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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy LLaVA on Amazon SageMaker\n",
"\n",
"Amazon SageMaker is a popular platform for running AI models, and models on huggingface deploy [Hugging Face Transformers](https://github.com/huggingface/transformers) using [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) and the [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/).\n",
"\n",
"\n",
"\n",
"Install sagemaker sdk:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install sagemaker --upgrade\n",
"!pip install -r code/requirements.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bundle llava model weights and code into a `model.tar.gz`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Create SageMaker model.tar.gz artifact\n",
"!tar -cf model.tar.gz --use-compress-program=pigz *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After we created the `model.tar.gz` archive we can upload it to Amazon S3. We will use the `sagemaker` SDK to upload the model to our sagemaker session bucket.\n",
"\n",
"Initialize sagemaker session first:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Couldn't call 'get_role' to get Role ARN from role name arn:aws:iam::297308036828:root to get Role path.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"sagemaker role arn: arn:aws:iam::297308036828:role/service-role/AmazonSageMaker-ExecutionRole-20231008T201275\n",
"sagemaker bucket: sagemaker-us-west-2-297308036828\n",
"sagemaker session region: us-west-2\n"
]
}
],
"source": [
"import sagemaker\n",
"import boto3\n",
"sess = sagemaker.Session()\n",
"# sagemaker session bucket -> used for uploading data, models and logs\n",
"# sagemaker will automatically create this bucket if it not exists\n",
"sagemaker_session_bucket=None\n",
"if sagemaker_session_bucket is None and sess is not None:\n",
" # set to default bucket if a bucket name is not given\n",
" sagemaker_session_bucket = sess.default_bucket()\n",
"\n",
"try:\n",
" role = sagemaker.get_execution_role()\n",
"except ValueError:\n",
" iam = boto3.client('iam')\n",
" # setup your own rolename in sagemaker\n",
" role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231008T201275')['Role']['Arn']\n",
"\n",
"sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
"\n",
"print(f\"sagemaker role arn: {role}\")\n",
"print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
"print(f\"sagemaker session region: {sess.boto_region_name}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Upload the `model.tar.gz` to our sagemaker session bucket:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"model uploaded to: s3://sagemaker-us-west-2-297308036828/llava-v1.5-7b/model.tar.gz\n"
]
}
],
"source": [
"from sagemaker.s3 import S3Uploader\n",
"\n",
"# upload model.tar.gz to s3\n",
"s3_model_uri = S3Uploader.upload(local_path=\"./model.tar.gz\", desired_s3_uri=f\"s3://{sess.default_bucket()}/llava-v1.5-7b\")\n",
"\n",
"print(f\"model uploaded to: {s3_model_uri}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use `HuggingfaceModel` to create our real-time inference endpoint:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"-----------------!"
]
}
],
"source": [
"\n",
"from sagemaker.huggingface.model import HuggingFaceModel\n",
"\n",
"# create Hugging Face Model Class\n",
"huggingface_model = HuggingFaceModel(\n",
" model_data=s3_model_uri, # path to your model and script\n",
" role=role, # iam role with permissions to create an Endpoint\n",
" transformers_version=\"4.28.1\", # transformers version used\n",
" pytorch_version=\"2.0.0\", # pytorch version used\n",
" py_version='py310', # python version used\n",
" model_server_workers=1\n",
")\n",
"\n",
"# deploy the endpoint endpoint\n",
"predictor = huggingface_model.deploy(\n",
" initial_instance_count=1,\n",
" instance_type=\"ml.g5.xlarge\",\n",
" # container_startup_health_check_timeout=600, # increase timeout for large models\n",
" # model_data_download_timeout=600, # increase timeout for large models\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `.deploy()` returns an `HuggingFacePredictor` object which can be used to request inference using the `.predict()` method. Our endpoint expects a `json` with at least `image` and `question` key."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The image features a red and black toy horse with a pair of glasses on its face. The horse is wearing a pair of red glasses, which adds a unique and quirky touch to the toy. The horse's legs are also painted in red and black colors, further enhancing its appearance. The toy horse is standing on a grey surface, which serves as a backdrop for the scene.\n"
]
}
],
"source": [
"data = {\n",
" \"image\" : 'https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png', \n",
" \"question\" : \"Describe the image and color details.\",\n",
" # \"max_new_tokens\" : 1024,\n",
" # \"temperature\" : 0.2,\n",
" # \"conv_mode\" : \"llava_v1\"\n",
"}\n",
"\n",
"# request\n",
"output = predictor.predict(data)\n",
"print(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The inference ` predictor` can also be initilized like with your deployed `endpoint_name` :"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
"sagemaker.config INFO - Not applying SDK defaults from location: /Users/tom/Library/Application Support/sagemaker/config.yaml\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Couldn't call 'get_role' to get Role ARN from role name arn:aws:iam::297308036828:root to get Role path.\n"
]
}
],
"source": [
"import sagemaker\n",
"import boto3\n",
"sess = sagemaker.Session()\n",
"try:\n",
" role = sagemaker.get_execution_role()\n",
"except ValueError:\n",
" iam = boto3.client('iam')\n",
" # setup your own rolename in sagemaker\n",
" role = iam.get_role(RoleName='AmazonSageMaker-ExecutionRole-20231008T201275')['Role']['Arn']\n",
"\n",
"from sagemaker.huggingface.model import HuggingFacePredictor\n",
"# initial the endpoint predictor\n",
"predictor2 = HuggingFacePredictor(\n",
" endpoint_name=\"huggingface-pytorch-inference-2023-10-19-05-57-37-847\",\n",
" sagemaker_session=sess\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To clean up, we can delete the model and endpoint by `delete_endpoint()`or using sagemaker console:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# delete sagemaker endpoint\n",
"predictor.delete_model()\n",
"predictor.delete_endpoint()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llava",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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