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<p><a href="../zh/usage/environment_variables.md">简体中文</a></p>
<h1 id="fastdeploy-environment-variables">FastDeploy Environment Variables</h1>
<p>FastDeploy's environment variables are defined in <code>fastdeploy/envs.py</code> at the root of the repository. Below is the documentation:</p>
<pre><code class="language-python">environment_variables: dict[str, Callable[[], Any]] = {
# Whether to use BF16 on CPU
&quot;FD_CPU_USE_BF16&quot;: lambda: os.getenv(&quot;FD_CPU_USE_BF16&quot;, &quot;False&quot;),
# CUDA architecture versions used when building FastDeploy (string list, e.g. [80,90])
&quot;FD_BUILDING_ARCS&quot;: lambda: os.getenv(&quot;FD_BUILDING_ARCS&quot;, &quot;[]&quot;),
# Log directory
&quot;FD_LOG_DIR&quot;: lambda: os.getenv(&quot;FD_LOG_DIR&quot;, &quot;log&quot;),
# Enable debug mode (0 or 1)
&quot;FD_DEBUG&quot;: lambda: int(os.getenv(&quot;FD_DEBUG&quot;, &quot;0&quot;)),
# FastDeploy log retention days
&quot;FD_LOG_BACKUP_COUNT&quot;: lambda: os.getenv(&quot;FD_LOG_BACKUP_COUNT&quot;, &quot;7&quot;),
# Model download source, can be &quot;AISTUDIO&quot;, &quot;MODELSCOPE&quot; or &quot;HUGGINGFACE&quot;
&quot;FD_MODEL_SOURCE&quot;: lambda: os.getenv(&quot;FD_MODEL_SOURCE&quot;, &quot;AISTUDIO&quot;),
# Model download cache directory
&quot;FD_MODEL_CACHE&quot;: lambda: os.getenv(&quot;FD_MODEL_CACHE&quot;, None),
# Maximum number of stop sequences
&quot;FD_MAX_STOP_SEQS_NUM&quot;: lambda: int(os.getenv(&quot;FD_MAX_STOP_SEQS_NUM&quot;, &quot;5&quot;)),
# Maximum length of stop sequences
&quot;FD_STOP_SEQS_MAX_LEN&quot;: lambda: int(os.getenv(&quot;FD_STOP_SEQS_MAX_LEN&quot;, &quot;8&quot;)),
# GPU devices to use (comma-separated string, e.g. 0,1,2)
&quot;CUDA_VISIBLE_DEVICES&quot;: lambda: os.getenv(&quot;CUDA_VISIBLE_DEVICES&quot;, None),
# Whether to use HuggingFace tokenizer (0 or 1)
&quot;FD_USE_HF_TOKENIZER&quot;: lambda: bool(int(os.getenv(&quot;FD_USE_HF_TOKENIZER&quot;, &quot;0&quot;))),
# ZMQ send high-water mark (HWM) during initialization
&quot;FD_ZMQ_SNDHWM&quot;: lambda: os.getenv(&quot;FD_ZMQ_SNDHWM&quot;, 0),
# Directory for caching KV quantization parameters
&quot;FD_CACHE_PARAMS&quot;: lambda: os.getenv(&quot;FD_CACHE_PARAMS&quot;, &quot;none&quot;),
# Attention backend (&quot;NATIVE_ATTN&quot;, &quot;APPEND_ATTN&quot;, or &quot;MLA_ATTN&quot;)
&quot;FD_ATTENTION_BACKEND&quot;: lambda: os.getenv(&quot;FD_ATTENTION_BACKEND&quot;, &quot;APPEND_ATTN&quot;),
# Sampling class (&quot;base&quot;, &quot;base_non_truncated&quot;, &quot;air&quot;, or &quot;rejection&quot;)
&quot;FD_SAMPLING_CLASS&quot;: lambda: os.getenv(&quot;FD_SAMPLING_CLASS&quot;, &quot;base&quot;),
# MoE backend (&quot;cutlass&quot;, &quot;marlin&quot;, or &quot;triton&quot;)
&quot;FD_MOE_BACKEND&quot;: lambda: os.getenv(&quot;FD_MOE_BACKEND&quot;, &quot;cutlass&quot;),
# Whether to use Machete for wint4 dense GEMM
&quot;FD_USE_MACHETE&quot;: lambda: os.getenv(&quot;FD_USE_MACHETE&quot;, &quot;1&quot;),
# Whether to disable recompute the request when the KV cache is full
&quot;FD_DISABLED_RECOVER&quot;: lambda: os.getenv(&quot;FD_DISABLED_RECOVER&quot;, &quot;0&quot;),
