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221 lines
8.5 KiB
221 lines
8.5 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import json
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import signal
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import sys
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from pathlib import Path
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from time import sleep, time
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import requests
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from ultralytics.hub.utils import HUB_API_ROOT, check_dataset_disk_space, smart_request
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from ultralytics.yolo.utils import LOGGER, PREFIX, __version__, emojis, is_colab, threaded
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from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
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AGENT_NAME = f"python-{__version__}-colab" if is_colab() else f"python-{__version__}-local"
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session = None
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class HubTrainingSession:
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def __init__(self, model_id, auth):
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self.agent_id = None # identifies which instance is communicating with server
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self.model_id = model_id
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self.api_url = f"{HUB_API_ROOT}/v1/models/{model_id}"
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self.auth_header = auth.get_auth_header()
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self._rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0} # rate limits (seconds)
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self._timers = {} # rate limit timers (seconds)
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self._metrics_queue = {} # metrics queue
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self.model = self._get_model()
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self._start_heartbeat() # start heartbeats
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self._register_signal_handlers()
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def _register_signal_handlers(self):
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signal.signal(signal.SIGTERM, self._handle_signal)
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signal.signal(signal.SIGINT, self._handle_signal)
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def _handle_signal(self, signum, frame):
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"""
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Prevent heartbeats from being sent on Colab after kill.
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This method does not use frame, it is included as it is
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passed by signal.
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"""
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if self.alive is True:
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LOGGER.info(f"{PREFIX}Kill signal received! ❌")
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self._stop_heartbeat()
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sys.exit(signum)
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def _stop_heartbeat(self):
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"""End the heartbeat loop"""
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self.alive = False
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def upload_metrics(self):
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payload = {"metrics": self._metrics_queue.copy(), "type": "metrics"}
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smart_request(f"{self.api_url}", json=payload, headers=self.auth_header, code=2)
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def upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
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# Upload a model to HUB
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file = None
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if Path(weights).is_file():
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with open(weights, "rb") as f:
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file = f.read()
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if final:
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smart_request(
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f"{self.api_url}/upload",
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data={
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"epoch": epoch,
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"type": "final",
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"map": map},
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files={"best.pt": file},
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headers=self.auth_header,
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retry=10,
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timeout=3600,
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code=4,
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)
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else:
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smart_request(
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f"{self.api_url}/upload",
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data={
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"epoch": epoch,
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"type": "epoch",
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"isBest": bool(is_best)},
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headers=self.auth_header,
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files={"last.pt": file},
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code=3,
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)
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def _get_model(self):
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# Returns model from database by id
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api_url = f"{HUB_API_ROOT}/v1/models/{self.model_id}"
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headers = self.auth_header
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try:
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response = smart_request(api_url, method="get", headers=headers, thread=False, code=0)
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data = response.json().get("data", None)
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if data.get("status", None) == "trained":
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raise ValueError(
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emojis(f"Model is already trained and uploaded to "
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f"https://hub.ultralytics.com/models/{self.model_id} 🚀"))
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if not data.get("data", None):
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raise ValueError("Dataset may still be processing. Please wait a minute and try again.") # RF fix
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self.model_id = data["id"]
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# TODO: restore when server keys when dataset URL and GPU train is working
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self.train_args = {
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"batch": data["batch_size"],
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"epochs": data["epochs"],
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"imgsz": data["imgsz"],
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"patience": data["patience"],
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"device": data["device"],
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"cache": data["cache"],
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"data": data["data"]}
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self.input_file = data.get("cfg", data["weights"])
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# hack for yolov5 cfg adds u
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if "cfg" in data and "yolov5" in data["cfg"]:
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self.input_file = data["cfg"].replace(".yaml", "u.yaml")
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return data
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except requests.exceptions.ConnectionError as e:
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raise ConnectionRefusedError("ERROR: The HUB server is not online. Please try again later.") from e
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except Exception:
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raise
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def check_disk_space(self):
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if not check_dataset_disk_space(self.model["data"]):
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raise MemoryError("Not enough disk space")
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def register_callbacks(self, trainer):
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trainer.add_callback("on_pretrain_routine_end", self.on_pretrain_routine_end)
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trainer.add_callback("on_fit_epoch_end", self.on_fit_epoch_end)
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trainer.add_callback("on_model_save", self.on_model_save)
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trainer.add_callback("on_train_end", self.on_train_end)
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def on_pretrain_routine_end(self, trainer):
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"""
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Start timer for upload rate limit.
