Rename vllm dockerfile

README.md

@@ -74,9 +74,9 @@ curl -X POST -d '{"model":"meta-llama/Meta-Llama-3-8B-Instruct", "prompt":"Hello
 
 ```bash
 #export token=<HUGGINGFACE_HUB_TOKEN>
-docker build --pull . -f docker/Dockerfile.llm -t ts/llm
+docker build --pull . -f docker/Dockerfile.vllm -t ts/vllm
 
-docker run --rm -ti --shm-size 10g --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:8080 -v data:/data ts/llm --model_id meta-llama/Meta-Llama-3-8B-Instruct --disable_token_auth
+docker run --rm -ti --shm-size 10g --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:8080 -v data:/data ts/vllm --model_id meta-llama/Meta-Llama-3-8B-Instruct --disable_token_auth
 
 # Try it out
 curl -X POST -d '{"model":"meta-llama/Meta-Llama-3-8B-Instruct", "prompt":"Hello, my name is", "max_tokens": 200}' --header "Content-Type: application/json" "http://localhost:8080/predictions/model/1.0/v1/completions"

docs/llm_deployment.md

@@ -11,7 +11,7 @@ The launcher can either be used standalone or in combination with our provided T
 
 To launch the docker we first need to build it:
 ```bash
-docker build . -f docker/Dockerfile.llm -t ts/llm
+docker build . -f docker/Dockerfile.vllm -t ts/vllm
 ```
 
 Models are usually loaded from the HuggingFace hub and are cached in a [docker volume](https://docs.docker.com/storage/volumes/) for faster reload.
@@ -22,7 +22,7 @@ export token=<HUGGINGFACE_HUB_TOKEN>
 
 You can then go ahead and launch a TorchServe instance serving your selected model:
 ```bash
-docker run --rm -ti --shm-size 1g --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:8080 -v data:/data ts/llm --model_id meta-llama/Meta-Llama-3-8B-Instruct --disable_token_auth
+docker run --rm -ti --shm-size 1g --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:8080 -v data:/data ts/vllm --model_id meta-llama/Meta-Llama-3-8B-Instruct --disable_token_auth
 ```
 
 To change the model you just need to exchange the identifier given to the `--model_id` parameter.

examples/large_models/vllm/llama3/Readme.md

@@ -9,7 +9,7 @@ To leverage the power of vLLM we fist need to install it using pip in out develo
 ```bash
 python -m pip install -r ../requirements.txt
 ```
-For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.llm).
+For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.vllm).
 
 ### Step 1: Download Model from HuggingFace
 

examples/large_models/vllm/lora/Readme.md

@@ -9,7 +9,7 @@ To leverage the power of vLLM we fist need to install it using pip in out develo
 ```bash
 python -m pip install -r ../requirements.txt
 ```
-For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.llm).
+For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.vllm).
 
 ### Step 1: Download Model from HuggingFace
 

examples/large_models/vllm/mistral/Readme.md

@@ -9,7 +9,7 @@ To leverage the power of vLLM we fist need to install it using pip in out develo
 ```bash
 python -m pip install -r ../requirements.txt
 ```
-For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.llm).
+For later deployments we can make vLLM part of the deployment environment by adding the requirements.txt while building the model archive in step 2 (see [here](../../../../model-archiver/README.md#model-specific-custom-python-requirements) for details) or we can make it part of a docker image like [here](../../../../docker/Dockerfile.vllm).
 
 ### Step 1: Download Model from HuggingFace