From 27b8ee5e4430e5558ed46edea514a7d5ec141b09 Mon Sep 17 00:00:00 2001 From: hsteude Date: Wed, 4 Oct 2023 09:39:14 +0000 Subject: [PATCH 1/9] Created first running version for llama2-chat deployment in kserve --- serving/llama2/Dockerfile | 10 + serving/llama2/README.md | 92 + serving/llama2/hf-token-secret.yaml | 8 + serving/llama2/llama2_chat/__init__.py | 0 serving/llama2/llama2_chat/predictor.py | 106 + serving/llama2/main.py | 47 + serving/llama2/poetry.lock | 3979 +++++++++++++++++++++++ serving/llama2/predictor.yaml | 32 + serving/llama2/pyproject.toml | 29 + 9 files changed, 4303 insertions(+) create mode 100644 serving/llama2/Dockerfile create mode 100644 serving/llama2/README.md create mode 100644 serving/llama2/hf-token-secret.yaml create mode 100644 serving/llama2/llama2_chat/__init__.py create mode 100644 serving/llama2/llama2_chat/predictor.py create mode 100644 serving/llama2/main.py create mode 100644 serving/llama2/poetry.lock create mode 100644 serving/llama2/predictor.yaml create mode 100644 serving/llama2/pyproject.toml diff --git a/serving/llama2/Dockerfile b/serving/llama2/Dockerfile new file mode 100644 index 0000000..4f1e169 --- /dev/null +++ b/serving/llama2/Dockerfile @@ -0,0 +1,10 @@ +FROM nvcr.io/nvidia/pytorch:23.09-py3 +RUN apt-get update && apt-get install -y libglib2.0-0 +RUN mkdir /app +WORKDIR /app +COPY pyproject.toml /app/ +COPY llama2_chat /app/llama2_chat +RUN python -m pip install . +ADD main.py ./main.py +ENV PORT=8080 +EXPOSE 8080 diff --git a/serving/llama2/README.md b/serving/llama2/README.md new file mode 100644 index 0000000..cc0f742 --- /dev/null +++ b/serving/llama2/README.md @@ -0,0 +1,92 @@ +# Llama2 Deployment + +For deploying the Llama2 model, we will create a custom predictor using KServe. KServe provides a way to implement a custom model server, enabling you to bring your own models and pre/post-processing code. If you are new to creating custom predictors with KServe, you may read more about it in the [KServe documentation](https://kserve.github.io/website/0.8/modelserving/v1beta1/custom/custom_model/). + +For a gentle introduction and detailed guide on working with Llama2, consider reading this blog post: [How to prompt Llama](https://replicate.com/blog/how-to-prompt-llama). + +## Hugging Face Token + +You'll need a Hugging Face token to utilize the transformers library for downloading +the model weights. Additionally, you'll need to ask Meta for permission to use the +Llama model itself. You can read more about that [here](https://huggingface.co/blog/llama2). + +The code expects an environment variable named `HF_ACCESS_TOKEN` that holds the token as a string. + +### Setting the Variable Locally + +To set this variable for a local deployment, run: +```sh +export HF_ACCESS_TOKEN= +``` + +### Creating a Kubernetes Secret + +We've created a file called hf-token-secret.yaml in this directory to create a +Kubernetes secret that holds the access token (which we'll need for the kserve +deployment later on). First, encode the token to base64 by running: +```sh +echo -n | base64 +``` + +Now, the output of the above command can be used to create the Kubernetes secret: +```sh +sed 's/HF_ACCESS_TOKEN//' hf-token-secret.yaml | kubectl apply -n -f - +``` + +## Run/develop Locally + +To run it locally, you'll need to first install the Python dependencies. To +do so, run the following from this directory: +``` +poetry install . +export HF_ACCESS_TOKEN= +poetry run python main.py --name "llama2-chat" --device "cpu" --hf-model-string "meta-llama/Llama-2-13b-chat-hf" +``` + +### Run Query Against Local Deployment + +Once the server is up and running, you might try something like: +```sh +curl localhost:8080/v1/models/llama2-chat:predict -d '{"top_k": 3, "max_length": 200, "instances": ["Why is MLOps so important?"]