add sagemaker to docs

docs/my-website/docs/tutorials/sagemaker_llms.md

@@ -0,0 +1,72 @@
+import Image from '@theme/IdealImage';
+
+# Deploy & Query Llama2-7B on Sagemaker
+
+This tutorial has 2 major components:
+1. Deploy Llama2-7B on Jumpstart
+2. Use LiteLLM to Query Llama2-7B on Sagemaker
+
+## Deploying Llama2-7B on AWS Sagemaker
+### Pre-requisites
+Ensure you have AWS quota for deploying your selected LLM. You can apply for a quota increase here: https://console.aws.amazon.com/servicequotas/home
+* ml.g5.48xlarge
+* ml.g5.2xlarge
+
+### Create an Amazon SageMaker domain to use Studio and Studio Notebooks
+
+- Head to AWS console https://aws.amazon.com/console/ 
+- Navigate to AWS Sagemaker from the console
+- On AWS Sagemaker select 'Create a Sagemaker Domain' 
+<Image img={require('../../img/sagemaker_domain.png')} />
+
+### Deploying Llama2-7B using AWS Sagemaker Jumpstart
+
+- After creating your sagemaker domain, click 'Open Studio', which should take you to AWS sagemaker studio 
+
+- On the left sidebar navigate to SageMaker Jumpstart -> Models, notebooks, solutions
+- Now select the LLM you want to deploy by clicking 'View Model' - (in this case select Llama2-7B)
+<Image img={require('../../img/sagemaker_jumpstart.png')} />
+- Click `Deploy` for the Model you want to deploy
+<Image img={require('../../img/sagemaker_deploy.png')} />
+
+- After deploying Llama2, copy your model endpoint 
+<Image img={require('../../img/sagemaker_endpoint.png')} />
+
+### Use LiteLLM to Query Llama2-7B on Sagemaker
+
+#### Prerequisites
+* `pip install boto3`
+* `pip install litellm`
+* Create your AWS Access Key, get your `AWS_ACCESS_KEY_ID` and  `AWS_SECRET_ACCESS_KEY`. You can create a new aws access key on the aws console under `Security Credentials` under your profile
+
+#### Querying deployed Llama2-7b 
+Set `model` = `sagemaker/<your model endpoint>` for `completion`. Use the model endpoint you got after deploying llama2-7b on sagemaker. If you used jumpstart your model endpoint will look like this `jumpstart-dft-meta-textgeneration-llama-2-7b`
+
+Code Example:
+```python
+from litellm import completion
+os.environ['AWS_ACCESS_KEY_ID'] = "your-access-key-id"
+os.environ['AWS_SECRET_ACCESS_KEY'] = "your-secret-key"
+
+response = completion(
+            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
+            messages=[{'role': 'user', 'content': 'are you a llama'}],
+            temperature=0.2, # optional params
+            max_tokens=80,
+        )
+
+```
+
+That's it! Happy completion()!
+
+#### Next Steps: 
+- Add Caching: https://docs.litellm.ai/docs/caching/gpt_cache
+- Add Logging and Observability to your deployed LLM: https://docs.litellm.ai/docs/observability/callbacks
+
+
+
+
+
+
+
+

docs/my-website/sidebars.js

@@ -52,6 +52,7 @@ const sidebars = {
         'tutorials/huggingface_tutorial', 
         'tutorials/TogetherAI_liteLLM', 
         'tutorials/finetuned_chat_gpt',
+        'tutorials/sagemaker_llms',
         'tutorials/text_completion',
         'tutorials/litellm_Test_Multiple_Providers',
         "tutorials/first_playground",