GPU利用率prometheus查询

问题描述 投票：0回答：1

我可以从 Prometheus 的以下指标中找出 GPU 利用率吗？我不知道如何查询它。 我没有适用于 PPC64lE 环境的 dcgm-exporter 映像。您还可以分享用于制作 ppc64le 环境的 dcgm-exporter 的 docker 镜像的链接

 HELP go_gc_duration_seconds A summary of the pause duration of garbage collection cycles.
# TYPE go_gc_duration_seconds summary
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go_gc_duration_seconds{quantile="0.25"} 0
go_gc_duration_seconds{quantile="0.5"} 0
go_gc_duration_seconds{quantile="0.75"} 0
go_gc_duration_seconds{quantile="1"} 0
go_gc_duration_seconds_sum 0
go_gc_duration_seconds_count 0
# HELP go_goroutines Number of goroutines that currently exist.
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# HELP go_info Information about the Go environment.
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# HELP go_memstats_alloc_bytes Number of bytes allocated and still in use.
# TYPE go_memstats_alloc_bytes gauge
go_memstats_alloc_bytes 2.499048e+06
# HELP go_memstats_alloc_bytes_total Total number of bytes allocated, even if freed.
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go_memstats_alloc_bytes_total 2.499048e+06
# HELP go_memstats_buck_hash_sys_bytes Number of bytes used by the profiling bucket hash table.
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go_memstats_buck_hash_sys_bytes 4593
# HELP go_memstats_frees_total Total number of frees.
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go_memstats_frees_total 761
# HELP go_memstats_gc_cpu_fraction The fraction of this program's available CPU time used by the GC since the program started.
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go_memstats_gc_cpu_fraction 0
# HELP go_memstats_gc_sys_bytes Number of bytes used for garbage collection system metadata.
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go_memstats_gc_sys_bytes 4.368032e+06
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go_memstats_heap_alloc_bytes 2.499048e+06
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go_memstats_heap_idle_bytes 4.13696e+06
# HELP go_memstats_heap_inuse_bytes Number of heap bytes that are in use.
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go_memstats_heap_inuse_bytes 3.760128e+06
# HELP go_memstats_heap_objects Number of allocated objects.
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go_memstats_heap_objects 5731
# HELP go_memstats_heap_released_bytes Number of heap bytes released to OS.
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go_memstats_heap_released_bytes 4.13696e+06
# HELP go_memstats_heap_sys_bytes Number of heap bytes obtained from system.
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go_memstats_heap_sys_bytes 7.897088e+06
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# HELP go_memstats_mcache_inuse_bytes Number of bytes in use by mcache structures.
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# HELP go_memstats_mspan_inuse_bytes Number of bytes in use by mspan structures.
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go_memstats_mspan_inuse_bytes 58752
# HELP go_memstats_mspan_sys_bytes Number of bytes used for mspan structures obtained from system.
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go_memstats_mspan_sys_bytes 65536
# HELP go_memstats_next_gc_bytes Number of heap bytes when next garbage collection will take place.
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# HELP go_memstats_other_sys_bytes Number of bytes used for other system allocations.
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# HELP go_memstats_stack_sys_bytes Number of bytes obtained from system for stack allocator.
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go_memstats_sys_bytes 1.4027792e+07
# HELP go_threads Number of OS threads created.
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go_threads 9
# HELP nvidia_gpu_duty_cycle Percent of time over the past sample period during which one or more kernels were executing on the GPU device
# TYPE nvidia_gpu_duty_cycle gauge
nvidia_gpu_duty_cycle{minor_number="0",name="Tesla V100-SXM2-32GB",uuid="GPU-5481fdc1-1b2c-381d-90d9-2df35fc8cecf"} 0
nvidia_gpu_duty_cycle{minor_number="1",name="Tesla V100-SXM2-32GB",uuid="GPU-af66b351-1498-c103-f39e-7592b645dc80"} 0