}' +``` +We decided to allow the user to set the inference options for top_k (number of +samples with the best scores, from which we sample) and max_length in the API query, along with the prompt. + +## Build and Push Image + +To build the model using Docker, do: +```sh +docker build -t /llama2-chat: . +``` + +To use this image in kserve, we'll also need to push it to an image registry. +First, log in: +```sh +docker login :4567 -u -p +``` + +Note: This may require setting up a project access token in the GitLab UI first. +Navigate to: GitLab UI -> this Repository -> Settings -> Access Token -> Add a project access token. + +Now, you can push the image using Docker: +```sh +docker push /llama2-chat: +``` + +## Deploy on KServe + +Ensure there is a secret called hf-token-secret in your namespace and run this +to create the KServe inference service: +``` +sed 's/IMAGE_REGISTRY//;s/TAG//' predictor.yaml | kubectl create -n -f - +``` +Note that you might need to change the command in the `prediction.yaml` to match your hardware. + +### Run Query Against the KServe Deployment + +First, wait for the API to be ready; you can observe the progress and logs, etc., +in the Kubeflow UI under models. Once it's ready, you can copy the internal URL from +the Kubeflow UI and try: +```sh +curl -d '{"top_k": 3, "max_length": 100, "instances": ["Why is MLOps so important?"]}' +``` diff --git a/serving/llama2/hf-token-secret.yaml b/serving/llama2/hf-token-secret.yaml new file mode 100644 index 0000000..1cb8245 --- /dev/null +++ b/serving/llama2/hf-token-secret.yaml @@ -0,0 +1,8 @@ +apiVersion: v1 +kind: Secret +metadata: + name: hf-token-secret +type: Opaque +data: + HF_TOKEN: HF_ACCESS_TOKEN + diff --git a/serving/llama2/llama2_chat/__init__.py b/serving/llama2/llama2_chat/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/serving/llama2/llama2_chat/predictor.py b/serving/llama2/llama2_chat/predictor.py new file mode 100644 index 0000000..ad576fb --- /dev/null +++ b/serving/llama2/llama2_chat/predictor.py @@ -0,0 +1,106 @@ +from transformers import AutoTokenizer +import transformers +import torch +import os +import json +from loguru import logger +from huggingface_hub import login +from typing import Dict, Union +from kserve import ( + Model, + InferRequest, + InferResponse, +) + +import os +import json +import transformers +from typing import Union, Dict +from kserve import Model, InferRequest, InferResponse +from transformers import AutoTokenizer + + +class Llama2ChatPredictor(Model): + def __init__( + self, + name: str, + hf_model_string: str = "meta-llama/Llama-2-7b-chat-hf", + loguru_loglevel: str = "INFO", + device: Union[int, str] = "auto", + ): + """ + Initialize the Llama2ChatPredictor. + + Parameters: + name: The name of the model. + hf_model_string: The identifier of the Hugging Face model. + loguru_loglevel: Logging level for loguru. + device: The device to run the model on (0, 1, ..., 'cpu', 'gpu'). + """ + super().__init__(name=name) + os.environ["LOGURU_LEVEL"] = loguru_loglevel + self.device = device + self.hf_model_string = hf_model_string + self.load() + self.ready = True + + def load(self): + """ + Load the model weights and tokenizer from Hugging Face and create the pipeline. + """ + login(token=os.environ["HF_ACCESS_TOKEN"]) + logger.info(f"Loading weights and tokenizer for model: {self.hf_model_string}") + self.tokenizer = AutoTokenizer.from_pretrained(self.hf_model_string) + self.tokenizer.pad_token = self.tokenizer.eos_token + self.pipeline = transformers.pipeline( + "text-generation", + model=self.hf_model_string, + torch_dtype=torch.float32 + if not torch.cuda.is_available() + else torch.float16, + device=self.device, + ) + + + def preprocess( + self, + payload: Union[bytes, InferRequest], + headers: Dict[str, str] = None, + ) -> Dict: + """ + Preprocess the payload into the format expected by the predict method. + + Parameters: + payload: The input payload, as a bytes string or InferRequest. + headers: Additional headers (unused here, but expected in base class). + + Returns: + the payload as a dict + """ + return payload if type(payload) == dict else json.loads(payload) + + + def predict( + self, + payload: Dict, + headers: Dict[str, str] = None, + ) -> Union[str, InferResponse]: + """ + Generate predictions using the input payload. + + Parameters: + payload: The input payload, preprocessed to dict format. + headers: Additional headers (unused here, but expected in base class). + + Returns: + A string containing the generated text. + """ + sequences = self.pipeline( + f"{payload['instances']}\n", + do_sample=True, + top_k=payload["top_k"], + num_return_sequences=1, + eos_token_id=self.tokenizer.eos_token_id, + max_length=payload["max_length"], + ) + return