BioCoder integration (#2076)

* prepare execution and inference

* Create README.md

* Update README.md

* Update evaluation/biocoder/README.md

* Update evaluation/swe_bench/swe_env_box.py

* switch to biocoder docker container and test-specific code

* code for copying and running test files into container

* add metrics

* add readme

* Biocoder evaluation code finished (rewrite testing infrastructure, prompt tuning, and bug fixes)

* Update README.md

---------

Co-authored-by: lilbillybiscuit <[email protected]>
Co-authored-by: Yufan Song <[email protected]>
Co-authored-by: yufansong <[email protected]>

Author: 4 people

evaluation/biocoder/README.md

@@ -0,0 +1,59 @@
+# BioCoder Evaluation with Opendevin
+
+Implements evaluation of agents on BioCoder from the BioCoder benchmark introduced in [BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models](https://arxiv.org/abs/2308.16458). Please see [here](https://github.com/bigcode-project/bigcode-evaluation-harness/blob/main/bigcode_eval/tasks/humanevalpack.py) for the reference implementation used in the paper.
+
+## Setup Environment
+
+Please follow [this document](https://github.com/OpenDevin/OpenDevin/blob/main/Development.md) to setup local develop environment for OpenDevin.
+
+
+## Configure OpenDevin and your LLM
+Create a `config.toml` file if it does not exist at the root of the workspace. Please check [README.md](../../README.md) for how to set this up.
+
+## BioCoder Docker Image
+In the opendevin branch of the Biocoder repository, we have slightly modified our original Docker image to work with the OpenDevin environment. In the Docker image are testing scripts (`/testing/start_test_opendevin.py` and aux files in `/testing_files/`) to assist with evaluation. Additionally, we have installed all dependencies, including OpenJDK, mamba (with Python 3.6), and many system libraries. Notably, we have **not** packaged all repositories into the image, so they are downloaded at runtime.
+
+**Before first execution, pull our Docker image with the following command**
+```bash
+docker pull public.ecr.aws/i5g0m1f6/eval_biocoder:v1.0
+```
+
+To reproduce this image, please see the Dockerfile_Opendevin in the `biocoder` repository.
+
+## Start the evaluation
+
+
+```bash
+./evaluation/biocoder/scripts/run_infer.sh [model_config] [agent] [eval_limit]
+```
+
+where `model_config` is mandatory, while `agent`, `dataset` and `eval_limit` are optional.
+
+- `model_config`, e.g. `eval_gpt4_1106_preview`, is the config group name for your
+LLM settings, as defined in your `config.toml`.
+
+- `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 it infers all instances.
+
+Let's say you'd like to run 10 instances using `eval_gpt4_1106_eval_gpt4o_2024_05_13preview` and CodeActAgent,
+then your command would be:
+
+## Examples
+
+```bash
+./evaluation/biocoder/scripts/run_infer.sh eval_gpt4o_2024_05_13 CodeActAgent 1
+```
+
+## Reference
+```
+@misc{tang2024biocoder,
+      title={BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models},
+      author={Xiangru Tang and Bill Qian and Rick Gao and Jiakang Chen and Xinyun Chen and Mark Gerstein},
+      year={2024},
+      eprint={2308.16458},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```