code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _CalculateMetricsOverAllIterations(result_dicts, metadata={}):
"""Calculates stats given list of result dictionaries.
Each item in the list represends the results from a single
iteration.
Args:
result_dicts: a list of result dictionaries. Each result dictionary
represents a single run of the... | Calculates stats given list of result dictionaries.
Each item in the list represends the results from a single
iteration.
Args:
result_dicts: a list of result dictionaries. Each result dictionary
represents a single run of the CUDA device bandwidth test, parsed by
_ParseOutputFromSingleItera... | _CalculateMetricsOverAllIterations | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/gpu_pcie_bandwidth_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/gpu_pcie_bandwidth_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Sets the GPU clock speed and runs the CUDA PCIe benchmark.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
vm = benchmark_spec.vms[0]
num_iterations = FLAGS.g... | Sets the GPU clock speed and runs the CUDA PCIe benchmark.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/gpu_pcie_bandwidth_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/gpu_pcie_bandwidth_benchmark.py | Apache-2.0 |
def Prepare(bm_spec: benchmark_spec.BenchmarkSpec) -> None:
"""Install and set up GPU PingPong on the target vm.
Args:
bm_spec: The benchmark specification
"""
for vm in bm_spec.vms:
vm.RemoteCommand(f'{_ENV.value} pip install absl-py')
vm.AuthenticateVm()
for script in [_TEST_SCRIPT, _SERVER_S... | Install and set up GPU PingPong on the target vm.
Args:
bm_spec: The benchmark specification
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | Apache-2.0 |
def _RunGpuPingpong(
vm: virtual_machine.BaseVirtualMachine, addr: str
) -> List[Tuple[float, float]]:
"""Returns the Ping and Pong latency times."""
stdout, stderr = vm.RemoteCommand(
f'{_ENV.value} python {_TEST_SCRIPT} {addr}'
)
ping_bws = [
float(bw)
for bw in regex_util.ExtractAllMatc... | Returns the Ping and Pong latency times. | _RunGpuPingpong | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | Apache-2.0 |
def Run(bm_spec: benchmark_spec.BenchmarkSpec) -> List[sample.Sample]:
"""Run GPU PingPong test.
It tests the latency between 2 GPU in 2 VMs using TensorFlow gPRC server which
were started during prepare phrase.
Args:
bm_spec: The benchmark specification
Returns:
A list of sample.Sample objects.
... | Run GPU PingPong test.
It tests the latency between 2 GPU in 2 VMs using TensorFlow gPRC server which
were started during prepare phrase.
Args:
bm_spec: The benchmark specification
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/gpu_pingpong_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec: bm_spec.BenchmarkSpec):
"""Prepare Hammerdbcli by installing dependencies and uploading binaries.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
client_vms = benchmark_spec.vm_groups['clients']
assert len(c... | Prepare Hammerdbcli by installing dependencies and uploading binaries.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def SetOptimizedServerConfiguration(
optimized_server_config: str,
server_vm: virtual_machine.BaseVirtualMachine,
relational_db: r_db.BaseRelationalDb,
db_engine: str,
):
"""Set the optimized configuration for hammerdb.
Args:
optimized_server_config: The optimized server configuration type. Cur... | Set the optimized configuration for hammerdb.
Args:
optimized_server_config: The optimized server configuration type. Currently
support MINIMUM_RECOVERY and RESTORABLE
server_vm: Server VM to host the database.
relational_db: Relational database class.
db_engine: Database engine type.
| SetOptimizedServerConfiguration | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def SetMysqlOptimizedServerConfiguration(
optimized_server_config: str,
server_vm: virtual_machine.BaseVirtualMachine,
relational_db: r_db.BaseRelationalDb,
):
"""Set the optimized configuration for hammerdb for Mysql.
Args:
optimized_server_config: The optimized server configuration type. Currentl... | Set the optimized configuration for hammerdb for Mysql.
Args:
optimized_server_config: The optimized server configuration type. Currently
support MINIMUM_RECOVERY and RESTORABLE
server_vm: Server VM to host the database.
relational_db: Relational database class.
| SetMysqlOptimizedServerConfiguration | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def SetPostgresOptimizedServerConfiguration(
optimized_server_config: str,
server_vm: virtual_machine.BaseVirtualMachine,
relational_db: r_db.BaseRelationalDb,
custom_server_config: str = '',
):
"""Set the optimized configuration for hammerdb for postgres.
Args:
optimized_server_config: The opt... | Set the optimized configuration for hammerdb for postgres.
Args:
optimized_server_config: The optimized server configuration type. Currently
support MINIMUM_RECOVERY and RESTORABLE
server_vm: Server VM to host the database.
relational_db: Relational database class.
custom_server_config: str = '... | SetPostgresOptimizedServerConfiguration | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def AddMetadata(metadata: Dict[str, Any], updates: Any) -> Dict[str, Any]:
"""Returns a copy of the metadata with dictionary update applied."""
result = metadata.copy()
result.update(updates)
return result | Returns a copy of the metadata with dictionary update applied. | AddMetadata | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:
"""Run the Hammerdbcli benchmark.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample instances.
Raises:
Exception: if the script i... | Run the Hammerdbcli benchmark.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample instances.
Raises:
Exception: if the script is unknown
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hammerdbcli_benchmark.py | Apache-2.0 |
def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> list[sample.Sample]:
"""Runs the hardinfo2 benchmark and returns a dict of performance data.
Args:
benchmark_spec: The benchmark spec for this sample benchmark.
Returns:
A list of performance samples.
"""
vm = benchmark_spec.vms[0]
samples = []
#... | Runs the hardinfo2 benchmark and returns a dict of performance data.
Args:
benchmark_spec: The benchmark spec for this sample benchmark.
