def test_trace_and_metrics(self):
port = xu.get_free_tcp_ports()[0]
training_started = multiprocessing.Event()
def train_worker():
flags = args_parse.parse_common_options(datadir='/tmp/mnist-data',
batch_size=16,
momentum=0.5,
lr=0.01,
num_epochs=10)
flags.fake_data = True
flags.profiler_port = port
test_profile_mp_mnist.train_mnist(
flags,
training_started=training_started,
dynamic_graph=True,
fetch_often=True)
p = multiprocessing.Process(target=train_worker, daemon=True)
p.start()
training_started.wait(60)
logdir = tempfile.mkdtemp()
xp.trace(f'localhost:{port}',
logdir,
duration_ms=5000,
num_tracing_attempts=5,
delay_ms=1000)
p.terminate()
path = self._check_xspace_pb_exist(logdir)
self._check_trace_namespace_exists(path)
self._check_metrics_warnings_exist(self.fname)
def test_xla_profiler_prog_capture(tmpdir):
port = xu.get_free_tcp_ports()[0]
training_started = Event()
def train_worker():
model = BoringModel()
trainer = Trainer(default_root_dir=tmpdir,
max_epochs=4,
profiler="xla",
accelerator="tpu",
devices=8)
trainer.fit(model)
p = Process(target=train_worker, daemon=True)
p.start()
training_started.wait(120)
logdir = str(tmpdir)
xp.trace(f"localhost:{port}",
logdir,
duration_ms=2000,
num_tracing_attempts=5,
delay_ms=1000)
p.terminate()
assert os.isfile(
os.path.join(logdir, "plugins", "profile", "*", "*.xplane.pb"))
def trace():
xp.trace(
service_addr=args.service_addr,
logdir=args.logdir,
duration_ms=args.duration_ms,
)
print(f"Saved profiling output to {args.logdir}")
if __name__ == "__main__":
xla_enabled = True
amp_enabled = True
debug_enabled = False
dlprof_enabled = False
cpu_mem_usage = False
if dlprof_enabled and not xla_enabled and False:
import nvidia_dlprof_pytorch_nvtx
nvidia_dlprof_pytorch_nvtx.init()
if xla_enabled:
port_number = 8192
training_started = multiprocessing.Event()
dataset_path = '/pytorch/xla/test/IMDB Dataset.csv'
download_dataset()
def target_fn():
xmp.spawn(_mp_fn, nprocs=1)
p = multiprocessing.Process(target=target_fn, args=())
p.start()
training_started.wait()
xp.trace(f'localhost:{port_number}',
'/pytorch/xla/test/bert_tensorboard')
# xmp.spawn(_mp_fn, nprocs=1)
else:
dataset_path = os.path.join(os.getcwd(), "IMDB Dataset.csv")
download_dataset()
train_bert(dataset_path, xla_enabled, amp_enabled)
