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Commit0 Evaluation with OpenHands

This folder contains the evaluation harness that we built on top of the original Commit0 (paper).

The evaluation consists of three steps:

  1. Environment setup: install python environment, configure LLM config.
  2. Run Evaluation: Generate a edit patch for each Commit0 Repo, and get the evaluation results

Setup Environment and LLM Configuration

Please follow instruction here to setup your local development environment and LLM.

OpenHands Commit0 Instance-level Docker Support

OpenHands supports using the Commit0 Docker for **inference. This is now the default behavior.

Run Inference on Commit0 Instances

Make sure your Docker daemon is running, and you have ample disk space (at least 200-500GB, depends on the Commit0 set you are running on) for the instance-level docker image.

When the run_infer.sh script is started, it will automatically pull the lite split in Commit0. For example, for instance ID commit-0/minitorch, it will try to pull our pre-build docker image wentingzhao/minitorch from DockerHub. This image will be used create an OpenHands runtime image where the agent will operate on.

./evaluation/benchmarks/commit0_bench/scripts/run_infer.sh [repo_split] [model_config] [git-version] [agent] [eval_limit] [max_iter] [num_workers] [dataset] [dataset_split]

# Example
./evaluation/benchmarks/commit0_bench/scripts/run_infer.sh lite llm.eval_sonnet HEAD CodeActAgent 16 100 8 wentingzhao/commit0_combined test

where model_config is mandatory, and the rest are optional.

  • repo_split, e.g. lite, is the split of the Commit0 dataset you would like to evaluate on. Available options are lite, all and each individual repo.
  • model_config, e.g. eval_gpt4_1106_preview, is the config group name for your LLM settings, as defined in your config.toml.
  • git-version, e.g. HEAD, is the git commit hash of the OpenHands version you would like to evaluate. It could also be a release tag like 0.6.2.
  • agent, e.g. CodeActAgent, is the name of the agent for benchmarks, defaulting to CodeActAgent.
  • eval_limit, e.g. 10, limits the evaluation to the first eval_limit instances. By default, the script evaluates the lite split of the Commit0 dataset (16 repos). Note: in order to use eval_limit, you must also set agent.
  • max_iter, e.g. 20, is the maximum number of iterations for the agent to run. By default, it is set to 30.
  • num_workers, e.g. 3, is the number of parallel workers to run the evaluation. By default, it is set to 1.
  • dataset, a huggingface dataset name. e.g. wentingzhao/commit0_combined, specifies which dataset to evaluate on.
  • dataset_split, split for the huggingface dataset. Notice only test is supported for Commit0.

Note that the USE_INSTANCE_IMAGE environment variable is always set to true for Commit0.

Let's say you'd like to run 10 instances using llm.eval_sonnet and CodeActAgent,

then your command would be:

./evaluation/benchmarks/commit0_bench/scripts/run_infer.sh lite llm.eval_sonnet HEAD CodeActAgent 10 30 1 wentingzhao/commit0_combined test

Run Inference on RemoteRuntime (experimental)

This is in limited beta. Contact Xingyao over slack if you want to try this out!

./evaluation/benchmarks/commit0_bench/scripts/run_infer.sh [repo_split] [model_config] [git-version] [agent] [eval_limit] [max_iter] [num_workers] [dataset] [dataset_split]

# Example - This runs evaluation on CodeActAgent for 10 instances on "wentingzhao/commit0_combined"'s test set, with max 30 iteration per instances, with 1 number of workers running in parallel
ALLHANDS_API_KEY="YOUR-API-KEY" RUNTIME=remote SANDBOX_REMOTE_RUNTIME_API_URL="https://runtime.eval.all-hands.dev" EVAL_DOCKER_IMAGE_PREFIX="docker.io/wentingzhao" \
./evaluation/benchmarks/commit0_bench/scripts/run_infer.sh lite llm.eval_sonnet HEAD CodeActAgent 10 30 1 wentingzhao/commit0_combined test

To clean-up all existing runtime you've already started, run:

ALLHANDS_API_KEY="YOUR-API-KEY" ./evaluation/utils/scripts/cleanup_remote_runtime.sh

Specify a subset of tasks to run infer

If you would like to specify a list of tasks you'd like to benchmark on, you just need to pass selected repo through repo_split option.