Returns:
A list of performance samples.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hardinfo2_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hardinfo2_benchmark.py | Apache-2.0 |
def _GetVMsByRole(vm_groups):
"""Partition "vms" by role in the benchmark.
* The first VM is the master.
* The first FLAGS.hbase_zookeeper_nodes form the Zookeeper quorum.
* The last FLAGS.ycsb_client_vms are loader nodes.
* The nodes which are neither the master nor loaders are HBase region servers.
Args... | Partition "vms" by role in the benchmark.
* The first VM is the master.
* The first FLAGS.hbase_zookeeper_nodes form the Zookeeper quorum.
* The last FLAGS.ycsb_client_vms are loader nodes.
* The nodes which are neither the master nor loaders are HBase region servers.
Args:
vm_groups: The benchmark_spec... | _GetVMsByRole | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Prepare the virtual machines to run hadoop.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
by_role = _GetVMsByRole(benchmark_spec.vm_groups)
loaders = by_role['clients']
assert loaders, 'No loader VM... | Prepare the virtual machines to run hadoop.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Spawn YCSB and gather the results.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
by_role = _GetVMsByRole(benchmark_spec.vm_groups)
loaders = by_role['client... | Spawn YCSB and gather the results.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hbase_ycsb_benchmark.py | Apache-2.0 |
def _CopyAndUpdateRunScripts(model, vm):
"""Copy and update all necessary run scripts on the given vm.
Args:
model: name of the model
vm: vm to place and update run scripts on
"""
vm.RemoteCommand(
'[ -d "DeepLearningExamples" ] || git clone --branch clan-dev %s'
% GITHUB_MODELS_URL
)
... | Copy and update all necessary run scripts on the given vm.
Args:
model: name of the model
vm: vm to place and update run scripts on
| _CopyAndUpdateRunScripts | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def PrepareHorovod(vm):
"""Install dependencies on a single vm.
Args:
vm: vm to operate on
"""
logging.info('Installing Horovod on %s', vm)
vm.AuthenticateVm()
vm.Install('google_cloud_sdk')
vm.Install('openmpi')
vm.InstallPackages('wget git unzip')
vm.Install('nccl')
pip = 'pip'
if FLAGS.c... | Install dependencies on a single vm.
Args:
vm: vm to operate on
| PrepareHorovod | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install and set up Horovod on the target vms.
Args:
benchmark_spec: The benchmark specification
"""
vms = benchmark_spec.vms
background_tasks.RunThreaded(PrepareHorovod, vms)
hpc_util.CreateMachineFile(vms, nvidia_driver.QueryNumberOfGpus, MACHINEFILE) | Install and set up Horovod on the target vms.
Args:
benchmark_spec: The benchmark specification
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def _CreateMetadataDict(vms):
"""Create metadata dict to be used in run results.
Args:
vms: A list of worker VMs.
Returns:
metadata dict
"""
vm = vms[0]
gpus_per_node = nvidia_driver.QueryNumberOfGpus(vm)
num_vms = len(vms)
total_gpus = gpus_per_node * num_vms
metadata = dict()
metadata.u... | Create metadata dict to be used in run results.
Args:
vms: A list of worker VMs.
Returns:
metadata dict
| _CreateMetadataDict | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def _ExtractResNetThroughput(output):
"""Extract throughput from Horovod output.
Args:
output: Horovod output
Returns:
A tuple of:
Average throuput in images per second (float)
Unit of the throughput metric (str)
"""
# Start from last line and iterate backwards.
avg_throughput = 0
fo... | Extract throughput from Horovod output.
Args:
output: Horovod output
Returns:
A tuple of:
Average throuput in images per second (float)
Unit of the throughput metric (str)
| _ExtractResNetThroughput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def _ExtractBertThroughput(output):
"""Extract throughput from Horovod output.
Args:
output: Horovod output
Returns:
A tuple of:
Average throughput in sentences per second (float)
Unit of the throughput metric (str)
"""
# Start from last line and iterate backwards.
avg_throughput = 0
... | Extract throughput from Horovod output.
Args:
output: Horovod output
Returns:
A tuple of:
Average throughput in sentences per second (float)
Unit of the throughput metric (str)
| _ExtractBertThroughput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def _MakeSamplesFromOutput(vms, stdout, stderr):
"""Create a sample continaing the measured Horovod throughput.
Args:
vms: a list of worker VMs
stdout: stdout
stderr: stderr
Returns:
list of a Sample containing the Horovod throughput
"""
metadata = _CreateMetadataDict(vms)
output = stdout ... | Create a sample continaing the measured Horovod throughput.
Args:
vms: a list of worker VMs
stdout: stdout
stderr: stderr
Returns:
list of a Sample containing the Horovod throughput
| _MakeSamplesFromOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def RunWithVMs(vms, extra_envs=None):
"""Run Horovod on the cluster.
Args:
vms: A list of worker VMs.
extra_envs: A dictionary of environment variables.
Returns:
A list of sample.Sample objects.
"""
background_tasks.RunThreaded(
lambda vm: vm.RemoteCommand('rm -rf /tmp/models'), vms
)
... | Run Horovod on the cluster.
Args:
vms: A list of worker VMs.
extra_envs: A dictionary of environment variables.
Returns:
A list of sample.Sample objects.
| RunWithVMs | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/horovod_benchmark.py | Apache-2.0 |
def CheckPrerequisites(_) -> None:
"""Verifies that the required resources are present.
Raises:
perfkitbenchmarker.data.ResourceNotFound: On missing resource.
NotImplementedError: On certain flag combination not currently supported.
"""
data.ResourcePath(LOCAL_HPCCINF_FILE)
if FLAGS['hpcc_binary'].pr... | Verifies that the required resources are present.
