From 47a07767d069b6bed2f0663009da96846f4a8133 Mon Sep 17 00:00:00 2001 From: Sudeep Pillai Date: Thu, 1 Feb 2024 12:44:21 -0800 Subject: [PATCH 1/9] [blog] Introducing `inf2` runtime blog post (#540) ## Summary - Added blog post for inf2 runtime - Updated README with cleaner API / usage and value-props ## Related issues ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- README.md | 179 ++++------- docs/blog/assets/nos-inf2.jpg | Bin 0 -> 77461 bytes .../blog/posts/04-introducing-inf2-runtime.md | 292 ++++++++++++++++++ docs/concepts/runtime-environments.md | 2 +- 4 files changed, 360 insertions(+), 113 deletions(-) create mode 100644 docs/blog/assets/nos-inf2.jpg create mode 100644 docs/blog/posts/04-introducing-inf2-runtime.md diff --git a/README.md b/README.md index 39092893..e6e5d562 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,30 @@ -
Nitrous Oxide for your AI Infrastructure
+
Nitro Boost for your AI Infrastructure

+Website | Docs | Tutorials | Playground | Blog | Discord +

+

PyPI Version PyPI Version PyPI Downloads -PyPi Downloads
+Docker Pulls
+PyPi Downloads Discord PyPi Version

-

-Website | Docs | Blog | Discord -

-## What is NOS? -**NOS (`torch-nos`)** is a fast and flexible Pytorch inference server, specifically designed for optimizing and running inference of popular foundational AI models. -
+**NOS** is a fast and flexible PyTorch inference server that runs on any cloud or AI HW. -## 🛠️ **Why use NOS?** +## 🛠️ Key Features - 👩‍💻 **Easy-to-use**: Built for [PyTorch](https://pytorch.org/) and designed to optimize, serve and auto-scale Pytorch models in production without compromising on developer experience. -- 🥷 **Flexible**: Run and serve several foundational AI models ([Stable Diffusion](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), [CLIP](https://huggingface.co/openai/clip-vit-base-patch32), [Whisper](https://huggingface.co/openai/whisper-large-v2)) in a single place. -- 🔌 **Pluggable:** Plug your front-end to NOS with out-of-the-box high-performance gRPC/REST APIs, avoiding all kinds of ML model deployment hassles. -- 🚀 **Scalable**: Optimize and scale models easily for maximum HW performance without a PhD in ML, distributed systems or infrastructure. -- 📦 **Extensible**: Easily hack and add custom models, optimizations, and HW-support in a Python-first environment. -- ⚙️ **HW-accelerated:** Take full advantage of your underlying HW (GPUs, ASICs) without compromise. -- ☁️ **Cloud-agnostic:** Run on any cloud HW (AWS, GCP, Azure, Lambda Labs, On-Prem) with our ready-to-use inference server containers. - - -> **NOS** inherits its name from **N**itrous **O**xide **S**ystem, the performance-enhancing system typically used in racing cars. NOS is designed to be modular and easy to extend. -
+- 🥷 **Multi-modal & Multi-model**: Serve multiple foundational AI models ([LLMs](https://github.com/autonomi-ai/nos/blob/main/nos/models/llm.py), [Diffusion](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0), [Embeddings](https://github.com/autonomi-ai/nos/blob/main/nos/models/clip.py), [Speech-to-Text](https://github.com/autonomi-ai/nos/blob/main/nos/models/clip.py) and [Object Detection](https://github.com/autonomi-ai/nos/blob/main/nos/models/yolox.py)) simultaneously, in a single server. +- ⚙️ **HW-aware Runtime:** Deploy PyTorch models effortlessly on modern AI accelerators (NVIDIA GPUs, AWS Inferentia2, AMD - coming soon, and even CPUs). +- ☁️ **Cloud-agnostic Containers:** Run on any cloud (AWS, GCP, Azure, Lambda Labs, On-Prem) with our ready-to-use inference server containers. ## 🔥 What's New +* **[Feb 2024]** ✍️ [blog] [Introducing the NOS Inferentia2 (`inf2`) runtime](https://docs.nos.run/docs/blog/introducing-the-nos-inferentia2-runtime.html). * **[Jan 2024]** ✍️ [blog] [Serving LLMs on a budget](https://docs.nos.run/docs/blog/serving-llms-on-a-budget.html) with [SkyServe](https://skypilot.readthedocs.io/en/latest/serving/sky-serve.html). * **[Jan 2024]** 📚 [docs] [NOS x SkyPilot Integration](https://docs.nos.run/docs/integrations/skypilot.html) page! * **[Jan 2024]** ✍️ [blog] [Getting started with NOS tutorials](https://docs.nos.run/docs/blog/-getting-started-with-nos-tutorials.html) is available [here](./examples/tutorials/)! @@ -42,7 +35,7 @@ We highly recommend that you go to our [quickstart guide](https://docs.nos.run/docs/quickstart.html) to get started. To install the NOS client, you can run the following command: ```bash -conda create -n nos python=3.8 +conda create -n nos python=3.8 -y conda activate nos pip install torch-nos ``` @@ -50,56 +43,51 @@ pip install torch-nos Once the client is installed, you can start the NOS server via the NOS `serve` CLI. This will automatically detect your local environment, download the docker runtime image and spin up the NOS server: ```bash -nos serve up --http +nos serve up --http --logging-level INFO ``` -You are now ready to run your first inference request with NOS! You can run any of the following commands to try things out. - -*Note:* For the above quickstart to work out of the box, we expect the user to have [Docker](https://docs.docker.com/get-docker/), [Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker) and [Docker Compose](https://docs.docker.com/compose/install/) pre-installed on their machine. If you run into any issues, please visit our [quickstart](https://docs.nos.run/docs/quickstart.html) page or ping us on [Discord](https://discord.gg/QAGgvTuvgg). +You are now ready to run your [first inference request](#👩‍💻-what-can-nos-do) with NOS! You can run any of the following commands to try things out. You can set the logging level to `DEBUG` if you want more detailed information from the server. ## 👩‍💻 **What can NOS do?** ### 💬 Chat / LLM Agents (ChatGPT-as-a-Service) --- -NOS provides an OpenAI-compatible server with streaming support so that you can connect your favorite LLM client. +NOS provides an OpenAI-compatible server with streaming support so that you can connect your favorite OpenAI-compatible LLM client to talk to NOS. - - - - - - - - - -
gRPC API ⚡ REST API
+
+
+ API / Usage +
+gRPC API ⚡ ```python from nos.client import Client client = Client("[::]:50051") -model = client.Module("meta-llama/Llama-2-7b-chat-hf") +model = client.Module("TinyLlama/TinyLlama-1.1B-Chat-v1.0") response = model.chat(message="Tell me a story of 1000 words with emojis", _stream=True) ``` -
- +REST API ```bash curl \ -X POST http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "meta-llama/Llama-2-7b-chat-hf", - "messages": [{"role": "user", "content": "Tell me a story of 1000 words with emojis"}], - "temperature": 0.7, "stream": true + "model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", + "messages": [{ + "role": "user", + "content": "Tell me a story of 1000 words with emojis" + }], + "temperature": 0.7, + "stream": true }' ``` -
- + ### 🏞️ Image Generation (Stable-Diffusion-as-a-Service) --- @@ -107,13 +95,12 @@ Build MidJourney discord bots in seconds. - - - - - - - - - -
gRPC API ⚡ REST API
+
+
+ API / Usage +
+ +gRPC API ⚡ ```python from nos.client import Client @@ -125,9 +112,7 @@ image, = sdxl(prompts=["hippo with glasses in a library, cartoon styling"], width=1024, height=1024, num_images=1) ``` -
- +REST API ```bash curl \ @@ -137,32 +122,26 @@ curl \ "model_id": "stabilityai/stable-diffusion-xl-base-1-0", "inputs": { "prompts": ["hippo with glasses in a library, cartoon styling"], - "width": 1024, - "height": 1024, + "width": 1024, "height": 1024, "num_images": 1 } }' ``` -
- - + ### 🧠 Text & Image Embedding (CLIP-as-a-Service) --- -Build scalable semantic search of images/videos in minutes. +Build [scalable semantic search of images/videos](https://docs.nos.run/docs/demos/video-search.html) in minutes. - - - - - - - - - -
gRPC API ⚡ REST API
+
+
+ API / Usage +
+ +gRPC API ⚡ ```python from nos.client import Client @@ -173,8 +152,7 @@ clip = client.Module("openai/clip-vit-base-patch32") txt_vec = clip.encode_text(texts=["fox jumped over the moon"]) ``` -
+REST API ```bash curl \ @@ -189,26 +167,21 @@ curl \ }' ``` -
- + ### 🎙️ Audio Transcription (Whisper-as-a-Service) --- - -Perform real-time audio transcription using Whisper. +Perform [real-time audio transcription](./examples/tutorials/04-serving-multiple-models/) using Whisper. - - - - - - - - - -
gRPC API ⚡ REST API
+
+
+ API / Usage +