# Triton kernel JIT compilation directory
&quot;FD_TRITON_KERNEL_CACHE_DIR&quot;: lambda: os.getenv(&quot;FD_TRITON_KERNEL_CACHE_DIR&quot;, None),
# Switch from standalone PD to centralized inference (0 or 1)
&quot;FD_PD_CHANGEABLE&quot;: lambda: os.getenv(&quot;FD_PD_CHANGEABLE&quot;, &quot;0&quot;),
# Whether to use DeepGemm for FP8 blockwise MoE
&quot;FD_USE_DEEP_GEMM&quot;: lambda: bool(int(os.getenv(&quot;FD_USE_DEEP_GEMM&quot;, &quot;0&quot;))),
# Whether to use aggregate send
&quot;FD_USE_AGGREGATE_SEND&quot;: lambda: bool(int(os.getenv(&quot;FD_USE_AGGREGATE_SEND&quot;, &quot;0&quot;))),
# Whether to open Trace
&quot;TRACES_ENABLE&quot;: lambda: os.getenv(&quot;TRACES_ENABLE&quot;, &quot;false&quot;),
# Set trace server name
&quot;FD_SERVICE_NAME&quot;: lambda: os.getenv(&quot;FD_SERVICE_NAME&quot;, &quot;FastDeploy&quot;),
# Set trace host name
&quot;FD_HOST_NAME&quot;: lambda: os.getenv(&quot;FD_HOST_NAME&quot;, &quot;localhost&quot;),
# Set trace exporter
&quot;TRACES_EXPORTER&quot;: lambda: os.getenv(&quot;TRACES_EXPORTER&quot;, &quot;console&quot;),
# Set trace exporter_otlp_endpoint
&quot;EXPORTER_OTLP_ENDPOINT&quot;: lambda: os.getenv(&quot;EXPORTER_OTLP_ENDPOINT&quot;),
# Set trace exporter_otlp_headers
&quot;EXPORTER_OTLP_HEADERS&quot;: lambda: os.getenv(&quot;EXPORTER_OTLP_HEADERS&quot;),
# Enable kv cache block scheduler v1 (no need for kv_cache_ratio)
&quot;ENABLE_V1_KVCACHE_SCHEDULER&quot;: lambda: int(os.getenv(&quot;ENABLE_V1_KVCACHE_SCHEDULER&quot;, &quot;1&quot;)),
# Set prealloc block num for decoder
&quot;FD_ENC_DEC_BLOCK_NUM&quot;: lambda: int(os.getenv(&quot;FD_ENC_DEC_BLOCK_NUM&quot;, &quot;2&quot;)),
# Enable max prefill of one execute step
&quot;FD_ENABLE_MAX_PREFILL&quot;: lambda: int(os.getenv(&quot;FD_ENABLE_MAX_PREFILL&quot;, &quot;0&quot;)),
# Whether to use PLUGINS
&quot;FD_PLUGINS&quot;: lambda: None if &quot;FD_PLUGINS&quot; not in os.environ else os.environ[&quot;FD_PLUGINS&quot;].split(&quot;,&quot;),
# Set trace attribute job_id
&quot;FD_JOB_ID&quot;: lambda: os.getenv(&quot;FD_JOB_ID&quot;),
# Support max connections
&quot;FD_SUPPORT_MAX_CONNECTIONS&quot;: lambda: int(os.getenv(&quot;FD_SUPPORT_MAX_CONNECTIONS&quot;, &quot;1024&quot;)),
# Offset for Tensor Parallelism group GID
&quot;FD_TP_GROUP_GID_OFFSET&quot;: lambda: int(os.getenv(&quot;FD_TP_GROUP_GID_OFFSET&quot;, &quot;1000&quot;)),
# Enable multi api server
&quot;FD_ENABLE_MULTI_API_SERVER&quot;: lambda: bool(int(os.getenv(&quot;FD_ENABLE_MULTI_API_SERVER&quot;, &quot;0&quot;))),
# Whether to use Torch model format
&quot;FD_FOR_TORCH_MODEL_FORMAT&quot;: lambda: bool(int(os.getenv(&quot;FD_FOR_TORCH_MODEL_FORMAT&quot;, &quot;0&quot;))),
# Force disable default chunked prefill
&quot;FD_DISABLE_CHUNKED_PREFILL&quot;: lambda: bool(int(os.getenv(&quot;FD_DISABLE_CHUNKED_PREFILL&quot;, &quot;0&quot;))),
# Whether to use new get_output and save_output method (0 or 1)
&quot;FD_USE_GET_SAVE_OUTPUT_V1&quot;: lambda: bool(int(os.getenv(&quot;FD_USE_GET_SAVE_OUTPUT_V1&quot;, &quot;0&quot;))),
