musicgen-small/deploy-musicgen-small.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88facfc2-7a17-4a44-8b34-29761d350b72",
   "metadata": {},
   "source": [
    "# MusicGen Async SageMaker Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dda1f938-35f2-4eee-a319-ac2e199ff29e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -Uq sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "104af8c1-e7ef-42e2-b419-28a899800f01",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir model\n",
    "!mkdir model/code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bec2f848-b6fa-4320-8e2f-470e04696271",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## requirements.txt https://github.com/facebookresearch/audiocraft/blob/main/README.md\n",
    "'''\n",
    "# Best to make sure you have torch installed first, in particular before installing xformers.\n",
    "# Don't run this if you already have PyTorch installed.\n",
    "pip install 'torch>=2.0'\n",
    "# Then proceed to one of the following\n",
    "pip install -U audiocraft  # stable release\n",
    "pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge\n",
    "pip install -e .  # or if you cloned the repo locally (mandatory if you want to train).\n",
    "'''\n",
    "with open(\"model/code/requirements.txt\", \"w\") as f:\n",
    "    f.write(\"transformers==4.34.1\\n\")\n",
    "    f.write(\"boto3\\n\")\n",
    "    f.write(\"torch>=2.0\\n\")\n",
    "    f.write(\"scipy\\n\")\n",
    "    f.write(\"uuid\\n\")\n",
    "    f.write(\"audiocraft\\n\")\n",
    "    f.write(\"git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7449cccb-5535-4bcc-af1f-9b9ff057ca8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile model/code/inference.py\n",
    "\n",
    "import boto3\n",
    "from urllib.parse import urlparse\n",
    "from transformers import AutoProcessor, MusicgenForConditionalGeneration\n",
    "import scipy\n",
    "import uuid\n",
    "\n",
    "def model_fn(model_dir):\n",
    "    model = MusicgenForConditionalGeneration.from_pretrained(\"facebook/musicgen-small\")\n",
    "    return model\n",
    "\n",
    "\n",
    "def process_input(model, texts):\n",
    "    processor = AutoProcessor.from_pretrained(\"facebook/musicgen-small\")\n",
    "    inputs = processor(\n",
    "        text = texts, #[\"80s pop track with bassy drums and synth\", \"90s rock song with loud guitars and heavy drums\"],\n",
    "        padding=True,\n",
    "        return_tensors=\"pt\",\n",
    "    )\n",
    "    audio_values = model.generate(**inputs, max_new_tokens=256)\n",
    "    return audio_values\n",
    "\n",
    "\n",
    "def upload_to_s3(wav_on_disk, bucket_name):\n",
    "    s3 = boto3.resource('s3')\n",
    "    target_file = wav_on_disk.split('/')[-1]\n",
    "    s3.Bucket(bucket_name).upload_file(wav_on_disk, f'musicgen/output/{target_file}')\n",
    "    return f\"s3://{bucket_name}/musicgen/output/{target_file}\"\n",
    "\n",
    "\n",
    "def write_to_s3(sampling_rate, audio_values, bucket_name):\n",
    "    suffix = str(uuid.uuid1())\n",
    "    wav_file = f\"musicgen_out-{suffix}.wav\"\n",
    "    wav_on_disk = f'/tmp/{wav_file}'\n",
    "    try:\n",
    "        scipy.io.wavfile.write(wav_on_disk, rate=sampling_rate, data=audio_values[0, 0].numpy())\n",
    "    except:\n",
    "        wav_on_disk = f'/opt/ml/output/data/{wav_file}'\n",
    "        scipy.io.wavfile.write(wav_on_disk, rate=sampling_rate, data=audio_values[0, 0].numpy())\n",
    "    return upload_to_s3(wav_on_disk, bucket_name)\n",
    "\n",
    "\n",
    "def predict_fn(data, model):\n",
    "    texts = data.pop('texts')\n",
    "    bucket_name = data.pop('bucket_name')\n",
    "    audio_values = process_input(model, texts)\n",
    "    sampling_rate = model.config.audio_encoder.sampling_rate\n",
    "    s3_location = write_to_s3(sampling_rate, audio_values, bucket_name)\n",
    "    return {\n",
    "        \"generated_output_s3\": s3_location,\n",
    "        \"audio_values\": audio_values\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d35645-e796-40f3-bfe5-8dc8378da653",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe3098e-1194-4805-8cfd-f33880dd7211",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm model.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8be2a9d-63a6-4938-bbfb-7ab687ad69ca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf code/.ipynb_checkpoints*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20a8ea5b-753f-4ae8-aeff-226fad09eabd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!tar zcvf model.tar.gz *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de657b6f-7c22-4496-8c74-05d1661527c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "sagemaker_session_bucket = None\n",
    "if sagemaker_session_bucket is None and sess is not None:\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",
    "    role = iam.get_role(RoleName=\"sagemaker_execution_role\")[\"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}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89056fe5-98e8-44a6-b484-f01d83872293",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3_location = f\"s3://{sagemaker_session_bucket}/musicgen/model/model.tar.gz\"\n",
    "s3_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd3ec343-edf1-40f1-abc8-f0a317ca4661",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!aws s3 cp model.tar.gz $s3_location"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968baaea-6613-4f5e-9469-ccc55ea35ef7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Async Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb012660-5957-4ced-b2f3-e1527d99839a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.huggingface.model import HuggingFaceModel\n",
    "from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig\n",
    "from sagemaker.s3 import s3_path_join\n",
    "from sagemaker.utils import name_from_base\n",
    "\n",
    "async_endpoint_name = name_from_base(\"musicgen-small-v1-asyc\")\n",
    "\n",
    "# create Hugging Face Model Class\n",
    "huggingface_model = HuggingFaceModel(\n",
    "    name=async_endpoint_name,\n",
    "    model_data=s3_location,  # path to your model and script\n",
    "    role=role,  # iam role with permissions to create an Endpoint\n",
    "    transformers_version=\"4.28\",  # transformers version used\n",
    "    pytorch_version=\"2.0\",  # pytorch version used\n",
    "    py_version=\"py310\",  # python version used\n",
    ")\n",
    "\n",
    "# create async endpoint configuration\n",
    "async_config = AsyncInferenceConfig(\n",
    "    output_path=s3_path_join(\n",
    "        \"s3://\", sagemaker_session_bucket, \"musicgen/async_inference/music_output\"\n",
    "    ),  # Where our results will be stored\n",
    "    # Add nofitication SNS if needed\n",
    "    notification_config={\n",
    "        # \"SuccessTopic\": \"PUT YOUR SUCCESS SNS TOPIC ARN\",\n",
    "        # \"ErrorTopic\": \"PUT YOUR ERROR SNS TOPIC ARN\",\n",
    "    },  #  Notification configuration\n",
    ")\n",
    "\n",
    "# deploy the endpoint endpoint\n",
    "async_predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.g5.2xlarge\",\n",
    "    async_inference_config=async_config,\n",
    "    endpoint_name=async_endpoint_name,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea3be25-db70-4e9c-9054-e3978ecd62b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_name=async_predictor.endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "626079df-5dec-4c67-be91-52698f52e567",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store \\\n",
    "endpoint_name \\\n",
    "sagemaker_session_bucket"
   ]
  }
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