Step3-VL-10B-Base与Java开发实战SpringBoot微服务集成指南1. 开篇当Java开发者遇见多模态AI如果你是一个Java开发者可能已经习惯了SpringBoot的优雅和微服务的高效。但当你第一次听说Step3-VL-10B-Base这个多模态模型时可能会有点懵——这玩意儿能和我的Java项目结合起来吗答案是肯定的而且比你想象的更简单。Step3-VL-10B-Base不仅能看懂图片、理解文字还能通过API方式为你的Java应用提供智能能力。想象一下你的电商系统可以自动识别用户上传的商品图片你的内容平台可以智能分析图片中的文字信息这些都不再是科幻电影里的场景。今天我就带你一步步实现这个融合用最熟悉的SpringBoot框架让AI能力成为你微服务架构中的一环。2. 环境准备10分钟搞定基础配置开始之前我们先确保环境就绪。这里不需要复杂的机器学习知识只要你会用Java和SpringBoot就行。2.1 基础环境要求JDK 11或更高版本推荐JDK 17Maven 3.6 或 Gradle 7.xSpringBoot 2.7 或 3.x一个可访问的Step3-VL-10B-Base服务端点可以是本地部署或远程API2.2 创建SpringBoot项目用你熟悉的方式创建一个新项目。如果你喜欢命令行spring init --dependenciesweb,json --java-version17 --package-namecom.example.aimicroservice ai-microservice或者用IDE的Spring Initializr选择这些依赖Spring WebSpring Boot DevToolsJSON Processing2.3 添加必要的依赖在pom.xml中添加这些依赖确保能与AI服务顺畅通信dependencies !-- Spring Boot基础依赖 -- dependency groupIdorg.springframework.boot/groupId artifactIdspring-boot-starter-web/artifactId /dependency !-- HTTP客户端 -- dependency groupIdorg.apache.httpcomponents/groupId artifactIdhttpclient/artifactId version4.5.13/version /dependency !-- JSON处理 -- dependency groupIdcom.fasterxml.jackson.core/groupId artifactIdjackson-databind/artifactId /dependency /dependencies3. 核心集成三步连接AI能力现在来到最关键的部分——如何让SpringBoot应用与Step3-VL-10B-Base对话。3.1 配置服务连接首先创建一个配置类来管理AI服务连接Configuration public class AIConfig { Value(${ai.service.url:http://localhost:8000}) private String aiServiceUrl; Value(${ai.service.timeout:30000}) private int timeout; Bean public RestTemplate aiRestTemplate() { RestTemplate restTemplate new RestTemplate(); restTemplate.setRequestFactory(new HttpComponentsClientHttpRequestFactory()); return restTemplate; } Bean public AIClient aiClient(RestTemplate restTemplate) { return new AIClient(restTemplate, aiServiceUrl); } }在application.properties中添加配置# AI服务配置 ai.service.urlhttp://your-ai-service:8000 ai.service.timeout30000 # 服务端口 server.port80803.2 创建AI服务客户端接下来创建一个专门的客户端来处理与AI服务的通信Component public class AIClient { private final RestTemplate restTemplate; private final String baseUrl; public AIClient(RestTemplate restTemplate, String baseUrl) { this.restTemplate restTemplate; this.baseUrl baseUrl; } public String analyzeImage(String imageUrl, String question) { // 构建请求体 MapString, Object request new HashMap(); request.put(image_url, imageUrl); request.put(question, question); // 发送请求 ResponseEntityMap response restTemplate.postForEntity( baseUrl /analyze, request, Map.class ); return (String) response.getBody().get(answer); } public String describeImage(String imageUrl) { MapString, Object request new HashMap(); request.put(image_url, imageUrl); ResponseEntityMap response restTemplate.postForEntity( baseUrl /describe, request, Map.class ); return (String) response.getBody().get(description); } }3.3 实现业务服务层创建一个服务类来封装业务逻辑Service public class AIService { private final AIClient aiClient; public AIService(AIClient aiClient) { this.aiClient aiClient; } public ProductInfo analyzeProductImage(String imageUrl) { String description aiClient.describeImage(imageUrl); String category aiClient.analyzeImage(imageUrl, 这是什么类别的商品); String priceRange aiClient.analyzeImage(imageUrl, 这个商品大概什么价格区间); return new ProductInfo(description, category, priceRange); } public ContentAnalysis analyzeContentImage(String imageUrl) { String textContent aiClient.analyzeImage(imageUrl, 图片中有哪些文字内容); String sentiment aiClient.analyzeImage(imageUrl, 这张图片传达了什么情绪); return new ContentAnalysis(textContent, sentiment); } }4. 