Code translation is the process of converting code written in one programming language to another programming language while maintaining the same functionality. This process is also known as code conversion, source-to-source translation, or transpilation. Code translation is often performed when developers want to take advantage of new programming languages, improve code performance, or maintain legacy systems. Some common examples include translating code from Python to Java, or from JavaScript to TypeScript.
The workflow falls into the following architecture:
The CodeTrans example is implemented using the component-level microservices defined in GenAIComps. The flow chart below shows the information flow between different microservices for this example.
---
config:
flowchart:
nodeSpacing: 400
rankSpacing: 100
curve: linear
themeVariables:
fontSize: 50px
---
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef invisible fill:transparent,stroke:transparent;
style CodeTrans-MegaService stroke:#000000
%% Subgraphs %%
subgraph CodeTrans-MegaService["CodeTrans MegaService "]
direction LR
LLM([LLM MicroService]):::blue
end
subgraph UserInterface[" User Interface "]
direction LR
a([User Input Query]):::orchid
UI([UI server<br>]):::orchid
end
LLM_gen{{LLM Service <br>}}
GW([CodeTrans GateWay<br>]):::orange
NG([Nginx MicroService]):::blue
%% Questions interaction
direction LR
NG <==> UserInterface
a[User Input Query] --> UI
UI --> GW
GW <==> CodeTrans-MegaService
%% Embedding service flow
direction LR
LLM <-.-> LLM_gen
This Code Translation use case demonstrates Text Generation Inference across multiple platforms. Currently, we provide examples for Intel Gaudi2 and Intel Xeon Scalable Processors, and we invite contributions from other hardware vendors to expand OPEA ecosystem.
The Code Translation service can be effortlessly deployed on either Intel Gaudi2 or Intel Xeon Scalable Processor.
Currently we support two ways of deploying Code Translation services on docker:
-
Start services using the docker image on
docker hub
:docker pull opea/codetrans:latest
-
Start services using the docker images
built from source
: Guide
By default, the LLM model is set to a default value as listed below:
Service | Model |
---|---|
LLM | mistralai/Mistral-7B-Instruct-v0.3 |
Change the LLM_MODEL_ID
in docker_compose/set_env.sh
for your needs.
To set up environment variables for deploying Code Translation services, follow these steps:
-
Set the required environment variables:
# Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" # Example: NGINX_PORT=80 export NGINX_PORT=${your_nginx_port}
-
If you are in a proxy environment, also set the proxy-related environment variables:
export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy"
-
Set up other environment variables:
source ./docker_compose/set_env.sh
Find the corresponding compose.yaml.
cd GenAIExamples/CodeTrans/docker_compose/intel/hpu/gaudi
docker compose up -d
Refer to the Gaudi Guide to build docker images from source.
Find the corresponding compose.yaml.
cd GenAIExamples/CodeTrans/docker_compose/intel/cpu/xeon
docker compose up -d
Refer to the Xeon Guide for more instructions on building docker images from source.
Refer to the Code Translation Kubernetes Guide
Refer to the Code Translation Kubernetes Guide
Install Helm (version >= 3.15) first. Refer to the Helm Installation Guide for more information.
Refer to the CodeTrans helm chart for instructions on deploying CodeTrans into Kubernetes on Xeon & Gaudi.
Two ways of consuming Code Translation Service:
-
Use cURL command on terminal
curl http://${host_ip}:7777/v1/codetrans \ -H "Content-Type: application/json" \ -d '{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n}"}'
-
Access via frontend
To access the frontend, open the following URL in your browser: http://{host_ip}:5173.
By default, the UI runs on port 5173 internally.
-
If you get errors like "Access Denied", validate micro service first. A simple example:
http_proxy="" curl http://${host_ip}:8008/generate \ -X POST \ -d '{"inputs":" ### System: Please translate the following Golang codes into Python codes. ### Original codes: '\'''\'''\''Golang \npackage main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n '\'''\'''\'' ### Translated codes:","parameters":{"max_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
-
(Docker only) If all microservices work well, check the port ${host_ip}:7777, the port may be allocated by other users, you can modify the
compose.yaml
. -
(Docker only) If you get errors like "The container name is in use", change container name in
compose.yaml
.