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    • Comprehensive LLM Benchmarking
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      • Command Line Options
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      • Metrics Reference
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On this page
  • CLI RUNNER
  • APISERVER
  • COMPRESSION
  • DAG
  • DATASET
  • GPU
  • HTTP
  • LOGGING
  • METRICS
  • MLFLOW
  • OTEL
  • RECORD
  • SEARCHPLANNER
  • SERVERMETRICS
  • SERVICE
  • TIMING
  • TOKENIZER
  • UI
  • WORKER
  • ZMQ
  • DEV
Reference

Environment Variables

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AIPerf can be configured using environment variables with the AIPERF_ prefix. All settings are organized into logical subsystems for better discoverability.

Pattern: AIPERF_{SUBSYSTEM}_{SETTING_NAME}

Examples:

$export AIPERF_HTTP_CONNECTION_LIMIT=5000
$export AIPERF_WORKER_CPU_UTILIZATION_FACTOR=0.8
$export AIPERF_ZMQ_RCVTIMEO=600000

Environment variable names, default values, and definitions are subject to change. These settings may be modified, renamed, or removed in future releases.

CLI RUNNER

CLI runner post-run callback behavior. Controls whether OnComplete callback exceptions abort the run after all callbacks attempt or are isolated and logged. Default is isolated so that a single misbehaving callback (e.g. auto-plot in strict mode, third-party hook) cannot bypass the deliberate os._exit hang-protection that guards against multiprocessing/ZMQ teardown hangs in the parent process.

Environment VariableDefaultConstraintsDescription
AIPERF_RAISE_ON_CALLBACK_ERRORFalse—When true, re-raise the first OnComplete callback exception after running all remaining callbacks but before os._exit. Provides a strict-mode contract where a callback raise propagates out of the runner. When false (default) the exception is logged with full traceback, the exit code is forced non-zero, and the process still terminates via os._exit so leftover ZMQ/multiprocessing state cannot hang the interpreter.

APISERVER

API server settings. Controls the host and port of the API server.

Environment VariableDefaultConstraintsDescription
AIPERF_API_SERVER_HOST'127.0.0.1'—Host to bind the API server to
AIPERF_API_SERVER_PORTNone≥ 1, ≤ 65535Port to bind the API server to
AIPERF_API_SERVER_CORS_ORIGINS[]—List of CORS origins to allow (empty = no CORS, [’*’] = all origins)
AIPERF_API_SERVER_SHUTDOWN_TIMEOUT5.0≥ 1.0, ≤ 300.0Timeout in seconds for graceful API server shutdown before force-cancelling
AIPERF_API_SERVER_POST_COMPLETE_GRACE5.0≥ 0.0, ≤ 300.0Seconds the API listener stays open after a benchmark terminates so polling clients can observe the final status before the server shuts down. Set to 0 to skip the grace window and shut down immediately.

COMPRESSION

Compression settings for streaming file transfers. Controls chunk size and compression levels for zstd and gzip encodings used in dataset and results file transfers.

Environment VariableDefaultConstraintsDescription
AIPERF_COMPRESSION_CHUNK_SIZE65536≥ 1024, ≤ 1048576Chunk size in bytes for streaming compressed data (default: 64KB)
AIPERF_COMPRESSION_ZSTD_LEVEL3≥ 1, ≤ 22Zstandard compression level (1=fastest, 22=best compression, default: 3)
AIPERF_COMPRESSION_GZIP_LEVEL6≥ 1, ≤ 9Gzip compression level (1=fastest, 9=best compression, default: 6)

DAG

Settings for DAG benchmark mode (dag_jsonl input type).

Environment VariableDefaultConstraintsDescription
AIPERF_DAG_FAIL_FASTFalse—When True, abort the whole run on the first DAG child error (cancel pending siblings, raise to PhaseRunner, terminate phase). Default False - the orchestrator counts the error in BranchStats.children_errored, releases the join slot, drains pending siblings, and continues the run. Set via AIPERF_DAG_FAIL_FAST=1 for strict CI assertions.