构建REST API暴露AI能力给前端现在让我们创建控制器提供HTTP接口给前端或其他服务调用。4.1 商品图片分析接口RestController RequestMapping(/api/ai) public class AIController { private final AIService aiService; public AIController(AIService aiService) { this.aiService aiService; } PostMapping(/analyze-product) public ResponseEntityProductInfo analyzeProduct( RequestParam String imageUrl) { try { ProductInfo result aiService.analyzeProductImage(imageUrl); return ResponseEntity.ok(result); } catch (Exception e) { return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).build(); } } PostMapping(/analyze-content) public ResponseEntityContentAnalysis analyzeContent( RequestParam String imageUrl) { try { ContentAnalysis result aiService.analyzeContentImage(imageUrl); return ResponseEntity.ok(result); } catch (Exception e) { return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).build(); } } }4.2 数据结构定义创建对应的数据模型类public class ProductInfo { private String description; private String category; private String priceRange; public ProductInfo(String description, String category, String priceRange) { this.description description; this.category category; this.priceRange priceRange; } // getters 和 setters } public class ContentAnalysis { private String textContent; private String sentiment; public ContentAnalysis(String textContent, String sentiment) { this.textContent textContent; this.sentiment sentiment; } // getters 和 setters }5. 进阶功能让集成更智能实用基础集成完成后我们可以添加一些进阶功能来提升实用性和可靠性。5.1 添加缓存机制避免重复分析相同的图片提升响应速度Service public class CachedAIService { private final AIService aiService; private final CacheManager cacheManager; public CachedAIService(AIService aiService, CacheManager cacheManager) { this.aiService aiService; this.cacheManager cacheManager; } Cacheable(value imageAnalysis, key #imageUrl) public ProductInfo analyzeProductWithCache(String imageUrl) { return aiService.analyzeProductImage(imageUrl); } }在配置类中启用缓存Configuration EnableCaching public class CacheConfig { Bean public CacheManager cacheManager() { return new ConcurrentMapCacheManager(imageAnalysis); } }5.2 实现批量处理如果需要处理多张图片可以添加批量处理功能Service public class BatchAIService { private final AIService aiService; private final ExecutorService executorService; public BatchAIService(AIService aiService) { this.aiService aiService; this.executorService Executors.newFixedThreadPool(5); } public ListProductInfo batchAnalyzeProducts(ListString imageUrls) { ListCompletableFutureProductInfo futures imageUrls.stream() .map(url - CompletableFuture.supplyAsync( () - aiService.analyzeProductImage(url), executorService)) .collect(Collectors.toList()); return futures.stream() .map(CompletableFuture::join) .collect(Collectors.toList()); } }5.3 添加健康检查确保AI服务可用性Component public class AIHealthCheck implements HealthIndicator { private final AIClient aiClient; public AIHealthCheck(AIClient aiClient) { this.aiClient aiClient; } Override public Health health() { try { // 发送一个简单的测试请求 aiClient.describeImage(https://example.com/test.jpg); return Health.up().build(); } catch (Exception e) { return Health.down() .withDetail(error, AI service unavailable) .build(); } } }6. 实际应用示例让我们看几个具体的应用场景了解如何在实际项目中使用这个集成。