872 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -570,7 +570,7 @@ You can also visualize the differences in a scatter plot. In each experiment who with a single point, x-axis for the hyperparameter value, and Y-axis for the accuracy. In the code below, the task reports a single-point scatter plot with `number_layers` as the x-axis and -`accuracy` as the Y-axis : +`accuracy` as the Y-axis: ```python number_layers = 10 diff --git a/docs/getting_started/mlops/mlops_best_practices.md b/docs/getting_started/mlops/mlops_best_practices.md index a2052c75..24283698 100644 --- a/docs/getting_started/mlops/mlops_best_practices.md +++ b/docs/getting_started/mlops/mlops_best_practices.md @@ -31,7 +31,7 @@ Once you have a Task in ClearML, you can clone and edit its definitions in the U ## Manage Your Data Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction. Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder / S3 / Gs / Azure). -ClearML Data supports efficient Dataset storage and caching, differentiable & compressed. +ClearML Data supports efficient Dataset storage and caching, differentiable and compressed. ## Scale Your Work Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage diff --git a/docs/getting_started/mlops/mlops_first_steps.md b/docs/getting_started/mlops/mlops_first_steps.md index 8a123fd5..2c50868c 100644 --- a/docs/getting_started/mlops/mlops_first_steps.md +++ b/docs/getting_started/mlops/mlops_first_steps.md @@ -21,7 +21,7 @@ ClearML Agent was designed to deal with such issues and more! It is a tool respo machine of choice through the ClearML WebApp with no need for additional code. The agent will set up the environment for a specific Task’s execution (inside a Docker, or bare-metal), install the -required python packages, and execute & monitor the process. +required python packages, and execute and monitor the process. ## Set up an Agent diff --git a/docs/guides/clearml_agent/reproduce_exp.md b/docs/guides/clearml_agent/reproduce_exp.md index 36697b63..2aa1a5b1 100644 --- a/docs/guides/clearml_agent/reproduce_exp.md +++ b/docs/guides/clearml_agent/reproduce_exp.md @@ -18,13 +18,13 @@ Sometimes, you may need to recreate your experiment environment on a different m code. ClearML logs everything needed to reproduce your experiment and its environment (uncommitted changes, used packages, and -more), making it easy to reproduce your experiment's execution environment using ClearML. +more), making it easy to reproduce your experiment's execution environment using ClearML. You can reproduce the execution environment of any experiment you’ve run with ClearML on any workload: 1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md), :::tip - Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments + Use the UI's [filtering and sorting](../../webapp/webapp_exp_table.md#filtering-columns) to find the best performing experiments. ::: 1. Copy the desired experiment's ID 1. Use the ClearML Agent's [`build`](../../clearml_agent/clearml_agent_ref.md#build) command to rebuild the experiment's diff --git a/docs/integrations/monai.md b/docs/integrations/monai.md index cd3009c6..c968dab0 100644 --- a/docs/integrations/monai.md +++ b/docs/integrations/monai.md @@ -1,5 +1,5 @@ --- -title: Monai +title: MONAI --- :::tip @@ -7,8 +7,8 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d instructions. ::: -[Monai](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare -imaging. You can integrate ClearML into your code using Monai's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler), +[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare +imaging. You can integrate ClearML into your code using MONAI's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler), and [`ModelCheckpoint`](#modelcheckpoint). ## ClearMLImageHandler and ClearMLStatsHandler diff --git a/docs/webapp/webapp_orchestration_dash.md b/docs/webapp/webapp_orchestration_dash.md index 785193a1..30a388a5 100644 --- a/docs/webapp/webapp_orchestration_dash.md +++ b/docs/webapp/webapp_orchestration_dash.md @@ -18,7 +18,7 @@ Use the orchestration dashboard to monitor all of your available and in-use comp The orchestration dashboard shows your workers by groups and categories, specified by the following naming policy: `::`. -When no category is specified, workers are assigned the `DEFAULT` category. +When no category is specified, workers are assigned the `DEFAULT` category. When no group is specified, workers are assigned the `Default Group` group.