Remove solo pack benchmark/MLAgentBench (kept team contests only)

benchmark/MLAgentBench/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) [year] [fullname]
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

benchmark/MLAgentBench/SOURCE_README.md

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-# MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
-
-MLAgentBench is a suite of end-to-end Machine Learning (ML) experimentation tasks for benchmarking AI agents, where the agent aims to take a given 
-dataset and a machine learning task description and autonomously develop or improve an ML model. Paper: https://arxiv.org/abs/2310.03302
-![](figs/main.png)
-
-Our AI agent in action on MLAgentBench:
-[![Watch the video](https://img.youtube.com/vi/s9NANrjLEZs/maxresdefault.jpg)](https://youtu.be/s9NANrjLEZs)
-
-Each task is an interactive environment that directly resembles what human researchers see,
-where an agent can read available files, run multiple experiments on a compute cluster, and analyze results to achieve the specified research goal. 
-Specifically, we include 13 diverse ML engineering tasks,
-achievable by trying different machine learning methods, data processing, architectures, training processes, etc:
-![](figs/table.png)
-
-
-# Setup
-
-The MLAgentBench package can be installed with
-```
-pip install -e .
-```
-
-Install dependencies with python 3.10 by running 
-```
-bash install.sh
-```
-or use our [docker image](https://hub.docker.com/layers/qhwang123/researchassistant/latest/images/sha256-6b3690a13ba44fd089086e9860a298ed49a179d9a04a5406c0df074569a3aabe?context=repo). Since agent will modify and execute files, we recommend running experiments within sandboxes such as docker container.
-For docker, use the following instructions: 
-1. Pull the docker image:
-```
-docker pull qhwang123/researchassistant:latest
-```
-2. Run the docker container from the image, mounting the current directory to `/MLAgentBench` inside the container with root user permissions to install other packages:
-- On Windows PowerShell
-```
-docker run -it --user root -v ${PWD}:/MLAgentBench -w /MLAgentBench qhwang123/researchassistant:latest
-```
-- On Mac or Linux
-```
-docker run -it --user root -v "$(pwd)":/MLAgentBench -w /MLAgentBench qhwang123/researchassistant:latest
-```
-
-Each dataset will be prepared when it is run the first time. You can also prepare them beforehand with 
-```
-python -u -m MLAgentBench.prepare_task <task_name> $(which python)
-```
-For Kaggle datasets, you need to set up Kaggle API and authentication (~/.kaggle/kaggle.json) as described [here](https://www.kaggle.com/docs/api). You may also need to provide manual consent to the rules of specific competitions by following the prompts. For docker, use the following instructions:
-1. Ensure that you have ".kaggle/kaggle.json" with your API credentials in the MLAgentBench root folder.
-2. Once your container is mounted (instructions above), run
-```
-export KAGGLE_CONFIG_DIR=/MLAgentBench/.kaggle
-pip install kaggle
-sudo apt-get install unzip
-```
-
-Finally, put API keys under the root directory of this repo (or wherever you run scripts from). Currently, we support OpenAI (openai_api_key.txt in the format of organization:APIkey), Claude (claude_api_key.txt), and CRFM API (crfm_api_key.txt). To use an AutoGPT agent, setup the directory as described [here](https://docs.agpt.co/setup/).
-
-Update: We support gemini pro and huggingface now! To run gemini, fill in PROJECT_ID in LLM.py to your project id. To run huggingface, specifiy model as huggingface/<org name>/<model name>.
-
-# Quick Start
-
-To run our research agent on cifar10 task with openai API using gpt-4 and gpt-3.5-turbo:
-
-```
-python -u -m MLAgentBench.runner --python $(which python) --task cifar10 --device 0 --log-dir first_test  --work-dir workspace --llm-name gpt-4 --edit-script-llm-name gpt-4 --fast-llm-name gpt-3.5-turbo >  first_test/log 2>&1
-```
-
-Note: capturing log is necessary for oom error etc runtime error detection.
-
-This will produce logs in `first_test` directory with the following structure
-```
-first_test/
-    agent_log/
-        main_log # main log showing agent's research process
-        agent_*.json # saved agent states
-        ...
-    env_log/
-        tool_logs/ 
-        traces/ # snap shots of the agent workspace
-        trace.json # interaction trace of the agent
-        overall_time.txt # overall time
-        error.txt # will be generated if there is a system error
-```
-
-If llm names are not specified in the args, we use claude-v1 model by default for all LLM calls. See example logs with GPT-4 over cifar10 [here](https://drive.google.com/drive/folders/1Ozy_zKYdvwcSq3EFnkaudgUXKJmBwQ5t?usp=drive_link).
-
-# Evaluation
-
-To run evaluation:
-```
-python -m MLAgentBench.eval --log-folder <log_folder>  --task <task_name> --output-file <output_name>
-```
-
-This will evaluate all runs under <log_folder> as a json.
-
-To run baseline, run the trivial policy of directly running train.py then submit with ``--agent_type Agent`` as in baseline.sh:
-
-```
-python -u -m MLAgentBench.runner --python $(which python) --task cifar10 --device 0 --log-dir first_test  --work-dir workspace --agent_type Agent
-```
-
-Finally, to reproduce plots with jsons genereated, run plot.py in MLAgentBench.
-
-# Workflow
-
-To run the benchmark systematically, we recommend the following workflow:
-
-1. Run parallel experiments over different tasks and different agents using `run_experiments.sh`. This will generate log folders in structure of final_exp_logs/<model_name>/<run_timestamp>/...
-2. Run baseline.sh on all tasks to provide baselines.
-2. Run eval.sh with properly specified models and tasks to generate evaluation jsons, including baselines.
-3. Use plot.py in MLAgentBench to analyze the results. Note you need to fix some paths and names in the file as marked with TODO.
-
-# Tasks
-
-Each task is a folder in `MLAgentBench/benchmarks/`, under which the `env/` folder contains files that the research agent will see at the beginning, and `script/` folder contains additional hidden files such as `prepare.py` for downloading data and `eval.py` for evaluation.
-
-# Agents
-
-We currently support variants of our research agent along with langchain and autogpt agents. See `run_experiments.sh` for their commands.
-
-# Results
-Success Rate, i.e. the percentages of runs that achieve more than 10% improvement at the
-last step over the average performance of the baseline in starter code:
-![](figs/final_improve_10.png)
-
-
-
-Average Improvement over the baseline in starter code among the runs that made a valid
-submission at the last step:
-![](figs/final_improve.png)
-
-See all logs here: https://github.com/q-hwang/MLAgentBench_logs
-
-# Interactive Mode (Under construction)
-
-You can also specify tasks interactively to the research agent by running `research_agent_interactive.sh`, or ideally as a vscode extension.
-