6.1 电商商品自动分类Service public class ProductService { private final CachedAIService aiService; private final ProductRepository productRepository; public ProductService(CachedAIService aiService, ProductRepository productRepository) { this.aiService aiService; this.productRepository productRepository; } Transactional public Product processNewProduct(String imageUrl, String productName) { // 使用AI分析图片 ProductInfo info aiService.analyzeProductWithCache(imageUrl); // 创建商品记录 Product product new Product(); product.setName(productName); product.setImageUrl(imageUrl); product.setCategory(info.getCategory()); product.setDescription(info.getDescription()); return productRepository.save(product); } }6.2 内容审核增强Service public class ContentModerationService { private final AIService aiService; public ContentModerationService(AIService aiService) { this.aiService aiService; } public ModerationResult moderateImage(String imageUrl) { ContentAnalysis analysis aiService.analyzeContentImage(imageUrl); ModerationResult result new ModerationResult(); result.setContainsText(!analysis.getTextContent().isEmpty()); result.setSentiment(analysis.getSentiment()); result.setNeedsReview(shouldReview(analysis)); return result; } private boolean shouldReview(ContentAnalysis analysis) { // 根据分析结果决定是否需要人工审核 return analysis.getTextContent().contains(敏感词) || analysis.getSentiment().contains(负面); } }7. 测试与调试确保集成质量好的集成需要充分的测试来保证质量。7.1 单元测试示例SpringBootTest public class AIServiceTest { MockBean private AIClient aiClient; Autowired private AIService aiService; Test public void testAnalyzeProductImage() { // 准备mock数据 when(aiClient.describeImage(anyString())) .thenReturn(这是一款黑色的智能手机); when(aiClient.analyzeImage(anyString(), eq(这是什么类别的商品))) .thenReturn(电子产品); when(aiClient.analyzeImage(anyString(), eq(这个商品大概什么价格区间))) .thenReturn(2000-3000元); // 执行测试 ProductInfo result aiService.analyzeProductImage(test_url); // 验证结果 assertNotNull(result); assertEquals(电子产品, result.getCategory()); assertEquals(2000-3000元, result.getPriceRange()); } }7.2 集成测试配置创建测试配置文件SpringBootTest(webEnvironment SpringBootTest.WebEnvironment.RANDOM_PORT) TestPropertySource(properties { ai.service.urlhttp://localhost:${wiremock.server.port}, ai.service.timeout5000 }) AutoConfigureWireMock(port 0) public class AIControllerIntegrationTest { LocalServerPort private int port; Autowired private TestRestTemplate restTemplate; Test public void testAnalyzeProductEndpoint() { // 配置WireMock模拟AI服务响应 stubFor(post(urlEqualTo(/analyze)) .willReturn(aResponse() .withHeader(Content-Type, application/json) .withBody({\answer\:\电子产品\}))); // 发送测试请求 ResponseEntityProductInfo response restTemplate.postForEntity( http://localhost: port /api/ai/analyze-product?imageUrltest.jpg, null, ProductInfo.class); assertEquals(HttpStatus.OK, response.getStatusCode()); } }8. 部署与运维生产环境准备当集成开发完成后我们需要考虑如何部署到生产环境。8.1 Docker容器化创建DockerfileFROM openjdk:17-jdk-slim WORKDIR /app COPY target/ai-microservice.jar app.jar EXPOSE 8080 ENTRYPOINT [java, -jar, app.jar]构建和运行docker build -t ai-microservice . docker run -p 8080:8080 -e AI_SERVICE_URLhttp://ai-service:8000 ai-microservice8.2 环境配置管理使用环境变量配置export AI_SERVICE_URLhttp://production-ai-service:8000 export AI_SERVICE_TIMEOUT30000 java -jar ai-microservice.jar或者使用配置文件# application-production.yml ai: service: url: http://production-ai-service:8000 timeout: 300009. 总结通过这篇指南我们完整走过了将Step3-VL-10B-Base集成到SpringBoot微服务的全过程。从最初的环境准备到核心的服务集成再到进阶的功能增强最后到测试和部署每个步骤都提供了具体的代码示例和实践建议。实际使用下来这种集成方式确实能给Java应用带来很大的价值。特别是在处理图片内容理解的场景下原本需要复杂算法团队支持的功能现在通过简单的API调用就能实现。对于大多数业务场景来说这种程度的集成已经足够满足需求了。如果你刚开始尝试AI集成建议先从简单的单图片分析做起熟悉了整个流程后再逐步尝试批量处理、缓存优化等进阶功能。遇到网络超时或者服务不可用的情况时记得添加重试机制和降级策略保证主业务的稳定性。获取更多AI镜像想探索更多AI镜像和应用场景访问 CSDN星图镜像广场提供丰富的预置镜像覆盖大模型推理、图像生成、视频生成、模型微调等多个领域支持一键部署。
Step3-VL-10B-Base与Java开发实战：SpringBoot微服务集成指南
发布时间：2026/7/14 0:08:15
