Generate TensorFlow ops from example input and output
Submit code models for evaluation on benchmarks
Generate code for your app with a description
Generate code snippets from descriptions
Autocomplete code snippets in Python
Obfuscate code
Generate code snippets and answer programming questions
Find programs from input-output examples
Convert a GitHub repo to a text file for any LLM to use
Provide a link to a quantization notebook
Ask questions and get answers with code execution
Example for running a multi-agent autogen workflow.
Explore code snippets with Nomic Atlas
Tensorflow Coder is a specialized code generation tool designed to help developers and data scientists work with TensorFlow. It generates TensorFlow operations (ops) based on example input and output, simplifying the process of creating custom TensorFlow code. By leveraging patterns or examples, the tool can automate the writing of low-level TensorFlow code, saving time and reducing errors.
• Generate TensorFlow Ops: Create custom TensorFlow operations from example input and output. • Intelligent Code Completion: Autocomplete suggestions based on TensorFlow best practices. • Support for TensorFlow Versions: Compatibility with multiple versions of TensorFlow. • Debugging Assistance: Built-in tools to help identify and fix errors in generated code. • Integration with TensorFlow Ecosystem: Seamlessly works with existing TensorFlow workflows and libraries. • Customizable Output: Options to tailor the generated code to specific use cases.
What types of input and output does Tensorflow Coder support?
Tensorflow Coder supports a wide range of tensor types, including standard numerical tensors, string tensors, and more complex structured tensors.
Can I customize the generated code?
Yes, Tensorflow Coder allows customization of the generated code, such as specifying data types, adding comments, or modifying variable names.
Is Tensorflow Coder compatible with older versions of TensorFlow?
Yes, Tensorflow Coder is designed to be backward-compatible with earlier versions of TensorFlow, ensuring it can be used in legacy projects as well as new ones.