feat(api): add server setting for CUDA memory limit (#211)
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@ -7,6 +7,10 @@ from .model_cache import ModelCache
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logger = getLogger(__name__)
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DEFAULT_CACHE_LIMIT = 2
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DEFAULT_JOB_LIMIT = 10
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DEFAULT_IMAGE_FORMAT = "png"
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class ServerContext:
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def __init__(
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@ -19,12 +23,14 @@ class ServerContext:
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any_platform: bool = True,
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block_platforms: Optional[List[str]] = None,
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default_platform: Optional[str] = None,
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image_format: str = "png",
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cache_limit: Optional[int] = 1,
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image_format: str = DEFAULT_IMAGE_FORMAT,
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cache_limit: int = DEFAULT_CACHE_LIMIT,
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cache_path: Optional[str] = None,
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show_progress: bool = True,
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optimizations: Optional[List[str]] = None,
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extra_models: Optional[List[str]] = None,
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job_limit: int = DEFAULT_JOB_LIMIT,
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memory_limit: Optional[int] = None,
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) -> None:
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self.bundle_path = bundle_path
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self.model_path = model_path
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@ -35,15 +41,22 @@ class ServerContext:
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self.block_platforms = block_platforms or []
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self.default_platform = default_platform
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self.image_format = image_format
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self.cache = ModelCache(cache_limit)
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self.cache_limit = cache_limit
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self.cache_path = cache_path or path.join(model_path, ".cache")
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self.show_progress = show_progress
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self.optimizations = optimizations or []
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self.extra_models = extra_models or []
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self.job_limit = job_limit
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self.memory_limit = memory_limit
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self.cache = ModelCache(self.cache_limit)
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@classmethod
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def from_environ(cls):
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memory_limit = environ.get("ONNX_WEB_MEMORY_LIMIT", None)
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if memory_limit is not None:
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memory_limit = int(memory_limit)
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return cls(
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bundle_path=environ.get(
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"ONNX_WEB_BUNDLE_PATH", path.join("..", "gui", "out")
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@ -57,8 +70,10 @@ class ServerContext:
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block_platforms=environ.get("ONNX_WEB_BLOCK_PLATFORMS", "").split(","),
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default_platform=environ.get("ONNX_WEB_DEFAULT_PLATFORM", None),
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image_format=environ.get("ONNX_WEB_IMAGE_FORMAT", "png"),
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cache_limit=int(environ.get("ONNX_WEB_CACHE_MODELS", 2)),
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cache_limit=int(environ.get("ONNX_WEB_CACHE_MODELS", DEFAULT_CACHE_LIMIT)),
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show_progress=get_boolean(environ, "ONNX_WEB_SHOW_PROGRESS", True),
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optimizations=environ.get("ONNX_WEB_OPTIMIZATIONS", "").split(","),
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extra_models=environ.get("ONNX_WEB_EXTRA_MODELS", "").split(","),
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job_limit=int(environ.get("ONNX_WEB_JOB_LIMIT", DEFAULT_JOB_LIMIT)),
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memory_limit=memory_limit,
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)
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@ -268,13 +268,19 @@ def load_platforms(context: ServerContext) -> None:
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):
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if potential == "cuda":
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for i in range(torch.cuda.device_count()):
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options = {
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"device_id": i,
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}
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if context.memory_limit is not None:
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options["arena_extend_strategy"] = "kSameAsRequested"
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options["gpu_mem_limit"] = context.memory_limit
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available_platforms.append(
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DeviceParams(
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potential,
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platform_providers[potential],
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{
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"device_id": i,
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},
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options,
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context.optimizations,
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)
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)
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@ -41,13 +41,12 @@ class DevicePoolExecutor:
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self,
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server: ServerContext,
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devices: List[DeviceParams],
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max_jobs_per_worker: int = 10,
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max_pending_per_worker: int = 100,
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join_timeout: float = 1.0,
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):
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self.server = server
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self.devices = devices
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self.max_jobs_per_worker = max_jobs_per_worker
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self.max_jobs_per_worker = server.job_limit
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self.max_pending_per_worker = max_pending_per_worker
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self.join_timeout = join_timeout
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