@@ -125,7 +125,7 @@ to manually kill the profiler and generate your `nsys-rep` report.
You can view these profiles either as summaries in the CLI, using `nsys stats [profile-file]`, or in the GUI by installing Nsight [locally following the directions here](https://developer.nvidia.com/nsight-systems/get-started).
[Anyscale](https://www.anyscale.com) is a managed, multi-cloud platform developed by the creators of Ray.
Anyscale automates the entire lifecycle of Ray clusters in your AWS, GCP, or Azure account, delivering the flexibility of open-source Ray
without the operational overhead of maintaining Kubernetes control planes, configuring autoscalers, managing observability stacks, or manually managing head and worker nodes with helper scripts like <gh-file:examples/online_serving/run_cluster.sh>.
When serving large language models with vLLM, Anyscale can rapidly provision [production-ready HTTPS endpoints](https://docs.anyscale.com/examples/deploy-ray-serve-llms) or [fault-tolerant batch inference jobs](https://docs.anyscale.com/examples/ray-data-llm).
[Anything LLM](https://github.com/Mintplex-Labs/anything-llm) is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting.
[AutoGen](https://github.com/microsoft/autogen) is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.
[BentoML](https://github.com/bentoml/BentoML) allows you to deploy a large language model (LLM) server with vLLM as the backend, which exposes OpenAI-compatible endpoints. You can serve the model locally or containerize it as an OCI-compliant image and deploy it on Kubernetes.
Next, let us add our code to handle inference for the LLM of your choice (`mistralai/Mistral-7B-Instruct-v0.1` for this example), add the following code to your `main.py`:
??? Code
??? code
```python
from vllm import LLM, SamplingParams
...
...
@@ -64,7 +61,7 @@ cerebrium deploy
If successful, you should be returned a CURL command that you can call inference against. Just remember to end the url with the function name you are calling (in our case`/run`)
??? Command
??? console "Command"
```python
curl -X POST https://api.cortex.cerebrium.ai/v4/p-xxxxxx/vllm/run \
...
...
@@ -82,7 +79,7 @@ If successful, you should be returned a CURL command that you can call inference
[Dify](https://github.com/langgenius/dify) is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more, allowing you to quickly move from prototype to production.
Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-7b-chat-hf` for this example), create the following `serve.dstack.yml` file for the dstack `Service`:
??? Config
??? code "Config"
```yaml
type: service
...
...
@@ -48,7 +45,7 @@ Next, to provision a VM instance with LLM of your choice (`NousResearch/Llama-2-
Then, run the following CLI for provisioning:
??? Command
??? console "Command"
```console
$ dstack run . -f serve.dstack.yml
...
...
@@ -79,7 +76,7 @@ Then, run the following CLI for provisioning:
After the provisioning, you can interact with the model by using the OpenAI SDK:
[LiteLLM](https://github.com/BerriAI/litellm) call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
[Retrieval-augmented generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information. Use cases include providing chatbot access to internal company data or generating responses based on authoritative sources.
SkyPilot can scale up the service to multiple service replicas with built-in autoscaling, load-balancing and fault-tolerance. You can do it by adding a services section to the YAML file.
??? Yaml
??? code "Yaml"
```yaml
service:
...
...
@@ -111,7 +108,7 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
max_completion_tokens: 1
```
??? Yaml
??? code "Yaml"
```yaml
service:
...
...
@@ -186,7 +183,7 @@ vllm 2 1 xx.yy.zz.245 18 mins ago 1x GCP([Spot]{'L4': 1}) R
After the service is READY, you can find a single endpoint for the service and access the service with the endpoint:
??? Commands
??? console "Commands"
```bash
ENDPOINT=$(sky serve status --endpoint 8081 vllm)
...
...
@@ -220,7 +217,7 @@ service:
This will scale the service up to when the QPS exceeds 2 for each replica.
??? Yaml
??? code "Yaml"
```yaml
service:
...
...
@@ -285,7 +282,7 @@ sky serve down vllm
It is also possible to access the Llama-3 service with a separate GUI frontend, so the user requests send to the GUI will be load-balanced across replicas.
[Streamlit](https://github.com/streamlit/streamlit) lets you transform Python scripts into interactive web apps in minutes, instead of weeks. Build dashboards, generate reports, or create chat apps.
