Environment:Volcengine Verl Python Core Dependencies

Metadata

Field	Value
Sources	verl\|https://github.com/volcengine/verl
Domains	Infrastructure
Last Updated	2026-02-07 17:00 GMT

Overview

Core Python dependencies for the verl RL training framework including PyTorch, transformers, Ray, and tensordict.

Description

verl requires a set of core Python packages. PyTorch >= 2.4 is needed for FSDP2 (fully_shard API). tensordict >= 0.8.0 and <= 0.10.0 (excluding 0.9.0) is required for the DataProto protocol. Ray >= 2.41.0 is needed for distributed orchestration. numpy must be < 2.0.0 for compatibility.

Usage

These are mandatory for all verl workflows.

System Requirements

Python 3.10+ recommended
Linux OS

Dependencies

From setup.py:

torch >= 2.4.0 (required for FSDP2)
tensordict >= 0.8.0, <= 0.10.0, != 0.9.0
ray[default] >= 2.41.0
transformers
accelerate
datasets
peft
hydra-core
numpy < 2.0.0
pyarrow >= 19.0.0
packaging >= 20.0
wandb
pandas
tensorboard

Credentials

WANDB_ENTITY: W&B logging entity
MLFLOW_TRACKING_URI: MLflow tracking URI
SWANLAB_API_KEY: SwanLab logging API key
TENSORBOARD_DIR: TensorBoard output directory

Quick Install

pip install verl

Or install dependencies directly:

pip install torch>=2.4.0 transformers accelerate datasets peft hydra-core "numpy<2.0.0" "ray[default]>=2.41.0" "tensordict>=0.8.0,<=0.10.0,!=0.9.0" wandb pandas tensorboard "pyarrow>=19.0.0" "packaging>=20.0"

Code Evidence

From verl/utils/fsdp_utils.py:511:

assert CPUOffloadPolicy is not None, "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)"

And from verl/protocol.py:567-568:

assert version.parse(tensordict.__version__) >= version.parse("0.10.0")

Common Errors

Error	Solution
"PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)"	Upgrade PyTorch
tensordict version assertion	Install correct tensordict version
"No module named 'ray'"	pip install "ray[default]>=2.41.0"

Compatibility Notes

numpy 2.0+ is explicitly excluded. tensordict 0.9.0 has known incompatibilities.

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