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]] = {
# Whether to use BF16 on CPU
"FD_CPU_USE_BF16": lambda: os.getenv("FD_CPU_USE_BF16", "False"),
# 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: int(os.getenv("FD_DEBUG", "0")),
# FastDeploy log retention days
"FD_LOG_BACKUP_COUNT": lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
# Model download source, can be "AISTUDIO", "MODELSCOPE" or "HUGGINGFACE"
"FD_MODEL_SOURCE": lambda: os.getenv("FD_MODEL_SOURCE", "AISTUDIO"),
# 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: int(os.getenv("FD_MAX_STOP_SEQS_NUM", "5")),
# Maximum length of stop sequences
"FD_STOP_SEQS_MAX_LEN": lambda: int(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", 0),
# 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"),
# Whether to use Machete for wint4 dense GEMM
"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
# Whether to disable recompute the request when the KV cache is full
"FD_DISABLED_RECOVER": lambda: os.getenv("FD_DISABLED_RECOVER", "0"),
# 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", "0"),
# Whether to use DeepGemm for FP8 blockwise MoE
"FD_USE_DEEP_GEMM": lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "0"))),
# Whether to use aggregate send
"FD_USE_AGGREGATE_SEND": lambda: bool(int(os.getenv("FD_USE_AGGREGATE_SEND", "0"))),
# Whether to open Trace
"TRACES_ENABLE": lambda: os.getenv("TRACES_ENABLE", "false"),
# Set trace server name
"FD_SERVICE_NAME": lambda: os.getenv("FD_SERVICE_NAME", "FastDeploy"),
# Set trace host name
"FD_HOST_NAME": lambda: os.getenv("FD_HOST_NAME", "localhost"),
# Set trace exporter
"TRACES_EXPORTER": lambda: os.getenv("TRACES_EXPORTER", "console"),
# Set trace exporter_otlp_endpoint
"EXPORTER_OTLP_ENDPOINT": lambda: os.getenv("EXPORTER_OTLP_ENDPOINT"),
# Set trace exporter_otlp_headers
"EXPORTER_OTLP_HEADERS": lambda: os.getenv("EXPORTER_OTLP_HEADERS"),
# Enable kv cache block scheduler v1 (no need for kv_cache_ratio)
"ENABLE_V1_KVCACHE_SCHEDULER": lambda: int(os.getenv("ENABLE_V1_KVCACHE_SCHEDULER", "1")),
# Set prealloc block num for decoder
"FD_ENC_DEC_BLOCK_NUM": lambda: int(os.getenv("FD_ENC_DEC_BLOCK_NUM", "2")),
# Enable max prefill of one execute step
"FD_ENABLE_MAX_PREFILL": lambda: int(os.getenv("FD_ENABLE_MAX_PREFILL", "0")),
# Whether to use PLUGINS
"FD_PLUGINS": lambda: None if "FD_PLUGINS" not in os.environ else os.environ["FD_PLUGINS"].split(","),
# Set trace attribute job_id
"FD_JOB_ID": lambda: os.getenv("FD_JOB_ID"),
# Support max connections
"FD_SUPPORT_MAX_CONNECTIONS": lambda: int(os.getenv("FD_SUPPORT_MAX_CONNECTIONS", "1024")),
# Offset for Tensor Parallelism group GID
"FD_TP_GROUP_GID_OFFSET": lambda: int(os.getenv("FD_TP_GROUP_GID_OFFSET", "1000")),
# Enable multi api server
"FD_ENABLE_MULTI_API_SERVER": lambda: bool(int(os.getenv("FD_ENABLE_MULTI_API_SERVER", "0"))),
# Whether to use Torch model format
"FD_FOR_TORCH_MODEL_FORMAT": lambda: bool(int(os.getenv("FD_FOR_TORCH_MODEL_FORMAT", "0"))),
# Force disable default chunked prefill
"FD_DISABLE_CHUNKED_PREFILL": lambda: bool(int(os.getenv("FD_DISABLE_CHUNKED_PREFILL", "0"))),
# Whether to use new get_output and save_output method (0 or 1)
"FD_USE_GET_SAVE_OUTPUT_V1": lambda: bool(int(os.getenv("FD_USE_GET_SAVE_OUTPUT_V1", "0"))),
# Whether to enable model cache feature
"FD_ENABLE_MODEL_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_CACHE", "0"))),
# Enable internal module to access LLMEngine
"FD_ENABLE_INTERNAL_ADAPTER": lambda: int(os.getenv("FD_ENABLE_INTERNAL_ADAPTER", "0")),