Raises:
perfkitbenchmarker.data.ResourceNotFound: On missing resource.
NotImplementedError: On certain flag combination not currently supported.
| CheckPrerequisites | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _CalculateHpccDimensions(
num_vms: int, num_cpus: int, vm_memory_size_actual: int
) -> HpccDimensions:
"""Calculates the HPCC dimensions for the run."""
if FLAGS.memory_size_mb:
total_memory = FLAGS.memory_size_mb * 1024 * 1024 * num_vms
else:
total_memory = vm_memory_size_actual * 1024 * num_vms
... | Calculates the HPCC dimensions for the run. | _CalculateHpccDimensions | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _PrepareBinaries(
headnode_vm: linux_vm.BaseLinuxVirtualMachine,
vm: linux_vm.BaseLinuxVirtualMachine,
) -> None:
"""Prepares the binaries on the vm."""
vm.Install('fortran')
headnode_vm.MoveFile(vm, 'hpcc', 'hpcc')
headnode_vm.MoveFile(vm, '/usr/bin/orted', 'orted')
vm.RemoteCommand('sudo mv orte... | Prepares the binaries on the vm. | _PrepareBinaries | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec: bm_spec.BenchmarkSpec) -> None:
"""Install HPCC on the target vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
vms = benchmark_spec.vms
headnode_vm = vms[0]
PrepareHpcc(headnode_vm)
CreateHpccinf(he... | Install HPCC on the target vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def BaseMetadata(vm: linux_vm.BaseLinuxVirtualMachine) -> Dict[str, str]:
"""Update metadata with hpcc-related flag values."""
metadata = {}
metadata['memory_size_mb'] = FLAGS.memory_size_mb
if FLAGS['hpcc_binary'].present:
metadata['override_binary'] = FLAGS.hpcc_binary
if FLAGS['hpcc_mpi_env'].present:
... | Update metadata with hpcc-related flag values. | BaseMetadata | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def ParseOutput(hpcc_output: str) -> List[sample.Sample]:
"""Parses the output from HPCC.
Args:
hpcc_output: A string containing the text of hpccoutf.txt.
Returns:
A list of samples to be published (in the same format as Run() returns).
"""
results = []
# Parse all metrics from metric=value lines... | Parses the output from HPCC.
Args:
hpcc_output: A string containing the text of hpccoutf.txt.
Returns:
A list of samples to be published (in the same format as Run() returns).
| ParseOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:
"""Run HPCC on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
# recreate the HPL config file with each ... | Run HPCC on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _AddCommonMetadata(
samples: List[sample.Sample],
benchmark_spec: bm_spec.BenchmarkSpec,
dimensions: Dict[str, Any],
) -> None:
"""Adds metadata common to all samples."""
for item in samples:
item.metadata.update(BaseMetadata(benchmark_spec.vms[0]))
item.metadata['num_machines'] = len(benchm... | Adds metadata common to all samples. | _AddCommonMetadata | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def RunHpccSource(
vms: List[linux_vm.BaseLinuxVirtualMachine],
) -> List[sample.Sample]:
"""Returns the parsed output from running the compiled from source HPCC."""
headnode_vm = vms[0]
# backup existing HPCC output, if any
headnode_vm.RemoteCommand(
'if [ -f hpccoutf.txt ]; then '
'mv hpccoutf... | Returns the parsed output from running the compiled from source HPCC. | RunHpccSource | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _CreateHpccWrapper(vm: linux_vm.BaseLinuxVirtualMachine) -> None:
"""Creates a bash script to run HPCC on the VM.
This is required for when MKL is installed via the Intel repos as the
libiomp5.so file is not in /lib but rather in one found via sourcing the
mklvars.sh file.
Args:
vm: Virtual machine ... | Creates a bash script to run HPCC on the VM.
This is required for when MKL is installed via the Intel repos as the
libiomp5.so file is not in /lib but rather in one found via sourcing the
mklvars.sh file.
Args:
vm: Virtual machine to put file on.
| _CreateHpccWrapper | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def Cleanup(benchmark_spec: bm_spec.BenchmarkSpec) -> None:
"""Cleanup HPCC on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
vms = benchmark_spec.vms
headnode_vm = vms[0]
headnode_vm.RemoveFile('hpcc*')
headnode_vm.R... | Cleanup HPCC on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Cleanup | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def RunIntelLinpack(
vms: List[linux_vm.BaseLinuxVirtualMachine], dimensions: HpccDimensions
) -> sample.Sample:
"""Returns the parsed output from running the Intel compiled HPCC.
Unlike the compiled from source linpack run the Intel compiled linpack can
handle being cut off after --hpcc_timeout_hours as it ... | Returns the parsed output from running the Intel compiled HPCC.
Unlike the compiled from source linpack run the Intel compiled linpack can
handle being cut off after --hpcc_timeout_hours as it parses the continuous
output of linpack, reporting the last value found as the HPL_Tflops.
The metadata argument valu... | RunIntelLinpack | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _CreateIntelMpiRunCommand(
vms: List[linux_vm.BaseLinuxVirtualMachine], dimensions: HpccDimensions
) -> Tuple[str, int]:
"""Creates the command to run HPL for Intel compiled linpack.
Args:
vms: List of virtual machines to run on.
dimensions: The HpccDimensions for the run
Returns:
Tuple of t... | Creates the command to run HPL for Intel compiled linpack.
Args:
vms: List of virtual machines to run on.
dimensions: The HpccDimensions for the run
Returns:
Tuple of the mpirun command and the number of processes to be used.
| _CreateIntelMpiRunCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _ParseIntelLinpackOutputFile(file_text: str) -> float:
"""Returns the tflops for the hpcc run.
The last entry that matches
WR11C2R4 50688 192 6 10 551.85 1.57334e+02
is the Gflops for the run: 157.33
Args:
file_text: The hpcc output file contents.
