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fix(monitoring): report NVML metrics for the GPUs visible to CUDA - #165
NvmlMetricProvider opens a handle for every GPU in the machine (nvmlDeviceGetHandleByIndex(i) for i in range(nvmlDeviceGetCount())) and stores the i-th one as cuda:{i}. NVML ignores CUDA_VISIBLE_DEVICES, so whenever that variable is set, the cuda:N memory, utilization, and power columns describe other GPUs than the ones the module runs on.
Example: with CUDA_VISIBLE_DEVICES=4 on an 8-GPU node, the module runs on physical GPU 4, which is cuda:0 for PyTorch. The hardware metrics summary reports eight GPUs, and its cuda:0 column shows physical GPU 0. On a shared node, that is another job's GPU. This affects the common one-GPU-per-process launch.
Fix
Select the NVML handles from CUDA_VISIBLE_DEVICES the same way the CUDA driver parses it:
entries are device indices or (unique prefixes of) GPU- UUIDs, and the first entry decides which;
parsing stops at the first invalid entry: out of range, ambiguous, or of the other kind;
a repeated entry hides every GPU.
Index entries assume that CUDA enumerates GPUs in PCI bus order, as NVML does. This holds for identical GPUs or with CUDA_DEVICE_ORDER=PCI_BUS_ID. On mixed-GPU nodes with the default FASTEST_FIRST order, index entries can still map to the wrong GPU, as the unset case already does on main; UUID entries are always exact.
When the variable is unset, behavior is unchanged. The mapping uses NVML only and does not initialize CUDA. MIG- entries are not resolved, so no NVML GPU metrics are reported for them.
Verification
New CPU-only unit tests with a fake 8-GPU NVML cover the parsing cases and check that cuda:N keys report the N-th visible GPU. The existing GPU test now also asserts that the number of reported GPUs equals torch.cuda.device_count().
On an 8×B300 node I compared the selected GPUs against the CUDA driver (cuInit + cuDeviceGetCount + cuDeviceGetUuid_v2, no context created) for 15 CUDA_VISIBLE_DEVICES values. On main, all 14 values that set the variable disagree with the driver. With this change, all 15 values match.
CUDA_VISIBLE_DEVICES
driver
main
this PR
unset
0–7
0–7
0–7
4
4
0–7
4
6,4
6, 4
0–7
6, 4
GPU-<uuid6>
6
0–7
6
GPU-<prefix7>,GPU-<uuid4>
7, 4
0–7
7, 4
4,9,5
4
0–7
4
4,GPU-<uuid5>
4
0–7
4
GPU-<uuid6>,4
6
0–7
6
4,4
none (cuInit = 101)
0–7
none
-1, empty
none (cuInit = 100)
0–7
none
pytest tests/unit/utils/monitoring/ with CUDA_VISIBLE_DEVICES=6,4: 91 passed. pre-commit passes on the changed files. pyrefly reports no new errors; its 3 errors in examples/LLM/llm/benchmark.py are also present on main.
Summary by CodeRabbit
Bug Fixes
GPU memory, utilization, and power metrics now correspond to GPUs visible through CUDA_VISIBLE_DEVICES, in the same order CUDA uses. When the variable is unset, metrics include all available GPUs.
GPU metrics use visible-device numbering, so their cuda:N labels align with the device indices seen by the application.
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Inline comments:
Review comments at @aitune/utils/monitoring/hardware_metrics/metrics.py:
- Around line 63-64: Update the device mapping logic around the
entry.isdecimal() check so a CUDA device index is not used directly as an NVML
index. Match CUDA and NVML devices using a stable shared identity, such as UUID
or PCI bus ID, before assigning cuda:N labels; establish which identity is
available in this flow and preserve the correct physical GPU mapping when
enumeration orders differ.
Walkthrough
The NVML metric provider now selects GPUs according to CUDA_VISIBLE_DEVICES order. It supports decimal device indices and UUID prefixes, and labels metrics with visible-device indices. The changelog and tests document and verify the change.
Changes: CUDA-visible GPU metrics
Layer / File(s)
Summary
Resolve visible GPU indices aitune/utils/monitoring/hardware_metrics/metrics.py, tests/unit/utils/monitoring/hardware_metrics/test_metrics.py
The resolver handles unset visibility, decimal indices, UUID prefixes, invalid entries, duplicates, and byte-valued UUIDs. Tests verify selected-device ordering.
Use visible indices for metrics aitune/utils/monitoring/hardware_metrics/metrics.py, tests/unit/utils/monitoring/hardware_metrics/test_metrics.py, CHANGELOG.md
NvmlMetricProvider builds handles for resolved devices. Tests verify that metric labels follow visible-device indices and that the handle count matches PyTorch’s CUDA device count. The changelog describes the indexing change.
GPU metrics can be attributed to the wrong device on mixed-GPU nodes. Resolve the numeric-index mapping before merging unless that limitation is explicitly accepted.
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The title clearly and concisely describes the main change: reporting NVML metrics for GPUs visible to CUDA.
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Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
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Summary
NvmlMetricProvideropens a handle for every GPU in the machine (nvmlDeviceGetHandleByIndex(i) for i in range(nvmlDeviceGetCount())) and stores the i-th one ascuda:{i}. NVML ignoresCUDA_VISIBLE_DEVICES, so whenever that variable is set, thecuda:Nmemory, utilization, and power columns describe other GPUs than the ones the module runs on.Example: with
CUDA_VISIBLE_DEVICES=4on an 8-GPU node, the module runs on physical GPU 4, which iscuda:0for PyTorch. The hardware metrics summary reports eight GPUs, and itscuda:0column shows physical GPU 0. On a shared node, that is another job's GPU. This affects the common one-GPU-per-process launch.Fix
Select the NVML handles from
CUDA_VISIBLE_DEVICESthe same way the CUDA driver parses it:GPU-UUIDs, and the first entry decides which;Index entries assume that CUDA enumerates GPUs in PCI bus order, as NVML does. This holds for identical GPUs or with
CUDA_DEVICE_ORDER=PCI_BUS_ID. On mixed-GPU nodes with the defaultFASTEST_FIRSTorder, index entries can still map to the wrong GPU, as the unset case already does onmain; UUID entries are always exact.When the variable is unset, behavior is unchanged. The mapping uses NVML only and does not initialize CUDA.
MIG-entries are not resolved, so no NVML GPU metrics are reported for them.Verification
New CPU-only unit tests with a fake 8-GPU NVML cover the parsing cases and check that
cuda:Nkeys report the N-th visible GPU. The existing GPU test now also asserts that the number of reported GPUs equalstorch.cuda.device_count().On an 8×B300 node I compared the selected GPUs against the CUDA driver (
cuInit+cuDeviceGetCount+cuDeviceGetUuid_v2, no context created) for 15CUDA_VISIBLE_DEVICESvalues. Onmain, all 14 values that set the variable disagree with the driver. With this change, all 15 values match.CUDA_VISIBLE_DEVICESmain46,4GPU-<uuid6>GPU-<prefix7>,GPU-<uuid4>4,9,54,GPU-<uuid5>GPU-<uuid6>,44,4cuInit= 101)-1, emptycuInit= 100)pytest tests/unit/utils/monitoring/withCUDA_VISIBLE_DEVICES=6,4: 91 passed. pre-commit passes on the changed files. pyrefly reports no new errors; its 3 errors inexamples/LLM/llm/benchmark.pyare also present onmain.Summary by CodeRabbit
CUDA_VISIBLE_DEVICES, in the same order CUDA uses. When the variable is unset, metrics include all available GPUs.cuda:Nlabels align with the device indices seen by the application.