DATASET

Dataset loading and configuration. Controls timeouts and behavior for dataset loading operations, as well as memory-mapped dataset storage settings.

Environment VariableDefaultConstraintsDescription
AIPERF_DATASET_CONFIGURATION_TIMEOUT300.0≥ 1.0, ≤ 100000.0Timeout in seconds for dataset configuration operations
AIPERF_DATASET_MMAP_BASE_PATHNone—Base path for memory-mapped dataset files. If None, uses system temp directory. Set to a shared filesystem path for Kubernetes mounted volumes. Example: AIPERF_DATASET_MMAP_BASE_PATH=/mnt/shared-pvc creates files at /mnt/shared-pvc/aiperf_mmap_{benchmark_id}/
AIPERF_DATASET_PUBLIC_DATASET_TIMEOUT300.0≥ 1.0, ≤ 100000.0Timeout in seconds for public dataset loading operations
AIPERF_DATASET_MEDIA_DOWNLOAD_TIMEOUT60.0≥ 1.0, ≤ 100000.0Timeout in seconds per media URL download when inline encoding is required
AIPERF_DATASET_MEDIA_DOWNLOAD_MAX_CONCURRENCY10≥ 1, ≤ 100Maximum number of concurrent media URL downloads
AIPERF_DATASET_INLINE_RECORDS_WARN_THRESHOLD500≥ 1Soft warning threshold for the number of inline records: entries on a FileDataset. When total inline records exceed this value, the config loader logs a warning suggesting the user move the dataset to a JSONL file. No hard cap.

GPU

GPU telemetry collection configuration. Controls GPU metrics collection frequency, endpoint detection, and shutdown behavior. Metrics are collected from DCGM endpoints at the specified interval.

Environment VariableDefaultConstraintsDescription
AIPERF_GPU_COLLECTION_INTERVAL0.333≥ 0.01, ≤ 300.0GPU telemetry metrics collection interval in seconds (default: 333ms, ~3Hz)
AIPERF_GPU_DEFAULT_DCGM_ENDPOINTS['http://localhost:9400/metrics', 'http://localhost:9401/metrics']—Default DCGM endpoint URLs to check for GPU telemetry (comma-separated string or JSON array)
AIPERF_GPU_EXPORT_BATCH_SIZE100≥ 1, ≤ 1000000Batch size for telemetry record export results processor
AIPERF_GPU_REACHABILITY_TIMEOUT10≥ 1, ≤ 300Timeout in seconds for checking GPU telemetry endpoint reachability during init
AIPERF_GPU_SHUTDOWN_DELAY5.0≥ 1.0, ≤ 300.0Delay in seconds before shutting down GPU telemetry service to allow command response transmission
AIPERF_GPU_THREAD_JOIN_TIMEOUT5.0≥ 1.0, ≤ 300.0Timeout in seconds for joining GPU telemetry collection threads during shutdown

HTTP

HTTP client socket and connection configuration. Controls low-level socket options, keepalive settings, DNS caching, and connection pooling for HTTP clients. These settings optimize performance for high-throughput streaming workloads. Video Generation Polling: For async video generation APIs that use job polling (e.g., SGLang /v1/videos), the poll interval is controlled by AIPERF_HTTP_VIDEO_POLL_INTERVAL. The max poll time uses the —request-timeout-seconds CLI argument.

