diff --git a/.python-version b/.python-version new file mode 100644 index 000000000..c8cfe3959 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/README.md b/README.md index 1039f7272..0bb5129a7 100644 --- a/README.md +++ b/README.md @@ -54,15 +54,22 @@ rLLM is an open-source project to fully democratize reinforcement learning (RL) ## Getting Started 🎯 ### Installation -```bash -# Installing Python 3.10 Environment. -conda create -n rllm python=3.10 -y -conda activate rllm -# Installing RLLM dependencies. +First, make sure [uv](https://docs.astral.sh/uv/getting-started/installation/) is installed. + +Then, clone the repository and install dependencies with `uv`: + +```bash +git clone git@github.com:agentica-project/rllm.git cd rllm -pip install -e ./verl -pip install -e . +uv sync --extra build +uv sync --extra build --extra compile +``` + +Next, log into wandb: + +```bash +uv run wandb login ``` ### Data @@ -70,10 +77,10 @@ Our raw training data is in `rllm/data/[train|test]/[code|math]/`, along with pr ```bash # Download datasets from GDrive, populates rllm/data/[train|test]/[math|code]/*.json -python scripts/data/download_datasets.py +uv run scripts/data/download_datasets.py # Generate parquet files for Deepcoder/DeepscaleR in data/*.parquet -python scripts/data/[deepcoder|deepscaler]_dataset.py +uv run scripts/data/[deepcoder|deepscaler]_dataset.py ``` ### Training Scripts diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..7dc8cc7f0 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,57 @@ +[project] +name = "rllm" +version = "0.0.0" +description = "Distributed Post-Training RL Library for LLMs" +authors = [{ name = "Agentica Team", email = "agenticaproject@gmail.com" }] +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + 'torch==2.4.0', + 'torchaudio', + 'torchvision', + 'hydra-core==1.3.2', + 'deepspeed', + 'dm-tree', + 'gdown', + 'google-cloud-aiplatform', + 'latex2sympy2', + 'pybind11', + 'pylatexenc', + 'pytest', + 'sentence_transformers', + 'sortedcontainers', + 'tabulate', + 'torchmetrics', + 'gym', + 'selenium', + 'PyMuPDF', + 'nltk', + 'browsergym', + 'gradio', + 'fire', + 'vertexai', + 'e2b_code_interpreter', + 'firecrawl', + 'wandb', + 'verl', + 'datasets==3.5.0' +] + +[project.optional-dependencies] +build = ["torch", "setuptools", "packaging"] +compile = ["flash-attn"] + +[tool.uv] +no-build-isolation-package = ["flash-attn"] + +[tool.uv.sources] +verl = { path = "verl", editable = true } + +[[tool.uv.dependency-metadata]] +name = "flash-attn" +version = "2.7.4.post1" +requires-dist = ["torch", "einops"] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/scripts/deepscaler/train/deepscaler_1.5b_16k.sh b/scripts/deepscaler/train/deepscaler_1.5b_16k.sh index c699888e8..7cf4f2c07 100755 --- a/scripts/deepscaler/train/deepscaler_1.5b_16k.sh +++ b/scripts/deepscaler/train/deepscaler_1.5b_16k.sh @@ -24,7 +24,7 @@ if [ -z "$MODEL_PATH" ]; then fi # Train over 4 nodes, 8 A100-80GB GPUs per node. -python3 -m verl.trainer.main_ppo \ +uv run -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/rllm/data/deepscaler_train.parquet \ data.val_files=$HOME/rllm/data/aime.parquet \ diff --git a/scripts/deepscaler/train/deepscaler_1.5b_24k.sh b/scripts/deepscaler/train/deepscaler_1.5b_24k.sh index 6955191d0..6efc23a08 100755 --- a/scripts/deepscaler/train/deepscaler_1.5b_24k.sh +++ b/scripts/deepscaler/train/deepscaler_1.5b_24k.sh @@ -24,7 +24,7 @@ if [ -z "$MODEL_PATH" ]; then fi # Train over 4 nodes, 8 A100-80GB GPUs per node. -python3 -m verl.trainer.main_ppo \ +uv run -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/rllm/data/deepscaler_train.parquet \ data.val_files=$HOME/rllm/data/aime.parquet \ diff --git a/scripts/deepscaler/train/deepscaler_1.5b_8k.sh b/scripts/deepscaler/train/deepscaler_1.5b_8k.sh index 2e74eabed..51dfa7677 100755 --- a/scripts/deepscaler/train/deepscaler_1.5b_8k.sh +++ b/scripts/deepscaler/train/deepscaler_1.5b_8k.sh @@ -24,7 +24,7 @@ if [ -z "$MODEL_PATH" ]; then fi # Train over a single node, 8 A100-80GB GPUs. -python3 -m verl.trainer.main_ppo \ +uv run -m verl.trainer.main_ppo \ algorithm.adv_estimator=grpo \ data.train_files=$HOME/rllm/data/deepscaler_train.parquet \ data.val_files=$HOME/rllm/data/aime.parquet \ diff --git a/uv.lock b/uv.lock new file mode 100644 index 000000000..53fea0be0 --- /dev/null +++ b/uv.lock @@ -0,0 +1,5497 @@ +version = 1 +revision = 1 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'", + "python_full_version == '3.12.*' and 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