"""
line_re = re.compile(
... | Returns the tflops for the hpcc run.
The last entry that matches
WR11C2R4 50688 192 6 10 551.85 1.57334e+02
is the Gflops for the run: 157.33
Args:
file_text: The hpcc output file contents.
| _ParseIntelLinpackOutputFile | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _ParseIntelLinpackStdout(stdout: str) -> Tuple[float, Dict[str, float]]:
"""Parse the stdout of Intel HPL returning a condensed sample of results.
Sample stdout:
pkb-123-0 : Column=000576 Fraction=0.005 Kernel= 0.58 Mflops=1265648.19
pkb-123-0 : Column=001152 Fraction=0.010 Kernel=969908.14 Mflops=1... | Parse the stdout of Intel HPL returning a condensed sample of results.
Sample stdout:
pkb-123-0 : Column=000576 Fraction=0.005 Kernel= 0.58 Mflops=1265648.19
pkb-123-0 : Column=001152 Fraction=0.010 Kernel=969908.14 Mflops=1081059.81
pkb-123-0 : Column=001728 Fraction=0.015 Kernel=956391.64 Mflops=104... | _ParseIntelLinpackStdout | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcc_benchmark.py | Apache-2.0 |
def _UpdateBenchmarkSpecWithFlags(benchmark_spec):
"""Update the benchmark_spec with supplied command line flags.
Args:
benchmark_spec: benchmark specification to update
"""
gpus_per_node = FLAGS.hpcg_gpus_per_node or nvidia_driver.QueryNumberOfGpus(
benchmark_spec.vms[0]
)
cpus_per_rank = int(
... | Update the benchmark_spec with supplied command line flags.
Args:
benchmark_spec: benchmark specification to update
| _UpdateBenchmarkSpecWithFlags | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _CopyAndUpdateRunScripts(vm, benchmark_spec):
"""Copy and update all necessary run scripts on the given vm.
Args:
vm: vm to place and update run scripts on
benchmark_spec: benchmark specification
"""
config_file_context = {
'PROBLEM_SIZE': '%s %s %s' % (
benchmark_spec.hpcg_problem_... | Copy and update all necessary run scripts on the given vm.
Args:
vm: vm to place and update run scripts on
benchmark_spec: benchmark specification
| _CopyAndUpdateRunScripts | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _PrepareHpcg(vm):
"""Install HPCG on a single vm.
Args:
vm: vm to operate on
"""
if FLAGS.azure_infiniband:
vm.Install('mofed')
logging.info('Installing HPCG on %s', vm)
vm.Install('hpcg')
vm.AuthenticateVm() | Install HPCG on a single vm.
Args:
vm: vm to operate on
| _PrepareHpcg | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install and set up HPCG on the target vms.
Args:
benchmark_spec: The benchmark specification
"""
vms = benchmark_spec.vms
background_tasks.RunThreaded(_PrepareHpcg, vms)
_UpdateBenchmarkSpecWithFlags(benchmark_spec)
for vm in vms:
_CopyAndUpdateRunScripts(vm, bench... | Install and set up HPCG on the target vms.
Args:
benchmark_spec: The benchmark specification
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _CreateMetadataDict(benchmark_spec):
"""Create metadata dict to be used in run results.
Args:
benchmark_spec: benchmark spec
Returns:
metadata dict
"""
vm = benchmark_spec.vms[0]
metadata = dict()
metadata.update(cuda_toolkit.GetMetadata(vm))
metadata['num_nodes'] = len(benchmark_spec.vms)... | Create metadata dict to be used in run results.
Args:
benchmark_spec: benchmark spec
Returns:
metadata dict
| _CreateMetadataDict | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _GetEnvironmentVars(benchmark_spec):
"""Return a string containing HPCG-related environment variables.
Args:
benchmark_spec: benchmark spec
Returns:
string of environment variables
"""
return ' '.join([
'NUM_GPUS=%s' % benchmark_spec.total_gpus,
'OMP_NUM_THREADS=%s' % benchmark_spec.... | Return a string containing HPCG-related environment variables.
Args:
benchmark_spec: benchmark spec
Returns:
string of environment variables
| _GetEnvironmentVars | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _ExtractProblemSize(output):
"""Extract problem size from HPCG output.
Args:
output: HPCG output
Returns:
problem size in list format
"""
regex = r'(\d+)x(\d+)x(\d+)\s+local domain'
match = re.search(regex, output)
try:
return [int(match.group(1)), int(match.group(2)), int(match.group(3)... | Extract problem size from HPCG output.
Args:
output: HPCG output
Returns:
problem size in list format
| _ExtractProblemSize | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _ExtractThroughput(output):
"""Extract throughput from HPCG output.
Args:
output: HPCG output
Returns:
throuput (float)
"""
regex = r'final\s+=\s+(\S+)\s+GF'
match = re.search(regex, output)
try:
return float(match.group(1))
except:
raise HpcgParseOutputException('Unable to parse H... | Extract throughput from HPCG output.
Args:
output: HPCG output
Returns:
throuput (float)
| _ExtractThroughput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def _MakeSamplesFromOutput(benchmark_spec, output):
"""Create a sample continaing the measured HPCG throughput.
Args:
benchmark_spec: benchmark spec
output: HPCG output
Returns:
a Sample containing the HPCG throughput in Gflops
"""
actual_hpcg_problem_size = _ExtractProblemSize(output)
if actu... | Create a sample continaing the measured HPCG throughput.