nvidia_gpu_duty_cycle{minor_number="2",name="Tesla V100-SXM2-32GB",uuid="GPU-95887069-482a-9a95-d02a-7c6e79c47893"} 0
nvidia_gpu_duty_cycle{minor_number="3",name="Tesla V100-SXM2-32GB",uuid="GPU-a38af12e-e2f7-ee15-b064-4628cf1fc5da"} 0
# HELP nvidia_gpu_memory_total_bytes Total memory of the GPU device in bytes
# TYPE nvidia_gpu_memory_total_bytes gauge
nvidia_gpu_memory_total_bytes{minor_number="0",name="Tesla V100-SXM2-32GB",uuid="GPU-5481fdc1-1b2c-381d-90d9-2df35fc8cecf"} 3.4089730048e+10
nvidia_gpu_memory_total_bytes{minor_number="1",name="Tesla V100-SXM2-32GB",uuid="GPU-af66b351-1498-c103-f39e-7592b645dc80"} 3.4089730048e+10
nvidia_gpu_memory_total_bytes{minor_number="2",name="Tesla V100-SXM2-32GB",uuid="GPU-95887069-482a-9a95-d02a-7c6e79c47893"} 3.4089730048e+10
nvidia_gpu_memory_total_bytes{minor_number="3",name="Tesla V100-SXM2-32GB",uuid="GPU-a38af12e-e2f7-ee15-b064-4628cf1fc5da"} 3.4089730048e+10
# HELP nvidia_gpu_memory_used_bytes Memory used by the GPU device in bytes
# TYPE nvidia_gpu_memory_used_bytes gauge
nvidia_gpu_memory_used_bytes{minor_number="0",name="Tesla V100-SXM2-32GB",uuid="GPU-5481fdc1-1b2c-381d-90d9-2df35fc8cecf"} 4.470079488e+09
nvidia_gpu_memory_used_bytes{minor_number="1",name="Tesla V100-SXM2-32GB",uuid="GPU-af66b351-1498-c103-f39e-7592b645dc80"} 2.588934144e+09
nvidia_gpu_memory_used_bytes{minor_number="2",name="Tesla V100-SXM2-32GB",uuid="GPU-95887069-482a-9a95-d02a-7c6e79c47893"} 0
nvidia_gpu_memory_used_bytes{minor_number="3",name="Tesla V100-SXM2-32GB",uuid="GPU-a38af12e-e2f7-ee15-b064-4628cf1fc5da"} 5.640290304e+09
# HELP nvidia_gpu_num_devices Number of GPU devices
# TYPE nvidia_gpu_num_devices gauge
nvidia_gpu_num_devices 4
# HELP nvidia_gpu_power_usage_milliwatts Power usage of the GPU device in milliwatts
# TYPE nvidia_gpu_power_usage_milliwatts gauge
nvidia_gpu_power_usage_milliwatts{minor_number="0",name="Tesla V100-SXM2-32GB",uuid="GPU-5481fdc1-1b2c-381d-90d9-2df35fc8cecf"} 68088
nvidia_gpu_power_usage_milliwatts{minor_number="1",name="Tesla V100-SXM2-32GB",uuid="GPU-af66b351-1498-c103-f39e-7592b645dc80"} 56426
nvidia_gpu_power_usage_milliwatts{minor_number="2",name="Tesla V100-SXM2-32GB",uuid="GPU-95887069-482a-9a95-d02a-7c6e79c47893"} 38826
nvidia_gpu_power_usage_milliwatts{minor_number="3",name="Tesla V100-SXM2-32GB",uuid="GPU-a38af12e-e2f7-ee15-b064-4628cf1fc5da"} 71068
# HELP nvidia_gpu_temperature_celsius Temperature of the GPU device in celsius
# TYPE nvidia_gpu_temperature_celsius gauge
nvidia_gpu_temperature_celsius{minor_number="0",name="Tesla V100-SXM2-32GB",uuid="GPU-5481fdc1-1b2c-381d-90d9-2df35fc8cecf"} 45
nvidia_gpu_temperature_celsius{minor_number="1",name="Tesla V100-SXM2-32GB",uuid="GPU-af66b351-1498-c103-f39e-7592b645dc80"} 46
nvidia_gpu_temperature_celsius{minor_number="2",name="Tesla V100-SXM2-32GB",uuid="GPU-95887069-482a-9a95-d02a-7c6e79c47893"} 37
nvidia_gpu_temperature_celsius{minor_number="3",name="Tesla V100-SXM2-32GB",uuid="GPU-a38af12e-e2f7-ee15-b064-4628cf1fc5da"} 51
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# HELP process_open_fds Number of open file descriptors.
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# HELP process_resident_memory_bytes Resident memory size in bytes.
# TYPE process_resident_memory_bytes gauge
process_resident_memory_bytes 1.6646144e+07
# HELP process_start_time_seconds Start time of the process since unix epoch in seconds.
# TYPE process_start_time_seconds gauge
process_start_time_seconds 1.63059385687e+09
# HELP process_virtual_memory_bytes Virtual memory size in bytes.
# TYPE process_virtual_memory_bytes gauge
process_virtual_memory_bytes 1.264910336e+09
# HELP process_virtual_memory_max_bytes Maximum amount of virtual memory available in bytes.
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process_virtual_memory_max_bytes 1.8446744073709552e+19
# HELP promhttp_metric_handler_requests_in_flight Current number of scrapes being served.
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promhttp_metric_handler_requests_in_flight 1
# HELP promhttp_metric_handler_requests_total Total number of scrapes by HTTP status code.
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promhttp_metric_handler_requests_total{code="500"} 0
promhttp_metric_handler_requests_total{code="503"} 0
prometheus grafana nvidia ppc64le
1个回答
0
投票