+ +gRPC API ⚡ ```python from pathlib import Path @@ -222,8 +195,7 @@ with client.UploadFile(Path("audio.wav")) as remote_path: # {"chunks": ...} ``` -
+REST API ```bash curl \ @@ -234,24 +206,20 @@ curl \ -F 'file=@audio.wav' ``` -
+ ### 🧐 Object Detection (YOLOX-as-a-Service) --- - Run classical computer-vision tasks in 2 lines of code. - - - - - - - - - -
gRPC API ⚡ REST API
+
+
+ API / Usage +
+ +gRPC API ⚡ ```python from pathlib import Path @@ -263,8 +231,7 @@ model = client.Module("yolox/medium") response = model(images=[Image.open("image.jpg")]) ``` -
+REST API ```bash curl \ @@ -275,24 +242,12 @@ curl \ -F 'file=@image.jpg' ``` -
- + ### ⚒️ Custom models --- Want to run models not supported by NOS? You can easily add your own models following the examples in the [NOS Playground](https://github.com/autonomi-ai/nos-playground/tree/main/examples). - -## 📚 Documentation - -- [Tutorials](./examples/tutorials/) -- [Quickstart](https://docs.nos.run/docs/quickstart.html) -- [Models](https://docs.nos.run/docs/models/supported-models.html) -- **Concepts**: [Architecture Overview](https://docs.nos.run/docs/concepts/architecture-overview.html), [ModelSpec](https://docs.nos.run/docs/concepts/model-spec.html), [ModelManager](https://docs.nos.run/docs/concepts/model-manager.html), [Runtime Environments](https://docs.nos.run/docs/concepts/runtime-environments.html) -- **Demos**: [Building a Discord Image Generation Bot](https://docs.nos.run/docs/demos/discord-bot.html), [Video Search Demo](https://docs.nos.run/docs/demos/video-search.html) - ## 📄 License This project is licensed under the [Apache-2.0 License](LICENSE). @@ -304,7 +259,7 @@ NOS collects anonymous usage data using [Sentry](https://sentry.io/). This is us ## 🤝 Contributing We welcome contributions! Please see our [contributing guide](CONTRIBUTING.md) for more information. -### 🔗 Quick Links +## 🔗 Quick Links * 💬 Send us an email at [support@autonomi.ai](mailto:support@autonomi.ai) or join our [Discord](https://discord.gg/QAGgvTuvgg) for help. * 📣 Follow us on [Twitter](https://twitter.com/autonomi\_ai), and [LinkedIn](https://www.linkedin.com/company/autonomi-ai) to keep up-to-date on our products. diff --git a/docs/blog/assets/nos-inf2.jpg b/docs/blog/assets/nos-inf2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..942af4458cb1740889d88714e2383edac1f3ef80 GIT binary patch literal 77461 zcmeFZ2{@Hq+c12YQj|@R*{&!g^O$KXnNpdOnIxG)W{JH+hzywv*$Thi)y4Mc`j2UE@V6cSeZog={o`-{$2&TkC+?o$Qv@(eJhb(&0l^Ui z%T7L?Pr!PLG_cJ4!0ESg(NWp`uD&W*-wc)+?0zrzAC({cUYHw)06-@j0H_IntCNZXfQ%~u07?8?-6e4VU=#&_x-@bdc zcv(tXMpo{syy^`#bq!6en+A6c?-_wzw0UUz$j;uu(Zkcr+sF5*U+{~N(6E=`5pnSe ziAl+C-llxW%*y_l^C>s4XmDIca~3T<|6h8wBI26uK^bH--7I~fc*`wF+dfd z{4G&YQc}@SQBlzxqXE;g+^wpN+pqQRy>ao6(3nlT 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z6yHwS7tg4B{56mtgT#enQEgH5c?iEbYo*Nh6erEfl9O_FPl{}qj5VJo!YX6h(R=1T zP9`Ze(}8)b{^jbs)R&kkc{3JK;@;Oh(y~N)3p~E-^we68HVUG- z%cd-!>#X|2R61l;Etr6z#;+FNb;xsVTqeten9*qPG)IW{z7IFfOE@ptNif;A*X7x|(6M}v=-A-Wan#-9@YN^{xjK4g!#modc1T-24tt8>5 zd{60D!{*0(NnF%*79(t5!)TuRo^U!NaM7thLg2--{FS`bCJpxJ@W>zS<14q~J#P1Z zak!XqmvVR9n8&;91*=o+hiH*=|FZUvzitKjzxY4n&+*?U1<=2j$$x*D{L#nm-+g-j zo;!cEHSq5Sfq&2WKir%j`nPZMN4w{L^y~dM%I4qu{2y%%{JWg~?>Ya6oAZ + +We are excited to announce the availability of the [AWS Inferentia2](https://aws.amazon.com/en/ec2/instance-types/inf2/) runtime on [NOS](https://github.com/autonomi-ai/nos) - a.k.a. our **[`inf2`](../../concepts/runtime-environments.md#🏃‍♂️-supported-runtimes)** runtime. This runtime is designed to easily serve models on AWS Inferentia2, a high-performance, purpose-built chip for inference. In this blog post, we will introduce the AWS Inferentia2 runtime, and show you how to trivially deploy a model on the AWS Inferentia2 device using NOS. If you have followed our previous tutorial on [serving LLMs on a budget (on NVIDIA hardware)](./03-serving-llms-on-a-budget.md), you will be pleasantly surprised to see how easy it is to deploy a model on the AWS Inferentia2 device using the pre-baked NOS **[`inf2`](../../concepts/runtime-environments.md#🏃‍♂️-supported-runtimes)** runtime we provide. + +## ⚡️ What is AWS Inferentia2? + +[AWS Inferentia2](https://aws.amazon.com/en/ec2/instance-types/inf2/) (Inf2 for short) is the second-generation inference accelerator from AWS. Inf2 instances raise the performance of [Inf1](https://aws.amazon.com/ec2/instance-types/inf1/) (originally launched in 2019) by delivering 3x higher compute performance, 4x larger total accelerator memory, up to 4x higher throughput, and up to 10x lower latency. Inf2 instances are the first inference-optimized instances in Amazon EC2 to support scale-out distributed inference with ultra-high-speed connectivity between accelerators. + +Relative to the [AWS G5 instances](https://aws.amazon.com/ec2/instance-types/g5/) ([NVIDIA A10G](https://www.nvidia.com/en-us/data-center/products/a10-gpu/)), Inf2 instances promise up to 50% better performance-per-watt. Inf2 instances are ideal for applications such as natural language processing, recommender systems, image classification and recognition, speech recognition, and language translation that can take advantage of scale-out distributed inference. + +| Instance Size | Inf2 Accelerators | Accelerator Memory (GB) | vCPU | Memory (GiB) | On-Demand Price | Spot Price | +|---------------|-------------------|-------------------------|------|--------------|-----------------|-------------| +| inf2.xlarge | 1 | 32 | 4 | 16 | $0.76 | $0.32 | +| inf2.8xlarge | 1 | 32 | 32 | 128 | $1.97 | $0.79 | +| inf2.24xlarge | 6 | 192 | 96 | 384 | $6.49 | $2.45 | +| inf2.48xlarge | 12 | 384 | 192 | 768 | $12.98 | $5.13 | + +## 🏃‍♂️ NOS Inference Runtime + +The NOS inference server supports custom runtime environments through the use of the [InferenceServiceRuntime](../api/server.md#inferenceserviceruntime) class - a high-level interface for defining new **containerized** and **hardware-aware** runtime environments. NOS already ships with [runtime environments](../../concepts/runtime-environments.md#🏃‍♂️-supported-runtimes) for NVIDIA GPUs (`gpu`) and Intel/ARM CPUs (`cpu`). Today, we're adding the [NOS Inferentia2 runtime](https://hub.docker.com/repository/docker/autonomi/nos/general) (`inf2`) with the [AWS Neuron drivers](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/setup/neuron-setup/pytorch/neuronx/ubuntu/torch-neuronx-ubuntu22.html#setup-torch-neuronx-ubuntu22), the [AWS Neuron SDK](https://github.com/aws-neuron/aws-neuron-sdk) and NOS pre-installed. This allows developers to quickly develop applications for AWS Inferentia2, without wasting any precious time on the complexities of setting up the AWS Neuron SDK and the AWS Inferentia2 driver environments. + +## 📦 Deploying a PyTorch model on Inferentia2 with NOS + +Deploying PyTorch models on AWS Inferentia2 chips presents a unique set of challenges, distinct from the experience with NVIDIA GPUs. This is primarily due to the static graph execution requirement of ASICs, requiring the user to [trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) and [compile models ahead-of-time](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html), making them less accessible to entry-level developers. In some cases, custom model tracing and compilation are essential steps to fully utilize the AWS Inferentia2 accelerators. This demands a deep understanding of the HW-specific deployment/compiler toolchain ([TensorRT](https://developer.nvidia.com/tensorrt), [AWS Neuron SDK](https://github.com/aws-neuron/aws-neuron-sdk)), the captured and data-dependent traced PyTorch graph, and the underlying HW-specific kernel/op-support to name just a few challenges. + +!!!tip "**Simplifying AI hardware access with NOS**" + NOS aims to bridge this gap and streamline the deployment process, making it more even accessible for both entry-level and expert developers to leverage the powerful inference capabilities of AWS Inferentia2 for their inference needs. + + +### 1. Define your custom `inf2` model + +In this example, we'll be using the [`inf2/embeddings`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/) sentence embedding tutorial on [NOS](https://github.com/autonomi-ai/nos). First, we'll define our custom [`EmbeddingServiceInf2`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L24) model [`models/embeddings_inf2.py`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py) and a [`serve.yaml`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/serve.yaml) serve specification that will be used by NOS to serve our model on the AWS Inferentia2 device. The relevant files are shown below: + +
