FlexAI’s Interactive Development features are designed to optimize your development workflows and enhance productivity. You can leverage these tools to streamline your coding-testing-debugging cycles, all within the FlexAI ecosystem.
Visual Studio Code Integration
Connect directly to your Interactive Development environment from Visual Studio Code to leverage all of its supported features, like language servers, integrated terminal for system-wide command execution, debugging tools, and a large set of extensions.
SSH Connection Freedom
Access your an Interactive Development environment via SSH from any terminal or tool that can leverage it, giving you complete control over your development environment.
You can use the Interactive Training mode to create a dynamic FlexAI Training runtime environment that you can interact with directly via SSH from your terminal or through a code editor —in this case, Visual Studio Code (VSCode) 🔗.
The most straightforward way to integrate FlexAI into your development workflow is to use the Interactive Training mode to create a dynamic training environment that you can connect to from VSCode.
Within the Interactive Training runtime environment you will have access to its file system —including your code base, Datasets, generated outputs and Checkpoints—, as well as the ability to run system commands to query logs and monitor performance.
Currently, the FlexAI Console does not support this feature. Please refer to the "Using the FlexAI CLI" instructions instead.
Starting an Interactive Training session using the flexai training debug-ssh command with the required --repository-url and --authorized-keys flags, as well as the optional, but highly recommended --vscode flag.
flexai training debug-ssh \ --repository-url https://github.com/flexaihq/nanoGPT \ --authorized-keys ~/.ssh/id_ed25519.pub \ --vscode \Once you’re happy with the results after testing and iterating on your code, you can save your work by pushing changes to the source Git repository. This can be done because the CLI will attempt to get your Git Author name and Email from your local Git configuration and set them in the Interactive Training Runtime environment.
You can go deeper by integrating more FlexAI services, here’s an example workflow:
Currently, the FlexAI Console does not support this feature. Please refer to the "Using the FlexAI CLI" instructions instead.
You can deploy an Inference Endpoint of a coding-assistant LLM, such as StarCoder 🔗 or Mamba Codestral 🔗, either as a base model, or a Fine-tuned one with your own data.
Start an Interactive Training session making sure to use the --vscode flag.
Once the Interactive Training session is running and you have connected to it from VSCode, you can install an extension such as CodeGPT 🔗 or Continue 🔗 and configure it to use your deployed Inference Endpoint and API Key.
How about testing your changes by deploying an Inference Endpoint from an “Inference-ready” Checkpoint you created during your Interactive Training session? You can learn how to do this and more in the Inference-ready Checkpoints documentation page.
If you prefer not to use Visual Studio Code, you can still connect to the Interactive Training Runtime using any SSH client. Simply skip the --vscode flag when starting the Interactive Training session, then use the provided SSH command to connect to the Interactive Training Runtime from your terminal or SSH client of choice.