Environment VariableDefaultConstraintsDescription
AIPERF_HTTP_CONNECTION_LIMIT2500≥ 1, ≤ 65000Maximum number of concurrent HTTP connections
AIPERF_HTTP_KEEPALIVE_TIMEOUT300≥ 0, ≤ 10000HTTP connection keepalive timeout in seconds for connection pooling
AIPERF_HTTP_SO_RCVBUF10485760≥ 1024Socket receive buffer size in bytes (default: 10MB for high-throughput streaming)
AIPERF_HTTP_SO_RCVTIMEO30≥ 1, ≤ 100000Socket receive timeout in seconds
AIPERF_HTTP_SO_SNDBUF10485760≥ 1024Socket send buffer size in bytes (default: 10MB for high-throughput streaming)
AIPERF_HTTP_SO_SNDTIMEO30≥ 1, ≤ 100000Socket send timeout in seconds
AIPERF_HTTP_TCP_KEEPCNT1≥ 1, ≤ 100Maximum number of keepalive probes to send before considering the connection dead
AIPERF_HTTP_TCP_KEEPIDLE60≥ 1, ≤ 100000Time in seconds before starting TCP keepalive probes on idle connections
AIPERF_HTTP_TCP_KEEPINTVL30≥ 1, ≤ 100000Interval in seconds between TCP keepalive probes
AIPERF_HTTP_TCP_USER_TIMEOUT30000≥ 1, ≤ 1000000TCP user timeout in milliseconds (Linux-specific, detects dead connections)
AIPERF_HTTP_TTL_DNS_CACHE300≥ 0, ≤ 1000000DNS cache TTL in seconds for aiohttp client sessions
AIPERF_HTTP_FORCE_CLOSEFalse—Force close connections after each request
AIPERF_HTTP_ENABLE_CLEANUP_CLOSEDFalse—Enable cleanup of closed ssl connections
AIPERF_HTTP_USE_DNS_CACHETrue—Enable DNS cache
AIPERF_HTTP_SSL_VERIFYTrue—Enable SSL certificate verification. Set to False to disable verification. WARNING: Disabling this is insecure and should only be used for testing in a trusted environment.
AIPERF_HTTP_REQUEST_CANCELLATION_SEND_TIMEOUT300.0≥ 10.0, ≤ 3600.0Safety net timeout in seconds for waiting for HTTP request to be fully sent when request cancellation is enabled. Used as fallback when no explicit timeout is configured to prevent hanging indefinitely while waiting for the request to be written to the socket.
AIPERF_HTTP_IP_VERSION'4'—IP version for HTTP socket connections. Options: ‘4’ (AF_INET, default), ‘6’ (AF_INET6), or ‘auto’ (AF_UNSPEC, system chooses).
AIPERF_HTTP_TRUST_ENVFalse—Trust environment variables for HTTP client configuration. When enabled, aiohttp will read proxy settings from HTTP_PROXY, HTTPS_PROXY, and NO_PROXY environment variables.
AIPERF_HTTP_VIDEO_POLL_INTERVAL0.1≥ 0.001, ≤ 10.0Interval in seconds between status polls for async video generation jobs. Lower values provide faster completion detection but increase server load. Applies to the aiohttp transport.

LOGGING

Logging system configuration. Controls multiprocessing log queue size and other logging behavior.

Environment VariableDefaultConstraintsDescription
AIPERF_LOGGING_QUEUE_MAXSIZE1000≥ 1, ≤ 1000000Maximum size of the multiprocessing logging queue

METRICS

Metrics collection and storage configuration. Controls metrics storage allocation and collection behavior.

Environment VariableDefaultConstraintsDescription
AIPERF_METRICS_ARRAY_INITIAL_CAPACITY10000≥ 100, ≤ 1000000Initial array capacity for metric storage dictionaries to minimize reallocation
AIPERF_METRICS_USAGE_PCT_DIFF_THRESHOLD10.0≥ 0.0, ≤ 100.0Percentage difference threshold for flagging discrepancies between API usage and client token counts (default: 10%)
AIPERF_METRICS_OSL_MISMATCH_PCT_THRESHOLD5.0≥ 0.0, ≤ 100.0Percentage difference threshold for flagging discrepancies between requested and actual output sequence length (default: 5%)
AIPERF_METRICS_OSL_MISMATCH_MAX_TOKEN_THRESHOLD50≥ 1Maximum absolute token threshold for OSL mismatch. The effective threshold is min(requested_osl * pct_threshold, this value). Makes threshold tighter for large OSL values (default: 50 tokens)
AIPERF_METRICS_TDIGEST_COMPRESSION500≥ 20, ≤ 10000t-digest sketch compression for list-valued record metric aggregation. Higher = more centroids, tighter percentile accuracy, larger sketch. Default 500 measured to keep worst-case relative percentile error under 0.05% on 50M-sample workloads (40x under the 0.5% claimed accuracy band) at ~4 KB sketch size.

MLFLOW

MLflow export configuration. Controls timeout behavior for post-run MLflow artifact uploads.

Environment VariableDefaultConstraintsDescription
AIPERF_MLFLOW_EXPORT_TIMEOUT_SECONDS30.0≥ 1.0, ≤ 600.0Timeout in seconds for the post-run MLflow export operation. If the MLflow tracking server is unreachable, the export will be abandoned after this duration rather than blocking indefinitely.

OTEL

OpenTelemetry metrics streaming configuration. Controls buffering and flush behavior for OTLP metric streaming.

Environment VariableDefaultConstraintsDescription
AIPERF_OTEL_FLUSH_INTERVAL_SECONDS2.0≥ 0.1, ≤ 60.0Interval in seconds between periodic OTel metrics flushes
AIPERF_OTEL_MAX_BATCH_RECORDS500≥ 1, ≤ 1000000Maximum number of metric records to include in a single OTel flush
AIPERF_OTEL_MAX_BUFFERED_RECORDS10000≥ 1, ≤ 10000000Maximum number of buffered metric records before oldest records are dropped
AIPERF_OTEL_REQUEST_TIMEOUT_SECONDS10.0≥ 0.1, ≤ 300.0Timeout in seconds for OTel collector HTTP requests

RECORD

Record processing and export configuration. Controls batch sizes, processor scaling, and progress reporting for record processing.

Environment VariableDefaultConstraintsDescription
AIPERF_RECORD_EXPORT_BATCH_SIZE100≥ 1, ≤ 1000000Batch size for record export results processor
AIPERF_RECORD_RAW_EXPORT_BATCH_SIZE10≥ 1, ≤ 1000000Batch size for raw record writer processor
AIPERF_RECORD_PROCESSOR_SCALE_FACTOR4≥ 1, ≤ 100Scale factor for number of record processors to spawn based on worker count. Formula: 1 record processor for every X workers
AIPERF_RECORD_PROGRESS_REPORT_INTERVAL2.0≥ 0.1, ≤ 600.0Interval in seconds between records progress report messages
AIPERF_RECORD_PROCESS_RECORDS_TIMEOUT300.0≥ 1.0, ≤ 100000.0Timeout in seconds for processing record results

SEARCHPLANNER

Adaptive-search planner tunables. Controls precision targets, warmup-phase injection, and request-count presets for the smooth-isotonic and monotonic SLA-saturation search planners. All values are read at planner-construction or iteration-mutate time, so changes take effect on the next search run.

Environment VariableDefaultConstraintsDescription
AIPERF_SEARCH_PLANNER_SLA_PRECISION_DEFAULT0.05> 0.0, < 1.0Default SLA boundary search precision target. The bisection / smooth-isotonic bracket halts when (infeasible_min - feasible_max) / infeasible_min < this value, and the cliff detector requires bracket_gap > this * x_hi to report a cliff. 5% mirrors perf_analyzer’s —binary-search default.
AIPERF_SEARCH_PLANNER_DEFAULT_WARMUP_SECONDS30.0> 0.0, ≤ 100000.0Smooth-isotonic SLA planner: default warmup phase duration in seconds injected into each iteration’s cfg when cfg.sla_warmup_seconds is unset. Spec calls for max(30s, 3*inter-batch-time) but inter-batch-time is unknown at planner-time, so 30s is the safe floor. Must be strictly positive: zero defeats the cold-KV-cache rationale that motivates the floor.
AIPERF_SEARCH_PLANNER_FIRST_PROBE_WARMUP_FLOOR60.0> 0.0, ≤ 100000.0Smooth-isotonic SLA planner: minimum warmup duration in seconds for the first probe at each swept-dim value. Cold KV-cache and CUDA-graph compilation cost is largest the first time we hit a given concurrency, so floor that probe at 60s. Must be strictly positive: zero defeats the cold-KV-cache rationale.
AIPERF_SEARCH_PLANNER_REPLICATE_WARMUP_FLOOR15.0> 0.0, ≤ 100000.0Smooth-isotonic SLA planner: minimum warmup duration in seconds for replicate probes at an already-probed swept-dim value. Replicates reuse the warm KV-cache / CUDA-graph state, so a shorter warmup suffices. Must be strictly positive: zero defeats the floor.
AIPERF_SEARCH_PLANNER_SLA_PRECISION_REQUESTS{'tight': 10000, 'normal': 1000, 'coarse': 300}—Mapping from cfg.sla_precision preset name to the phases.profiling.requests value injected when the user did not set requests explicitly on the profiling phase. Drives p99 CI width. Each value must be strictly positive — zero/negative request counts surface as iteration-time failures otherwise. Override via JSON, e.g. AIPERF_SEARCH_PLANNER_SLA_PRECISION_REQUESTS='{"tight": 20000}'.