Args:
benchmark_spec: benchmark spec
output: HPCG output
Returns:
a Sample containing the HPCG throughput in Gflops
| _MakeSamplesFromOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Run HPCG on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
vms = benchmark_spec.vms
master_vm = vms[0]
run_command = 'cd %s && %s ./%s' % (
... | Run HPCG on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/hpcg_benchmark.py | Apache-2.0 |
def Prepare(bm_spec: benchmark_spec.BenchmarkSpec) -> None:
"""Install and set up HuggingFace BERT pretraining on the target vm.
Args:
bm_spec: The benchmark specification
"""
vm = bm_spec.vms[0]
vm.Install('neuron')
run_dir = 'aws-neuron-samples/torch-neuronx/training/dp_bert_hf_pretrain'
bm_spec.ru... | Install and set up HuggingFace BERT pretraining on the target vm.
Args:
bm_spec: The benchmark specification
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | Apache-2.0 |
def MakeSamplesFromOutput(output: str) -> List[sample.Sample]:
"""Create samples containing metrics.
Args:
output: string, command output Example output:
perfkitbenchmarker/tests/linux_benchmarks/huggingface_bert_pretraining_benchmark_test.py
Returns:
Samples containing training metrics, and the b... | Create samples containing metrics.
Args:
output: string, command output Example output:
perfkitbenchmarker/tests/linux_benchmarks/huggingface_bert_pretraining_benchmark_test.py
Returns:
Samples containing training metrics, and the bandwidth
| MakeSamplesFromOutput | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | Apache-2.0 |
def Run(bm_spec: benchmark_spec.BenchmarkSpec) -> List[sample.Sample]:
"""Run HuggingFace BRET pretaining test.
Args:
bm_spec: The benchmark specification
Returns:
A list of sample.Sample objects.
"""
vm = bm_spec.vms[0]
stdout, _ = vm.RobustRemoteCommand(
f'XLA_USE_BF16=1 {bm_spec.path} '
... | Run HuggingFace BRET pretaining test.
Args:
bm_spec: The benchmark specification
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/huggingface_bert_pretraining_benchmark.py | Apache-2.0 |
def _CreateMetadataDict(benchmark_spec):
"""Create metadata dict to be used in run results.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
metadata dict
"""
metadata = mnist_benchmark.CreateMetadataDict(benchmark_spec)
met... | Create metadata dict to be used in run results.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
metadata dict
| _CreateMetadataDict | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/inception3_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/inception3_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Run Inception V3 on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
_UpdateBenchmarkSpecWithFlags(benchmark_spec)
vm = benchmark_spec.vms[0]
in... | Run Inception V3 on the cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/inception3_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/inception3_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install IOR on the vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
vms = benchmark_spec.vms
background_tasks.RunThreaded(lambda vm: vm.Install('ior'), vms)
background_tasks.RunThreaded(lambda vm: ... | Install IOR on the vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/ior_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/ior_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Run the IOR benchmark on the vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
master_vm = benchmark_spec.vms[0]
results = []
# Run IOR benchmark.
if F... | Run the IOR benchmark on the vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/ior_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/ior_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install iperf and start the server on all machines.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
vms = benchmark_spec.vms
if len(vms) != 2:
raise ValueError(
f'iperf benchmark requires exa... | Install iperf and start the server on all machines.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | Apache-2.0 |
def _RunIperf(
sending_vm,
receiving_vm,
receiving_ip_address,
thread_count,
ip_type,
protocol,
interval_size=None,
):
"""Run iperf using sending 'vm' to connect to 'ip_address'.
Args:
sending_vm: The VM sending traffic.
receiving_vm: The VM receiving traffic.
receiving_ip_a... | Run iperf using sending 'vm' to connect to 'ip_address'.
Args:
sending_vm: The VM sending traffic.
receiving_vm: The VM receiving traffic.
receiving_ip_address: The IP address of the iperf server (ie the receiver).
thread_count: The number of threads the server will use.
ip_type: The IP type of '... | _RunIperf | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | Apache-2.0 |
def Run(benchmark_spec):
"""Run iperf on the target vm.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
"""
vms = benchmark_spec.vms
results = []
logging.info('Iperf Results:')
for pro... | Run iperf on the target vm.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
Returns:
A list of sample.Sample objects.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | Apache-2.0 |
def Cleanup(benchmark_spec):
"""Cleanup iperf on the target vm (by uninstalling).
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
vms = benchmark_spec.vms
for vm in vms:
if TCP in FLAGS.iperf_benchmarks:
vm.RemoteCommand(
... | Cleanup iperf on the target vm (by uninstalling).
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Cleanup | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/iperf_benchmark.py | Apache-2.0 |
def Prepare(bm_spec: _BenchmarkSpec) -> None:
"""Install KeyDB on one VM and memtier_benchmark on others."""
server_count = len(bm_spec.vm_groups['servers'])
if server_count != 1:
raise errors.Benchmarks.PrepareException(
f'Expected servers vm count to be 1, got {server_count}'
)
client_vms = bm... | Install KeyDB on one VM and memtier_benchmark on others. | Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/keydb_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/keydb_memtier_benchmark.py | Apache-2.0 |
def _PrepareCluster(benchmark_spec: bm_spec.BenchmarkSpec):
"""Prepares a cluster to run the hpa benchmark."""
cluster: container_service.KubernetesCluster = (
benchmark_spec.container_cluster
)
fib_image = benchmark_spec.container_specs['kubernetes_fib'].image
cluster.ApplyManifest(
'container/k... | Prepares a cluster to run the hpa benchmark. | _PrepareCluster | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec: bm_spec.BenchmarkSpec):
"""Install fib workload (and associated hpa) on the K8s Cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
prepare_fns = [
functools.partial(_PrepareCluster, benchmark_spec... | Install fib workload (and associated hpa) on the K8s Cluster.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[Sample]:
"""Run a benchmark against the Nginx server."""
vm = benchmark_spec.vms[0]
cluster: container_service.KubernetesCluster = typing.cast(
container_service.KubernetesCluster, benchmark_spec.container_cluster
)
addr = cluster.DeployIngress('fib... | Run a benchmark against the Nginx server. | Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def _PollServer(vm: virtual_machine.BaseVirtualMachine, addr: str) -> None:
"""Polls the server to confirm it is responding."""
poller = http_poller.HttpPoller()
response = poller.Run(vm, addr + '/calculate')
if not response.success:
raise errors.Resource.RetryableGetError(
'Failed to contact server... | Polls the server to confirm it is responding. | _PollServer | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def __init__(self, samples: List[Sample], stop: threading.Event):
"""Creates a KubernetesMetricsCollector.