Step3-VL-10B-Base与Java开发实战SpringBoot微服务集成指南1. 开篇当Java开发者遇见多模态AI如果你是一个Java开发者可能已经习惯了SpringBoot的优雅和微服务的高效。但当你第一次听说Step3-VL-10B-Base这个多模态模型时可能会有点懵——这玩意儿能和我的Java项目结合起来吗答案是肯定的而且比你想象的更简单。Step3-VL-10B-Base不仅能看懂图片、理解文字还能通过API方式为你的Java应用提供智能能力。想象一下你的电商系统可以自动识别用户上传的商品图片你的内容平台可以智能分析图片中的文字信息这些都不再是科幻电影里的场景。今天我就带你一步步实现这个融合用最熟悉的SpringBoot框架让AI能力成为你微服务架构中的一环。2. 环境准备10分钟搞定基础配置开始之前我们先确保环境就绪。这里不需要复杂的机器学习知识只要你会用Java和SpringBoot就行。2.1 基础环境要求JDK 11或更高版本推荐JDK 17Maven 3.6 或 Gradle 7.xSpringBoot 2.7 或 3.x一个可访问的Step3-VL-10B-Base服务端点可以是本地部署或远程API2.2 创建SpringBoot项目用你熟悉的方式创建一个新项目。如果你喜欢命令行spring init --dependenciesweb,json --java-version17 --package-namecom.example.aimicroservice ai-microservice或者用IDE的Spring Initializr选择这些依赖Spring WebSpring Boot DevToolsJSON Processing2.3 添加必要的依赖在pom.xml中添加这些依赖确保能与AI服务顺畅通信dependencies !-- Spring Boot基础依赖 -- dependency groupIdorg.springframework.boot/groupId artifactIdspring-boot-starter-web/artifactId /dependency !-- HTTP客户端 -- dependency groupIdorg.apache.httpcomponents/groupId artifactIdhttpclient/artifactId version4.5.13/version /dependency !-- JSON处理 -- dependency groupIdcom.fasterxml.jackson.core/groupId artifactIdjackson-databind/artifactId /dependency /dependencies3. 核心集成三步连接AI能力现在来到最关键的部分——如何让SpringBoot应用与Step3-VL-10B-Base对话。3.1 配置服务连接首先创建一个配置类来管理AI服务连接Configuration public class AIConfig { Value(${ai.service.url:http://localhost:8000}) private String aiServiceUrl; Value(${ai.service.timeout:30000}) private int timeout; Bean public RestTemplate aiRestTemplate() { RestTemplate restTemplate new RestTemplate(); restTemplate.setRequestFactory(new HttpComponentsClientHttpRequestFactory()); return restTemplate; } Bean public AIClient aiClient(RestTemplate restTemplate) { return new AIClient(restTemplate, aiServiceUrl); } }在application.properties中添加配置# AI服务配置 ai.service.urlhttp://your-ai-service:8000 ai.service.timeout30000 # 服务端口 server.port80803.2 创建AI服务客户端接下来创建一个专门的客户端来处理与AI服务的通信Component public class AIClient { private final RestTemplate restTemplate; private final String baseUrl; public AIClient(RestTemplate restTemplate, String baseUrl) { this.restTemplate restTemplate; this.baseUrl baseUrl; } public String analyzeImage(String imageUrl, String question) { // 构建请求体 MapString, Object request new HashMap(); request.put(image_url, imageUrl); request.put(question, question); // 发送请求 ResponseEntityMap response restTemplate.postForEntity( baseUrl /analyze, request, Map.class ); return (String) response.getBody().get(answer); } public String describeImage(String imageUrl) { MapString, Object request new HashMap(); request.put(image_url, imageUrl); ResponseEntityMap response restTemplate.postForEntity( baseUrl /describe, request, Map.class ); return (String) response.getBody().get(description); } }3.3 实现业务服务层创建一个服务类来封装业务逻辑Service public class AIService { private final AIClient aiClient; public AIService(AIClient aiClient) { this.aiClient aiClient; } public ProductInfo analyzeProductImage(String imageUrl) { String description aiClient.describeImage(imageUrl); String category aiClient.analyzeImage(imageUrl, 这是什么类别的商品); String priceRange aiClient.analyzeImage(imageUrl, 这个商品大概什么价格区间); return new ProductInfo(description, category, priceRange); } public ContentAnalysis analyzeContentImage(String imageUrl) { String textContent aiClient.analyzeImage(imageUrl, 图片中有哪些文字内容); String sentiment aiClient.analyzeImage(imageUrl, 这张图片传达了什么情绪); return new ContentAnalysis(textContent, sentiment); } }4. 