Add new training loop with PyTorch Lightning, remove stale files

1 files changed, 0 insertions, 62 deletions

diff --git a/training/gpu_manager.py b/training/gpu_manager.py
deleted file mode 100644
index ce1b3dd..0000000
--- a/training/gpu_manager.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""GPUManager class."""
-import os
-import time
-from typing import Optional
-
-import gpustat
-from loguru import logger
-import numpy as np
-from redlock import Redlock
-
-
-GPU_LOCK_TIMEOUT = 5000  # ms
-
-
-class GPUManager:
-    """Class for allocating GPUs."""
-
-    def __init__(self, verbose: bool = False) -> None:
-        """Initializes Redlock manager."""
-        self.lock_manager = Redlock([{"host": "localhost", "port": 6379, "db": 0}])
-        self.verbose = verbose
-
-    def get_free_gpu(self) -> int:
-        """Gets a free GPU.
-
-        If some GPUs are available, try reserving one by checking out an exclusive redis lock.
-        If none available or can not get lock, sleep and check again.
-
-        Returns:
-            int: The gpu index.
-
-        """
-        while True:
-            gpu_index = self._get_free_gpu()
-            if gpu_index is not None:
-                return gpu_index
-
-            if self.verbose:
-                logger.debug(f"pid {os.getpid()} sleeping")
-            time.sleep(GPU_LOCK_TIMEOUT / 1000)
-
-    def _get_free_gpu(self) -> Optional[int]:
-        """Fetches an available GPU index."""
-        try:
-            available_gpu_indices = [
-                gpu.index
-                for gpu in gpustat.GPUStatCollection.new_query()
-                if gpu.memory_used < 0.5 * gpu.memory_total
-            ]
-        except Exception as e:
-            logger.debug(f"Got the following exception: {e}")
-            return None
-
-        if available_gpu_indices:
-            gpu_index = np.random.choice(available_gpu_indices)
-            if self.verbose:
-                logger.debug(f"pid {os.getpid()} picking gpu {gpu_index}")
-            if self.lock_manager.lock(f"gpu_{gpu_index}", GPU_LOCK_TIMEOUT):
-                return int(gpu_index)
-            if self.verbose:
-                logger.debug(f"pid {os.getpid()} could not get lock.")
-        return None