vllm.envs
VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE module-attribute
¶
VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = (
False
)
VLLM_ALLOW_INSECURE_SERIALIZATION module-attribute
¶
VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING module-attribute
¶
VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
VLLM_FLASHINFER_FORCE_TENSOR_CORES module-attribute
¶
VLLM_FLASHINFER_FORCE_TENSOR_CORES: bool = False
VLLM_LOGITS_PROCESSOR_THREADS module-attribute
¶
VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE module-attribute
¶
VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16 module-attribute
¶
VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB module-attribute
¶
VLLM_ROCM_QUICK_REDUCE_QUANTIZATION module-attribute
¶
VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: str = 'NONE'
VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL module-attribute
¶
VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS module-attribute
¶
VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
VLLM_TORCH_PROFILER_RECORD_SHAPES module-attribute
¶
VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY module-attribute
¶
VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
VLLM_USE_FLASHINFER_MOE_MXFP4_BF16 module-attribute
¶
VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8 module-attribute
¶
VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE module-attribute
¶
VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: str = 'auto'
VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM module-attribute
¶
VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
VLLM_V1_USE_PREFILL_DECODE_ATTENTION module-attribute
¶
VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
VLLM_XLA_CACHE_PATH module-attribute
¶
VLLM_XLA_CACHE_PATH: str = join(
VLLM_CACHE_ROOT, "xla_cache"
)
environment_variables module-attribute
¶
environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_TARGET_DEVICE": lambda: lower(),
"MAX_JOBS": lambda: getenv("MAX_JOBS", None),
"NVCC_THREADS": lambda: getenv("NVCC_THREADS", None),
"VLLM_USE_PRECOMPILED": lambda: bool(
get("VLLM_USE_PRECOMPILED")
)
or bool(get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
"VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL": lambda: bool(
int(
getenv(
"VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL",
"0",
)
)
),
"CMAKE_BUILD_TYPE": lambda: getenv("CMAKE_BUILD_TYPE"),
"VERBOSE": lambda: bool(int(getenv("VERBOSE", "0"))),
"VLLM_CONFIG_ROOT": lambda: expanduser(
getenv(
"VLLM_CONFIG_ROOT",
join(get_default_config_root(), "vllm"),
)
),
"VLLM_CACHE_ROOT": lambda: expanduser(
getenv(
"VLLM_CACHE_ROOT",
join(get_default_cache_root(), "vllm"),
)
),
"VLLM_HOST_IP": lambda: getenv("VLLM_HOST_IP", ""),
"VLLM_PORT": get_vllm_port,
"VLLM_RPC_BASE_PATH": lambda: getenv(
"VLLM_RPC_BASE_PATH", gettempdir()
),
"VLLM_USE_MODELSCOPE": lambda: lower() == "true",
"VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
),
"CUDA_HOME": lambda: get("CUDA_HOME", None),
"VLLM_NCCL_SO_PATH": lambda: get(
"VLLM_NCCL_SO_PATH", None
),
"LD_LIBRARY_PATH": lambda: get("LD_LIBRARY_PATH", None),
"VLLM_USE_TRITON_FLASH_ATTN": lambda: lower()
in ("true", "1"),
"VLLM_V1_USE_PREFILL_DECODE_ATTENTION": lambda: lower()
in ("true", "1"),
"VLLM_USE_AITER_UNIFIED_ATTENTION": lambda: lower()
in ("true", "1"),
"VLLM_FLASH_ATTN_VERSION": lambda: maybe_convert_int(
get("VLLM_FLASH_ATTN_VERSION", None)
),
"VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE": lambda: bool(
get("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1")
!= "0"
),
"VLLM_USE_STANDALONE_COMPILE": lambda: get(
"VLLM_USE_STANDALONE_COMPILE", "1"
)
== "1",
"LOCAL_RANK": lambda: int(get("LOCAL_RANK", "0")),
"CUDA_VISIBLE_DEVICES": lambda: get(
"CUDA_VISIBLE_DEVICES", None
),
"VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")
),
"VLLM_API_KEY": lambda: get("VLLM_API_KEY", None),
"VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: lower()
== "true",
"S3_ACCESS_KEY_ID": lambda: get(
"S3_ACCESS_KEY_ID", None
),
"S3_SECRET_ACCESS_KEY": lambda: get(