SERVERMETRICS

Server metrics collection configuration. Controls server metrics collection frequency, endpoint detection, and shutdown behavior. Metrics are collected from Prometheus-compatible endpoints at the specified interval. Use --no-server-metrics CLI flag to disable collection.

Environment VariableDefaultConstraintsDescription
AIPERF_SERVER_METRICS_COLLECTION_FLUSH_PERIOD2.0≥ 0.0, ≤ 30.0Time in seconds to continue collecting metrics after profiling completes, allowing server-side metrics to flush/finalize before shutting down (default: 2.0s)
AIPERF_SERVER_METRICS_COLLECTION_INTERVAL0.333≥ 0.001, ≤ 300.0Server metrics collection interval in seconds (default: 333ms, ~3Hz)
AIPERF_SERVER_METRICS_EXPORT_BATCH_SIZE100≥ 1, ≤ 1000000Batch size for server metrics jsonl writer export results processor
AIPERF_SERVER_METRICS_REACHABILITY_TIMEOUT10≥ 1, ≤ 300Timeout in seconds for checking server metrics endpoint reachability during init
AIPERF_SERVER_METRICS_SHUTDOWN_DELAY5.0≥ 1.0, ≤ 300.0Delay in seconds before shutting down server metrics service to allow command response transmission

SERVICE

Service lifecycle and inter-service communication configuration. Controls timeouts for service registration, startup, shutdown, command handling, connection probing, heartbeats, and profile operations.

