Quick Start
Quick Start
🚀 Quick Start Guide: Master Core Features in 3 Minutes
This guide helps you quickly understand and use the core functions of the qModel algorithm model platform, including logging in, navigating the dashboard, accessing models, managing model assets, and running computation tasks. Get up to speed fast and start managing your algorithm models efficiently.
1. Log In to the System
✨✨✨ Demo Site ✨✨✨ https://demo.qmodel.tech — Username: qModel / Password: qModel123
✨✨✨ Demo Video ✨✨✨ https://www.bilibili.com/video/BV1s6TszmEnF
2. Dashboard Overview
After logging in, you'll land on the dashboard, which displays key metrics and quick access links:
- Core statistics: total registered models, computation tasks, and service calls;
- Recent activity: latest model uploads and task execution trends;
- News/announcements section and shortcuts to frequently used functions.
📌 Tip: The dashboard is your first window into platform status. Check it daily for updates.
3. Access Your First Model
- Go to Model Access & Execution;
- Click Upload, select a model package (ZIP/TAR format) and drag it into the upload area;
- The system automatically parses the entry script, input/output parameters, and dependencies;
- After compatibility detection passes, the model is ready for registration.
📌 Tip: Supported model formats include Python scripts, Java JARs, and executable programs. Start with a simple model to verify the workflow.
4. Register and Manage Models
- Go to Model Management;
- Click Register, fill in the model name, description, tags, author, and version information;
- Define input/output structures using JSON Schema or YAML;
- Submit for approval, then publish the model once approved.
📌 Tip: A well-organized classification and tagging system helps manage large model inventories. Plan your categories based on industry, task type, or technical stack.
5. Run a Computation Task
- Go to Model Computation;
- Click Create Task, select a published model;
- Configure task parameters: timeout, retry count, and priority;
- Bind input data to the model parameters;
- Launch the task and monitor its status in real time (queued → running → completed);
- View results with automatic chart and table visualization, and download as CSV/JSON.
📌 Tip: Start with small test datasets to verify computation correctness before processing large-scale data.
6. Publish a Model as a Service
- Go to Service Governance & Scheduling;
- Select a published model and click Publish as Service;
- The system automatically generates a RESTful API with Swagger/OpenAPI documentation;
- Use the generated API endpoint to call the model from external applications.
📌 Tip: The commercial edition adds authentication, rate limiting, monitoring, and call chain tracing for production-grade service governance.
7. Common Issues & Help
Q: Why does my model upload fail?
A: Check that the model package is in ZIP/TAR format, contains a valid entry file (e.g., main.py), and passes compatibility detection. For local deployments, ensure the model runtime environment is properly configured.
Q: Why is my computation task stuck in "queued" status?
A: For local deployments, check if the scheduled task service (Quartz) is enabled. For the demo site, contact technical support.
Q: Where can I find more guidance?
A: Refer to the full User Manual or contact your administrator for training materials.
🎉 Congratulations on completing the quick start!
You now understand the platform's core workflow. Dive deeper into advanced features like model fusion, workflow orchestration, and model training in the commercial edition to unlock the full potential of your algorithm model assets!