+ +```title="Directory structure of nos/examples/inf2/embeddings" +$ tree . +├── job-inf2-embeddings-deployment.yaml +├── models +│   └── embeddings_inf2.py (1) +├── README.md +├── serve.yaml (2) +└── tests + ├── test_embeddings_inf2_client.py (3) + └── test_embeddings_inf2.py +``` +
+1. Main python module that defines the [`EmbeddingServiceInf2`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L24) model. +2. The [`serve.yaml`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/serve.yaml) serve specification that defines the custom `inf2` runtime and registers the [`EmbeddingServiceInf2`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L24) model with NOS. +3. The pytest test for calling the [`EmbeddingServiceInf2`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L24) service via gRPC. + + +The embeddings interface is defined in the [`EmbeddingServiceInf2`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L24) module in [`models/embeddings_inf2.py`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py), where the [`__call__`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/models/embeddings_inf2.py#L67) method returns the embedding of the text prompt using [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) embedding model. + +### 2. Define the custom `inf2` runtime with the NOS serve specification + +The [`serve.yaml`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/serve.yaml) serve specification defines the custom embedding model, and a custom `inf2` runtime that NOS uses to execute our model. Follow the annotations below to understand the different components of the serve specification. + +
+ +```yaml title="serve.yaml" +images: + embeddings-inf2: + base: autonomi/nos:latest-inf2 (1) + env: + NOS_LOGGING_LEVEL: DEBUG + NOS_NEURON_CORES: 2 + run: + - python -m pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com + - pip install sentence-transformers (2) + +models: + BAAI/bge-small-en-v1.5: + model_cls: EmbeddingServiceInf2 + model_path: models/embeddings_inf2.py + default_method: __call__ + runtime_env: embeddings-inf2 (3) +``` +
+1. Specifies the base runtime image to use - we use the pre-baked `autonomi/nos:latest-inf2` runtime image to build our custom runtime image. This custom NOS runtime comes pre-installed with the AWS Neuron drivers and the AWS Neuron SDK. +2. Installs the `sentence-transformers` library, which is used to embed the text prompt using the `BAAI/bge-small-en-v1.5` model. +3. Specifies the custom runtime environment to use for the specific model deployment - `embeddings-inf2` - which is used to execute the `EmbeddingServiceInf2` model. + +In this example, we'll be using the [Huggingface Optimum](https://github.com/huggingface/optimum-neuron) library to help us simplify the deployment process to the Inf2 chip. However, for custom model architectures and optimizations, we have built our own PyTorch tracer and compiler for a growing list of popular models on the [Huggingface Hub](https://huggingface.co/models). + +???question "Need support for custom models on AWS Inferentia2?" + If you're interested in deploying a custom model on the AWS Inferentia2 chip with NOS, please reach out to us on our [GitHub Issues](https://github.com/autonomi-ai/nos/issues) page or at [support@autonomi.ai](mailto:support@autonomi.ai), and we'll be happy to help you out. + +### 3. Deploy the embedding service on AWS `inf2.xlarge` with SkyPilot + +Now that we have defined our custom model, let's deploy this service on [AWS Inferentia2](https://aws.amazon.com/en/ec2/instance-types/inf2/) using [SkyPilot](https://github.com/skypilot-org/skypilot). In this example, we're going to use SkyPilot's `sky launch` command to deploy our NOS service on an AWS `inf2.xlarge` on-demand instance. + +Before we launch the service, let's look at the [`job-inf2-embeddings-deployment.yaml`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/job-inf2-embeddings-deployment.yaml) file that we will use to provision the `inf2` instance and deploy the `EmbeddingServiceInf2` model. + +
+ +```yaml title="job-inf2-embeddings-deployment.yaml" + +file_mounts: (1) + /app: . + +resources: + cloud: aws + region: us-west-2 + instance_type: inf2.xlarge (2) + image_id: ami-096319086cc3d5f23 # us-west-2 (3) + disk_size: 256 + ports: 8000 + +setup: | + sudo apt-get install -y docker-compose-plugin + + cd /app + cd /app && python3 -m venv .venv && source .venv/bin/activate + pip install git+https://github.com/autonomi-ai/nos.git pytest (4) + +run: | + source /app/.venv/bin/activate + cd /app && nos serve up -c serve.yaml --http (5) + +``` +
+1. Mounts the local `./app` directory so that the `serve.yaml`, `models/` and `tests/` directories are available on the remote instance. +2. Specifies the AWS Inferentia2 instance type to use - we use the `inf2.xlarge` instance type. +3. Specifies the Amazon Machine Instance (AMI) use that come pre-installed with AWS Neuron drivers. +4. We simply need `pytest` for testing the client-side logic in `tests/test_embeddings_inf2_client.py` +5. Starts the NOS server with the `serve.yaml` specification. The runtime flag `--runtime inf2` is optional, and automatically detected by NOS as illustrated here. + +!!!note "Provisioning `inf2.xlarge` instances" + To provision an `inf2.xlarge` instance, you will need to have an AWS account and the necessary [service quotas](https://console.aws.amazon.com/servicequotas/home/services/ec2/quotas/) set for the `inf2` instance nodes. For more information on service quotas, please refer to the [AWS documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-resource-limits.html). + +!!!warning "Using SkyPilot with `inf2` instances" + Due to a [job submission bug](https://github.com/skypilot-org/skypilot/issues/2968) in the SkyPilot CLI for `inf2` instances, you will need to use the `skypilot-nightly[aws]` (`pip install skypilot-nightly[aws]`) package to provision `inf2` instances correctly with the `sky launch` command below. + +Let's deploy the `inf2` embeddings service using the following command: +```bash +sky launch -c inf2-embeddings-service job-inf2-embeddings-deployment.yaml +``` + +???success "`sky launch` output" + You should see the following output from the `sky launch` command: + ```bash + (nos-infra-py38) inf2/embeddings spillai-desktop [ sky launch -c inf2-embeddings-service job-inf2-embeddings-deployment.yaml + Task from YAML spec: job-inf2-embeddings-deployment.yaml + I 01-31 21:48:06 optimizer.py:694] == Optimizer == + I 01-31 21:48:06 optimizer.py:717] Estimated cost: $0.8 / hour + I 01-31 21:48:06 optimizer.py:717] + I 01-31 21:48:06 optimizer.py:840] Considered resources (1 node): + I 01-31 21:48:06 optimizer.py:910] ------------------------------------------------------------------------------------------ + I 01-31 21:48:06 optimizer.py:910] CLOUD INSTANCE vCPUs Mem(GB) ACCELERATORS REGION/ZONE COST ($) CHOSEN + I 01-31 21:48:06 optimizer.py:910] ------------------------------------------------------------------------------------------ + I 01-31 21:48:06 optimizer.py:910] AWS inf2.xlarge 4 16 Inferentia:1 us-west-2 0.76 ✔ + I 01-31 21:48:06 optimizer.py:910] ------------------------------------------------------------------------------------------ + I 01-31 21:48:06 optimizer.py:910] + Launching a new cluster 'inf2-embeddings-service'. Proceed? [Y/n]: y + I 01-31 21:48:18 cloud_vm_ray_backend.py:4389] Creating a new cluster: 'inf2-embeddings-service' [1x AWS(inf2.xlarge, {'Inferentia': 1}, image_id={'us-west-2': 'ami-096319086cc3d5f23'}, ports=['8000'])]. + I 01-31 21:48:18 cloud_vm_ray_backend.py:4389] Tip: to reuse an existing cluster, specify --cluster (-c). Run `sky status` to see existing clusters. + I 01-31 21:48:18 cloud_vm_ray_backend.py:1386] To view detailed progress: tail -n100 -f /home/spillai/sky_logs/sky-2024-01-31-21-48-06-108390/provision.log + I 01-31 21:48:19 provisioner.py:79] Launching on AWS us-west-2 (us-west-2a,us-west-2b,us-west-2c,us-west-2d) + I 01-31 21:49:37 provisioner.py:429] Successfully provisioned or found existing instance. + I 01-31 21:51:03 provisioner.py:531] Successfully