Environment VariableDefaultConstraintsDescription
AIPERF_SERVICE_COMMAND_RESPONSE_TIMEOUT30.0≥ 1.0, ≤ 1000.0Timeout in seconds for command responses
AIPERF_SERVICE_COMMS_REQUEST_TIMEOUT90.0≥ 1.0, ≤ 1000.0Timeout in seconds for requests from req_clients to rep_clients
AIPERF_SERVICE_CONNECTION_PROBE_INTERVAL0.1≥ 0.1, ≤ 600.0Interval in seconds for connection probes while waiting for initial connection to the zmq message bus
AIPERF_SERVICE_CONNECTION_PROBE_TIMEOUT90.0≥ 1.0, ≤ 100000.0Maximum time in seconds to wait for connection probe response while waiting for initial connection to the zmq message bus
AIPERF_SERVICE_CREDIT_PROGRESS_REPORT_INTERVAL2.0≥ 1, ≤ 100000.0Interval in seconds between credit progress report messages
AIPERF_SERVICE_DISABLE_UVLOOPFalse—Disable uvloop and use default asyncio event loop instead
AIPERF_SERVICE_HEARTBEAT_INTERVAL5.0≥ 1.0, ≤ 100000.0Interval in seconds between heartbeat messages for component services
AIPERF_SERVICE_PROFILE_CONFIGURE_TIMEOUT600.0≥ 1.0, ≤ 100000.0Timeout in seconds for profile configure command
AIPERF_SERVICE_PROFILE_START_TIMEOUT60.0≥ 1.0, ≤ 100000.0Timeout in seconds for profile start command
AIPERF_SERVICE_PROFILE_CANCEL_TIMEOUT10.0≥ 1.0, ≤ 100000.0Timeout in seconds for profile cancel command
AIPERF_SERVICE_REGISTRATION_INTERVAL1.0≥ 1.0, ≤ 100000.0Interval in seconds between registration attempts for component services
AIPERF_SERVICE_REGISTRATION_MAX_ATTEMPTS10≥ 1, ≤ 100000Maximum number of registration attempts before giving up
AIPERF_SERVICE_REGISTRATION_TIMEOUT30.0≥ 1.0, ≤ 100000.0Timeout in seconds for service registration
AIPERF_SERVICE_START_TIMEOUT30.0≥ 1.0, ≤ 100000.0Timeout in seconds for service start operations
AIPERF_SERVICE_TASK_CANCEL_TIMEOUT_SHORT2.0≥ 1.0, ≤ 100000.0Maximum time in seconds to wait for simple tasks to complete when cancelling
AIPERF_SERVICE_EVENT_LOOP_HEALTH_ENABLEDTrue—Enable event loop health monitoring to detect blocked event loops. When enabled, TimingManager and Worker services periodically check if the event loop is responsive and log warnings when latency exceeds the threshold.
AIPERF_SERVICE_EVENT_LOOP_HEALTH_INTERVAL0.25≥ 0.05, ≤ 10.0Interval in seconds between event loop health checks (default: 250ms). The monitor sleeps for this duration and measures actual elapsed time to detect blocking.
AIPERF_SERVICE_EVENT_LOOP_HEALTH_WARN_THRESHOLD_MS25.0> 1.0, ≤ 10000.0Warning threshold in milliseconds for event loop latency (default: 25ms). If the actual sleep duration exceeds the expected duration by this amount, a warning is logged.
AIPERF_SERVICE_HEALTH_ENABLEDFalse—Enable the lightweight health server for Kubernetes liveness/readiness probes. When enabled, non-API services will start an HTTP server serving /healthz and /readyz endpoints.
AIPERF_SERVICE_HEALTH_HOST'127.0.0.1'—Host to bind the health server to. Use ‘0.0.0.0’ for Kubernetes deployments.
AIPERF_SERVICE_HEALTH_PORT8080≥ 1, ≤ 65535Port for the health server HTTP endpoints (/healthz, /readyz).
AIPERF_SERVICE_HEALTH_REQUEST_TIMEOUT5.0≥ 0.1, ≤ 60.0Timeout in seconds for reading health check HTTP requests.

TIMING

Timing manager configuration. Controls timing-related settings for credit phase execution and scheduling.

Environment VariableDefaultConstraintsDescription
AIPERF_TIMING_CANCEL_DRAIN_TIMEOUT10.0≥ 1.0, ≤ 300.0Timeout in seconds for waiting for cancelled credits to drain after phase timeout
AIPERF_TIMING_RATE_RAMP_UPDATE_INTERVAL0.1≥ 0.01, ≤ 10.0Update interval in seconds for continuous rate ramping (default 0.1s = 100ms)

TOKENIZER

Tokenizer pre-warm and loading configuration. Controls how the CLI parent pre-warms tokenizer caches before spawning AIPerf services. Pre-warming runs in subprocesses so the parent never imports the heavy native libraries (transformers, Rust-backed tokenizers, tiktoken).

Environment VariableDefaultConstraintsDescription
AIPERF_TOKENIZER_PRELOAD_TIMEOUT120.0≥ 1.0, ≤ 100000.0Timeout in seconds for the parent’s tokenizer pre-warm phase. Bounds the total wall-clock time for all parallel subprocess pre-warms. On timeout, subprocesses are killed and AIPerf continues; child services may then download tokenizers themselves on first use.
AIPERF_TOKENIZER_SKIP_PRELOADFalse—Skip parent-process tokenizer cache pre-warming. Intended for test harnesses that replace tokenizer loading and must avoid forked prefetch subprocesses. Production defaults to preloading.

