def test_mxnet_with_profiler_and_debugger_then_disable_framework_metrics(
sagemaker_session,
mxnet_training_latest_version,
mxnet_training_latest_py_version,
cpu_instance_type,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
rules = [
Rule.sagemaker(rule_configs.vanishing_gradient()),
Rule.sagemaker(base_config=rule_configs.all_zero(),
rule_parameters={"tensor_regex": ".*"}),
ProfilerRule.sagemaker(rule_configs.ProfilerReport(),
name="CustomProfilerReportRule"),
]
debugger_hook_config = DebuggerHookConfig(
s3_output_path=
f"s3://{sagemaker_session.default_bucket()}/{str(uuid.uuid4())}/tensors",
)
profiler_config = ProfilerConfig(
s3_output_path=
f"s3://{sagemaker_session.default_bucket()}/{str(uuid.uuid4())}/system",
system_monitor_interval_millis=1000,
framework_profile_params=FrameworkProfile(),
)
script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_gluon.py")
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
framework_version=mxnet_training_latest_version,
py_version=mxnet_training_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
rules=rules,
debugger_hook_config=debugger_hook_config,
profiler_config=profiler_config,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/mxnet_mnist/train")
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/mxnet_mnist/test")
training_job_name = unique_name_from_base(
"test-profiler-mxnet-training")
mx.fit(
inputs={
"train": train_input,
"test": test_input
},
job_name=training_job_name,
wait=False,
)
job_description = mx.latest_training_job.describe()
assert job_description[
"ProfilerConfig"] == profiler_config._to_request_dict()
assert job_description[
"DebugHookConfig"] == debugger_hook_config._to_request_dict()
assert job_description.get("ProfilingStatus") == "Enabled"
profiler_rule_configuration = job_description.get(
"ProfilerRuleConfigurations")[0]
assert profiler_rule_configuration[
"RuleConfigurationName"] == "CustomProfilerReportRule"
assert profiler_rule_configuration["RuleEvaluatorImage"] == mx.rules[
0].image_uri
assert profiler_rule_configuration["RuleParameters"] == {
"rule_to_invoke": "ProfilerReport",
}
for index, rule in enumerate(mx.debugger_rules):
assert (job_description["DebugRuleConfigurations"][index]
["RuleConfigurationName"] == rule.name)
assert (job_description["DebugRuleConfigurations"][index]
["RuleEvaluatorImage"] == rule.image_uri)
_wait_until_training_can_be_updated(sagemaker_session.sagemaker_client,
training_job_name)
mx.update_profiler(disable_framework_metrics=True)
job_description = mx.latest_training_job.describe()
assert job_description["ProfilerConfig"]["ProfilingParameters"] == {}
def test_mxnet_with_enable_framework_metrics_then_update_framework_metrics(
sagemaker_session,
mxnet_training_latest_version,
mxnet_training_latest_py_version,
cpu_instance_type,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
profiler_config = ProfilerConfig(
framework_profile_params=FrameworkProfile(start_step=1,
num_steps=5))
script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_gluon.py")
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
framework_version=mxnet_training_latest_version,
py_version=mxnet_training_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
profiler_config=profiler_config,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/mxnet_mnist/train")
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/mxnet_mnist/test")
training_job_name = unique_name_from_base(
"test-profiler-mxnet-training")
mx.fit(
inputs={
"train": train_input,
"test": test_input
},
job_name=training_job_name,
wait=False,
)
job_description = mx.latest_training_job.describe()
assert (job_description["ProfilerConfig"]["ProfilingParameters"] ==
profiler_config._to_request_dict()["ProfilingParameters"])
assert job_description.get("ProfilingStatus") == "Enabled"
_wait_until_training_can_be_updated(sagemaker_session.sagemaker_client,
training_job_name)
updated_framework_profile = FrameworkProfile(
detailed_profiling_config=DetailedProfilingConfig(
profile_default_steps=True))
mx.update_profiler(framework_profile_params=updated_framework_profile)
job_description = mx.latest_training_job.describe()
assert (job_description["ProfilerConfig"]["ProfilingParameters"] ==
updated_framework_profile.profiling_parameters)
profiler_rule_configuration = job_description.get(
"ProfilerRuleConfigurations")[0]
assert re.match(r"ProfilerReport-\d*",
profiler_rule_configuration["RuleConfigurationName"])
assert profiler_rule_configuration[
"RuleEvaluatorImage"] == get_rule_container_image_uri(
mx.sagemaker_session.boto_region_name)
assert profiler_rule_configuration["RuleParameters"] == {
"rule_to_invoke": "ProfilerReport"
}
def test_mxnet_with_custom_profiler_config_then_update_rule_and_config(
sagemaker_session,
mxnet_training_latest_version,
mxnet_training_latest_py_version,
cpu_instance_type,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
profiler_config = ProfilerConfig(
s3_output_path=
f"s3://{sagemaker_session.default_bucket()}/{str(uuid.uuid4())}/system",
system_monitor_interval_millis=1000,
)
script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_gluon.py")
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
framework_version=mxnet_training_latest_version,
py_version=mxnet_training_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
profiler_config=profiler_config,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/mxnet_mnist/train")
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/mxnet_mnist/test")
training_job_name = unique_name_from_base(
"test-profiler-mxnet-training")
mx.fit(
inputs={
"train": train_input,
"test": test_input
},
job_name=training_job_name,
wait=False,
)
job_description = mx.latest_training_job.describe()
assert job_description.get(
"ProfilerConfig") == profiler_config._to_request_dict()
assert job_description.get("ProfilingStatus") == "Enabled"
profiler_rule_configuration = job_description.get(
"ProfilerRuleConfigurations")[0]
assert re.match(r"ProfilerReport-\d*",
profiler_rule_configuration["RuleConfigurationName"])
assert profiler_rule_configuration[
"RuleEvaluatorImage"] == get_rule_container_image_uri(
mx.sagemaker_session.boto_region_name)
assert profiler_rule_configuration["RuleParameters"] == {
"rule_to_invoke": "ProfilerReport"
}
_wait_until_training_can_be_updated(sagemaker_session.sagemaker_client,
training_job_name)
mx.update_profiler(
rules=[ProfilerRule.sagemaker(rule_configs.CPUBottleneck())],
system_monitor_interval_millis=500,
)
job_description = mx.latest_training_job.describe()
assert job_description["ProfilerConfig"][
"S3OutputPath"] == profiler_config.s3_output_path
assert job_description["ProfilerConfig"][
"ProfilingIntervalInMilliseconds"] == 500
profiler_report_rule_config = job_description.get(
"ProfilerRuleConfigurations")[0]
assert re.match(r"ProfilerReport-\d*",
profiler_report_rule_config["RuleConfigurationName"])
assert profiler_report_rule_config[
"RuleEvaluatorImage"] == get_rule_container_image_uri(
mx.sagemaker_session.boto_region_name)
assert profiler_report_rule_config["RuleParameters"] == {
"rule_to_invoke": "ProfilerReport"
}