# Whether to enable model cache feature
&quot;FD_ENABLE_MODEL_CACHE&quot;: lambda: bool(int(os.getenv(&quot;FD_ENABLE_MODEL_CACHE&quot;, &quot;0&quot;))),
# Enable internal module to access LLMEngine
&quot;FD_ENABLE_INTERNAL_ADAPTER&quot;: lambda: int(os.getenv(&quot;FD_ENABLE_INTERNAL_ADAPTER&quot;, &quot;0&quot;)),
# LLMEngine receive requests port, used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ZMQ_RECV_REQUEST_SERVER_PORT&quot;: lambda: os.getenv(&quot;FD_ZMQ_RECV_REQUEST_SERVER_PORT&quot;, &quot;8200&quot;),
# LLMEngine send response port, used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ZMQ_SEND_RESPONSE_SERVER_PORT&quot;: lambda: os.getenv(&quot;FD_ZMQ_SEND_RESPONSE_SERVER_PORT&quot;, &quot;8201&quot;),
# LLMEngine receive requests port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ZMQ_RECV_REQUEST_SERVER_PORTS&quot;: lambda: os.getenv(&quot;FD_ZMQ_RECV_REQUEST_SERVER_PORTS&quot;, &quot;8200&quot;),
# LLMEngine send response port (multiple ports), used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ZMQ_SEND_RESPONSE_SERVER_PORTS&quot;: lambda: os.getenv(&quot;FD_ZMQ_SEND_RESPONSE_SERVER_PORTS&quot;, &quot;8201&quot;),
# LLMEngine receive control command port, used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ZMQ_CONTROL_CMD_SERVER_PORTS&quot;: lambda: os.getenv(&quot;FD_ZMQ_CONTROL_CMD_SERVER_PORTS&quot;, &quot;8202&quot;),
# Whether to enable the decode caches requests for preallocating resource
&quot;FD_ENABLE_CACHE_TASK&quot;: lambda: os.getenv(&quot;FD_ENABLE_CACHE_TASK&quot;, &quot;0&quot;),
# Batched token timeout in EP
&quot;FD_EP_BATCHED_TOKEN_TIMEOUT&quot;: lambda: float(os.getenv(&quot;FD_EP_BATCHED_TOKEN_TIMEOUT&quot;, &quot;0.1&quot;)),
# Max pre-fetch requests number in PD
&quot;FD_EP_MAX_PREFETCH_TASK_NUM&quot;: lambda: int(os.getenv(&quot;FD_EP_MAX_PREFETCH_TASK_NUM&quot;, &quot;8&quot;)),
# Enable or disable model caching. When enabled, the quantized model is stored as a cache for future inference to improve loading efficiency
&quot;FD_ENABLE_MODEL_LOAD_CACHE&quot;: lambda: bool(int(os.getenv(&quot;FD_ENABLE_MODEL_LOAD_CACHE&quot;, &quot;0&quot;))),
# Whether to clear cpu cache when clearing model weights
&quot;FD_ENABLE_SWAP_SPACE_CLEARING&quot;: lambda: int(os.getenv(&quot;FD_ENABLE_SWAP_SPACE_CLEARING&quot;, &quot;0&quot;)),
# Enable return text, used when FD_ENABLE_INTERNAL_ADAPTER=1
&quot;FD_ENABLE_RETURN_TEXT&quot;: lambda: bool(int(os.getenv(&quot;FD_ENABLE_RETURN_TEXT&quot;, &quot;0&quot;))),
# Used to truncate the string inserted during thinking when reasoning in a model. (&lt;/think&gt; for ernie-45-vl, \n&lt;/think&gt;\n\n for ernie-x1)
&quot;FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR&quot;: lambda: os.getenv(&quot;FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR&quot;, &quot;&lt;/think&gt;&quot;),
# Timeout for cache_transfer_manager process exit
&quot;FD_CACHE_PROC_EXIT_TIMEOUT&quot;: lambda: int(os.getenv(&quot;FD_CACHE_PROC_EXIT_TIMEOUT&quot;, &quot;600&quot;)),
# Count for cache_transfer_manager process error
&quot;FD_CACHE_PROC_ERROR_COUNT&quot;: lambda: int(os.getenv(&quot;FD_CACHE_PROC_ERROR_COUNT&quot;, &quot;10&quot;)),
