async def _report_usage(self):
if not ray_usage_lib._usage_stats_enabled():
return
"""
- Always write usage_stats.json regardless of report success/failure.
- If report fails, the error message should be written to usage_stats.json
- If file write fails, the error will just stay at dashboard.log.
usage_stats.json won't be written.
"""
try:
data = ray_usage_lib.generate_report_data(
self.cluster_metadata,
self.total_success,
self.total_failed,
self.seq_no,
)
error = None
try:
await self.client.report_usage_data_async(
ray_usage_lib._usage_stats_report_url(), data)
except Exception as e:
logger.info(f"Usage report request failed. {e}")
error = str(e)
self.total_failed += 1
else:
self.total_success += 1
finally:
self.seq_no += 1
data = ray_usage_lib.generate_write_data(data, error)
await self.client.write_usage_data_async(data, self.session_dir)
except Exception as e:
logger.exception(e)
logger.info(f"Usage report failed: {e}")
def test_usage_lib_report_data(monkeypatch, shutdown_only, tmp_path):
with monkeypatch.context() as m:
m.setenv("RAY_USAGE_STATS_ENABLED", "1")
# Runtime env is required to run this test in minimal installation test.
ray.init(num_cpus=0, runtime_env={"pip": ["ray[serve]"]})
"""
Make sure the generated data is following the schema.
"""
cluster_metadata = ray_usage_lib.get_cluster_metadata(
ray.experimental.internal_kv.internal_kv_get_gcs_client(), num_retries=20
)
d = ray_usage_lib.generate_report_data(cluster_metadata, 2, 2, 2)
validate(instance=asdict(d), schema=schema)
"""
Make sure writing to a file works as expected
"""
client = ray_usage_lib.UsageReportClient()
temp_dir = Path(tmp_path)
client._write_usage_data(d, temp_dir)
wait_for_condition(lambda: file_exists(temp_dir))
"""
Make sure report usage data works as expected
"""
@ray.remote(num_cpus=0, runtime_env={"pip": ["ray[serve]"]})
class ServeInitator:
def __init__(self):
# Start the ray serve server to verify requests are sent
# to the right place.
from ray import serve
serve.start()
@serve.deployment(ray_actor_options={"num_cpus": 0})
async def usage(request):
body = await request.json()
if body == asdict(d):
return True
else:
return False
usage.deploy()
def ready(self):
pass
# We need to start a serve with runtime env to make this test
# work with minimal installation.
s = ServeInitator.remote()
ray.get(s.ready.remote())
# Query our endpoint over HTTP.
r = client._report_usage_data("http://127.0.0.1:8000/usage", d)
r.raise_for_status()
assert json.loads(r.text) is True
def test_usage_lib_report_data(
monkeypatch, ray_start_cluster, tmp_path, reset_lib_usage
):
with monkeypatch.context() as m:
m.setenv("RAY_USAGE_STATS_ENABLED", "1")
m.setenv("RAY_USAGE_STATS_REPORT_URL", "http://127.0.0.1:8000")
cluster = ray_start_cluster
cluster.add_node(num_cpus=0)
# Runtime env is required to run this test in minimal installation test.
ray.init(address=cluster.address, runtime_env={"pip": ["ray[serve]"]})
"""
Make sure the generated data is following the schema.
"""
cluster_metadata = ray_usage_lib.get_cluster_metadata(
ray.experimental.internal_kv.internal_kv_get_gcs_client(), num_retries=20
)
cluster_config_file_path = tmp_path / "ray_bootstrap_config.yaml"
cluster_config_file_path.write_text(
"""
cluster_name: minimal
max_workers: 1
provider:
type: aws
region: us-west-2
availability_zone: us-west-2a
"""
)
cluster_config_to_report = ray_usage_lib.get_cluster_config_to_report(
cluster_config_file_path
)
d = ray_usage_lib.generate_report_data(
cluster_metadata, cluster_config_to_report, 2, 2, 2
)
validate(instance=asdict(d), schema=schema)
"""
Make sure writing to a file works as expected
"""
client = ray_usage_lib.UsageReportClient()
temp_dir = Path(tmp_path)
client.write_usage_data(d, temp_dir)
wait_for_condition(lambda: file_exists(temp_dir))
"""
Make sure report usage data works as expected
"""
@ray.remote(num_cpus=0)
class ServeInitator:
def __init__(self):
# Start the ray serve server to verify requests are sent
# to the right place.
from ray import serve
serve.start()
@serve.deployment(ray_actor_options={"num_cpus": 0})
async def usage(request):
body = await request.json()
if body == asdict(d):
return True
else:
return False
usage.deploy()
def ready(self):
pass
# We need to start a serve with runtime env to make this test
# work with minimal installation.
s = ServeInitator.remote()
ray.get(s.ready.remote())
# Query our endpoint over HTTP.
r = client.report_usage_data("http://127.0.0.1:8000/usage", d)
r.raise_for_status()
assert json.loads(r.text) is True