Implement HTTPMonitor to send node status and training progress to ge…

…neric HTTP Server

configs/150M/3090.toml

@@ -1,5 +1,10 @@
 name_model = "150M"
 project = "debug_150m_zero_band"
+run_id = "2c774d7c830b49e7855f4f9be6ea4d09"
+
+[metric_logger]
+type = "dummy"
+base_url = "https://protocol-api.primeintellect.ai"
 
 [train]
 micro_bs = 16 # change this base on the gpu
@@ -9,4 +14,4 @@ sharding_strategy = "NO_SHARD"
 batch_size = 512
 warmup_steps = 1000
 total_steps = 88_000
-lr = 4e-4
+lr = 4e-4

configs/debug/diloco.toml

@@ -1,10 +1,12 @@
 name_model = "debugmodel"
 project = "/tmp/debug"
-metric_logger_type = "dummy"
+
+[metric_logger]
+type = "dummy"
 
 [train]
 micro_bs = 8
-sharding_strategy = "FULL_SHARD"
+sharding_strategy = "SHARD_GRAD_OP"
 
 [optim]
 batch_size = 16

configs/debug/diloco_http_logger.toml

@@ -0,0 +1,22 @@
+name_model = "debugmodel"
+project = "/tmp/debug"
+
+[metric_logger]
+type = "http"
+base_url = "https://protocol-api.primeintellect.ai"
+
+[train]
+micro_bs = 8
+sharding_strategy = "SHARD_GRAD_OP"
+
+[optim]
+batch_size = 16
+warmup_steps = 10
+total_steps = 4
+
+[data]
+fake = true
+
+[diloco]
+inner_steps = 2
+

configs/debug/normal.toml

@@ -1,6 +1,8 @@
 name_model = "debugmodel"
 project = "/tmp/debug"
-metric_logger_type = "dummy"
+
+[metric_logger]
+type = "dummy"
 
 [train]
 micro_bs = 8

src/zeroband/train.py

@@ -22,7 +22,7 @@
 from zeroband.diloco import Diloco, DilocoConfig, ElasticDeviceMesh
 
 from zeroband.utils import get_sharding_strategy
-from zeroband.utils.monitor import WandbMonitor, DummyMonitor
+from zeroband.utils.monitor import WandbMonitor, DummyMonitor, HttpMonitor
 from zeroband.data import TEST_VOCAB_SIZE, get_dataloader
 from zeroband.models.llama import get_model
 from zeroband.utils.world_info import get_world_info
@@ -52,19 +52,26 @@ class TrainConfig(BaseConfig):
     sharding_strategy: str = "SHARD_GRAD_OP"
 
 
+class MetricLogger(BaseConfig):
+    type: Literal["wandb", "dummy", "http"] = "http"
+    base_url: str | None = None
+    auth_token: str | None = None
+
+
 class Config(BaseConfig):
     # main config
     name_model: Literal["debugmodel", "150M", "271M", "1B", "7B", "13B", "26B", "70B"] = "150M"
     type_model: Literal["llama2", "llama3"] = "llama2"
 
     project: str = "zeroband"
-    metric_logger_type: Literal["wandb", "dummy"] = "wandb"
+    run_id: str | None = None
 
     # sub config
     diloco: DilocoConfig | None = None
     data: DataConfig = DataConfig()
     optim: OptimConfig = OptimConfig()
     train: TrainConfig
+    metric_logger: MetricLogger
 
 
 def train(config: Config):
@@ -148,7 +155,12 @@ def train(config: Config):
     model.train()
 
     if world_info.rank == 0:
-        logger_cls = WandbMonitor if config.metric_logger_type == "wandb" else DummyMonitor
+        if config.metric_logger.type == "wandb":
+            logger_cls = WandbMonitor
+        elif config.metric_logger.type == "http":
+            logger_cls = HttpMonitor
+        else:
+            logger_cls = DummyMonitor
         metric_logger = logger_cls(project=config.project, config=config.model_dump(), resume=False)
 
     train_dataloader_iterator = iter(train_dataloader)
@@ -210,6 +222,7 @@ def train(config: Config):
                 "Perplexity": torch.exp(loss_batch).item(),
                 "total_tokens": real_step * config.optim.batch_size * config.data.seq_length,
                 "mfu": mfu,
+                "time": time.time(),
             }
 
             if config.diloco is not None:

src/zeroband/utils/monitor.py

@@ -1,6 +1,9 @@
 import pickle
 from typing import Any, Protocol
 import importlib
+from zeroband.utils.logging import get_logger
+
+logger = get_logger()
 
 
 class Monitor(Protocol):
@@ -11,6 +14,85 @@ def log(self, metrics: dict[str, Any]): ...
     def finish(self): ...
 
 
+class HttpMonitor:
+    """
+    Logs the status of nodes, and training progress to an API
+    """
+
+    def __init__(self, config, *args, **kwargs):
+        self.data = []
+        self.batch_size = getattr(config.progress_logger, 'batch_size', 10)
+        self.run_id = config.get('run_id', 'default_run')
+        self.base_url = config['metric_logger']['base_url']
+        self.auth_token = config['metric_logger']['auth_token']
+
+    def _remove_duplicates(self):
+        seen = set()
+        unique_logs = []
+        for log in self.data:
+            log_tuple = tuple(sorted(log.items()))
+            if log_tuple not in seen:
+                unique_logs.append(log)
+                seen.add(log_tuple)
+        self.data = unique_logs
+
+    def log(self, data: dict[str, Any]):
+        # Lowercase the keys in the data dictionary
+        lowercased_data = {k.lower(): v for k, v in data.items()}
+        self.data.append(lowercased_data)
+        if len(self.data) >= self.batch_size:
+            self._remove_duplicates()  # Remove duplicates before sending
+            self._send_batch()
+
+    def _send_batch(self):
+        import requests
+        # Remove duplicates before sending
+        self._remove_duplicates()
+
+        # Send batch of logs to API endpoint
+        batch = self.data[:self.batch_size]
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.auth_token}"
+        }
+        payload = {
+            "logs": batch
+        }
+        api = f"{self.base_url}/training_runs/{self.run_id}/logs"
+        try:
+            response = requests.post(api, json=payload, headers=headers)
+            response.raise_for_status()
+        except requests.RequestException as e:
+            logger.debug(f"Failed to send batch of logs to http monitor: {e}")
+            return False
+
+        self.data = self.data[self.batch_size:]
+        return True
+
+    def _finish(self):
+        import requests
+        headers = {
+            "Content-Type": "application/json"
+        }
+        api = f"{self.base_url}/training_runs/{self.run_id}/finish"
+        try:
+            response = requests.post(api, headers=headers)
+            response.raise_for_status()
+            return True
+        except requests.RequestException as e:
+            return False
+
+    def finish(self):
+        # Remove duplicates before sending any remaining logs
+        self._remove_duplicates()
+
+        # Send any remaining logs
+        while self.data:
+            self._send_batch()
+
+        self._finish()
+
+
 class WandbMonitor:
     def __init__(self, project, config, resume: bool):
         if importlib.util.find_spec("wandb") is None: