Add Transformers integration page (#696)

docs/integrations/transformers.md

@@ -0,0 +1,93 @@
+---
+title: HuggingFace Transformers
+---
+
+HuggingFace's [Transformers](https://huggingface.co/docs/transformers/index) is a popular deep learning framework. You can 
+seamlessly integrate ClearML into your Transformer's PyTorch [Trainer](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer) 
+code using the built-in [`ClearMLCallback`](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/callback#transformers.integrations.ClearMLCallback). 
+ClearML automatically logs Transformer's models, parameters, scalars, and more. 
+
+All you have to do is install and set up ClearML:
+
+1. Install the `clearml` python package:
+
+   ```commandline
+   pip install clearml
+   ``` 
+   
+1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:
+    * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/) 
+    * Set up your own server, see [here](../deploying_clearml/clearml_server.md).  
+1. Connect the ClearML SDK to the server by creating credentials (go to the top right in the UI to **Settings > Workspace > Create new credentials**), 
+   then execute the command below and follow the instructions:
+
+   ```commandline
+   clearml-init
+   ```
+    
+That’s it! In every training run from now on, the ClearML experiment 
+manager will capture:
+* Source code and uncommitted changes
+* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments),
+and TensorFlow definitions
+* Installed packages
+* Model files (make sure the `CLEARML_LOG_MODEL` environment variable is set to `True`)
+* Scalars (loss, learning rates)
+* Console output
+* General details such as machine details, runtime, creation date etc.
+* And more
+
+All of this is captured into a [ClearML Task](../fundamentals/task.md). By default, a task called `Trainer` is created 
+in the `HuggingFace Transformers` project. To change the task’s name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK`
+environment variables
+
+:::tip project names 
+ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a name will create the `sample` 
+task within the `example` project. 
+:::
+
+In order to log the models created during training, set the `CLEARML_LOG_MODEL` environment variable to `True`. 
+
+You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md). 
+
+![transformers scalars](../img/integrations_transformers_scalars.png)
+
+Additionally, you can view all of your Transformers runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md). 
+Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model. 
+You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.   
+
+
+## Remote Execution
+ClearML logs all the information required to reproduce an experiment on a different machine (installed packages, 
+uncommitted changes etc.). The [ClearML Agent](../clearml_agent.md) listens to designated queues and when a task is 
+enqueued, the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the 
+experiment manager.
+
+Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running 
+the following command on it:
+
+```commandline
+clearml-agent daemon --queue <queues_to_listen_to> [--docker]
+```
+
+Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md) to help you manage cloud workloads in the 
+cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up 
+and shuts down instances as needed, according to a resource budget that you set.
+
+
+### Cloning, Editing, and Enqueuing
+
+![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif)
+
+Use ClearML’s web interface to edit task details, like configuration parameters or input models, then execute the task 
+with the new configuration on a remote machine:
+* Clone the experiment
+* Edit the hyperparameters and/or other details 
+* Enqueue the task
+
+The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md). 
+
+## Hyperparameter Optimization
+Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md) 
+for more information.

sidebars.js

@@ -60,7 +60,7 @@ module.exports = {
         {'CLI Tools': ['apps/clearml_session', 'apps/clearml_task', 'apps/clearml_param_search']},
         {'Integrations': [
                 'integrations/autokeras',
-                'integrations/catboost', 'integrations/click', 'integrations/fastai',
+                'integrations/catboost', 'integrations/click', 'integrations/fastai', 'integrations/transformers',
                 'integrations/hydra',
                 'integrations/keras', 'integrations/keras_tuner',
                 'integrations/lightgbm', 'integrations/matplotlib',