Args:
samples: List of samples. Observed metrics will be added to this list.
stop: Event indicating when the test is over. Once set, the Observe*()
methods will stop.
"""
self.... | Creates a KubernetesMetricsCollector.
Args:
samples: List of samples. Observed metrics will be added to this list.
stop: Event indicating when the test is over. Once set, the Observe*()
methods will stop.
| __init__ | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def ObserveNumNodes(
self,
cluster: container_service.KubernetesCluster,
) -> None:
"""Periodically samples the number of nodes.
Adds result to self._samples.
Expected to be run in a background thread. Never completes until self._stop
is signaled.
Args:
cluster: The cluster in... | Periodically samples the number of nodes.
Adds result to self._samples.
Expected to be run in a background thread. Never completes until self._stop
is signaled.
Args:
cluster: The cluster in question.
| ObserveNumNodes | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def _Observe(
self,
observe_fn: Callable[[], List[Sample]],
) -> None:
"""Call the specified function until self._stop is signalled.
Results are appended to self._samples. Timeouts are ignored.
Args:
observe_fn: The function to call.
"""
success_count = 0
failure_count = 0
... | Call the specified function until self._stop is signalled.
Results are appended to self._samples. Timeouts are ignored.
Args:
observe_fn: The function to call.
| _Observe | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_hpa_benchmark.py | Apache-2.0 |
def GetConfig(user_config: Dict[str, Any]) -> Dict[str, Any]:
"""Load and return benchmark config spec."""
config = configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME)
if FLAGS.memcached_memtier_client_machine_type:
vm_spec = config['container_cluster']['nodepools']['clients']['vm_spec']
fo... | Load and return benchmark config spec. | GetConfig | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | Apache-2.0 |
def CreateHelmCommand() -> list[str]:
"""Create command for helm to ccreate memcached instances."""
command = ['helm', 'install', 'benchmarkcache', 'bitnami/memcached']
for c in [
(
'extraEnvVars=- name: MEMCACHED_MAX_CONNECTIONS\n value: "32768"\n-'
f' name: MEMCACHED_THREADS\n valu... | Create command for helm to ccreate memcached instances. | CreateHelmCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | Apache-2.0 |
def _PrepareCluster() -> None:
"""Prepares a cluster to run the Memcached benchmark."""
vm_util.IssueCommand(
['helm', 'repo', 'add', 'bitnami', 'https://charts.bitnami.com/bitnami'],
cwd=vm_util.GetTempDir(),
)
vm_util.IssueCommand(
CreateHelmCommand(),
cwd=vm_util.GetTempDir(),
)
... | Prepares a cluster to run the Memcached benchmark. | _PrepareCluster | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | Apache-2.0 |
def Prepare(bm_spec: _BenchmarkSpec) -> None:
"""Install Memcached on K8s cluster and memtier_benchmark on client VMs."""
client_vms = bm_spec.vm_groups['clients']
# Install Memtier and Memcached on the cluster
prepare_fns = [functools.partial(_PrepareCluster)] + [
functools.partial(vm.Install, 'memtier')... | Install Memcached on K8s cluster and memtier_benchmark on client VMs. | Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | Apache-2.0 |
def Run(bm_spec: _BenchmarkSpec):
"""Runs memtier against memcached and gathers the results.
Args:
bm_spec: The benchmark specification. Contains all data that is required to
run the benchmark.
Returns:
A list of sample.Sample instances.
"""
client = bm_spec.vm_groups['clients'][0]
assert bm... | Runs memtier against memcached and gathers the results.
Args:
bm_spec: The benchmark specification. Contains all data that is required to
run the benchmark.
Returns:
A list of sample.Sample instances.
| Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_memcached_memtier_benchmark.py | Apache-2.0 |
def GetConfig(user_config):
"""Load and return benchmark config.
Args:
user_config: user supplied configuration (flags and config file)
Returns:
loaded benchmark configuration
"""
config = configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME)
if FLAGS['ycsb_client_vms'].present:
co... | Load and return benchmark config.
Args:
user_config: user supplied configuration (flags and config file)
Returns:
loaded benchmark configuration
| GetConfig | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | Apache-2.0 |
def _PrepareClient(vm):
"""Install YCSB on the client VM."""
vm.Install('ycsb')
# Disable logging for MongoDB driver, which is otherwise quite verbose.
log_config = """<configuration><root level="WARN"/></configuration>"""
vm.RemoteCommand(
"echo '{}' > {}/logback.xml".format(log_config, ycsb.YCSB_DIR)... | Install YCSB on the client VM. | _PrepareClient | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | Apache-2.0 |
def _PrepareDeployment(benchmark_spec):
"""Deploys MongoDB Operator and instance on the cluster."""
