FastDeploy/docs/usage/environment_variables.md

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FastDeploy Environment Variables

FastDeploy's environment variables are defined in fastdeploy/envs.py at the root of the repository. Below is the documentation:

environment_variables: dict[str, Callable[[], Any]] = {
    # CUDA architecture versions used when building FastDeploy (string list, e.g. [80,90])
    "FD_BUILDING_ARCS":
    lambda: os.getenv("FD_BUILDING_ARCS", "[]"),

    # Log directory
    "FD_LOG_DIR":
    lambda: os.getenv("FD_LOG_DIR", "log"),

    # Enable debug mode (0 or 1)
    "FD_DEBUG":
    lambda: os.getenv("FD_DEBUG", "0"),

    # FastDeploy log retention days
    "FD_LOG_BACKUP_COUNT":
    lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),

    # Model download cache directory
    "FD_MODEL_CACHE":
    lambda: os.getenv("FD_MODEL_CACHE", None),

    # Maximum number of stop sequences
    "FD_MAX_STOP_SEQS_NUM":
    lambda: os.getenv("FD_MAX_STOP_SEQS_NUM", "5"),

    # Maximum length of stop sequences
    "FD_STOP_SEQS_MAX_LEN":
    lambda: os.getenv("FD_STOP_SEQS_MAX_LEN", "8"),

    # GPU devices to use (comma-separated string, e.g. 0,1,2)
    "CUDA_VISIBLE_DEVICES":
    lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),

    # Whether to use HuggingFace tokenizer (0 or 1)
    "FD_USE_HF_TOKENIZER":
    lambda: bool(int(os.getenv("FD_USE_HF_TOKENIZER", 0))),

    # ZMQ send high-water mark (HWM) during initialization
    "FD_ZMQ_SNDHWM":
    lambda: os.getenv("FD_ZMQ_SNDHWM", 10000),

    # Directory for caching KV quantization parameters
    "FD_CACHE_PARAMS":
    lambda: os.getenv("FD_CACHE_PARAMS", "none"),

    # Attention backend ("NATIVE_ATTN", "APPEND_ATTN", or "MLA_ATTN")
    "FD_ATTENTION_BACKEND":
    lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),

    # Sampling class ("base", "base_non_truncated", "air", or "rejection")
    "FD_SAMPLING_CLASS":
    lambda: os.getenv("FD_SAMPLING_CLASS", "base"),

    # MoE backend ("cutlass", "marlin", or "triton")
    "FD_MOE_BACKEND":
    lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),

    # Triton kernel JIT compilation directory
    "FD_TRITON_KERNEL_CACHE_DIR":
    lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),

    # Switch from standalone PD to centralized inference (0 or 1)
    "FD_PD_CHANGEABLE":
    lambda: os.getenv("FD_PD_CHANGEABLE", "1"),

    # Whether to use DeepGemm for FP8 blockwise MoE.
    "FD_USE_DEEP_GEMM":
    lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "0"))),

    # Whether to enable model cache feature
    "FD_ENABLE_MODEL_LOAD_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_LOAD_CACHE", "0"))),

    # Whether to use Machete for wint4 dense GEMM.
    "FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "0"),

    # Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie4_5_vl, \n</think>\n\n for ernie_x1)
    "FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),

    # Timeout for cache_transfer_manager process exit
    "FD_CACHE_PROC_EXIT_TIMEOUT": lambda: int(os.getenv("FD_CACHE_PROC_EXIT_TIMEOUT", "600")),

    # Count for cache_transfer_manager process error
    "FD_CACHE_PROC_ERROR_COUNT": lambda: int(os.getenv("FD_CACHE_PROC_ERROR_COUNT", "10")),
}