Added gaudi example for rerank model finetuning (#794)

Signed-off-by: Ye, Xinyu <xinyu.ye@intel.com>

Author: XinyuYe-Intel

InstructionTuning/README.md

@@ -20,16 +20,16 @@ Download a training file `alpaca_data.json` and upload it to the server with bel
 
 ```bash
 # upload a training file
-curl http://${your_ip}:8005/v1/finetune/upload_training_files -X POST -H "Content-Type: multipart/form-data" -F "files=@./alpaca_data.json"
+curl http://${your_ip}:8015/v1/files -X POST -H "Content-Type: multipart/form-data" -F "file=@./alpaca_data.json" -F purpose="fine-tune"
 ```
 
 ### 2. Create fine-tuning job
 
-After a training file `alpaca_data.json` is uploaded, use the following command to launch a finetuning job using `meta-llama/Llama-2-7b-chat-hf` as base model:
+After a training file like `alpaca_data.json` is uploaded, use the following command to launch a finetuning job using `meta-llama/Llama-2-7b-chat-hf` as base model:
 
 ```bash
 # create a finetuning job
-curl http://${your_ip}:8005/v1/fine_tuning/jobs \
+curl http://${your_ip}:8015/v1/fine_tuning/jobs \
   -X POST \
   -H "Content-Type: application/json" \
   -d '{
@@ -44,17 +44,14 @@ Below commands show how to list finetuning jobs, retrieve a finetuning job, canc
 
 ```bash
 # list finetuning jobs
-curl http://${your_ip}:8005/v1/fine_tuning/jobs   -X GET
+curl http://${your_ip}:8015/v1/fine_tuning/jobs -X GET
 
 # retrieve one finetuning job
-curl http://localhost:8005/v1/fine_tuning/jobs/retrieve   -X POST   -H "Content-Type: application/json"   -d '{
-    "fine_tuning_job_id": ${fine_tuning_job_id}}'
+curl http://${your_ip}:8015/v1/fine_tuning/jobs/retrieve -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
 
 # cancel one finetuning job
-
-curl http://localhost:8005/v1/fine_tuning/jobs/cancel   -X POST   -H "Content-Type: application/json"   -d '{
-    "fine_tuning_job_id": ${fine_tuning_job_id}}'
+curl http://${your_ip}:8015/v1/fine_tuning/jobs/cancel -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
 
 # list checkpoints of a finetuning job
-curl http://${your_ip}:8005/v1/finetune/list_checkpoints -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
+curl http://${your_ip}:8015/v1/finetune/list_checkpoints -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
 ```

InstructionTuning/docker_compose/intel/cpu/xeon/README.md

@@ -27,5 +27,5 @@ docker build -t opea/finetuning:latest --build-arg https_proxy=$https_proxy --bu
 Start docker container with below command:
 
 ```bash
-docker run -d --name="finetuning-server" -p 8005:8005 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/finetuning:latest
+docker run -d --name="finetuning-server" -p 8015:8015 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/finetuning:latest
 ```

InstructionTuning/docker_compose/intel/hpu/gaudi/README.md

@@ -27,5 +27,5 @@ Start docker container with below command:
 
 ```bash
 export HF_TOKEN=${your_huggingface_token}
-docker run --runtime=habana -e HABANA_VISIBLE_DEVICES=all -p 8005:8005 -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host -e https_proxy=$https_proxy -e http_proxy=$http_proxy -e no_proxy=$no_proxy -e HF_TOKEN=$HF_TOKEN opea/finetuning-gaudi:latest
+docker run --runtime=habana -e HABANA_VISIBLE_DEVICES=all -p 8015:8015 -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host -e https_proxy=$https_proxy -e http_proxy=$http_proxy -e no_proxy=$no_proxy -e HF_TOKEN=$HF_TOKEN opea/finetuning-gaudi:latest
 ```

RerankFinetuning/README.md

@@ -8,6 +8,10 @@ Rerank model finetuning is the process of further training rerank model on a dat
 
 Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) for detail.
 
+### Deploy Rerank Model Finetuning Service on Gaudi
+
+Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) for detail.
+
 ## Consume Rerank Model Finetuning Service
 
 ### 1. Upload a training file
@@ -47,10 +51,10 @@ Below commands show how to list finetuning jobs, retrieve a finetuning job, canc
 curl http://${your_ip}:8015/v1/fine_tuning/jobs -X GET
 
 # retrieve one finetuning job
-curl http://localhost:8015/v1/fine_tuning/jobs/retrieve -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
+curl http://${your_ip}:8015/v1/fine_tuning/jobs/retrieve -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
 
 # cancel one finetuning job
-curl http://localhost:8015/v1/fine_tuning/jobs/cancel -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
+curl http://${your_ip}:8015/v1/fine_tuning/jobs/cancel -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'
 
 # list checkpoints of a finetuning job
 curl http://${your_ip}:8015/v1/finetune/list_checkpoints -X POST -H "Content-Type: application/json" -d '{"fine_tuning_job_id": ${fine_tuning_job_id}}'

RerankFinetuning/docker_compose/intel/hpu/gaudi/README.md

@@ -0,0 +1,31 @@
+# Deploy Rerank Model Finetuning Service on Gaudi
+
+This document outlines the deployment process for a rerank model finetuning service utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice on Intel Xeon server. The steps include Docker image creation, container deployment. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
+
+## 🚀 Build Docker Images
+
+First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
+
+### 1. Source Code install GenAIComps
+
+```bash
+git clone https://github.com/opea-project/GenAIComps.git
+cd GenAIComps
+```
+
+### 2. Build Docker Image
+
+Build docker image with below command:
+
+```bash
+docker build -t opea/finetuning-gaudi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/finetuning/Dockerfile.intel_hpu .
+```
+
+### 3. Run Docker with CLI
+
+Start docker container with below command:
+
+```bash
+export HF_TOKEN=${your_huggingface_token}
+docker run --runtime=habana -e HABANA_VISIBLE_DEVICES=all -p 8015:8015 -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host -e https_proxy=$https_proxy -e http_proxy=$http_proxy -e no_proxy=$no_proxy -e HF_TOKEN=$HF_TOKEN opea/finetuning-gaudi:latest
+```