构建REST API暴露AI能力给前端现在让我们创建控制器提供HTTP接口给前端或其他服务调用。4.1 商品图片分析接口RestController RequestMapping(/api/ai) public class AIController { private final AIService aiService; public AIController(AIService aiService) { this.aiService aiService; } PostMapping(/analyze-product) public ResponseEntityProductInfo analyzeProduct( RequestParam String imageUrl) { try { ProductInfo result aiService.analyzeProductImage(imageUrl); return ResponseEntity.ok(result); } catch (Exception e) { return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).build(); } } PostMapping(/analyze-content) public ResponseEntityContentAnalysis analyzeContent( RequestParam String imageUrl) { try { ContentAnalysis result aiService.analyzeContentImage(imageUrl); return ResponseEntity.ok(result); } catch (Exception e) { return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR).build(); } } }4.2 数据结构定义创建对应的数据模型类public class ProductInfo { private String description; private String category; private String priceRange; public ProductInfo(String description, String category, String priceRange) { this.description description; this.category category; this.priceRange priceRange; } // getters 和 setters } public class ContentAnalysis { private String textContent; private String sentiment; public ContentAnalysis(String textContent, String sentiment) { this.textContent textContent; this.sentiment sentiment; } // getters 和 setters }5. 进阶功能让集成更智能实用基础集成完成后我们可以添加一些进阶功能来提升实用性和可靠性。5.1 添加缓存机制避免重复分析相同的图片提升响应速度Service public class CachedAIService { private final AIService aiService; private final CacheManager cacheManager; public CachedAIService(AIService aiService, CacheManager cacheManager) { this.aiService aiService; this.cacheManager cacheManager; } Cacheable(value imageAnalysis, key #imageUrl) public ProductInfo analyzeProductWithCache(String imageUrl) { return aiService.analyzeProductImage(imageUrl); } }在配置类中启用缓存Configuration EnableCaching public class CacheConfig { Bean public CacheManager cacheManager() { return new ConcurrentMapCacheManager(imageAnalysis); } }5.2 实现批量处理如果需要处理多张图片可以添加批量处理功能Service public class BatchAIService { private final AIService aiService; private final ExecutorService executorService; public BatchAIService(AIService aiService) { this.aiService aiService; this.executorService Executors.newFixedThreadPool(5); } public ListProductInfo batchAnalyzeProducts(ListString imageUrls) { ListCompletableFutureProductInfo futures imageUrls.stream() .map(url - CompletableFuture.supplyAsync( () - aiService.analyzeProductImage(url), executorService)) .collect(Collectors.toList()); return futures.stream() .map(CompletableFuture::join) .collect(Collectors.toList()); } }5.3 添加健康检查确保AI服务可用性Component public class AIHealthCheck implements HealthIndicator { private final AIClient aiClient; public AIHealthCheck(AIClient aiClient) { this.aiClient aiClient; } Override public Health health() { try { // 发送一个简单的测试请求 aiClient.describeImage(https://example.com/test.jpg); return Health.up().build(); } catch (Exception e) { return Health.down() .withDetail(error, AI service unavailable) .build(); } } }6. 实际应用示例让我们看几个具体的应用场景了解如何在实际项目中使用这个集成。