provisioned cluster: inf2-embeddings-service + I 01-31 21:51:04 cloud_vm_ray_backend.py:4418] Processing file mounts. + I 01-31 21:51:05 cloud_vm_ray_backend.py:4450] To view detailed progress: tail -n100 -f ~/sky_logs/sky-2024-01-31-21-48-06-108390/file_mounts.log + I 01-31 21:51:05 backend_utils.py:1286] Syncing (to 1 node): . -> ~/.sky/file_mounts/app + I 01-31 21:51:06 cloud_vm_ray_backend.py:3158] Running setup on 1 node. + ... + (task, pid=23904) ✓ Launching docker compose with command: docker compose -f + (task, pid=23904) /home/ubuntu/.nos/tmp/serve/docker-compose.app.yml up + (task, pid=23904) Network serve_default Creating + (task, pid=23904) Network serve_default Created + (task, pid=23904) Container serve-nos-server-1 Creating + (task, pid=23904) Container serve-nos-server-1 Created + (task, pid=23904) Container serve-nos-http-gateway-1 Creating + (task, pid=23904) Container serve-nos-http-gateway-1 Created + (task, pid=23904) Attaching to serve-nos-http-gateway-1, serve-nos-server-1 + (task, pid=23904) serve-nos-http-gateway-1 | WARNING: Current configuration will not reload as not all conditions are met, please refer to documentation. + (task, pid=23904) serve-nos-server-1 | ✓ InferenceExecutor :: Backend initializing (as daemon) ... + (task, pid=23904) serve-nos-server-1 | ✓ InferenceExecutor :: Backend initialized (elapsed=2.9s). + (task, pid=23904) serve-nos-server-1 | ✓ InferenceExecutor :: Connected to backend. + (task, pid=23904) serve-nos-server-1 | ✓ Starting gRPC server on [::]:50051 + (task, pid=23904) serve-nos-server-1 | ✓ InferenceService :: Deployment complete (elapsed=0.0s) + (task, pid=23904) serve-nos-server-1 | (EmbeddingServiceInf2 pid=404) 2024-01-31 21:53:58.566 | INFO | nos.neuron.device:setup_environment:36 - Setting up neuron env with 2 cores + ... + (task, pid=23904) serve-nos-server-1 | (EmbeddingServiceInf2 pid=404) 2024-02-01T05:54:36Z Compiler status PASS + (task, pid=23904) serve-nos-server-1 | (EmbeddingServiceInf2 pid=404) 2024-01-31 21:54:46.928 | INFO | EmbeddingServiceInf2:__init__:61 - Saved model to /app/.nos/cache/neuron/BAAI/bge-small-en-v1.5-bs-1-sl-384 + (task, pid=23904) serve-nos-server-1 | (EmbeddingServiceInf2 pid=404) 2024-01-31 21:54:47.037 | INFO | EmbeddingServiceInf2:__init__:64 - Loaded neuron model: BAAI/bge-small-en-v1.5 + ... + (task, pid=23904) serve-nos-server-1 | 2024-01-31 22:25:43.710 | INFO | nos.server._service:Run:360 - Executing request [model=BAAI/bge-small-en-v1.5, method=None] + (task, pid=23904) serve-nos-server-1 | 2024-01-31 22:25:43.717 | INFO | nos.server._service:Run:362 - Executed request [model=BAAI/bge-small-en-v1.5, method=None, elapsed=7.1ms] + ``` + +Once complete, you should see the following (trimmed) output from the `sky launch` command: +```bash +✓ InferenceExecutor :: Backend initializing (as daemon) ... +✓ InferenceExecutor :: Backend initialized (elapsed=2.9s). +✓ InferenceExecutor :: Connected to backend. +✓ Starting gRPC server on [::]:50051 +✓ InferenceService :: Deployment complete (elapsed=0.0s) +Setting up neuron env with 2 cores +... +Compiler status PASS +Saved model to /app/.nos/cache/neuron/BAAI/bge-small-en-v1.5-bs-1-sl-384 +Loaded neuron model: BAAI/bge-small-en-v1.5 +``` + + +### 3. Test your custom model on AWS Inf2 instance + +Once the service is deployed, you should be able to simply make a cURL request to the `inf2` instance to test the server-side logic of the embeddings model. + +=== "Using cURL (remote)" + + ```bash + export IP=$(sky status --ip inf2-embeddings-service) + + curl \ + -X POST http://${IP}:8000/v1/infer \ + -H 'Content-Type: application/json' \ + -d '{ + "model_id": "BAAI/bge-small-en-v1.5", + "inputs": { + "texts": ["fox jumped over the moon"] + } + }' + ``` + +=== "Using the gRPC client (on the `inf2` instance)" + + Optionally, you can also test the gRPC service using the provided [`tests/test_embeddings_inf2_client.py`](https://github.com/autonomi-ai/nos/blob/main/examples/inf2/embeddings/tests/test_embeddings_inf2_client.py). For this test however, you'll need to ssh into the `inf2` instance and run the following command. + + ```bash + ssh inf2-embeddings-service + ``` + + Once you're on the `inf2.xlarge` instance, you can run `pytest -sv tests/test_embeddings_inf2_client.py` to test the server-side logic of the embeddings model. + + ```bash + $ pytest -sv tests/test_embeddings_inf2_client.py + ``` + + Here's a simplified version of the test to execute the embeddings model. + + ```python + from nos.client import Client + + # Create the client + client = Client("[::]:50051") + assert client.WaitForServer() + + # Load the embeddings model + model = client.Module("BAAI/bge-small-en-v1.5") + + # Embed text with the model + texts = "What is the meaning of life?" + response = model(texts=texts) + ``` + + +## 🤑 What's it going to cost me? + +The table below shows the costs of deploying one of these *latency-optimized* (`bsize=1`) embedding services on a single Inf2 instance on AWS. While the costs are only one part of the equation, it is important to note that the AWS Inf2 instances are ~25% cheaper than the NVIDIA A10G instances, and offer a more cost-effective solution for inference workloads on AWS. In the coming weeks, we'll be digging into evaluating the performance of the Inf2 instances with respect to their NVIDIA GPU counterparts on inference metrics such as latency/throughput and cost metrics such as number of requests / $, montly costs etc. + +| Model | Cloud Instance | Spot | Cost / hr | Cost / month | # of Req. / $ | +| ----- | -------------- | ---- | --------- | ------------ | ---------- | +| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | `inf2.xlarge` | - | $0.75 | ~$540 | ~685K / $1 | +| **[BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)** | `inf2.xlarge` | ✅ | **$0.32** | **~$230** | ~1.6M / $1 | + + +## 🎁 Wrapping up + +In this post, we introduced the new NOS **[`inf2`](../../concepts/runtime-environments.md#🏃‍♂️-supported-runtimes)** runtime that allows developers to easily develop, and serve models on the [AWS Inferentia2](https://aws.amazon.com/en/ec2/instance-types/inf2/) chip. With more cost-efficient, and inference-optimized chips coming to market ([Google TPUs](https://cloud.google.com/tpu/docs/v5e-inference), [Groq](https://groq.com/products/), [Tenstorrent](https://tenstorrent.com/cards/) etc), we believe it is important for developers to be able to easily access and deploy models on these devices. The specialized [NOS Inference Runtime](../../concepts/runtime-environments.md#⚡️-nos-inference-runtime) aims to do just that - a fast, and frictionless way to deploy models onto any of the AI accelerators, be it NVIDIA GPUs or AWS Inferentia2 chips, in the cloud, or on-prem. + +Thanks for reading, and we hope you found this post useful - and finally, give [NOS](https://github.com/autonomi-ai/nos) a try. If you have any questions, or would like to learn more about the [NOS](https://github.com/autonomi-ai/nos) `inf2` runtime, please reach out to us on our [GitHub Issues](https://github.com/autonomi-ai/nos/issues) page or join us on [Discord](https://discord.gg/QAGgvTuvgg). +