# API_KEY required for service authentication
&quot;FD_API_KEY&quot;: lambda: [] if &quot;FD_API_KEY&quot; not in os.environ else os.environ[&quot;FD_API_KEY&quot;].split(&quot;,&quot;),
# The AK of bos storing the features while multi_modal infer
&quot;ENCODE_FEATURE_BOS_AK&quot;: lambda: os.getenv(&quot;ENCODE_FEATURE_BOS_AK&quot;),
# The SK of bos storing the features while multi_modal infer
&quot;ENCODE_FEATURE_BOS_SK&quot;: lambda: os.getenv(&quot;ENCODE_FEATURE_BOS_SK&quot;),
# The ENDPOINT of bos storing the features while multi_modal infer
&quot;ENCODE_FEATURE_ENDPOINT&quot;: lambda: os.getenv(&quot;ENCODE_FEATURE_ENDPOINT&quot;),
# Enable offline perf test mode for PD disaggregation
&quot;FD_OFFLINE_PERF_TEST_FOR_PD&quot;: lambda: int(os.getenv(&quot;FD_OFFLINE_PERF_TEST_FOR_PD&quot;, &quot;0&quot;)),
# Enable E2W tensor convert
&quot;FD_ENABLE_E2W_TENSOR_CONVERT&quot;: lambda: int(os.getenv(&quot;FD_ENABLE_E2W_TENSOR_CONVERT&quot;, &quot;0&quot;)),
# Engine task queue with shared memory
&quot;FD_ENGINE_TASK_QUEUE_WITH_SHM&quot;: lambda: int(os.getenv(&quot;FD_ENGINE_TASK_QUEUE_WITH_SHM&quot;, &quot;0&quot;)),
# Fill bitmask batch size
&quot;FD_FILL_BITMASK_BATCH&quot;: lambda: int(os.getenv(&quot;FD_FILL_BITMASK_BATCH&quot;, &quot;4&quot;)),
# Enable PDL
&quot;FD_ENABLE_PDL&quot;: lambda: int(os.getenv(&quot;FD_ENABLE_PDL&quot;, &quot;1&quot;)),
# Disable guidance additional feature
&quot;FD_GUIDANCE_DISABLE_ADDITIONAL&quot;: lambda: bool(int(os.getenv(&quot;FD_GUIDANCE_DISABLE_ADDITIONAL&quot;, &quot;1&quot;))),
# LLGuidance log level
&quot;FD_LLGUIDANCE_LOG_LEVEL&quot;: lambda: int(os.getenv(&quot;FD_LLGUIDANCE_LOG_LEVEL&quot;, &quot;0&quot;)),
# Number of tokens in the group for Mixture of Experts (MoE) computation processing on HPU
&quot;FD_HPU_CHUNK_SIZE&quot;: lambda: int(os.getenv(&quot;FD_HPU_CHUNK_SIZE&quot;, &quot;64&quot;)),
# Enable FP8 calibration on HPU
&quot;FD_HPU_MEASUREMENT_MODE&quot;: lambda: os.getenv(&quot;FD_HPU_MEASUREMENT_MODE&quot;, &quot;0&quot;),
# Prefill wait decode resource seconds
&quot;FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS&quot;: lambda: int(os.getenv(&quot;FD_PREFILL_WAIT_DECODE_RESOURCE_SECONDS&quot;, &quot;30&quot;)),
# FMQ config JSON
&quot;FMQ_CONFIG_JSON&quot;: lambda: os.getenv(&quot;FMQ_CONFIG_JSON&quot;, None),
# OTLP Exporter schedule delay millis
&quot;FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS&quot;: lambda: int(os.getenv(&quot;FD_OTLP_EXPORTER_SCHEDULE_DELAY_MILLIS&quot;, &quot;500&quot;)),
# OTLP Exporter max export batch size
&quot;FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE&quot;: lambda: int(os.getenv(&quot;FD_OTLP_EXPORTER_MAX_EXPORT_BATCH_SIZE&quot;, &quot;64&quot;)),
# Token processor health timeout
&quot;FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT&quot;: lambda: int(os.getenv(&quot;FD_TOKEN_PROCESSOR_HEALTH_TIMEOUT&quot;, &quot;120&quot;)),
# XPU MoE FFN quant type map
&quot;FD_XPU_MOE_FFN_QUANT_TYPE_MAP&quot;: lambda: os.getenv(&quot;FD_XPU_MOE_FFN_QUANT_TYPE_MAP&quot;, &quot;&quot;),
}
</code></pre>
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