diff --git a/docs/Worker-Managers.md b/docs/Worker-Managers.md
new file mode 100644
index 000000000..a346ead49
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+++ b/docs/Worker-Managers.md
@@ -0,0 +1,71 @@
+# Worker Managers
+
+[Worker Managers](https://github.com/codalab/codalab-worksheets/blob/master/codalab/worker_manager/main.py) 
+allow you to automatically spin up VMs and start CodaLab workers on them to run your staged jobs.
+
+We support the following Worker Managers:
+
+| Name             | Description                                                  |
+|------------------|--------------------------------------------------------------|
+| aws-batch        | Worker manager for submitting jobs to AWS Batch.             | 
+| azure-batch      | Worker manager for submitting jobs to Azure Batch.           | 
+| slurm-batch      | Worker manager for submitting jobs using Slurm Batch.        | 
+| kubernetes       | Worker manager for submitting jobs to a Kubernetes cluster.  |
+
+
+## AWS Batch Worker Manager
+
+### Configure AWS Batch (one-time setup)
+
+1. Authenticate AWS on the command-line:
+    1. Install the CLI: `pip install awscli`.
+    1. Authenticate by running `aws configure` and fill out the form.
+1. Create a [launch template](https://docs.aws.amazon.com/batch/latest/userguide/launch-templates.html) 
+   for EC2 instances by running:
+   
+   ```commandline
+    aws ec2 --region <region> create-launch-template --cli-input-json file://lt.json
+   ```
+    
+   Your launch template `lt.json` should look something like this:
+
+   ```json
+    {
+        "LaunchTemplateName": "increase-root-volume",
+        "LaunchTemplateData": {
+            "BlockDeviceMappings": [
+                {
+                    "DeviceName": "/dev/xvda",
+                    "Ebs": {
+                        "Encrypted": true,
+                        "VolumeSize": <Desired volume size in GB as an integer>,
+                        "VolumeType": "gp2"
+                    }
+                }
+            ]
+        }
+    }
+   ```
+   
+1. Log on to the [AWS console](https://aws.amazon.com/console). 
+1. In the upper right corner, select the region.
+1. Type `Batch` in the search bar and click `Batch` under `Services`.
+1. Create a compute environment:
+    1. Click `Compute environments` and then `Create`.
+    1. Specify a name for `Compute Environment Name`.
+    1. Under `Instance Configuration`, select `On-Demand` or `Spot`.
+    1. Specify the [type of EC2 instances](https://aws.amazon.com/ec2/instance-types/) under the 
+       `Allowed Instance Types` dropdown menu.
+    1. Under `Additional Settings`, select the launch template you created.
+    1. Click `Create compute environment`.
+1. Configure a job queue:
+    1. Click `Job queues` and then `Create`.
+    1. Give your job queue a name.
+    1. Under `Connected compute environments`, select the compute environment from the previous step.
+    1. Click `Create`.
+1. Wait for the job queue and compute environment to have a status of `VALID`.
+
+### Start a AWS Batch Worker Manager
+
+Use the [AWS Batch Worker Manager](https://github.com/codalab/codalab-worksheets/blob/master/codalab/worker_manager/aws_batch_worker_manager.py),
+to start the worker manager. Pass in the name of the job queue for `--job-queue`.
diff --git a/mkdocs.yml b/mkdocs.yml
index e69ff34f8..c18ba9396 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -43,6 +43,7 @@ nav:
   - Contributing:
     - Code Overview: Code-Overview.md
     - Server Setup: Server-Setup.md
+    - Worker Managers: Worker-Managers.md
     - Multiple Bundle Stores (in development): Multiple-Bundle-Stores.md
   - About:
     - About: About.md