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This method does not use trainer. It is passed to all callbacks by default.
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"""
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# Start timer for upload rate limit
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LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
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self._timers = {"metrics": time(), "ckpt": time()} # start timer on self.rate_limit
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def on_fit_epoch_end(self, trainer):
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# Upload metrics after val end
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all_plots = {**trainer.label_loss_items(trainer.tloss, prefix="train"), **trainer.metrics}
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if trainer.epoch == 0:
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model_info = {
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"model/parameters": get_num_params(trainer.model),
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"model/GFLOPs": round(get_flops(trainer.model), 3),
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"model/speed(ms)": round(trainer.validator.speed[1], 3)}
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all_plots = {**all_plots, **model_info}
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self._metrics_queue[trainer.epoch] = json.dumps(all_plots)
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if time() - self._timers["metrics"] > self._rate_limits["metrics"]:
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self.upload_metrics()
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self._timers["metrics"] = time() # reset timer
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self._metrics_queue = {} # reset queue
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def on_model_save(self, trainer):
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# Upload checkpoints with rate limiting
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is_best = trainer.best_fitness == trainer.fitness
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if time() - self._timers["ckpt"] > self._rate_limits["ckpt"]:
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LOGGER.info(f"{PREFIX}Uploading checkpoint {self.model_id}")
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self._upload_model(trainer.epoch, trainer.last, is_best)
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self._timers["ckpt"] = time() # reset timer
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def on_train_end(self, trainer):
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# Upload final model and metrics with exponential standoff
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LOGGER.info(f"{PREFIX}Training completed successfully ✅")
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LOGGER.info(f"{PREFIX}Uploading final {self.model_id}")
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# hack for fetching mAP
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mAP = trainer.metrics.get("metrics/mAP50-95(B)", 0)
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self._upload_model(trainer.epoch, trainer.best, map=mAP, final=True) # results[3] is mAP0.5:0.95
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self.alive = False # stop heartbeats
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LOGGER.info(f"{PREFIX}View model at https://hub.ultralytics.com/models/{self.model_id} 🚀")
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def _upload_model(self, epoch, weights, is_best=False, map=0.0, final=False):
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# Upload a model to HUB
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file = None
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if Path(weights).is_file():
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with open(weights, "rb") as f:
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file = f.read()
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file_param = {"best.pt" if final else "last.pt": file}
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endpoint = f"{self.api_url}/upload"
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data = {"epoch": epoch}
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if final:
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data.update({"type": "final", "map": map})
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else:
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data.update({"type": "epoch", "isBest": bool(is_best)})
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smart_request(
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endpoint,
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data=data,
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files=file_param,
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headers=self.auth_header,
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retry=10 if final else None,
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timeout=3600 if final else None,
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code=4 if final else 3,
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)
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@threaded
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def _start_heartbeat(self):
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self.alive = True
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while self.alive:
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r = smart_request(
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f"{HUB_API_ROOT}/v1/agent/heartbeat/models/{self.model_id}",
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json={
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"agent": AGENT_NAME,
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"agentId": self.agent_id},
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headers=self.auth_header,
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retry=0,
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code=5,
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thread=False,
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)
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self.agent_id = r.json().get("data", {}).get("agentId", None)
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sleep(self._rate_limits["heartbeat"])
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