"S3_SECRET_ACCESS_KEY", None
),
"S3_ENDPOINT_URL": lambda: get("S3_ENDPOINT_URL", None),
"VLLM_USAGE_STATS_SERVER": lambda: get(
"VLLM_USAGE_STATS_SERVER", "https://stats.vllm.ai"
),
"VLLM_NO_USAGE_STATS": lambda: get(
"VLLM_NO_USAGE_STATS", "0"
)
== "1",
"VLLM_DO_NOT_TRACK": lambda: (
get("VLLM_DO_NOT_TRACK", None)
or get("DO_NOT_TRACK", None)
or "0"
)
== "1",
"VLLM_USAGE_SOURCE": lambda: get(
"VLLM_USAGE_SOURCE", "production"
),
"VLLM_CONFIGURE_LOGGING": lambda: int(
getenv("VLLM_CONFIGURE_LOGGING", "1")
),
"VLLM_LOGGING_CONFIG_PATH": lambda: getenv(
"VLLM_LOGGING_CONFIG_PATH"
),
"VLLM_LOGGING_LEVEL": lambda: upper(),
"VLLM_LOGGING_PREFIX": lambda: getenv(
"VLLM_LOGGING_PREFIX", ""
),
"VLLM_LOGITS_PROCESSOR_THREADS": lambda: int(
getenv("VLLM_LOGITS_PROCESSOR_THREADS", "0")
)
if "VLLM_LOGITS_PROCESSOR_THREADS" in environ
else None,
"VLLM_TRACE_FUNCTION": lambda: int(
getenv("VLLM_TRACE_FUNCTION", "0")
),
"VLLM_ATTENTION_BACKEND": lambda: getenv(
"VLLM_ATTENTION_BACKEND", None
),
"VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
int(environ["VLLM_USE_FLASHINFER_SAMPLER"])
)
if "VLLM_USE_FLASHINFER_SAMPLER" in environ
else None,
"VLLM_FLASHINFER_FORCE_TENSOR_CORES": lambda: bool(
int(
getenv(
"VLLM_FLASHINFER_FORCE_TENSOR_CORES", "0"
)
)
),
"VLLM_PP_LAYER_PARTITION": lambda: getenv(
"VLLM_PP_LAYER_PARTITION", None
),
"VLLM_CPU_KVCACHE_SPACE": lambda: int(
getenv("VLLM_CPU_KVCACHE_SPACE", "0")
)
if "VLLM_CPU_KVCACHE_SPACE" in environ
else None,
"VLLM_CPU_OMP_THREADS_BIND": lambda: getenv(
"VLLM_CPU_OMP_THREADS_BIND", "auto"
),
"VLLM_CPU_NUM_OF_RESERVED_CPU": lambda: int(
getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0")
)
if "VLLM_CPU_NUM_OF_RESERVED_CPU" in environ
else None,
"VLLM_CPU_MOE_PREPACK": lambda: bool(
int(getenv("VLLM_CPU_MOE_PREPACK", "1"))
),
"VLLM_CPU_SGL_KERNEL": lambda: bool(
int(getenv("VLLM_CPU_SGL_KERNEL", "0"))
),
"VLLM_USE_RAY_SPMD_WORKER": lambda: bool(
int(getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))
),
"VLLM_USE_RAY_COMPILED_DAG": lambda: bool(
int(getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))
),
"VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": lambda: getenv(
"VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto"
),
"VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
int(
getenv(
"VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM",
"0",
)
)
),
"VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
int(getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
),
"VLLM_WORKER_MULTIPROC_METHOD": lambda: getenv(
"VLLM_WORKER_MULTIPROC_METHOD", "fork"
),
"VLLM_ASSETS_CACHE": lambda: expanduser(
getenv(
"VLLM_ASSETS_CACHE",
join(
get_default_cache_root(), "vllm", "assets"
),
)
),
"VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(
getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")
),
"VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
),
"VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
),
"VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
),
"VLLM_VIDEO_LOADER_BACKEND": lambda: getenv(
"VLLM_VIDEO_LOADER_BACKEND", "opencv"
),
"VLLM_MM_INPUT_CACHE_GIB": lambda: int(
getenv("VLLM_MM_INPUT_CACHE_GIB", "4")
),
"VLLM_XLA_CACHE_PATH": lambda: expanduser(
getenv(
"VLLM_XLA_CACHE_PATH",
join(
get_default_cache_root(),
"vllm",
"xla_cache",
),
)
),
"VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
int(getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
),
"VLLM_XLA_USE_SPMD": lambda: bool(
int(getenv("VLLM_XLA_USE_SPMD", "0"))
),
"VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")
),
"VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
int(
getenv(
"VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING",
"1",
)
)
),
"VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", 0)
),
"VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: lower()
in ("1", "true"),
"VLLM_TEST_FORCE_FP8_MARLIN": lambda: lower()
in ("1", "true"),
"VLLM_TEST_FORCE_LOAD_FORMAT": lambda: getenv(
"VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"
),
"VLLM_RPC_TIMEOUT": lambda: int(
getenv("VLLM_RPC_TIMEOUT", "10000")
),
"VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
),
"VLLM_PLUGINS": lambda: None
if "VLLM_PLUGINS" not in environ
else split(","),
"VLLM_LORA_RESOLVER_CACHE_DIR": lambda: getenv(
"VLLM_LORA_RESOLVER_CACHE_DIR", None
),
"VLLM_TORCH_PROFILER_DIR": lambda: None
if getenv("VLLM_TORCH_PROFILER_DIR", None) is None
else expanduser(getenv("VLLM_TORCH_PROFILER_DIR", ".")),
"VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: bool(
getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0")
!= "0"
),
"VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: bool(
getenv(
"VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0"
)
!= "0"
),
"VLLM_TORCH_PROFILER_WITH_STACK": lambda: bool(
getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"
),
"VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: bool(
getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"
),
"VLLM_USE_TRITON_AWQ": lambda: bool(
int(getenv("VLLM_USE_TRITON_AWQ", "0"))
),
"VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: lower()
in ("1", "true"),
"VLLM_SKIP_P2P_CHECK": lambda: getenv(
"VLLM_SKIP_P2P_CHECK", "1"
)
== "1",
"VLLM_DISABLED_KERNELS": lambda: []
if "VLLM_DISABLED_KERNELS" not in environ
else split(","),
"VLLM_USE_V1": lambda: bool(
int(getenv("VLLM_USE_V1", "1"))
),
"VLLM_ROCM_USE_AITER": lambda: lower() in ("true", "1"),
"VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_AITER_LINEAR": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_AITER_MOE": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_AITER_RMSNORM": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_AITER_MLA": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_AITER_MHA": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_USE_SKINNY_GEMM": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_FP8_PADDING": lambda: bool(
int(getenv("VLLM_ROCM_FP8_PADDING", "1"))
),
"VLLM_ROCM_MOE_PADDING": lambda: bool(
int(getenv("VLLM_ROCM_MOE_PADDING", "1"))
),
"VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": lambda: upper(),
"VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: lower()
in ("true", "1"),
"VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
get(
"VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None
)
),
"Q_SCALE_CONSTANT": lambda: int(
getenv("Q_SCALE_CONSTANT", "200")
),
"K_SCALE_CONSTANT": lambda: int(
getenv("K_SCALE_CONSTANT", "200")
),
"V_SCALE_CONSTANT": lambda: int(
getenv("V_SCALE_CONSTANT", "100")
),
"VLLM_ENABLE_V1_MULTIPROCESSING": lambda: bool(
int(getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))
),
"VLLM_LOG_BATCHSIZE_INTERVAL": lambda: float(
getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")
),
"VLLM_DISABLE_COMPILE_CACHE": lambda: bool(
int(getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))
),
"VLLM_SERVER_DEV_MODE": lambda: bool(
int(getenv("VLLM_SERVER_DEV_MODE", "0"))
),
"VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
),
"VLLM_MLA_DISABLE": lambda: bool(
int(getenv("VLLM_MLA_DISABLE", "0"))
),
"VLLM_RAY_PER_WORKER_GPUS": lambda: float(
getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
),
"VLLM_RAY_BUNDLE_INDICES": lambda: getenv(
"VLLM_RAY_BUNDLE_INDICES", ""
),
"VLLM_CUDART_SO_PATH": lambda: getenv(
"VLLM_CUDART_SO_PATH", None
),
"VLLM_DP_RANK": lambda: int(
getenv("VLLM_DP_RANK", "0")
),
"VLLM_DP_RANK_LOCAL": lambda: int(
getenv("VLLM_DP_RANK_LOCAL", VLLM_DP_RANK)
),
"VLLM_DP_SIZE": lambda: int(
getenv("VLLM_DP_SIZE", "1")
),
"VLLM_DP_MASTER_IP": lambda: getenv(
"VLLM_DP_MASTER_IP", "127.0.0.1"
),
"VLLM_DP_MASTER_PORT": lambda: int(
getenv("VLLM_DP_MASTER_PORT", "0")
),
"VLLM_MOE_DP_CHUNK_SIZE": lambda: int(
getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")
),
"VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: get(
"VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
)
== "1",
"VLLM_CI_USE_S3": lambda: get("VLLM_CI_USE_S3", "0")
== "1",
"VLLM_MODEL_REDIRECT_PATH": lambda: get(
"VLLM_MODEL_REDIRECT_PATH", None
),
"VLLM_MARLIN_USE_ATOMIC_ADD": lambda: get(
"VLLM_MARLIN_USE_ATOMIC_ADD", "0"
)
== "1",
"VLLM_V0_USE_OUTLINES_CACHE": lambda: get(