# LLMEngine receive requests port, used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ZMQ_RECV_REQUEST_SERVER_PORT": lambda: os.getenv("FD_ZMQ_RECV_REQUEST_SERVER_PORT", "8200"),
# LLMEngine send response port, used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ZMQ_SEND_RESPONSE_SERVER_PORT": lambda: os.getenv("FD_ZMQ_SEND_RESPONSE_SERVER_PORT", "8201"),
# LLMEngine receive requests port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ZMQ_RECV_REQUEST_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_RECV_REQUEST_SERVER_PORTS", "8200"),
# LLMEngine send response port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ZMQ_SEND_RESPONSE_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_SEND_RESPONSE_SERVER_PORTS", "8201"),
# LLMEngine receive control command port, used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ZMQ_CONTROL_CMD_SERVER_PORTS": lambda: os.getenv("FD_ZMQ_CONTROL_CMD_SERVER_PORTS", "8202"),
# Whether to enable the decode caches requests for preallocating resource
"FD_ENABLE_CACHE_TASK": lambda: os.getenv("FD_ENABLE_CACHE_TASK", "0"),
# Batched token timeout in EP
"FD_EP_BATCHED_TOKEN_TIMEOUT": lambda: float(os.getenv("FD_EP_BATCHED_TOKEN_TIMEOUT", "0.1")),
# Max pre-fetch requests number in PD
"FD_EP_MAX_PREFETCH_TASK_NUM": lambda: int(os.getenv("FD_EP_MAX_PREFETCH_TASK_NUM", "8")),
# Enable or disable model caching. When enabled, the quantized model is stored as a cache for future inference to improve loading efficiency
"FD_ENABLE_MODEL_LOAD_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_LOAD_CACHE", "0"))),
# Whether to clear cpu cache when clearing model weights
"FD_ENABLE_SWAP_SPACE_CLEARING": lambda: int(os.getenv("FD_ENABLE_SWAP_SPACE_CLEARING", "0")),
# Enable return text, used when FD_ENABLE_INTERNAL_ADAPTER=1
"FD_ENABLE_RETURN_TEXT": lambda: bool(int(os.getenv("FD_ENABLE_RETURN_TEXT", "0"))),
# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-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")),
# API_KEY required for service authentication
"FD_API_KEY": lambda: [] if "FD_API_KEY" not in os.environ else os.environ["FD_API_KEY"].split(","),
# The AK of bos storing the features while multi_modal infer
"ENCODE_FEATURE_BOS_AK": lambda: os.getenv("ENCODE_FEATURE_BOS_AK"),
# The SK of bos storing the features while multi_modal infer
"ENCODE_FEATURE_BOS_SK": lambda: os.getenv("ENCODE_FEATURE_BOS_SK"),
# The ENDPOINT of bos storing the features while multi_modal infer
"ENCODE_FEATURE_ENDPOINT": lambda: os.getenv("ENCODE_FEATURE_ENDPOINT"),
# Enable offline perf test mode for PD disaggregation
"FD_OFFLINE_PERF_TEST_FOR_PD": lambda: int(os.getenv("FD_OFFLINE_PERF_TEST_FOR_PD", "0")),
# Enable E2W tensor convert
"FD_ENABLE_E2W_TENSOR_CONVERT": lambda: int(os.getenv("FD_ENABLE_E2W_TENSOR_CONVERT", "0")),
# Engine task queue with shared memory
"FD_ENGINE_TASK_QUEUE_WITH_SHM": lambda: int(os.getenv("FD_ENGINE_TASK_QUEUE_WITH_SHM", "0")),
# Fill bitmask batch size
"FD_FILL_BITMASK_BATCH": lambda: int(os.getenv("FD_FILL_BITMASK_BATCH", "4")),
# Enable PDL
"FD_ENABLE_PDL": lambda: int(os.getenv("FD_ENABLE_PDL", "1")),
# Disable guidance additional feature
"FD_GUIDANCE_DISABLE_ADDITIONAL": lambda: bool(int(os.getenv("FD_GUIDANCE_DISABLE_ADDITIONAL", "1"))),
# LLGuidance log level
"FD_LLGUIDANCE_LOG_LEVEL": lambda: int(os.getenv("FD_LLGUIDANCE_LOG_LEVEL", "0")),
# Number of tokens in the group for Mixture of Experts (MoE) computation processing on HPU
"FD_HPU_CHUNK_SIZE": lambda: int(os.getenv("FD_HPU_CHUNK_SIZE", "64")),
# Enable FP8 calibration on HPU
"FD_HPU_MEASUREMENT_MODE": lambda: os.getenv("FD_HPU_MEASUREMENT_MODE", "0"),
# Prefill wait decode resource seconds
"FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS": lambda: int(os.getenv("FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS", "30")),
# FMQ config JSON
"FMQ_CONFIG_JSON": lambda: os.getenv("FMQ_CONFIG_JSON", None),
# OTLP Exporter schedule delay millis
"FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS": lambda: int(os.getenv("FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS", "500")),
# OTLP Exporter max export batch size
"FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE": lambda: int(os.getenv("FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE", "64")),
# Token processor health timeout
"FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT": lambda: int(os.getenv("FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT", "120")),
# XPU MoE FFN quant type map
"FD_XPU_MOE_FFN_QUANT_TYPE_MAP": lambda: os.getenv("FD_XPU_MOE_FFN_QUANT_TYPE_MAP", ""),
}