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api-get-memory

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Vladimir Mandic 2023-01-07 07:51:35 -05:00 committed by GitHub
parent 151233399c
commit 47534577ed
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2 changed files with 41 additions and 0 deletions
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37
modules/api/api.py
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@ -130,6 +130,7 @@ class Api:
self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse) self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse)
self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
def add_api_route(self, path: str, endpoint, **kwargs): def add_api_route(self, path: str, endpoint, **kwargs):
if shared.cmd_opts.api_auth: if shared.cmd_opts.api_auth:
@ -465,6 +466,42 @@ class Api:
shared.state.end() shared.state.end()
return TrainResponse(info = "train embedding error: {error}".format(error = error)) return TrainResponse(info = "train embedding error: {error}".format(error = error))
def get_memory(self):
def gb(val: float):
return round(val / 1024 / 1024 / 1024, 2)
try:
import os, psutil
process = psutil.Process(os.getpid())
res = process.memory_info()
ram_total = 100 * res.rss / process.memory_percent()
ram = { 'free': gb(ram_total - res.rss), 'used': gb(res.rss), 'total': gb(ram_total) }
except Exception as err:
ram = { 'error': f'{err}' }
try:
import torch
if torch.cuda.is_available():
s = torch.cuda.mem_get_info()
system = { 'free': gb(s[0]), 'used': gb(s[1] - s[0]), 'total': gb(s[1]) }
s = dict(torch.cuda.memory_stats(shared.device))
allocated = { 'current': gb(s['allocated_bytes.all.current']), 'peak': gb(s['allocated_bytes.all.peak']) }
reserved = { 'current': gb(s['reserved_bytes.all.current']), 'peak': gb(s['reserved_bytes.all.peak']) }
active = { 'current': gb(s['active_bytes.all.current']), 'peak': gb(s['active_bytes.all.peak']) }
inactive = { 'current': gb(s['inactive_split_bytes.all.current']), 'peak': gb(s['inactive_split_bytes.all.peak']) }
warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] }
cuda = {
'system': system,
'active': active,
'allocated': allocated,
'reserved': reserved,
'inactive': inactive,
'events': warnings,
}
else:
cuda = { 'error': 'unavailable' }
except Exception as err:
cuda = { 'error': f'{err}' }
return MemoryResponse(ram = ram, cuda = cuda)
def launch(self, server_name, port): def launch(self, server_name, port):
self.app.include_router(self.router) self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port) uvicorn.run(self.app, host=server_name, port=port)

4
modules/api/models.py
View file

@ -260,3 +260,7 @@ class EmbeddingItem(BaseModel):
class EmbeddingsResponse(BaseModel): class EmbeddingsResponse(BaseModel):
loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
class MemoryResponse(BaseModel):
ram: dict[str, str] | dict[str, float] = Field(title="RAM", description="System memory stats")
cuda: dict[str, str] | dict[str, dict] = Field(title="CUDA", description="nVidia CUDA memory stats")