Weco CLI – Code Optimizer for Machine Learning Engineers

weco is a command-line interface for interacting with Weco AI's code optimizer, powered by AI-Driven Exploration. It helps you automate the improvement of your code for tasks like GPU kernel optimization, feature engineering, model development, and prompt engineering.

MLX.1.mp4

---

Overview

The weco CLI leverages a tree search approach guided by Large Language Models (LLMs) to iteratively explore and refine your code. It automatically applies changes, runs your evaluation script, parses the results, and proposes further improvements based on the specified goal.

---

Example Use Cases

Here's how weco can be applied to common ML engineering tasks:

• GPU Kernel Optimization:

  • Goal: Improve the speed or efficiency of low-level GPU code.
  • How: weco iteratively refines CUDA, Triton, Metal, or other kernel code specified in your --source file.
  • --eval-command: Typically runs a script that compiles the kernel, executes it, and benchmarks performance (e.g., latency, throughput).
  • --metric: Examples include latency, throughput, TFLOPS, memory_bandwidth. Optimize to minimize latency or maximize throughput.

• Feature Engineering:

  • Goal: Discover better data transformations or feature combinations for your machine learning models.
  • How: weco explores different processing steps or parameters within your feature transformation code (--source).
  • --eval-command: Executes a script that applies the features, trains/validates a model using those features, and prints a performance score.
  • --metric: Examples include accuracy, AUC, F1-score, validation_loss. Usually optimized to maximize accuracy/AUC/F1 or minimize loss.

• Model Development:

  • Goal: Tune hyperparameters or experiment with small architectural changes directly within your model's code.
  • How: weco modifies hyperparameter values (like learning rate, layer sizes if defined in the code) or structural elements in your model definition (--source).
  • --eval-command: Runs your model training and evaluation script, printing the key performance indicator.
  • --metric: Examples include validation_accuracy, test_loss, inference_time, perplexity. Optimize according to the metric's nature (e.g., maximize accuracy, minimize loss).

• Prompt Engineering:

  • Goal: Refine prompts used within larger systems (e.g., for LLM interactions) to achieve better or more consistent outputs.
  • How: weco modifies prompt templates, examples, or instructions stored in the --source file.
  • --eval-command: Executes a script that uses the prompt, generates an output, evaluates that output against desired criteria (e.g., using another LLM, checking for keywords, format validation), and prints a score.
  • --metric: Examples include quality_score, relevance, task_success_rate, format_adherence. Usually optimized to maximize.

---

Setup

1. Install the Package:

   pip install weco

2. Configure API Keys:

   Set the appropriate environment variables for your desired language model provider:

   • OpenAI: export OPENAI_API_KEY="your_key_here"
   • Anthropic: export ANTHROPIC_API_KEY="your_key_here"
   • Google DeepMind: export GEMINI_API_KEY="your_key_here" (Google AI Studio has a free API usage quota. Create a key here to use weco for free.)

---

Usage

⚠️ Warning: Code Modification
weco directly modifies the file specified by --source during the optimization process. It is strongly recommended to use version control (like Git) to track changes and revert if needed. Alternatively, ensure you have a backup of your original file before running the command. Upon completion, the file will contain the best-performing version of the code found during the run.

---

Examples

Example 1: Optimizing PyTorch operations

weco --source examples/simple-torch/optimize.py \
     --eval-command "python examples/simple-torch/evaluate.py --solution-path examples/simple-torch/optimize.py --device mps" \
     --metric speedup \
     --maximize true \
     --steps 15 \
     --model o3-mini \
     --additional-instructions "Fuse operations in the forward method while ensuring the max float deviation remains small. Maintain the same format of the code."

Example 2: Optimizing MLX operations with instructions from a file

Sometimes, additional context or instructions are too complex for a single command-line string. You can provide a path to a file containing these instructions.

weco --source examples/simple-mlx/optimize.py \
     --eval-command "python examples/simple-mlx/evaluate.py --solution-path examples/simple-mlx/optimize.py" \
     --metric speedup \
     --maximize true \
     --steps 30 \
     --model o3-mini \
     --additional-instructions examples/simple-mlx/metal-examples.rst

---

Command Line Arguments

Argument	Description	Required
--source	Path to the source code file that will be optimized (e.g., optimize.py).	Yes
--eval-command	Command to run for evaluating the code in --source. This command should print the target --metric and its value to the terminal (stdout/stderr). See note below.	Yes
--metric	The name of the metric you want to optimize (e.g., 'accuracy', 'speedup', 'loss'). This metric name should match what's printed by your --eval-command.	Yes
--maximize	Whether to maximize (true) or minimize (false) the metric.	Yes
--steps	Number of optimization steps (LLM iterations) to run.	Yes
--model	Model identifier for the LLM to use (e.g., gpt-4o, claude-3.5-sonnet). Recommended models to try include o3-mini, claude-3-haiku, and gemini-2.5-pro-exp-03-25.	Yes
--additional-instructions	(Optional) Natural language description of specific instructions OR path to a file containing detailed instructions to guide the LLM.	No

---

Important Note on Evaluation

The command specified by --eval-command is crucial. It's responsible for executing the potentially modified code from --source and assessing its performance. This command MUST print the metric you specified with --metric along with its numerical value to the terminal (standard output or standard error). Weco reads this output to understand how well each code version performs and guide the optimization process.

For example, if you set --metric speedup, your evaluation script (eval.py in the examples) should output a line like:

speedup: 1.5

or

Final speedup value = 1.5

Weco will parse this output to extract the numerical value (1.5 in this case) associated with the metric name ('speedup').

Contributing

We welcome contributions! To get started:

1. Fork and Clone the Repository:

   git clone https://github.com/WecoAI/weco-cli.git
   cd weco-cli

2. Install Development Dependencies:

   pip install -e ".[dev]"

3. Create a Feature Branch:

   git checkout -b feature/your-feature-name

4. Make Your Changes: Ensure your code adheres to our style guidelines and includes relevant tests.

5. Commit and Push your changes, then open a pull request with a clear description of your enhancements.

---