cluster = benchmark_spec.container_cluster
storage_class = STORAGE_CLASS.value or cluster.GetDefaultStorageClass()
cluster.ApplyManifest(
'container/kubernetes_mongodb/kubernetes_mongodb.yaml.j2',
cpu... | Deploys MongoDB Operator and instance on the cluster. | _PrepareDeployment | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install MongoDB on one VM and YCSB on another.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
server_partials = [functools.partial(_PrepareDeployment, benchmark_spec)]
client_partials = [
functool... | Install MongoDB on one VM and YCSB on another.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_mongodb_ycsb_benchmark.py | Apache-2.0 |
def _CreateNginxConfigMapDir():
"""Returns a TemporaryDirectory containing files in the Nginx ConfigMap."""
if FLAGS.nginx_conf:
nginx_conf_filename = FLAGS.nginx_conf
else:
relative_nginx_conf_filename = 'container/kubernetes_nginx/http.conf'
if FLAGS.nginx_use_ssl:
relative_nginx_conf_filename... | Returns a TemporaryDirectory containing files in the Nginx ConfigMap. | _CreateNginxConfigMapDir | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | Apache-2.0 |
def _PrepareCluster(benchmark_spec):
"""Prepares a cluster to run the Nginx benchmark."""
with _CreateNginxConfigMapDir() as nginx_config_map_dirname:
benchmark_spec.container_cluster.CreateConfigMap(
'default-config', nginx_config_map_dirname
)
container_image = benchmark_spec.container_specs['ku... | Prepares a cluster to run the Nginx benchmark. | _PrepareCluster | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Install Nginx on the K8s Cluster and a load generator on the clients.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
"""
clients = benchmark_spec.vm_groups['clients']
prepare_fns = [functools.partial(_Prep... | Install Nginx on the K8s Cluster and a load generator on the clients.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benchmark.
| Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_nginx_benchmark.py | Apache-2.0 |
def _PrepareCluster(bm_spec: _BenchmarkSpec):
"""Prepares a cluster to run the Redis benchmark."""
redis_port = redis_server.GetRedisPorts()[0]
replicas = bm_spec.container_cluster.nodepools['redis'].num_nodes * 2
with kubernetes_helper.CreateRenderedManifestFile(
'container/kubernetes_redis_memtier/kuber... | Prepares a cluster to run the Redis benchmark. | _PrepareCluster | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_redis_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_redis_memtier_benchmark.py | Apache-2.0 |
def Prepare(bm_spec: _BenchmarkSpec) -> None:
"""Install Redis on K8s cluster and memtier_benchmark on client VMs."""
client_vms = bm_spec.vm_groups['clients']
# Install Memtier and Redis on the cluster
prepare_fns = [functools.partial(_PrepareCluster, bm_spec)] + [
functools.partial(vm.Install, 'memtier'... | Install Redis on K8s cluster and memtier_benchmark on client VMs. | Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_redis_memtier_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_redis_memtier_benchmark.py | Apache-2.0 |
def _GetImage() -> str:
"""Get the image for the scale deployment."""
if virtual_machine.GPU_COUNT.value:
return NVIDIA_GPU_IMAGE
return DEFAULT_IMAGE | Get the image for the scale deployment. | _GetImage | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def _GetRolloutCreationTime(rollout_name: str) -> int:
"""Returns the time when the rollout was created."""
out, _, _ = container_service.RunRetryableKubectlCommand([
'rollout',
'history',
rollout_name,
'-o',
'jsonpath={.metadata.creationTimestamp}',
])
return ConvertToEpochTime(ou... | Returns the time when the rollout was created. | _GetRolloutCreationTime | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def _GetScaleTimeout() -> int:
"""Returns the timeout for the scale up & teardown."""
base_timeout = 60 * 10
per_pod_timeout = NUM_PODS.value * 3
proposed_timeout = base_timeout + per_pod_timeout
max_timeout = 2 * 60 * 60 # 2 hours
return min(proposed_timeout, max_timeout) | Returns the timeout for the scale up & teardown. | _GetScaleTimeout | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def Run(bm_spec: benchmark_spec.BenchmarkSpec) -> list[sample.Sample]:
"""Scales a large number of pods on kubernetes."""
assert bm_spec.container_cluster
cluster = bm_spec.container_cluster
assert isinstance(cluster, container_service.KubernetesCluster)
initial_nodes = set(cluster.GetNodeNames())
samples... | Scales a large number of pods on kubernetes. | Run | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def ScaleUpPods(
cluster: container_service.KubernetesCluster,
) -> tuple[list[sample.Sample], str]:
"""Scales up pods on a kubernetes cluster. Returns samples & rollout name."""
samples = []
initial_pods = set(cluster.GetPodNames())
logging.info('Initial pods: %s', initial_pods)
command = None
if virt... | Scales up pods on a kubernetes cluster. Returns samples & rollout name. | ScaleUpPods | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def _CheckForFailures(
cluster: container_service.KubernetesCluster,
pod_samples: list[sample.Sample],
):
"""Fails the benchmark if not enough pods were created.
Args:
cluster: The cluster to check for failures on.
pod_samples: The samples from pod transition times which includes pod Ready
co... | Fails the benchmark if not enough pods were created.
Args:
cluster: The cluster to check for failures on.
pod_samples: The samples from pod transition times which includes pod Ready
count.
Raises:
QuotaFailure: If a quota is exceeded.
RunError: If scale up failed for a non-quota reason.
| _CheckForFailures | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def FromJsonPathResult(
cls, resource_type: str, resource_name: str, condition: dict[str, Any]
) -> 'KubernetesResourceStatusCondition':
"""Parses the json result of kubectl get."""
str_time = condition['lastTransitionTime']
return cls(
resource_type,
resource_name,
epoch_tim... | Parses the json result of kubectl get. | FromJsonPathResult | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def _GetResourceStatusConditions(
resource_type: str, resource_name: str
) -> list[KubernetesResourceStatusCondition]:
"""Returns the status conditions for a resource.
Args:
resource_type: The type of the resource to get the status conditions for.
resource_name: The name of the resource to get the stat... | Returns the status conditions for a resource.
