Detailed Tutorial for Local Deployment of DEEPSEEK Service

I. Preparation

Ensure that your machine meets the following conditions:

• Operating System: Mainstream systems such as Linux and Windows are supported.
• Hardware: A high - performance GPU, such as an NVIDIA GPU, is recommended to accelerate model inference. Have sufficient memory, for example, 16GB or more, and ample disk space to store model files.
• Software Dependencies: Install a Python environment, preferably Python 3.8 or later. Install relevant deep - learning frameworks, such as PyTorch.

II. Download the DEEPSEEK Model

1. Visit the official DEEPSEEK model download address (make sure to obtain it from the official or a trusted source).
2. Select the appropriate model version according to your needs, such as the base model, fine - tuned model, etc.
3. Use a download tool (such as wget, curl, etc.) for downloading. For example, in a Linux system, use the following command to download:

   wget [model download link]

III. Install Dependencies

After downloading the model, you need to install the dependency packages required to run the model.

1. Create and activate a Python virtual environment (optional but recommended):

   python3 -m venv myenv
   source myenv/bin/activate  # On Windows, use myenv\Scripts\activate

2. Install the dependency packages. Usually, you can install them through the requirements.txt file provided by the project. Assuming you have extracted the model - related project files to the current directory, use the following command to install:

   pip install -r requirements.txt

IV. Configure the Service

Configure relevant parameters according to the requirements of the model operation.

1. Locate the model configuration file, which is usually in YAML or JSON format.
2. Modify the parameters in the configuration file, such as the model path, device type (CPU or GPU), port number (if the service needs to be provided externally), etc. For example, if you want to run the model using a GPU, find the configuration item related to the device in the configuration file and set it to the GPU - related parameters:

   device: cuda:0

V. Start the Service

After completing the above steps, you can start the DEEPSEEK service.

1. Enter the model project directory in the command line.
2. Execute the startup command. For example, if it is a Flask - based service, you may use the following command to start:

   python app.py

3. Wait for the service to start successfully. After successful startup, you can see relevant startup information in the command line, such as the port number the service is listening on.

VI. Test the Service

After the service is started, you can test it in the following ways.

1. If the service provides a web interface, open a browser and enter the service address (such as http://localhost:5000, where 5000 is the port number the service is listening on, modify it according to the actual situation). Enter test questions in the web interface and view the model's answers.
2. If the service provides an API interface, use a tool such as Postman for testing. Construct a request, send it to the API address, and view the returned results.