"VLLM_V0_USE_OUTLINES_CACHE", "0"
)
== "1",
"VLLM_V1_USE_OUTLINES_CACHE": lambda: get(
"VLLM_V1_USE_OUTLINES_CACHE", "0"
)
== "1",
"VLLM_TPU_BUCKET_PADDING_GAP": lambda: int(
environ["VLLM_TPU_BUCKET_PADDING_GAP"]
)
if "VLLM_TPU_BUCKET_PADDING_GAP" in environ
else 0,
"VLLM_TPU_MOST_MODEL_LEN": lambda: maybe_convert_int(
get("VLLM_TPU_MOST_MODEL_LEN", None)
),
"VLLM_TPU_USING_PATHWAYS": lambda: bool(
"proxy" in lower()
),
"VLLM_USE_DEEP_GEMM": lambda: bool(
int(getenv("VLLM_USE_DEEP_GEMM", "0"))
),
"VLLM_SKIP_DEEP_GEMM_WARMUP": lambda: bool(
int(getenv("VLLM_SKIP_DEEP_GEMM_WARMUP", "0"))
),
"VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
int(getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
),
"VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
int(getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
),
"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
int(
getenv(
"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"
)
)
),
"VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
int(
getenv(
"VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"
)
)
),
"VLLM_XGRAMMAR_CACHE_MB": lambda: int(
getenv("VLLM_XGRAMMAR_CACHE_MB", "512")
),
"VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
),
"VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
int(
getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0")
)
),
"VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: getenv(
"VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
),
"VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5557")
),
"VLLM_ALL2ALL_BACKEND": lambda: getenv(
"VLLM_ALL2ALL_BACKEND", "naive"
),
"VLLM_FLASHINFER_MOE_BACKEND": lambda: getenv(
"VLLM_FLASHINFER_MOE_BACKEND", "throughput"
),
"VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
getenv(
"VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840"
)
),
"VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: lower(),
"VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
),
"VLLM_SLEEP_WHEN_IDLE": lambda: bool(
int(getenv("VLLM_SLEEP_WHEN_IDLE", "0"))
),
"VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
),
"VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
),
"VLLM_KV_CACHE_LAYOUT": lambda: getenv(
"VLLM_KV_CACHE_LAYOUT", None
),
"VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
int(getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
),
"VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
int(getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
),
"VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "120")
),
"VLLM_USE_CUDNN_PREFILL": lambda: bool(
int(getenv("VLLM_USE_CUDNN_PREFILL", "0"))
),
"VLLM_USE_TRTLLM_ATTENTION": lambda: getenv(
"VLLM_USE_TRTLLM_ATTENTION", None
),
"VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
int(getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
),
"VLLM_LOOPBACK_IP": lambda: getenv(
"VLLM_LOOPBACK_IP", ""
),
"VLLM_PROCESS_NAME_PREFIX": lambda: getenv(
"VLLM_PROCESS_NAME_PREFIX", "VLLM"
),
"VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
int(
getenv(
"VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE",
"0",
)
)
),
"VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
int(getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
),
}
__dir__ ¶
compute_hash ¶
compute_hash() -> str
WARNING: Whenever a new key is added to this environment variables, ensure that it is included in the factors list if it affects the computation graph. For example, different values of VLLM_PP_LAYER_PARTITION will generate different computation graphs, so it is included in the factors list. The env vars that affect the choice of different kernels or attention backends should also be included in the factors list.
Source code in vllm/envs.py
get_default_cache_root ¶
get_default_config_root ¶
get_vllm_port ¶
Get the port from VLLM_PORT environment variable.
Returns:
Type | Description |
---|---|
Optional[int] | The port number as an integer if VLLM_PORT is set, None otherwise. |
Raises:
Type | Description |
---|---|
ValueError | If VLLM_PORT is a URI, suggest k8s service discovery issue. |