\ No newline at end of file diff --git a/docs/concepts/runtime-environments.md b/docs/concepts/runtime-environments.md index 92105366..17e18a7e 100644 --- a/docs/concepts/runtime-environments.md +++ b/docs/concepts/runtime-environments.md @@ -1,4 +1,4 @@ -The NOS inference server supports custom runtime environments through the use of the `InferenceServiceRuntime` class and the [configurations](../api/server.md#InferenceServiceRuntime.configs) defined within. This class provides a high-level interfa.ce for defining new custom runtime environments that can be used with NOS. +The NOS inference server supports custom runtime environments through the use of the `InferenceServiceRuntime` class and the [configurations](../api/server.md#InferenceServiceRuntime.configs) defined within. This class provides a high-level interface for defining new custom runtime environments that can be used with NOS. ### ⚡️ NOS Inference Runtime From e72d2418260ec12f1ef61f09be7e5e5b60cdf72f Mon Sep 17 00:00:00 2001 From: Scott <106728726+outtanames@users.noreply.github.com> Date: Thu, 1 Feb 2024 13:06:22 -0800 Subject: [PATCH 2/9] Add transcription interface for raw numpy arrays (#491) ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- nos/models/whisper.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/nos/models/whisper.py b/nos/models/whisper.py index ebe29049..6c470800 100644 --- a/nos/models/whisper.py +++ b/nos/models/whisper.py @@ -2,6 +2,7 @@ from pathlib import Path from typing import Any, Dict, List +import numpy as np import torch from nos import hub @@ -89,6 +90,16 @@ def transcribe( ) return response["chunks"] + def transcribe_raw( + self, raw: np.ndarray, chunk_length_s: int = 30, batch_size: int = 24, return_timestamps: bool = True + ) -> List[Dict[str, Any]]: + """Transcribe the audio file.""" + with torch.inference_mode(): + response = self.pipe( + raw, chunk_length_s=chunk_length_s, batch_size=batch_size, return_timestamps=return_timestamps + ) + return response["chunks"] + for model_name in Whisper.configs: cfg = Whisper.configs[model_name] @@ -101,3 +112,13 @@ def transcribe( inputs={"path": Path, "chunk_length_s": int, "batch_size": int, "return_timestamps": bool}, outputs={"chunks": List[Dict[str, Any]]}, ) + + hub.register( + model_name, + TaskType.AUDIO_TRANSCRIPTION, + Whisper, + init_args=(model_name,), + method="transcribe_raw", + inputs={"raw": np.ndarray, "chunk_length_s": int, "batch_size": int, "return_timestamps": bool}, + outputs={"chunks": List[Dict[str, Any]]}, + ) From ccd74e80ec5dbebdc010e4ecda3efd9cb0ba7251 Mon Sep 17 00:00:00 2001 From: Scott <106728726+outtanames@users.noreply.github.com> Date: Thu, 1 Feb 2024 15:50:33 -0800 Subject: [PATCH 3/9] Revert "Enable cli test (#529)" (#541) `test_cli_serve.py` failing on main: https://github.com/autonomi-ai/nos/actions/runs/7748335133/job/21130768377 ## Summary ## Related issues ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- nos/cli/serve.py | 18 +++++------------- pyproject.toml | 2 +- tests/cli/test_cli_serve.py | 25 +++++++------------------ 3 files changed, 13 insertions(+), 32 deletions(-) diff --git a/nos/cli/serve.py b/nos/cli/serve.py index d6b056cc..83a89946 100644 --- a/nos/cli/serve.py +++ b/nos/cli/serve.py @@ -431,28 +431,20 @@ def _serve( @serve_cli.command("down", help="Tear down the NOS server.") def _serve_down( - target: str = typer.Option(None, "--target", help="Tear down a specific target.", show_default=True), verbose: bool = typer.Option(False, "-v", "--verbose", help="Verbose output.", show_default=False), ) -> None: """Main entrypoint for teardown .""" from nos.common.system import docker_compose_command from nos.logging import logger - if target is not None: - from nos.constants import NOS_HOME - - compose_path = NOS_HOME / f"tmp/serve/docker-compose.{target}.yml" - if not compose_path.exists(): - raise FileNotFoundError(f"Target {target} compose file not found, cannot tear down.") - else: - sandbox_name: str = Path.cwd().name - compose_path = Path.cwd() / f"docker-compose.{sandbox_name}.yml" - if not compose_path.exists(): - raise FileNotFoundError(f"File {compose_path} not found, cannot tear down.") + sandbox_name: str = Path.cwd().name + compose_path = Path.cwd() / f"docker-compose.{sandbox_name}.yml" + if not compose_path.exists(): + raise FileNotFoundError(f"File {compose_path} not found, cannot tear down.") # Spin down the docker compose print(f"[green]✓[/green] Tearing down docker compose with command: [bold white]{compose_path.name} down") - cmd = f"{docker_compose_command()} -f {compose_path} down" + cmd = f"{docker_compose_command} -f {compose_path.name} down" proc = subprocess.run(cmd, shell=True) if proc.returncode != 0: logger.error(f"Failed to tear down, e={proc.stderr}") diff --git a/pyproject.toml b/pyproject.toml index 7d97fb0d..0571aa5b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,7 +81,7 @@ exclude = [ [tool.setuptools.package-data] -"*" = ["*.json", "*.proto", "*.j2", "test_data/**/*.*"] +"*" = ["*.json", "*.proto", "*.j2", "test_data/*.*"] [tool.black] line-length = 119 diff --git a/tests/cli/test_cli_serve.py b/tests/cli/test_cli_serve.py index 2b1b2b44..e2fcb1d6 100644 --- a/tests/cli/test_cli_serve.py +++ b/tests/cli/test_cli_serve.py @@ -1,4 +1,3 @@ -import contextlib import os from pathlib import Path from typing import Union @@ -11,7 +10,6 @@ runner = CliRunner() -@contextlib.contextmanager def path_ctx(path: Union[Path, str]): prev_cwd = Path.cwd() os.chdir(str(path)) @@ -26,24 +24,15 @@ def test_cli_serve_help(): assert result.exit_code == 0 -def test_cli_serve(): +@pytest.mark.skip +def test_cli_serve_up(): from nos.cli.cli import app_cli - from nos.test.utils import NOS_TEST_DATA_DIR + from nos.tests.utils import NOS_TEST_DATA_DIR - config_path = NOS_TEST_DATA_DIR / "hub/custom_model/config.yaml" # Move to the test data directory - with path_ctx(config_path.parent): - serve_up_result = runner.invoke(app_cli, ["serve", "up", "-c", "config.yaml", "-d"]) - assert serve_up_result.exit_code == 0 + path = NOS_TEST_DATA_DIR / "hub/custom_model/config.yaml" - from nos.client import Client - - # Wait for the server to be ready - client = Client() - client.WaitForServer() - assert client.IsHealthy() - - # Tear down the model - serve_down_result = runner.invoke(app_cli, ["serve", "down", "--target", "custom_model"]) - assert serve_down_result.exit_code == 0 + with path_ctx(path.parent): + result = runner.invoke(app_cli, ["serve", "-c", "config.yaml", "up"]) + assert result.exit_code == 0 From 7ced196545f00c0eed7b3e1fa856131d53cdaf43 Mon Sep 17 00:00:00 2001 From: Scott <106728726+outtanames@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:59:03 -0800 Subject: [PATCH 4/9] Add device name as profile column (#539) Adds torch device name (e.g. "nvidia-geforce-rtx-2080") to profiling output. ## Checks - [x] `make lint`: I've run `make lint` to lint the changes in this PR. - [x] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- nos/cli/profile.py | 19 +++++++++++++++---- nos/common/io/__init__.py | 1 - nos/common/profiler.py | 1 - 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/nos/cli/profile.py b/nos/cli/profile.py index b23cff19..acf87234 100644 --- a/nos/cli/profile.py +++ b/nos/cli/profile.py @@ -104,6 +104,7 @@ def profile_models( get_inputs=lambda: _model_inputs(task=TaskType.IMAGE_EMBEDDING, shape=(224, 224), batch_size=1), batch_size=1, shape=(224, 224), + device_name=profiler.device_name, ), ) elif task == TaskType.TEXT_EMBEDDING: @@ -114,6 +115,7 @@ def profile_models( get_inputs=lambda: _model_inputs(task=TaskType.TEXT_EMBEDDING, batch_size=1), batch_size=1, shape=None, + device_name=profiler.device_name, ), ) elif task == TaskType.OBJECT_DETECTION_2D: @@ -126,6 +128,7 @@ def profile_models( ), batch_size=1, shape=(640, 480), + device_name=profiler.device_name, ), ) elif task == TaskType.IMAGE_GENERATION: @@ -136,6 +139,7 @@ def profile_models( get_inputs=lambda: _model_inputs(task=TaskType.IMAGE_GENERATION, batch_size=1), batch_size=1, shape=None, + device_name=profiler.device_name, ), ) elif task == TaskType.AUDIO_TRANSCRIPTION: @@ -146,6 +150,7 @@ def profile_models( get_inputs=lambda: _model_inputs(task=TaskType.AUDIO_TRANSCRIPTION, batch_size=1), batch_size=1, shape=None, + device_name=profiler.device_name, ), ) elif task == TaskType.DEPTH_ESTIMATION_2D: @@ -158,6 +163,7 @@ def profile_models( ), batch_size=1, shape=(640, 480), + device_name=profiler.device_name, ), ) elif task == TaskType.IMAGE_SEGMENTATION_2D: @@ -170,6 +176,7 @@ def profile_models( ), batch_size=1, shape=(640, 480), + device_name=profiler.device_name, ), ) elif task == TaskType.IMAGE_SUPER_RESOLUTION: @@ -182,6 +189,7 @@ def profile_models( ), batch_size=1, shape=(160, 120), + device_name=profiler.device_name, ), ) else: @@ -261,7 +269,8 @@ def _profile_list( table.add_column("method") table.add_column("task") table.add_column("runtime") - table.add_column("device") + table.add_column("device_type") + table.add_column("device_name") table.add_column("it/s") table.add_column("cpu_memory") table.add_column("cpu_util") @@ -276,7 +285,7 @@ def _profile_list( try: if hasattr(metadata, "resources") and metadata.resources is not None: runtime = metadata.resources.runtime - device = "-".join(metadata.resources.device.split("-")[-2:]) + device_type = "-".join(metadata.resources.device.split("-")[-2:]) cpu_memory = metadata.resources.memory if type(cpu_memory) != str: cpu_memory = f"{humanize.naturalsize(metadata.resources.memory, binary=True)}" @@ -286,6 +295,7 @@ def _profile_list( it_s = f'{profile["profiling_data"]["forward::execution"]["num_iterations"] * 1e3 / profile["profiling_data"]["forward::execution"]["total_ms"]:.1f}' cpu_util = f'{profile["profiling_data"]["forward::execution"]["cpu_utilization"]:0.2f}' gpu_util = f'{profile["profiling_data"]["forward::execution"]["gpu_utilization"]:0.2f}' + device_name = f'{profile["device_name"]}' except Exception as e: logger.debug("Failed to load metadata: ", e) it_s = "-" @@ -293,13 +303,14 @@ def _profile_list( gpu_util = "-" cpu_memory = "-" gpu_memory = "-" - runtime, device = None, None + runtime, device_type, device_name = None, None, None table.add_row( f"[green]{model}[/green]", method, spec.task(method), runtime, - device, + device_type, + device_name, it_s, cpu_memory, cpu_util, diff --git a/nos/common/io/__init__.py b/nos/common/io/__init__.py index df1d1f1d..ef35eaea 100644 --- a/nos/common/io/__init__.py +++ b/nos/common/io/__init__.py @@ -36,5 +36,4 @@ def prepare_images(images: Union[Image.Image, np.ndarray, List[Image.Image], Lis raise TypeError(f"Invalid type for images: {type(images[0])}") else: raise TypeError(f"Invalid type for images: {type(images)}") - # import pdb; pdb.set_trace() return images diff --git a/nos/common/profiler.py b/nos/common/profiler.py index 7011ea4a..05b542fa 100644 --- a/nos/common/profiler.py +++ b/nos/common/profiler.py @@ -476,7 +476,6 @@ def run(self) -> None: print(f"[white]{self}[/white]") from nos.constants import NOS_PROFILE_CATALOG_PATH - # import pdb; pdb.set_trace() self.profiling_data = Profiler.from_json_path(NOS_PROFILE_CATALOG_PATH) with torch.inference_mode(): for _idx, request in enumerate(self.requests): From 7e8786a14a79b3b8faf207900568d8bf2c2976e7 Mon Sep 17 00:00:00 2001 From: Jim Burtoft <39492751+jimburtoft@users.noreply.github.com> Date: Fri, 2 Feb 2024 21:13:05 -0500 Subject: [PATCH 5/9] Fix code in docs example (#544) The code you have tries to send the second line as a parameter to the first. You can add the && or you can delete the backslash on the previous line. ## Summary ## Related issues ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- docs/quickstart.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/quickstart.md b/docs/quickstart.md index 85c4a987..33322c43 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -8,7 +8,7 @@ You will need to install [Docker](https://docs.docker.com/get-docker/), [Nvidia On Linux, you can install Docker and Docker Compose via the following commands: ```bash sudo apt-get update \ - sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin \ + && sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin \ && sudo systemctl restart docker ``` From 03f9c3bb0b26bd6319b59cdbccce4e603e0fa410 Mon Sep 17 00:00:00 2001 From: Sudeep Pillai Date: Fri, 2 Feb 2024 21:38:28 -0800 Subject: [PATCH 6/9] Minor updates to SDXL inf2 example --- examples/inf2/sdxl/README.md | 11 ++++++++--- examples/inf2/sdxl/models/sdxl_inf2.py | 4 ++-- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/examples/inf2/sdxl/README.md b/examples/inf2/sdxl/README.md index 43d8dc4f..ad93c2a2 100644 --- a/examples/inf2/sdxl/README.md +++ b/examples/inf2/sdxl/README.md @@ -14,7 +14,7 @@ nos serve up -c serve.yaml --http --runtime inf2 ### Run the tests ```bash -pytest -sv ./tests/test_embeddings_client.py +pytest -sv ./tests/test_sdxl_client.py ``` ### Call the service @@ -26,9 +26,14 @@ curl \ -X POST http://:8000/v1/infer \ -H 'Content-Type: application/json' \ -d '{ - "model_id": "BAAI/bge-small-en-v1.5", + "model_id": "stabilityai/stable-diffusion-xl-base-1.0-inf2", "inputs": { - "texts": ["fox jumped over the moon"] + "prompts": ["hippo with glasses in a library, cartoon styling"], + "width": 1024, "height": 1024, + "num_images": 1, + "num_inference_steps": 50, + "guidance_scale": 7.5, + "num_images": 1 } }' ``` diff --git a/examples/inf2/sdxl/models/sdxl_inf2.py b/examples/inf2/sdxl/models/sdxl_inf2.py index e610422a..7022a671 100644 --- a/examples/inf2/sdxl/models/sdxl_inf2.py +++ b/examples/inf2/sdxl/models/sdxl_inf2.py @@ -91,8 +91,8 @@ def __call__( num_images: int = 1, num_inference_steps: int = 50, guidance_scale: float = 7.5, - height: int = 512, - width: int = 512, + height: int = 1024, + width: int = 1024, ) -> List[Image.Image]: """Generate images from text prompt.""" From 78693236e585c307414fc13a1baeb8d1ed674ae8 Mon Sep 17 00:00:00 2001 From: Scott <106728726+outtanames@users.noreply.github.com> Date: Thu, 15 Feb 2024 13:54:26 -0800 Subject: [PATCH 7/9] Add substack blog post on ChatGPT profiling experiments to NOS blog. (#542) Should match https://scottloftin.substack.com/p/lets-build-an-ml-sre-bot-with-nos ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- docs/blog/assets/clip_embedding_times.png | Bin 0 -> 177739 bytes docs/blog/assets/clip_speed.png | Bin 0 -> 101333 bytes docs/blog/assets/nos_profile_list.png | Bin 0 -> 234392 bytes .../assets/reserved_vs_on_demand_first.png | Bin 0 -> 131348 bytes .../assets/reserved_vs_on_demand_second.png | Bin 0 -> 139685 bytes docs/blog/assets/t4_laion_price.png | Bin 0 -> 96373 bytes .../posts/05-playing-with-nos-profiler.md | 137 ++++++++++++++++++ 7 files changed, 137 insertions(+) create mode 100644 docs/blog/assets/clip_embedding_times.png create mode 100644 docs/blog/assets/clip_speed.png create mode 100644 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b/docs/blog/posts/05-playing-with-nos-profiler.md @@ -0,0 +1,137 @@ +--- +date: 2024-02-02 +tags: + - integrations + - skypilot + - chatGPT +categories: + - infra + - embeddings +authors: + - sloftin +links: + - posts/05-playing-with-nos-profiler.md +--- + +# OK Computer, Why are you slow? + +(Originally published at https://scottloftin.substack.com/p/lets-build-an-ml-sre-bot-with-nos) + +In this post, we'll be going over a set of experiments we've run with the NOS profiler and Skypilot to automatically answer questions about your infra using retrieval +on top of pricing and performance data. + +Figuring out the best platform for a given model begins with benchmarking, and unfortunately today this is still somewhat painful on Nvidia hardware let alone other platforms. Most folks rely on leaderboards published by Anyscale, Huggingface, Martian (my personal favorite!), and many others, but setting aside debates over methodology and fairness, we are still mostly looking at quoted numbers for a top-level inference API without a lot of knobs to turn on the underlying HW. NOS provides a profiling tool that can benchmark Text/Image embedding, Image Generation and Language across GPUs and across clouds. Let’s start with some local numbers on a 2080: + +```bash +nos profile method --method encode_image +nos profile list +``` + + + +We see a breakdown across four different image embedding models including the method and task (interchangeable in this case, each CLIP variant will support both Image and Text embedding as methods), the Iterations per Second, GPU memory footprint (how much space did this model have to allocate) and finally the GPU utilization, which measures (in a very broad sense) how efficiently we are using the HW. A few things to note: the image size is fixed to 224X224X1 across all runs with a batch size of 1. In practice, the Iterations/Second will depend tremendously on tuning the batch size and image resolution for our target HW, which will be the subject of a followup post. For now we’ll take these (admittedly naive) numbers at face value and see what we can work out about how exactly to run a large embedding workload. We’re going to use a fantastic tool called Skypilot to deploy the profiler to a Tesla T4 instance on GCP: + +```bash +sky launch -c nos-profiling-service skyserve.dev.yaml --gpus=t4 +``` + +This will run the above model variants on our chosen instance and write everything to a bucket in GCS, which I’ve already downloaded: + +``` +[ + { + "namespace":"nos::openai\/clip-vit-base-patch32", + "profiling_data":{ + "init::memory_gpu::pre":1001259008, + "init::memory_cpu::pre":18951331840, + "init::memory_gpu::post":1640890368, + "init::memory_cpu::post":19440001024, + "forward::memory_gpu::pre":1640890368, + "forward::memory_cpu::pre":19440001024, + "forward::memory_gpu::post":1642987520, + "forward::memory_cpu::post":19440001024, + "forward_warmup::execution":{ + "num_iterations":436, + "total_ms":2003.6902427673, + "cpu_utilization":9.8, + "gpu_utilization":38 + }, + "forward::execution":{ + "num_iterations":1101, + "total_ms":5002.1767616272, + "cpu_utilization":9.8, + "gpu_utilization":38 + }, + "cleanup::memory_gpu::pre":1642987520, + "cleanup::memory_cpu::pre":19443097600, + "cleanup::memory_gpu::post":1003356160, + "cleanup::memory_cpu::post":19445149696,... +``` + +This snippet shows a breakdown of memory allocation and runtime across various stages of execution (we’re mostly interested in forward::execution). Even this short list of profiling info is quite a lot to pick apart: we would ordinarily be doing a lot of envelope math and maybe scrub through each iteration in the chrome profiling tool, but lets see if we can streamline things a bit with more modern tools. + +## 🤖 Our InfraBot Chat Assistant + +The OpenAI assistants API is somewhat unstable at the moment, but after a few tries it was able to ingest the profiling catalog as raw JSON and answer a few questions about its contents. Let’s start simple: + +_Hey InfraBot, can you list the models in the profiling catalog by iterations per second?