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def str_to_bool(value) -> int:
"""
Converts a string representation of truth to `True` (1) or `False` (0).
True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1
elif value in ("n", "no", "f", "false", "off", "0"):
return 0
else:
raise ValueError(f"invalid truth value {value}")
def get_int_from_env(env_keys, default):
"""Returns the first positive env value found in the `env_keys` list or the default."""
for e in env_keys:
val = int(os.environ.get(e, -1))
if val >= 0:
return val
return default
def parse_flag_from_env(key, default=False):
"""Returns truthy value for `key` from the env if available else the default."""
value = os.environ.get(key, str(default))
return str_to_bool(value) == 1 # As its name indicates `str_to_bool` actually returns an int...
def parse_choice_from_env(key, default="no"):
value = os.environ.get(key, str(default))
return value
def are_libraries_initialized(*library_names: str) -> Dict[str, bool]:
"""
Checks if any of `library_names` are imported in the environment. Will return results as a `key:bool` pair.
"""
return [lib_name for lib_name in library_names if lib_name in sys.modules.keys()]
def get_gpu_info():
"""
Gets GPU count and names using `nvidia-smi` instead of torch to not initialize CUDA.
Largely based on the `gputil` library.
"""
if platform.system() == "Windows":
# If platform is Windows and nvidia-smi can't be found in path
# try from systemd rive with default installation path
command = spawn.find_executable("nvidia-smi")
if command is None:
command = "%s\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi.exe" % os.environ["systemdrive"]
else:
command = "nvidia-smi"
# Returns as list of `n` GPUs and their names
output = subprocess.check_output(
[command, "--query-gpu=count,name", "--format=csv,noheader"], universal_newlines=True
)
output = output.strip()
gpus = output.split(os.linesep)
# Get names from output
gpu_count = len(gpus)
gpu_names = [gpu.split(",")[1].strip() for gpu in gpus]
return gpu_names, gpu_count
def check_cuda_p2p_ib_support():
"""
Checks if the devices being used have issues with P2P and IB communications, namely any consumer GPU hardware after
the 3090.
Noteably uses `nvidia-smi` instead of torch to not initialize CUDA.
"""
try:
device_names, device_count = get_gpu_info()
unsupported_devices = {"RTX 3090", "RTX 40"}
if device_count > 1:
if any(
unsupported_device in device_name
for device_name in device_names
for unsupported_device in unsupported_devices
):
return False
except Exception:
pass
return True
def check_fp8_capability():
"""
Checks if all the current GPUs available support FP8.
Notably must initialize `torch.cuda` to check.
"""
cuda_device_capacity = torch.cuda.get_device_capability()
return cuda_device_capacity >= (8, 9)
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