sequences[0]["generated_text"] diff --git a/serving/llama2/main.py b/serving/llama2/main.py new file mode 100644 index 0000000..0a25346 --- /dev/null +++ b/serving/llama2/main.py @@ -0,0 +1,47 @@ +import click +import torch +from typing import Union +from loguru import logger +from kserve import ( + ModelServer, +) +from llama2_chat.predictor import Llama2ChatPredictor + + +@click.command() +@click.option("--name", type=str, default="llama2-chat", help="Model name.") +@click.option("--loglevel", type=str, default="INFO", help="Log level for loguru.") +@click.option( + "--hf-model-string", + type=str, + default="meta-llama/Llama-2-13b-chat-hf", + help="Index of the model in the hugging face model registry.", +) +@click.option( + "--device", + default="cpu", + help="Device (0, 1, ..., 'cpu', 'cuda').", +) +def main(name: str, loglevel: str, hf_model_string: str, device: Union[str, int]): + """ + Main function to initiate and start the KServe Model Server. + + Parameters: + name: The name of the model. + loglevel: The logging level for loguru. + hf_model_string: Identifier for the Hugging Face model. + device: Computational device to utilize (0, 1, 2, ..., 'cpu', 'gpu'). + """ + logger.info(f"Cuda is available? -> {torch.cuda.is_available()}") + device = int(device) if device not in ('cpu', 'gpu', 'mps') else device + kserve_model = Llama2ChatPredictor( + name=name, + hf_model_string=hf_model_string, + loguru_loglevel=loglevel, + device=device, + ) + ModelServer().start([kserve_model]) + + +if __name__ == "__main__": + main() diff --git a/serving/llama2/poetry.lock b/serving/llama2/poetry.lock new file mode 100644 index 0000000..1c4aabd --- /dev/null +++ b/serving/llama2/poetry.lock @@ -0,0 +1,3979 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "accelerate" +version = "0.23.0" +description = "Accelerate" +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "accelerate-0.23.0-py3-none-any.whl", hash = "sha256:fba5065ff4e7c8f0a39df50785023703cd9bf8388073aa13c6457920f3bc5225"}, + {file = "accelerate-0.23.0.tar.gz", hash = "sha256:2139d219fa9a37773c4279c9afebe9f681f2f29e85a29b0be8d76257bd8e4abe"}, +] + +[package.dependencies] +huggingface-hub = "*" +numpy = ">=1.17" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +torch = ">=1.10.0" + +[package.extras] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", 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+apiVersion: serving.kserve.io/v1beta1 +kind: InferenceService +metadata: + name: llama2-chat +spec: + predictor: + containers: + - name: kserve-container + image: IMAGE_REGISTRY/llama2-chat:TAG + command: + - "python" + args: + - "main.py" + - "--device" + - "0" + - "--hf-model-string" + - "meta-llama/Llama-2-13b-chat-hf" + - "--name" + - "llama2-chat" + - "--loglevel" + - "INFO" + env: + - name: HF_ACCESS_TOKEN + valueFrom: + secretKeyRef: + name: hf-token-secret + key: HF_TOKEN + resources: + limits: + memory: "32Gi" + cpu: "16" + diff --git a/serving/llama2/pyproject.toml b/serving/llama2/pyproject.toml new file mode 100644 index 0000000..1006bc6 --- /dev/null +++ b/serving/llama2/pyproject.toml @@ -0,0 +1,29 @@ +[tool.poetry] +name = "llama2-chat" +version = "0.1.0" +description = "" +authors = ["hsteude "] +packages = [{include = "llama2_chat"}] + +[tool.poetry.dependencies] +python = ">=3.9,<3.12" +transformers = "^4.33.3" +torch = ">=2.0.0, !=2.0.1" +accelerate = "^0.23.0" +datasets = "^2.14.5" +huggingface-hub = "^0.17.3" +loguru = "^0.7.2" +kserve = "^0.11.1" + +[tool.poetry.group.dev.dependencies] +ipython = "^8.16.1" +ipdb = "^0.13.13" +pdbpp = "^0.10.3" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.pyright] +venvPath = "." +venv = ".venv" From efc31fbab3ca49b16139efe820b365e3b8076466 Mon Sep 17 00:00:00 2001 From: hsteude Date: Fri, 26 Jan 2024 10:58:09 +0100 Subject: [PATCH 2/9] Repo reorga, to match samos pattern --- .gitlab-ci.yml | 14 +++++ .../llama2-chat-predictor}/Dockerfile | 0 .../llama2-chat-predictor}/README.md | 45 ++++++---------- .../llama2_chat/__init__.py | 0 .../llama2_chat/predictor.py | 0 .../llama2-chat-predictor}/main.py | 0 .../llama2-chat-predictor}/poetry.lock | 0 .../llama2-chat-predictor}/pyproject.toml | 0 serving/llama2-chat/README.md | 54 +++++++++++++++++++ .../hf-token-secret.yaml | 0 .../{llama2 => llama2-chat}/predictor.yaml | 0 11 files changed, 83 insertions(+), 30 deletions(-) rename {serving/llama2 => images/llama2-chat-predictor}/Dockerfile (100%) rename {serving/llama2 => images/llama2-chat-predictor}/README.md (57%) rename {serving/llama2 => images/llama2-chat-predictor}/llama2_chat/__init__.py (100%) rename {serving/llama2 => images/llama2-chat-predictor}/llama2_chat/predictor.py (100%) rename {serving/llama2 => images/llama2-chat-predictor}/main.py (100%) rename {serving/llama2 => images/llama2-chat-predictor}/poetry.lock (100%) rename {serving/llama2 => images/llama2-chat-predictor}/pyproject.toml (100%) create mode 100644 serving/llama2-chat/README.md rename serving/{llama2 => llama2-chat}/hf-token-secret.yaml (100%) rename serving/{llama2 => llama2-chat}/predictor.yaml (100%) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 38845c0..d3eca1d 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -52,3 +52,17 @@ mnsist-katib-build: rules: - changes: - images/minimal-mnist/**/* + +llama2-chat-predictor-build: + stage: build-image + script: + - > + /kaniko/executor + --context "${CI_PROJECT_DIR}/images/llama2-chat-predictor/" + --dockerfile "${CI_PROJECT_DIR}/images/llama2-chat-predictor/Dockerfile" + --destination "${CI_REGISTRY}/${CI_PROJECT_PATH}/llama2-chat-predictor:latest" + --destination "${CI_REGISTRY}/${CI_PROJECT_PATH}/llama2-chat-predictor:${CI_COMMIT_SHORT_SHA}" + + rules: + - changes: + - images/llama2-chat-predictor/**/* diff --git a/serving/llama2/Dockerfile b/images/llama2-chat-predictor/Dockerfile similarity index 100% rename from serving/llama2/Dockerfile rename to images/llama2-chat-predictor/Dockerfile diff --git a/serving/llama2/README.md b/images/llama2-chat-predictor/README.md similarity index 57% rename from serving/llama2/README.md rename to images/llama2-chat-predictor/README.md index cc0f742..23ce74a 100644 --- a/serving/llama2/README.md +++ b/images/llama2-chat-predictor/README.md @@ -1,6 +1,12 @@ # Llama2 Deployment -For deploying the Llama2 model, we will create a custom predictor using KServe. KServe provides a way to implement a custom model server, enabling you to bring your own models and pre/post-processing code. If you are new to creating custom predictors with KServe, you may read more about it in the [KServe documentation](https://kserve.github.io/website/0.8/modelserving/v1beta1/custom/custom_model/). +For deploying the Llama2 model, we will create a custom predictor using KServe. +This directory holds all the code as well as the Dockerfile to create +the image for the llama2 deployment in kserve. +The steps for the actual deployment in kserve can also be found in this repository +(`/serving/llama2-chat`). + +If you are new to creating custom predictors with KServe, you may read more about it in the [KServe documentation](https://kserve.github.io/website/0.8/modelserving/v1beta1/custom/custom_model/). For a gentle introduction and detailed guide on working with Llama2, consider reading this blog post: [How to prompt Llama](https://replicate.com/blog/how-to-prompt-llama). @@ -19,20 +25,6 @@ To set this variable for a local deployment, run: export HF_ACCESS_TOKEN= ``` -### Creating a Kubernetes Secret - -We've created a file called hf-token-secret.yaml in this directory to create a -Kubernetes secret that holds the access token (which we'll need for the kserve -deployment later on). First, encode the token to base64 by running: -```sh -echo -n | base64 -``` - -Now, the output of the above command can be used to create the Kubernetes secret: -```sh -sed 's/HF_ACCESS_TOKEN//' hf-token-secret.yaml | kubectl apply -n -f - -``` - ## Run/develop Locally To run it locally, you'll need to first install the Python dependencies. To @@ -54,6 +46,7 @@ samples with the best scores, from which we sample) and max_length in the API qu ## Build and Push Image +### Using docker To build the model using Docker, do: ```sh docker build -t /llama2-chat: . @@ -73,20 +66,12 @@ Now, you can push the image using Docker: docker push /llama2-chat: ``` -## Deploy on KServe - -Ensure there is a secret called hf-token-secret in your namespace and run this -to create the KServe inference service: -``` -sed 's/IMAGE_REGISTRY//;s/TAG//' predictor.yaml | kubectl create -n -f - -``` -Note that you might need to change the command in the `prediction.yaml` to match your hardware. +### Using GitLab Runner with Kaniko +Automated Build: Trigger an automatic build by committing a change to +any file in or under the directory where this README lives. This activates +a GitLab CI/CD pipeline using Kaniko. The built image will receive two tags: -### Run Query Against the KServe Deployment +latest: Always points to the most recent build. +commit-: Associates the image with a specific +Git commit. -First, wait for the API to be ready; you can observe the progress and logs, etc., -in the Kubeflow UI under models. Once it's ready, you can copy the internal URL from -the Kubeflow UI and try: -```sh -curl -d '{"top_k": 3, "max_length": 100, "instances": ["Why is MLOps so important?"]}' -``` diff --git a/serving/llama2/llama2_chat/__init__.py b/images/llama2-chat-predictor/llama2_chat/__init__.py similarity index 100% rename from serving/llama2/llama2_chat/__init__.py rename to images/llama2-chat-predictor/llama2_chat/__init__.py diff --git a/serving/llama2/llama2_chat/predictor.py b/images/llama2-chat-predictor/llama2_chat/predictor.py similarity index 100% rename from serving/llama2/llama2_chat/predictor.py rename to images/llama2-chat-predictor/llama2_chat/predictor.py diff --git a/serving/llama2/main.py b/images/llama2-chat-predictor/main.py similarity index 100% rename from serving/llama2/main.py rename to images/llama2-chat-predictor/main.py diff --git a/serving/llama2/poetry.lock b/images/llama2-chat-predictor/poetry.lock similarity index 100% rename from serving/llama2/poetry.lock rename to images/llama2-chat-predictor/poetry.lock diff --git a/serving/llama2/pyproject.toml b/images/llama2-chat-predictor/pyproject.toml similarity index 100% rename from serving/llama2/pyproject.toml rename to images/llama2-chat-predictor/pyproject.toml diff --git a/serving/llama2-chat/README.md b/serving/llama2-chat/README.md new file mode 100644 index 0000000..504f38b --- /dev/null +++ b/serving/llama2-chat/README.md @@ -0,0 +1,54 @@ +# Llama2 Deployment + +For deploying the Llama2 model, we will create a custom predictor using KServe. +KServe provides a way to implement a custom model server, enabling you to bring +your own models and pre- and post-processing code. +In the `images` directory of this repo, you'll find a directory holding all the +code and the Dockerfiles needed to create the image for this deployment. +Here, we'll assume the image is in a registry that KServe has access to and +focus on the Kserve specific steps of the deployment. +If you are interested, also feel free to check out our simple UI, which is +designed to use this deployment (`images/llama2-chat-ui`). + +## Hugging Face Token + +You'll need a Hugging Face token to utilize the transformers library for downloading +the model weights. Additionally, you'll need to ask Meta for permission to use the +Llama model itself. You can read more about that [here](https://huggingface.co/blog/llama2). + +The code expects an environment variable named `HF_ACCESS_TOKEN` that holds the token as a string. + +### Creating a Kubernetes Secret + +We've created a file called hf-token-secret.yaml in this directory to create a +Kubernetes secret that holds the access token (which we'll need for the kserve +deployment later on). First, encode the token to base64 by running: +```sh +echo -n | base64 +``` + +Now, the output of the above command can be used to create the Kubernetes secret: +```sh +sed 's/HF_ACCESS_TOKEN//' hf-token-secret.yaml | kubectl apply -n -f - +``` + +## Deploy on KServe + +Ensure there is a secret called hf-token-secret in your namespace and run this +to create the KServe inference service: +``` +sed 's/IMAGE_REGISTRY//;s/TAG//' predictor.yaml | kubectl create -n -f - +``` +Note that you might need to change the command in the `prediction.yaml` to match your hardware. + +### Run Query Against the KServe Deployment + +First, wait for the API to be ready; you can observe the progress and logs, etc., +in the Kubeflow UI under models. Note that this may take several minutes. +Once it's ready, you can copy the internal URL from +the Kubeflow UI and try: +```sh +curl -d '{"top_k": 3, "max_length": 100, "instances": ["Why is MLOps so important?"]