6.1 电商商品自动分类Service public class ProductService { private final CachedAIService aiService; private final ProductRepository productRepository; public ProductService(CachedAIService aiService, ProductRepository productRepository) { this.aiService aiService; this.productRepository productRepository; } Transactional public Product processNewProduct(String imageUrl, String productName) { // 使用AI分析图片 ProductInfo info aiService.analyzeProductWithCache(imageUrl); // 创建商品记录 Product product new Product(); product.setName(productName); product.setImageUrl(imageUrl); product.setCategory(info.getCategory()); product.setDescription(info.getDescription()); return productRepository.save(product); } }6.2 内容审核增强Service public class ContentModerationService { private final AIService aiService; public ContentModerationService(AIService aiService) { this.aiService aiService; } public ModerationResult moderateImage(String imageUrl) { ContentAnalysis analysis aiService.analyzeContentImage(imageUrl); ModerationResult result new ModerationResult(); result.setContainsText(!analysis.getTextContent().isEmpty()); result.setSentiment(analysis.getSentiment()); result.setNeedsReview(shouldReview(analysis)); return result; } private boolean shouldReview(ContentAnalysis analysis) { // 根据分析结果决定是否需要人工审核 return analysis.getTextContent().contains(敏感词) || analysis.getSentiment().contains(负面); } }7. 测试与调试确保集成质量好的集成需要充分的测试来保证质量。7.1 单元测试示例SpringBootTest public class AIServiceTest { MockBean private AIClient aiClient; Autowired private AIService aiService; Test public void testAnalyzeProductImage() { // 准备mock数据 when(aiClient.describeImage(anyString())) .thenReturn(这是一款黑色的智能手机); when(aiClient.analyzeImage(anyString(), eq(这是什么类别的商品))) .thenReturn(电子产品); when(aiClient.analyzeImage(anyString(), eq(这个商品大概什么价格区间))) .thenReturn(2000-3000元); // 执行测试 ProductInfo result aiService.analyzeProductImage(test_url); // 验证结果 assertNotNull(result); assertEquals(电子产品, result.getCategory()); assertEquals(2000-3000元, result.getPriceRange()); } }7.2 集成测试配置创建测试配置文件SpringBootTest(webEnvironment SpringBootTest.WebEnvironment.RANDOM_PORT) TestPropertySource(properties { ai.service.urlhttp://localhost:${wiremock.server.port}, ai.service.timeout5000 }) AutoConfigureWireMock(port 0) public class AIControllerIntegrationTest { LocalServerPort private int port; Autowired private TestRestTemplate restTemplate; Test public void testAnalyzeProductEndpoint() { // 配置WireMock模拟AI服务响应 stubFor(post(urlEqualTo(/analyze)) .willReturn(aResponse() .withHeader(Content-Type, application/json) .withBody({\answer\:\电子产品\}))); // 发送测试请求 ResponseEntityProductInfo response restTemplate.postForEntity( http://localhost: port /api/ai/analyze-product?imageUrltest.jpg, null, ProductInfo.class); assertEquals(HttpStatus.OK, response.getStatusCode()); } }8. 部署与运维生产环境准备当集成开发完成后我们需要考虑如何部署到生产环境。8.1 Docker容器化创建DockerfileFROM openjdk:17-jdk-slim WORKDIR /app COPY target/ai-microservice.jar app.jar EXPOSE 8080 ENTRYPOINT [java, -jar, app.jar]构建和运行docker build -t ai-microservice . docker run -p 8080:8080 -e AI_SERVICE_URLhttp://ai-service:8000 ai-microservice8.2 环境配置管理使用环境变量配置export AI_SERVICE_URLhttp://production-ai-service:8000 export AI_SERVICE_TIMEOUT30000 java -jar ai-microservice.jar或者使用配置文件# application-production.yml ai: service: url: http://production-ai-service:8000 timeout: 300009. 总结通过这篇指南我们完整走过了将Step3-VL-10B-Base集成到SpringBoot微服务的全过程。从最初的环境准备到核心的服务集成再到进阶的功能增强最后到测试和部署每个步骤都提供了具体的代码示例和实践建议。实际使用下来这种集成方式确实能给Java应用带来很大的价值。特别是在处理图片内容理解的场景下原本需要复杂算法团队支持的功能现在通过简单的API调用就能实现。对于大多数业务场景来说这种程度的集成已经足够满足需求了。如果你刚开始尝试AI集成建议先从简单的单图片分析做起熟悉了整个流程后再逐步尝试批量处理、缓存优化等进阶功能。遇到网络超时或者服务不可用的情况时记得添加重试机制和降级策略保证主业务的稳定性。获取更多AI镜像想探索更多AI镜像和应用场景访问 CSDN星图镜像广场提供丰富的预置镜像覆盖大模型推理、图像生成、视频生成、模型微调等多个领域支持一键部署。
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