Args:
resource_type: The type of the resource to get the status conditions for.
resource_name: The name of the resource to get the status conditions for.
Should not have a prefix (eg pods/).
Returns:
A list of status condition, where each condition i... | _GetResourceStatusConditions | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def ParseStatusChanges(
cluster: container_service.KubernetesCluster,
resource_type: str,
start_time: float,
resources_to_ignore: abc.Set[str] = frozenset(),
) -> list[sample.Sample]:
"""Parses status transitions into samples.
Status transitions are pulled from the cluster, and include e.g. when a ... | Parses status transitions into samples.
Status transitions are pulled from the cluster, and include e.g. when a pod
transitions to PodScheduled or Ready. See tests for more example input/output.
Args:
cluster: The cluster to parse the status changes for.
resource_type: The type of the resource to parse ... | ParseStatusChanges | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def _SummarizeTimestamps(timestamps: list[float]) -> dict[str, float]:
"""Returns a few metrics about a list of timestamps."""
percentiles = [0, 10, 50, 90, 95, 99.9, 100]
summary = {
'mean': np.mean(timestamps),
}
for percentile in percentiles:
summary[f'p{percentile}'] = np.percentile(timestamps, ... | Returns a few metrics about a list of timestamps. | _SummarizeTimestamps | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def Cleanup(_):
"""Cleanups scale benchmark. Runs before teardown."""
container_service.RunRetryableKubectlCommand(
['delete', '--all', 'pods', '-n', 'default'], timeout=_GetScaleTimeout()
) | Cleanups scale benchmark. Runs before teardown. | Cleanup | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/kubernetes_scale_benchmark.py | Apache-2.0 |
def GetAzBootVMStartIdByLauncher(launcher_name):
"""Returns the Azure boot VM id by launcher name.
We want to keep the VM id unique across all the vms in this resource group.
Since the ids are used later to calculate the private ip address. We have to
skip the first few ids that will match up to reserved reser... | Returns the Azure boot VM id by launcher name.
We want to keep the VM id unique across all the vms in this resource group.
Since the ids are used later to calculate the private ip address. We have to
skip the first few ids that will match up to reserved reserved ips.
E.g.
Azure reserved ip: 10.0.0.0, 10.0.... | GetAzBootVMStartIdByLauncher | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def _GetServerStartCommand(client_port, launcher_vm):
"""Returns the command to start the listener server."""
cloud = FLAGS.cloud
if cloud == 'GCP' and FLAGS.use_public_ip:
vms_name_pattern = UNDEFINED_HOSTNAME
elif cloud == 'GCP':
vms_name_pattern = (
'{name_pattern}-VM_ID.{zone}.c.{project}.in... | Returns the command to start the listener server. | _GetServerStartCommand | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def GetConfig(user_config):
"""Load and updates the benchmark config with user flags.
Args:
user_config: user supplied configuration (flags and config file)
Returns:
loaded benchmark configuration
"""
config = configs.LoadConfig(BENCHMARK_CONFIG, user_config, BENCHMARK_NAME)
launcher_config = conf... | Load and updates the benchmark config with user flags.
Args:
user_config: user supplied configuration (flags and config file)
Returns:
loaded benchmark configuration
| GetConfig | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def _BuildContext(launcher_vm, booter_template_vm):
"""Returns the context variables for Jinja2 template during rendering."""
context = {
'boot_machine_type': booter_template_vm.machine_type,
'cloud': FLAGS.cloud,
'contact_launcher': FLAGS.vms_contact_launcher,
'launcher_vm_name': launcher_v... | Returns the context variables for Jinja2 template during rendering. | _BuildContext | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def _Install(launcher_vm, booter_template_vm):
"""Installs benchmark scripts and packages on the launcher vm."""
launcher_vm.InstallCli()
# Render boot script on launcher server VM(s)
context = _BuildContext(launcher_vm, booter_template_vm)
launcher_vm.RenderTemplate(
data.ResourcePath(_BOOT_TEMPLATE), ... | Installs benchmark scripts and packages on the launcher vm. | _Install | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def Prepare(benchmark_spec):
"""Prepare the launcher server vm(s).
Prepare the launcher server vm(s) by:
1) Build the script that each launcher server will use to kick off boot.
2) Start a listening server to wait for booting vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that... | Prepare the launcher server vm(s).
Prepare the launcher server vm(s) by:
1) Build the script that each launcher server will use to kick off boot.
2) Start a listening server to wait for booting vms.
Args:
benchmark_spec: The benchmark specification. Contains all data that is
required to run the benc... | Prepare | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def _WaitForResponses(launcher_vms):
"""Wait for all results or server shutdown or TIMEOUT_SECONDS."""
# if any listener server exited, stop waiting.
def _LauncherError(vm):
error, _ = vm.RemoteCommand(
'grep ERROR ' + _LISTENER_SERVER_LOG, ignore_failure=True
)
return error
error_str = ba... | Wait for all results or server shutdown or TIMEOUT_SECONDS. | _WaitForResponses | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
def _ParseResult(launcher_vms):
"""Parse the results on the launcher VMs and send it back.
Boot time is the boot duration of the slowest machine.
Args:
launcher_vms: Launcher server VMs.
Returns:
A list of benchmark samples.
"""
vm_count = 0
slowest_time = -1
get_starttime_cmd = 'cat {startim... | Parse the results on the launcher VMs and send it back.
Boot time is the boot duration of the slowest machine.
Args:
launcher_vms: Launcher server VMs.
Returns:
A list of benchmark samples.
| _ParseResult | python | GoogleCloudPlatform/PerfKitBenchmarker | perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/perfkitbenchmarker/linux_benchmarks/large_scale_boot_benchmark.py | Apache-2.0 |
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