sage-guide

docs/pages/Deploying/Sagemaker-Docsgpt.md

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+# How to deploy LLM's on Sagemaker for DocsGPT
+
+This guide uses some of the methods from the [Phil Schmid's guides](https://www.philschmid.de/) so if you want to dive deeper into the topic, check out his guides.
+
+### 1. Create a new python notebook on Sagemaker and prep dependencies and permissions
+
+Install dependencies
+
+```python
+!pip install "sagemaker>=2.175.0" --upgrade --quiet
+```
+
+Check permissions
+
+```python
+import sagemaker
+import boto3
+sess = sagemaker.Session()
+# sagemaker session bucket -> used for uploading data, models and logs
+# sagemaker will automatically create this bucket if it not exists
+sagemaker_session_bucket=None
+if sagemaker_session_bucket is None and sess is not None:
+    # set to default bucket if a bucket name is not given
+    sagemaker_session_bucket = sess.default_bucket()
+
+try:
+    role = sagemaker.get_execution_role()
+except ValueError:
+    iam = boto3.client('iam')
+    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
+
+sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)
+
+print(f"sagemaker role arn: {role}")
+print(f"sagemaker session region: {sess.boto_region_name}")
+print(f"sagemaker session bucket: {sess.default_bucket()}")
+```
+
+Get huggingfaces llm image uri for the container
+
+```python
+from sagemaker.huggingface import get_huggingface_llm_image_uri
+
+# retrieve the llm image uri
+llm_image = get_huggingface_llm_image_uri(
+  "huggingface",
+  version="1.1.0",
+)
+
+# print ecr image uri
+print(f"llm image uri: {llm_image}")
+```
+
+### 2. Prepare the Model
+
+Running this code will create a model with some default parameters. You can change these parameters to suit your needs.
+There are two ways you can choose which model to use.
+
+You can either use the model_id from the huggingface.co/models page or you can use the model_data from a previous training job.
+
+```python
+import json
+from sagemaker.huggingface import HuggingFaceModel
+
+# sagemaker config
+instance_type = "ml.g5.xlarge"
+number_of_gpu = 1
+health_check_timeout = 600
+
+# Define Model and Endpoint configuration parameter
+config = {
+  'HF_MODEL_ID': "/opt/ml/model", # model_id from hf.co/models
+  #'HF_MODEL_ID': "Arc53/DocsGPT-7B",  
+  'SM_NUM_GPUS': json.dumps(number_of_gpu), # Number of GPU used per replica
+  'MAX_INPUT_LENGTH': json.dumps(7000),  # Max length of input text
+  'MAX_TOTAL_TOKENS': json.dumps(8000),  # Max length of the generation (including input text)
+  'MAX_BATCH_TOTAL_TOKENS': json.dumps(8192),  # Limits the number of tokens that can be processed in parallel during the generation
+  'MAX_BATCH_PREFILL_TOKENS': json.dumps(7000),
+}
+
+# create HuggingFaceModel with the image uri
+llm_model = HuggingFaceModel(
+  model_data="s3://docsgpt/models/hf-tensors/docsgpt-7b-O-hq-64-alpha-2023-11-22-15-04-04-455/model.tar.gz",
+  role=role,
+  image_uri=llm_image,
+  env=config
+)
+```
+
+### 3. Deploy the Model
+
+Running this line will create Model in the Sagemaker console. Next it will create an endpoint configuration and finally it will create an endpoint.
+
+```python
+llm = llm_model.deploy(
+  initial_instance_count=1,
+  endpoint_name="docsgpt-7b",
+  instance_type=instance_type,
+  container_startup_health_check_timeout=health_check_timeout, # 10 minutes to be able to load the model
+)
+```
+
+### 4. Connect it to the application
+
+Change you .env file and set the following variables:
+
+```python
+SAGEMAKER_ENDPOINT: str = None # SageMaker endpoint name (docsgpt-7b)
+SAGEMAKER_REGION: str = None # SageMaker region name
+SAGEMAKER_ACCESS_KEY: str = None # SageMaker access key
+SAGEMAKER_SECRET_KEY: str = None # SageMaker secret key
+```
+
+> **_NOTE:_** If you are using the same AWS account for the application and SageMaker, you can leave the access and secret keys empty.
+
+Also make sure you switch to appropriate embeddings if you want everything runs locally for example
+
+```python
+EMBEDDINGS_NAME=huggingface_sentence-transformers/all-mpnet-base-v2
+```