}' +``` + + diff --git a/serving/llama2/hf-token-secret.yaml b/serving/llama2-chat/hf-token-secret.yaml similarity index 100% rename from serving/llama2/hf-token-secret.yaml rename to serving/llama2-chat/hf-token-secret.yaml diff --git a/serving/llama2/predictor.yaml b/serving/llama2-chat/predictor.yaml similarity index 100% rename from serving/llama2/predictor.yaml rename to serving/llama2-chat/predictor.yaml From 21a2ed130e36e48b43099205a7651289f745d667 Mon Sep 17 00:00:00 2001 From: hsteude Date: Thu, 5 Oct 2023 17:57:10 +0000 Subject: [PATCH 3/9] Minor: removed double imports (weird that my linter did not complain) --- images/llama2-chat-predictor/llama2_chat/predictor.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/images/llama2-chat-predictor/llama2_chat/predictor.py b/images/llama2-chat-predictor/llama2_chat/predictor.py index ad576fb..f7d85e0 100644 --- a/images/llama2-chat-predictor/llama2_chat/predictor.py +++ b/images/llama2-chat-predictor/llama2_chat/predictor.py @@ -12,13 +12,6 @@ InferResponse, ) -import os -import json -import transformers -from typing import Union, Dict -from kserve import Model, InferRequest, InferResponse -from transformers import AutoTokenizer - class Llama2ChatPredictor(Model): def __init__( @@ -61,11 +54,10 @@ def load(self): device=self.device, ) - def preprocess( self, payload: Union[bytes, InferRequest], - headers: Dict[str, str] = None, + headers: Dict[str, str] = None, ) -> Dict: """ Preprocess the payload into the format expected by the predict method. @@ -79,7 +71,6 @@ def preprocess( """ return payload if type(payload) == dict else json.loads(payload) - def predict( self, payload: Dict, From efe258c87c051ac2d8a1fd7f8d224f662a1c2083 Mon Sep 17 00:00:00 2001 From: hsteude Date: Fri, 26 Jan 2024 11:18:13 +0100 Subject: [PATCH 4/9] Minor: Formatted readme --- images/llama2-chat-predictor/README.md | 1 - serving/llama2-chat/README.md | 30 +++++++++++++------------- 2 files changed, 15 insertions(+), 16 deletions(-) diff --git a/images/llama2-chat-predictor/README.md b/images/llama2-chat-predictor/README.md index 23ce74a..92e5e95 100644 --- a/images/llama2-chat-predictor/README.md +++ b/images/llama2-chat-predictor/README.md @@ -74,4 +74,3 @@ a GitLab CI/CD pipeline using Kaniko. The built image will receive two tags: latest: Always points to the most recent build. commit-: Associates the image with a specific Git commit. - diff --git a/serving/llama2-chat/README.md b/serving/llama2-chat/README.md index 504f38b..25ce14a 100644 --- a/serving/llama2-chat/README.md +++ b/serving/llama2-chat/README.md @@ -2,27 +2,29 @@ For deploying the Llama2 model, we will create a custom predictor using KServe. KServe provides a way to implement a custom model server, enabling you to bring -your own models and pre- and post-processing code. -In the `images` directory of this repo, you'll find a directory holding all the -code and the Dockerfiles needed to create the image for this deployment. -Here, we'll assume the image is in a registry that KServe has access to and -focus on the Kserve specific steps of the deployment. -If you are interested, also feel free to check out our simple UI, which is -designed to use this deployment (`images/llama2-chat-ui`). +your own models and pre- and post-processing code. In the `images` directory of +this repo, you'll find a directory holding all the code and the Dockerfiles +needed to create the image for this deployment. Here, we'll assume the image is +in a registry that KServe has access to and focus on the Kserve specific steps +of the deployment. If you are interested, also feel free to check out our +simple UI, which is designed to use this deployment (`images/llama2-chat-ui`). ## Hugging Face Token -You'll need a Hugging Face token to utilize the transformers library for downloading -the model weights. Additionally, you'll need to ask Meta for permission to use the -Llama model itself. You can read more about that [here](https://huggingface.co/blog/llama2). +You'll need a Hugging Face token to utilize the transformers library for +downloading the model weights. Additionally, you'll need to ask Meta for +permission to use the Llama model itself. You can read more