UI

User interface and dashboard configuration. Controls refresh rates, update thresholds, and notification behavior for the various UI modes (dashboard, tqdm, etc.).

Environment VariableDefaultConstraintsDescription
AIPERF_UI_LOG_REFRESH_INTERVAL0.1≥ 0.01, ≤ 100000.0Log viewer refresh interval in seconds (default: 10 FPS)
AIPERF_UI_MIN_UPDATE_PERCENT1.0≥ 0.01, ≤ 100.0Minimum percentage difference from last update to trigger a UI update (for non-dashboard UIs)
AIPERF_UI_NOTIFICATION_TIMEOUT3≥ 1, ≤ 100000Duration in seconds to display UI notifications before auto-dismissing
AIPERF_UI_REALTIME_METRICS_INTERVAL5.0≥ 1.0, ≤ 1000.0Interval in seconds between real-time metrics messages
AIPERF_UI_REALTIME_METRICS_ENABLEDFalse—Enable real-time metrics collection and reporting despite UI type
AIPERF_UI_SPINNER_REFRESH_RATE0.1≥ 0.1, ≤ 100.0Progress spinner refresh rate in seconds (default: 10 FPS)

WORKER

Worker management and auto-scaling configuration. Controls worker pool sizing, health monitoring, load detection, and recovery behavior. The CPU_UTILIZATION_FACTOR is used in the auto-scaling formula: max_workers = max(1, min(int(cpu_count * factor) - 1, MAX_WORKERS_CAP))

Environment VariableDefaultConstraintsDescription
AIPERF_WORKER_CHECK_INTERVAL1.0≥ 0.1, ≤ 100000.0Interval in seconds between worker status checks by WorkerManager
AIPERF_WORKER_CPU_UTILIZATION_FACTOR0.75≥ 0.1, ≤ 1.0Factor multiplied by CPU count to determine default max workers (0.0-1.0). Formula: max(1, min(int(cpu_count * factor) - 1, MAX_WORKERS_CAP))
AIPERF_WORKER_ERROR_RECOVERY_TIME3.0≥ 0.1, ≤ 1000.0Time in seconds from last error before worker is considered healthy again
AIPERF_WORKER_HEALTH_CHECK_INTERVAL2.0≥ 0.1, ≤ 1000.0Interval in seconds between worker health check messages
AIPERF_WORKER_HIGH_LOAD_CPU_USAGE85.0≥ 50.0, ≤ 100.0CPU usage percentage threshold for considering a worker under high load
AIPERF_WORKER_HIGH_LOAD_RECOVERY_TIME5.0≥ 0.1, ≤ 1000.0Time in seconds from last high load before worker is considered recovered
AIPERF_WORKER_MAX_WORKERS_CAP32≥ 1, ≤ 10000Absolute maximum number of workers to spawn, regardless of CPU count
AIPERF_WORKER_STALE_TIME10.0≥ 0.1, ≤ 1000.0Time in seconds from last status report before worker is considered stale
AIPERF_WORKER_STATUS_SUMMARY_INTERVAL0.5≥ 0.1, ≤ 1000.0Interval in seconds between worker status summary messages

ZMQ

ZMQ socket and communication configuration. Controls ZMQ socket timeouts, keepalive settings, retry behavior, and concurrency limits. These settings affect reliability and performance of the internal message bus.