根据您共享的指标，以下是将为您提供有关 GPU 利用率的信息的指标：

nvidia_gpu_duty_cycle
- 过去采样周期内一个或多个内核在 GPU 设备上执行的时间百分比

nvidia_gpu_memory_total_bytes
- GPU 设备可用的总内存（以字节为单位）

nvidia_gpu_memory_used_bytes
- GPU 设备使用的内存（以字节为单位）

nvidia_gpu_num_devices
- GPU 设备数量

nvidia_gpu_power_usage_milliwatts
- GPU 设备的功耗（以毫瓦为单位）

nvidia_gpu_temperature_celsius
- GPU 设备的温度（摄氏度）

从 Prometheus UI 或以 Prometheus 作为数据源的 Grafana，这些值可用于查询表达式中以检索关联的 GPU 指标。例如，如果您要执行像 

nvidia_gpu_memory_total_bytes
这样的简单查询，它将返回与此指标名称匹配的所有时间序列。

另请注意，您共享的指标包含上述每个值的 4 个条目，每个条目对应每个可用的 GPU 设备，编号为 

0-3
。如果您只想查询特定设备的指标，假设#2，您的查询需要如下所示：
nvidia_gpu_memory_total_bytes{minor_number="2"}
。请记下每个指标名称后面的 
{}
之间的各种逗号分隔标签，因为它们可用于根据您的喜好过滤查询。有关 Prometheus 查询的更多信息此处。

对于 

DCGM
本身，您可以使用官方 github repo 中的源代码专门为 PPC64IE 构建 Docker 镜像。 说明将首先让您创建一个单独的 Docker 映像，该映像将用于生成 DCGM 构建。 生成 DCGM 版本时，在执行 
--arch ppc
脚本以定位 PPC64IE 时，您需要包含 
./build.sh
选项。

对于 

dcgm-exporter
(github)，NVIDIA 在其 Docker Hub 存储库 上提供了许多预构建的映像，并可在此处找到官方文档。

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