_ + + + +Ok, our raw profiling data is slowly becoming more readable. Let’s see how this all scales with number of embeddings: + +_Can you compute how long it would take to generate embeddings with each model for workload sizes in powers of 10, starting at 1000 image embeddings and ending at 10,000,000. Please plot these for each model in a graph._ + + + +Reasonable: runtime will depend linearly on total embeddings (again, we’re using batch size 1 for illustration purposes). + +The more interesting question is: what hardware should we use given price constraints? While ChatGPT can probably provide a solid high level answer describing the tradeoffs between on-demand, spot and reserved instances, as well as the value of performance of the underlying card relative to time spent on other tasks like copying data, we’ll need to provide hard numbers on instance prices if we want something concrete. + +## 💵 Adding Pricing Information with Skypilot + +Skypilot proves a utility for fetching pricing and availability for a variety of instance types across the big 3 CSPs (AWS, Azure and GCP). I was able to generate a summary below (lightly edited to work with Substack formatting): + +``` +GPU QTY CLOUD INSTANCE_TYPE HOURLY_PRICE HOURLY_SPOT_PRICE +T4 1 AWS g4dn.xlarge $ 0.526 $ 0.063 +T4 1 AWS g4dn.2xlarge $ 0.752 $ 0.091 +T4 1 AWS g4dn.4xlarge $ 1.204 $ 0.141 +T4 1 AWS g4dn.8xlarge $ 2.176 $ 0.255 +T4 1 AWS g4dn.16xlarge $ 4.352 $ 0.483 +T4 4 AWS g4dn.12xlarge $ 3.912 $ 0.528 +T4 8 AWS g4dn.metal $ 7.824 $ 0.915 +T4 1 Azure Standard_NC4as_T4_v3 $ 0.526 $ 0.053 +T4 1 Azure Standard_NC8as_T4_v3 $ 0.752 $ 0.075 +T4 1 Azure Standard_NC16as_T4_v3 $ 1.204 $ 0.120 +T4 4 Azure Standard_NC64as_T4_v3 $ 4.352 $ 0.435 +T4 1 GCP (attachable) $ 0.350 $ 0.070 +T4 2 GCP (attachable) $ 0.700 $ 0.140 +T4 4 GCP (attachable) $ 1.400 $ 0.279 +``` + +Ok, lets add some dollar signs to our plot above: + +_Can you compute how much it would cost on a T4 with 1 GPU to generate embeddings with the cheapest model for workloads of powers of 10, starting at 1000 image embeddings and ending at 10,000,000. Please plot these in a graph._ + + + +The above looks reasonable assuming a minimum reservation of 1 hour (we aren’t doing serverless, we need to pay for the whole instance for the whole hour in our proposed cloud landscape). For 10 million embeddings, the total is something like 13 hours, so assuming an on-demand price of $0.35 we have 0.35*13 ~= 0.455, pretty close to the graph. But what if we wanted to index something like YouTube with ~500PB of videos? Ok, maybe not the whole site, but a substantial subset, maybe 10^11 images: if we extrapolate the above we’re looking at $40,000 in compute, which we would probably care about fitting to our workload. In particular, we might go with a reserved rather than an on-demand instance for a ~%50 discount, but at what point does that pay off? Unfortunately at time of writing, Skypilot doesn’t seem to include reserved instance pricing by default, but for a single instance type it’s easy enough to track down and feed to InfraBot: a 1 Year commitment brings us down to $0.220 per GPU, and a 3 Year commitment to $0.160 per GPU. It’s still higher than the spot price of course, but at this scale its reasonable to assume some SLA that prevents us from halting indexing on preemption. Let’s see if we can find a break-even point. + +_Can you add the cost to reserve a 1 and 3 year instance? A 1 year reservation is $0.220 per gou per hour, and a 3 year reservation is $0.160 per gpu per hour._ + + + +Looks like we need to go a little further to the right + +_Ok can you do the same plot, but at 10^9, 10^10, and 10^11_ + + + +10^10 embeddings at $0.35/hr is about $4,860, so this looks roughly correct. 10 Billion embeddings is about 100,000 Hours of (low resolution) video at full 30FPS, so while it’s quite large its not completely unheard of for a larger video service. + + +## The Verdict? + +This is a very simple example of a line of questioning that might confront someone managing an ML service, with a fairly simple answer that was straightforward to generate with an already non-bleeding-edge LLM. With the right catalog data and a lot of patience, it stands to reason that we could answer far more sophisticated questions about inference costs and even automatically act on these decision points by generating Infra-as-Code on the fly. In any case, it’s a useful way to pick apart what are normally very opaque inference stats, and something I’ve been pulling out of my own toolkit more and more. Just make sure to check the math every now and again! + From c579af955e4e41dc936e82594ffe00ed925f205f Mon Sep 17 00:00:00 2001 From: Sudeep Pillai Date: Mon, 19 Feb 2024 02:35:39 -0800 Subject: [PATCH 8/9] [docs] Updated blog --- ...ying-with-nos-profiler.md => 05-playing-with-nos-profiler.mdx} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/blog/posts/{05-playing-with-nos-profiler.md => 05-playing-with-nos-profiler.mdx} (100%) diff --git a/docs/blog/posts/05-playing-with-nos-profiler.md b/docs/blog/posts/05-playing-with-nos-profiler.mdx similarity index 100% rename from docs/blog/posts/05-playing-with-nos-profiler.md rename to docs/blog/posts/05-playing-with-nos-profiler.mdx From 148724f6f797557b0dc10552b7c4da2ed755a873 Mon Sep 17 00:00:00 2001 From: Scott <106728726+outtanames@users.noreply.github.com> Date: Tue, 20 Feb 2024 13:13:28 -0800 Subject: [PATCH 9/9] Blog cleanup for 05 (#547) Looks like the public blog requires absolute paths for image assets. Also corrected a couple of typos that slipped through. ## Checks - [ ] `make lint`: I've run `make lint` to lint the changes in this PR. - [ ] `make test`: I've made sure the tests (`make test-cpu` or `make test`) are passing. - Additional tests: - [ ] Benchmark tests (when contributing new models) - [ ] GPU/HW tests --- ...er.mdx => 05-playing-with-nos-profiler.md} | 27 +++++++++---------- 1 file changed, 13 insertions(+), 14 deletions(-) rename docs/blog/posts/{05-playing-with-nos-profiler.mdx => 05-playing-with-nos-profiler.md} (76%) diff --git a/docs/blog/posts/05-playing-with-nos-profiler.mdx b/docs/blog/posts/05-playing-with-nos-profiler.md similarity index 76% rename from docs/blog/posts/05-playing-with-nos-profiler.mdx rename to docs/blog/posts/05-playing-with-nos-profiler.md index bb1d874e..12e115e0 100644 --- a/docs/blog/posts/05-playing-with-nos-profiler.mdx +++ b/docs/blog/posts/05-playing-with-nos-profiler.md @@ -17,8 +17,7 @@ links: (Originally published at https://scottloftin.substack.com/p/lets-build-an-ml-sre-bot-with-nos) -In this post, we'll be going over a set of experiments we've run with the NOS profiler and Skypilot to automatically answer questions about your infra using retrieval -on top of pricing and performance data. +In this post, we'll be going over a set of experiments we've run with the NOS profiler and Skypilot to automatically answer questions about your infra using retrieval on top of pricing and performance data. Figuring out the best platform for a given model begins with benchmarking, and unfortunately today this is still somewhat painful on Nvidia hardware let alone other platforms. Most folks rely on leaderboards published by Anyscale, Huggingface, Martian (my personal favorite!), and many others, but setting