Environment VariableDefaultConstraintsDescription
AIPERF_ZMQ_CONTEXT_TERM_TIMEOUT10.0≥ 1.0, ≤ 100000.0Timeout in seconds for terminating the ZMQ context during shutdown
AIPERF_ZMQ_PULL_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received messages from ZMQ PULL clients. Prevents event loop starvation during message bursts. 0 disables yielding, 1 yields after every message, 10 yields every 10 messages, etc.
AIPERF_ZMQ_REPLY_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received requests from ZMQ ROUTER reply clients. Prevents event loop starvation during request bursts. 0 disables yielding, 1 yields after every request, 10 yields every 10 requests, etc.
AIPERF_ZMQ_REQUEST_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received responses from ZMQ DEALER request clients. Prevents event loop starvation during response bursts. 0 disables yielding, 1 yields after every response, 10 yields every 10 responses, etc.
AIPERF_ZMQ_STREAMING_DEALER_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received messages from ZMQ streaming DEALER clients. Prevents event loop starvation during message bursts. 0 disables yielding, 1 yields after every message, 10 yields every 10 messages, etc.
AIPERF_ZMQ_STREAMING_ROUTER_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received messages from ZMQ streaming ROUTER clients. Prevents event loop starvation during message bursts. 0 disables yielding, 1 yields after every message, 10 yields every 10 messages, etc.
AIPERF_ZMQ_SUB_YIELD_INTERVAL10≥ 0, ≤ 1000000Yield to the event loop after every N received messages from ZMQ SUB clients. Prevents event loop starvation during message bursts. 0 disables yielding, 1 yields after every message, 10 yields every 10 messages, etc.
AIPERF_ZMQ_PULL_MAX_CONCURRENCY100000≥ 1, ≤ 10000000Maximum concurrency for ZMQ PULL clients
AIPERF_ZMQ_PUSH_MAX_RETRIES2≥ 1, ≤ 100Maximum number of retry attempts when pushing messages to ZMQ PUSH socket
AIPERF_ZMQ_PUSH_RETRY_DELAY0.1≥ 0.1, ≤ 1000.0Delay in seconds between retry attempts for ZMQ PUSH operations
AIPERF_ZMQ_RCVTIMEO300000≥ 1, ≤ 10000000Socket receive timeout in milliseconds (default: 5 minutes)
AIPERF_ZMQ_SNDTIMEO300000≥ 1, ≤ 10000000Socket send timeout in milliseconds (default: 5 minutes)
AIPERF_ZMQ_TCP_KEEPALIVE_IDLE60≥ 1, ≤ 100000Time in seconds before starting TCP keepalive probes on idle ZMQ connections
AIPERF_ZMQ_TCP_KEEPALIVE_INTVL10≥ 1, ≤ 100000Interval in seconds between TCP keepalive probes for ZMQ connections
AIPERF_ZMQ_EVENT_BUS_PROXY_FRONTEND_PORT5663≥ 1, ≤ 65535Default TCP port for the event-bus XPUB/XSUB proxy frontend (producers connect here). Single source of truth for the non-k8s comm configs (TCP, dual-bind); k8s pod manifests pull the same value via K8sEnvironment.PORTS.EVENT_BUS_PROXY_PUB_FRONTEND (defaults match).
AIPERF_ZMQ_EVENT_BUS_PROXY_BACKEND_PORT5664≥ 1, ≤ 65535Default TCP port for the event-bus XPUB/XSUB proxy backend (subscribers connect here). See EVENT_BUS_PROXY_FRONTEND_PORT.

DEV

Development and debugging configuration. Controls developer-focused features like debug logging, profiling, and internal metrics. These settings are typically disabled in production environments.

Environment VariableDefaultConstraintsDescription
AIPERF_DEV_DEBUG_SERVICESNone—List of services to enable DEBUG logging for (comma-separated or multiple flags)
AIPERF_DEV_ENABLE_YAPPIFalse—Enable yappi profiling (Yet Another Python Profiler) for performance analysis. Requires ‘pip install yappi snakeviz’
AIPERF_DEV_MODEFalse—Enable AIPerf Developer mode for internal metrics and debugging
AIPERF_DEV_SHOW_EXPERIMENTAL_METRICSFalse—[Developer use only] Show experimental metrics in output (requires DEV_MODE)
AIPERF_DEV_SHOW_INTERNAL_METRICSFalse—[Developer use only] Show internal and hidden metrics in output (requires DEV_MODE)
AIPERF_DEV_TRACE_SERVICESNone—List of services to enable TRACE logging for (comma-separated or multiple flags)