about that +[here](https://huggingface.co/blog/llama2). -The code expects an environment variable named `HF_ACCESS_TOKEN` that holds the token as a string. +The code expects an environment variable named `HF_ACCESS_TOKEN` that holds the +token as a string. ### Creating a Kubernetes Secret -We've created a file called hf-token-secret.yaml in this directory to create a -Kubernetes secret that holds the access token (which we'll need for the kserve +We've created a file called hf-token-secret.yaml in this directory to create a +Kubernetes secret that holds the access token (which we'll need for the kserve deployment later on). First, encode the token to base64 by running: + ```sh echo -n | base64 ``` @@ -50,5 +52,3 @@ the Kubeflow UI and try: ```sh curl -d '{"top_k": 3, "max_length": 100, "instances": ["Why is MLOps so important?"]}' ``` - - From ffe9517723bbb8b11ad2041b575e7fadc83d661e Mon Sep 17 00:00:00 2001 From: Igor Kvachenok Date: Fri, 1 Nov 2024 10:46:22 +0100 Subject: [PATCH 5/9] Update dependencies --- images/llama2-chat-predictor/poetry.lock | 4803 +++++++++---------- images/llama2-chat-predictor/pyproject.toml | 4 +- 2 files changed, 2259 insertions(+), 2548 deletions(-) diff --git a/images/llama2-chat-predictor/poetry.lock b/images/llama2-chat-predictor/poetry.lock index 1c4aabd..4f6e950 100644 --- a/images/llama2-chat-predictor/poetry.lock +++ b/images/llama2-chat-predictor/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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ec63db01decd1e5896cd46b6edd201d51dd7150d Mon Sep 17 00:00:00 2001 From: Igor Kvachenok Date: Fri, 1 Nov 2024 15:53:30 +0100 Subject: [PATCH 6/9] Update README and image --- images/llama2-chat-predictor/Dockerfile | 2 +- images/llama2-chat-predictor/README.md | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/images/llama2-chat-predictor/Dockerfile b/images/llama2-chat-predictor/Dockerfile index 4f1e169..0104754 100644 --- a/images/llama2-chat-predictor/Dockerfile +++ b/images/llama2-chat-predictor/Dockerfile @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/pytorch:23.09-py3 +FROM nvcr.io/nvidia/pytorch:24.10-py3 RUN apt-get update && apt-get install -y libglib2.0-0 RUN mkdir /app WORKDIR /app diff --git a/images/llama2-chat-predictor/README.md b/images/llama2-chat-predictor/README.md index 92e5e95..f4c7c48 100644 --- a/images/llama2-chat-predictor/README.md +++ b/images/llama2-chat-predictor/README.md @@ -22,7 +22,7 @@ The code expects an environment variable named `HF_ACCESS_TOKEN` that holds the To set this variable for a local deployment, run: ```sh -export HF_ACCESS_TOKEN= + export HF_ACCESS_TOKEN= ``` ## Run/develop Locally @@ -30,9 +30,9 @@ export HF_ACCESS_TOKEN= To run it locally, you'll need to first install the Python dependencies. To do so, run the following from this directory: ``` -poetry install . -export HF_ACCESS_TOKEN= -poetry run python main.py --name "llama2-chat" --device "cpu" --hf-model-string "meta-llama/Llama-2-13b-chat-hf" +poetry install + export HF_ACCESS_TOKEN= +poetry run python main.py --name "llama2-chat" --device "cpu" --hf-model-string "meta-llama/Llama-2-7b-chat-hf" ``` ### Run Query Against Local Deployment @@ -49,7 +49,7 @@ samples with the best scores, from which we sample) and max_length in the API qu ### Using docker To build the model using Docker, do: ```sh -docker build -t /llama2-chat: . +docker build -t /llama2-chat: . --platform=linux/amd64 ``` To use this image in kserve, we'll also need to push it to an image registry. From 3180ecd1ad2e9cb8f7c45e062b1fe54c862f3679 Mon Sep 17 00:00:00 2001 From: Igor Kvachenok Date: Fri, 1 Nov 2024 15:54:57 +0100 Subject: [PATCH 7/9] Add github action for image build --- .github/workflows/llama.yaml | 45 ++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 .github/workflows/llama.yaml diff --git a/.github/workflows/llama.yaml b/.github/workflows/llama.yaml new file mode 100644 index 0000000..2aa01b9 --- /dev/null +++ b/.github/workflows/llama.yaml @@ -0,0 +1,45 @@ +name: Build Llama Image + +on: + workflow_dispatch: + +env: + DOCKERFILE_PATH: "images/llama2-chat-predictor/Dockerfile" + BUILD_CONTEXT_DIR: "images/llama2-chat-predictor/" + IMAGE_NAME: "mlflow-server" + IMAGE_PUSH_PATH: "${{ secrets.GCP_ARTIFACT_REGISTRY_PATH }}" + IMAGE_PUSH_SECRET: "${{ secrets.GCP_SA_KEY }}" + +jobs: + llama-image: + runs-on: ubuntu-latest + steps: + - name: Delete huge unnecessary folders + run: | + sudo rm -rf "$AGENT_TOOLSDIRECTORY" + sudo rm -rf /usr/share/dotnet + + - name: Checkout code + uses: actions/checkout@v4 + + - name: Auth GCloud CLI + uses: google-github-actions/auth@v2 + with: + credentials_json: ${{ env.IMAGE_PUSH_SECRET }} + + - name: Docker Login to Google Artifact Registry + uses: docker/login-action@v3 + with: + registry: europe-west3-docker.pkg.dev + username: _json_key + password: ${{ env.IMAGE_PUSH_SECRET }} + + - name: Build Llama image + run: | + LATEST_TAG=${{ env.IMAGE_PUSH_PATH }}/${{ env.IMAGE_NAME }}:latest + COMMIT_TAG=${{ env.IMAGE_PUSH_PATH }}/${{ env.IMAGE_NAME }}:commit-${{ github.sha }} + BUILD_CONTEXT="${GITHUB_WORKSPACE}/${{ env.BUILD_CONTEXT_DIR }}" + docker build -t $LATEST_TAG -f ${{ env.DOCKERFILE_PATH }} "${BUILD_CONTEXT}" + docker tag $LATEST_TAG $COMMIT_TAG + docker push $LATEST_TAG + docker push $COMMIT_TAG From e7ff3eed96168d8a81a4d161a59d2760208e9886 Mon Sep 17 00:00:00 2001 From: Igor Kvachenok Date: Fri, 1 Nov 2024 17:58:58 +0100 Subject: [PATCH 8/9] Bugfixes --- .github/workflows/llama.yaml | 2 +- serving/llama2-chat/README.md | 4 ++-- serving/llama2-chat/hf-token-secret.yaml | 2 +- serving/llama2-chat/predictor.yaml | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/llama.yaml b/.github/workflows/llama.yaml index 2aa01b9..2837e30 100644 --- a/.github/workflows/llama.yaml +++ b/.github/workflows/llama.yaml @@ -6,7 +6,7 @@ on: env: DOCKERFILE_PATH: "images/llama2-chat-predictor/Dockerfile" BUILD_CONTEXT_DIR: "images/llama2-chat-predictor/" - IMAGE_NAME: "mlflow-server" + IMAGE_NAME: "llama-model" IMAGE_PUSH_PATH: "${{ secrets.GCP_ARTIFACT_REGISTRY_PATH }}" IMAGE_PUSH_SECRET: "${{ secrets.GCP_SA_KEY }}" diff --git a/serving/llama2-chat/README.md b/serving/llama2-chat/README.md index 25ce14a..4de32c1 100644 --- a/serving/llama2-chat/README.md +++ b/serving/llama2-chat/README.md @@ -26,7 +26,7 @@ Kubernetes secret that holds the access token (which we'll need for the kserve deployment later on). First, encode the token to base64 by running: ```sh -echo -n | base64 +echo -n "" | tr -d '\n' | base64 ``` Now, the output of the above command can be used to create the Kubernetes secret: @@ -39,7 +39,7 @@ sed 's/HF_ACCESS_TOKEN//' hf-token-secret.yaml | kubectl a Ensure there is a secret called hf-token-secret in your namespace and run this to create the KServe inference service: ``` -sed 's/IMAGE_REGISTRY//;s/TAG//' predictor.yaml | kubectl create -n -f - +sed 's|IMAGE_REGISTRY||;s/TAG//' predictor.yaml | kubectl create -n -f - ``` Note that you might need to change the command in the `prediction.yaml` to match your hardware. diff --git a/serving/llama2-chat/hf-token-secret.yaml b/serving/llama2-chat/hf-token-secret.yaml index 1cb8245..5e49c14 100644 --- a/serving/llama2-chat/hf-token-secret.yaml +++ b/serving/llama2-chat/hf-token-secret.yaml @@ -4,5 +4,5 @@ metadata: name: hf-token-secret type: Opaque data: - HF_TOKEN: HF_ACCESS_TOKEN + HF_TOKEN: HF_ACCESS_TOKEN diff --git a/serving/llama2-chat/predictor.yaml b/serving/llama2-chat/predictor.yaml index d670016..574d924 100644 --- a/serving/llama2-chat/predictor.yaml +++ b/serving/llama2-chat/predictor.yaml @@ -6,7 +6,7 @@ spec: predictor: containers: - name: kserve-container - image: IMAGE_REGISTRY/llama2-chat:TAG + image: IMAGE_REGISTRY/TAG command: - "python" args: @@ -25,7 +25,7 @@ spec: secretKeyRef: name: hf-token-secret key: HF_TOKEN - resources: + resources: limits: memory: "32Gi" cpu: "16" From c4e0064e626e9ba3c2172c0bb7b72ef6bf3cf1c1 Mon Sep 17 00:00:00 2001 From: Igor Kvachenok Date: Fri, 1 Nov 2024 17:59:17 +0100 Subject: [PATCH 9/9] Trying different image --- images/llama2-chat-predictor/Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/images/llama2-chat-predictor/Dockerfile b/images/llama2-chat-predictor/Dockerfile index 0104754..fb0ac1f 100644 --- a/images/llama2-chat-predictor/Dockerfile +++ b/images/llama2-chat-predictor/Dockerfile @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/pytorch:24.10-py3 +FROM pytorch/pytorch:latest RUN apt-get update && apt-get install -y libglib2.0-0 RUN mkdir /app WORKDIR /app