aside debates over methodology and fairness, we are still mostly looking at quoted numbers for a top-level inference API without a lot of knobs to turn on the underlying HW. NOS provides a profiling tool that can benchmark Text/Image embedding, Image Generation and Language across GPUs and across clouds. Let’s start with some local numbers on a 2080: @@ -27,15 +26,15 @@ nos profile method --method encode_image nos profile list ``` - + -We see a breakdown across four different image embedding models including the method and task (interchangeable in this case, each CLIP variant will support both Image and Text embedding as methods), the Iterations per Second, GPU memory footprint (how much space did this model have to allocate) and finally the GPU utilization, which measures (in a very broad sense) how efficiently we are using the HW. A few things to note: the image size is fixed to 224X224X1 across all runs with a batch size of 1. In practice, the Iterations/Second will depend tremendously on tuning the batch size and image resolution for our target HW, which will be the subject of a followup post. For now we’ll take these (admittedly naive) numbers at face value and see what we can work out about how exactly to run a large embedding workload. We’re going to use a fantastic tool called Skypilot to deploy the profiler to a Tesla T4 instance on GCP: +We see a breakdown across four different image embedding models including the method and task (interchangeable in this case, each CLIP variant will support both Image and Text embedding as methods), the Iterations per Second, GPU memory footprint (how much space did this model have to allocate) and finally the GPU utilization, which measures how efficiently we are using the HW (in a very broad sense). A few things to note: the image size is fixed to 224X224X1 across all runs with a batch size of 1. In practice, the Iterations/Second will depend tremendously on tuning the batch size and image resolution for our target HW, which will be the subject of a followup post. For now, we’ll take these numbers at face value and see what we can work out about how exactly to run a large embedding workload. We’re going to use Skypilot to deploy the profiler to a Tesla T4 instance on GCP: ```bash sky launch -c nos-profiling-service skyserve.dev.yaml --gpus=t4 ``` -This will run the above model variants on our chosen instance and write everything to a bucket in GCS, which I’ve already downloaded: +This will run the above model variants on our chosen instance and write everything to a bucket in GCS as a JSON file, which I’ve already downloaded: ``` [ @@ -68,7 +67,7 @@ This will run the above model variants on our chosen instance and write everythi "cleanup::memory_cpu::post":19445149696,... ``` -This snippet shows a breakdown of memory allocation and runtime across various stages of execution (we’re mostly interested in forward::execution). Even this short list of profiling info is quite a lot to pick apart: we would ordinarily be doing a lot of envelope math and maybe scrub through each iteration in the chrome profiling tool, but lets see if we can streamline things a bit with more modern tools. +This snippet shows a breakdown of memory allocation and runtime across various stages of execution (we’re mostly interested in forward::execution). Even this short list of profiling info is quite a lot to pick apart: we would ordinarily be doing a lot of envelope math and might scrub through each iteration in the chrome profiler, but lets see if we can streamline things a bit with more 'modern' tools. ## 🤖 Our InfraBot Chat Assistant @@ -76,13 +75,13 @@ The OpenAI assistants API is somewhat unstable at the moment, but after a few tr _Hey InfraBot, can you list the models in the profiling catalog by iterations per second?_ - + -Ok, our raw profiling data is slowly becoming more readable. Let’s see how this all scales with number of embeddings: +Ok, our raw profiling data is slowly becoming more readable. Let’s see how this all scales with the number of embeddings: _Can you compute how long it would take to generate embeddings with each model for workload sizes in powers of 10, starting at 1000 image embeddings and ending at 10,000,000. Please plot these for each model in a graph._ - + Reasonable: runtime will depend linearly on total embeddings (again, we’re using batch size 1 for illustration purposes). @@ -90,7 +89,7 @@ The more interesting question is: what hardware should we use given price constr ## 💵 Adding Pricing Information with Skypilot -Skypilot proves a utility for fetching pricing and availability for a variety of instance types across the big 3 CSPs (AWS, Azure and GCP). I was able to generate a summary below (lightly edited to work with Substack formatting): +Skypilot proves a utility for fetching pricing and availability for a variety of instance types across the big 3 CSPs (AWS, Azure and GCP). I was able to generate a summary below (lightly edited for formatting): ``` GPU QTY CLOUD INSTANCE_TYPE HOURLY_PRICE HOURLY_SPOT_PRICE @@ -114,19 +113,19 @@ Ok, lets add some dollar signs to our plot above: _Can you compute how much it would cost on a T4 with 1 GPU to generate embeddings with the cheapest model for workloads of powers of 10, starting at 1000 image embeddings and ending at 10,000,000. Please plot these in a graph._ - + -The above looks reasonable assuming a minimum reservation of 1 hour (we aren’t doing serverless, we need to pay for the whole instance for the whole hour in our proposed cloud landscape). For 10 million embeddings, the total is something like 13 hours, so assuming an on-demand price of $0.35 we have 0.35*13 ~= 0.455, pretty close to the graph. But what if we wanted to index something like YouTube with ~500PB of videos? Ok, maybe not the whole site, but a substantial subset, maybe 10^11 images: if we extrapolate the above we’re looking at $40,000 in compute, which we would probably care about fitting to our workload. In particular, we might go with a reserved rather than an on-demand instance for a ~%50 discount, but at what point does that pay off? Unfortunately at time of writing, Skypilot doesn’t seem to include reserved instance pricing by default, but for a single instance type it’s easy enough to track down and feed to InfraBot: a 1 Year commitment brings us down to $0.220 per GPU, and a 3 Year commitment to $0.160 per GPU. It’s still higher than the spot price of course, but at this scale its reasonable to assume some SLA that prevents us from halting indexing on preemption. Let’s see if we can find a break-even point. +The above looks reasonable assuming a minimum reservation of 1 hour (we aren’t doing serverless; we need to pay for the whole instance for the whole hour in our proposed cloud landscape). For 10 million embeddings, the total is something like 13 hours, so assuming an on-demand price of $0.35 we have $0.35*13 ~= $4.55, pretty close to the graph. But what if we wanted to index something like YouTube with ~500PB of videos? Ok, maybe not the whole site, but a substantial subset, maybe 10^11 images. If we extrapolate the above we’re looking at $40,000 in compute, which we would probably care about fitting to our workload. In particular, we might go with a reserved rather than an on-demand instance for a ~%50 discount, but at what point does that pay off? Unfortunately at time of writing, Skypilot doesn’t seem to include reserved instance pricing by default, but for a single instance type it’s easy enough to track down and feed to InfraBot: a 1 Year commitment brings us down to $0.220 per GPU, and a 3 Year commitment to $0.160 per GPU. It’s still higher than the spot price of course, but at this scale its reasonable to assume some SLA that prevents us from halting indexing on preemption. Let’s see if we can find a break-even point. _Can you add the cost to reserve a 1 and 3 year instance? A 1 year reservation is $0.220 per gou per hour, and a 3 year reservation is $0.160 per gpu per hour._ - + Looks like we need to go a little further to the right _Ok can you do the same plot, but at 10^9, 10^10, and 10^11_ - + 10^10 embeddings at $0.35/hr is about $4,860, so this looks roughly correct. 10 Billion embeddings is about 100,000 Hours of (low resolution) video at full 30FPS, so while it’s quite large its not completely unheard of for a larger video service.