chore: import upstream snapshot with attribution

.devcontainer/Dockerfile

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+# 1. Pull down your Azure Container Registry image
+FROM rdagentappregistry.azurecr.io/rd-agent-mle:20250623
+
+# 2. (Optional) install any additional tools you need
+#    e.g. git, bash-completion, etc.
+# RUN apt update && \
+#     apt install -y git bash-completion && \
+#     rm -rf /var/lib/apt/lists/*
+RUN apt update && \
+    apt install -y git bash-completion

.devcontainer/README.md

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+# Introduction
+
+!!!!!This dev container is not for public development!!!!!!
+!!!!!Please don't use it if you are just a public open-source user.!!!!!!
+
+# Steps to run the dev container (for internal use only)
+
+Prerequisites(this is the reason why this dev container is not for public use):
+
+- Make sure you have the `rdagentappregistry.azurecr.io/rd-agent-mle:20250623` image locally & DevContainer is installed in your IDE
+- The kaggle dataset is located at `/home/shared/RD-Agent/kaggle`
+
+1. Open the project and select "Open In DevContainer"
+2. Set up your Kaggle Key (do not share this; other internal URLs are hardcoded in the config files)
+
+```bash
+export KAGGLE_USERNAME=
+export KAGGLE_KEY=
+```
+
+3. Run: python rdagent/app/data_science/loop.py --competition nomad2018-predict-transparent-conductors
+
+
+# Additional Notes
+- Please install and use this Dev Container in VS Code.
+- You **must open VS Code remotely and enter the `RD-Agent` directory before running the DevContainer configuration (`.devcontainer/devcontainer.json`)**. Otherwise, the workspace and path mappings will not work as expected.
+- To open the DevContainer correctly in VS Code:
+  1. Remotely connect to the machine and open the `RD-Agent` folder in VS Code.
+  2. Press `Ctrl+Shift+P` (or `Cmd+Shift+P` on Mac), type and select **"Dev Containers: Reopen in Container"**.
+
+
+
+# How to grade your submission in the DevContainer
+
+1. save your submission file in `./sumission.csv`
+
+2. Run evaluation
+DS_COMPETITION=<your competition name>
+conda run -n mlebench  mlebench grade-sample submission.csv $DS_COMPETITION --data-dir /tmp/kaggle/zip_files/

.devcontainer/devcontainer.json

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+{
+  "name": "rd-agent-mle DevContainer",
+  "build": {
+    "dockerfile": "Dockerfile",
+    "context": ".."
+  },
+  "workspaceFolder": "/workspace/RD-Agent",
+  "workspaceMount": "source=${localWorkspaceFolder},target=/workspace/RD-Agent,type=bind,consistency=cached",
+  "remoteUser": "root",
+  "settings": {
+    "terminal.integrated.shell.linux": "/bin/bash"
+  },
+  "mounts": [
+    "source=/home/shared/RD-Agent/kaggle,target=/tmp/kaggle,type=bind,consistency=cached,readonly"
+  ],
+  "extensions": [
+    "ms-python.python",
+    "ms-python.vscode-pylance",
+    "ms-toolsai.jupyter"
+  ],
+  "runArgs": [
+    "--init",
+    "--shm-size=1g",
+    "--env-file", "${localWorkspaceFolder}/.devcontainer/env",
+    "--network=host",
+    "--gpus=all"
+  ],
+  "postCreateCommand": "make dev"
+}

.devcontainer/env

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+# Global configs:
+
+MAX_RETRY=12000
+RETRY_WAIT_SECONDS=5
+TIMEOUT_FAIL_LIMIT=100
+
+# litellm
+# CHAT_MODEL=gpt-4o
+# CHAT_TEMPERATURE=0.7
+
+CHAT_STREAM=False
+CHAT_TEMPERATURE=1
+CHAT_MODEL=o1-preview
+SYSTEM_PROMPT_ROLE=user
+
+BACKEND=rdagent.oai.backend.LiteLLMAPIBackend
+OPENAI_API_KEY=sk-1234
+OPENAI_API_BASE=http://ep14.213428.xyz:38881
+
+
+# amc chat model configs:
+EMBEDDING_MODEL=text-embedding-ada-002
+
+# Cache Setting (Optional):
+DUMP_CHAT_CACHE=True
+USE_CHAT_CACHE=False
+DUMP_EMBEDDING_CACHE=True
+USE_EMBEDDING_CACHE=False
+LOG_LLM_CHAT_CONTENT=True
+
+DS_LOCAL_DATA_PATH=/tmp/kaggle
+
+DS_IF_USING_MLE_DATA=True
+
+
+PICKLE_CACHE_FOLDER_PATH_STR=./log/pickle_cache
+CACHE_WITH_PICKLE=False
+ENABLE_CACHE=False
+PROMPT_CACHE_PATH=./log/prompt_cache.db
+
+DS_CODER_COSTEER_ENV_TYPE=conda
+# DS_PROPOSAL_VERSION=v2 deprecated
+
+DS_CODER_ON_WHOLE_PIPELINE=True
+COSTEER_V2_QUERY_FORMER_TRACE_LIMIT=3
+
+# export PYTHONPATH=.  # this is for running researcher branch;