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Vendor Go bindings for NVML. Don't build a static binary.

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We can't build a static binary because that would require bundling the
closed source NVML library in cAdvisor.

Instead, gonvml uses dlopen to dynamically load NVML.
This commit is contained in:
Rohit Agarwal 2017-10-19 11:27:06 -07:00
parent 126fb2232e
commit 318f28bef6
8 changed files with 6162 additions and 1 deletions
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"ImportPath": "github.com/matttproud/golang_protobuf_extensions/pbutil",
"Rev": "fc2b8d3a73c4867e51861bbdd5ae3c1f0869dd6a"
},
{
"ImportPath": "github.com/mindprince/gonvml",
"Rev": "b49be04bdda2d9cd7544bb3a0bce7210ec3448c7"
},
{
"ImportPath": "github.com/mistifyio/go-zfs",
"Comment": "v2.1.1-31-g166dd29",

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@ -37,7 +37,6 @@ if [ "${go_version:0:3}" = "1.4" ]; then
fi
ldflags="
-extldflags '-static'
-X ${repo_path}/version.Version${ldseparator}${version}
-X ${repo_path}/version.Revision${ldseparator}${revision}
-X ${repo_path}/version.Branch${ldseparator}${branch}

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# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
PKG=github.com/mindprince/gonvml
.PHONY: build
build:
docker run -v $(shell pwd):/go/src/$(PKG) --workdir=/go/src/$(PKG) golang:1.8 go build cmd/example/example.go

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Copyright 1993-2016 NVIDIA Corporation. All rights reserved.
NOTICE TO USER:
This source code is subject to NVIDIA ownership rights under U.S. and
international Copyright laws. Users and possessors of this source code
are hereby granted a nonexclusive, royalty-free license to use this code
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NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH
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MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
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source code with only those rights set forth herein.
Any use of this source code in individual and commercial software must
include, in the user documentation and internal comments to the code,
the above Disclaimer and U.S. Government End Users Notice.

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Go Bindings for NVML
--------------------
[NVML or NVIDIA Management
Library](https://developer.nvidia.com/nvidia-management-library-nvml) is a
C-based API that can be used for monitoring NVIDIA GPU devices. It's closed
source but can be downloaded as part of the [GPU Deployment
Kit](https://developer.nvidia.com/gpu-deployment-kit).
The [NVML API
Reference](http://docs.nvidia.com/deploy/nvml-api/nvml-api-reference.html)
describe various methods that are available as part of NVML.
The `nvml.h` file is included in this repository so that we don't depend on
the presence of NVML in the build environment.
The `bindings.go` file is the cgo bridge which calls the NVML functions. The
cgo preamble in `bindings.go` uses `dlopen` to dynamically load NVML and makes
its functions available.

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/*
Copyright 2017 Google Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package gonvml
// #cgo LDFLAGS: -ldl -Wl,--unresolved-symbols=ignore-in-object-files
/*
#include <stddef.h>
#include <dlfcn.h>
#include <stdlib.h>
#include "nvml.h"
// nvmlHandle is the handle for dynamically loaded libnvidia-ml.so
void *nvmlHandle;
// Loads the "libnvidia-ml.so.1" shared library and initializes NVML.
// Call this before calling any other methods.
nvmlReturn_t nvmlInit_dl(void) {
nvmlHandle = dlopen("libnvidia-ml.so.1", RTLD_LAZY | RTLD_GLOBAL);
if (nvmlHandle == NULL) {
return (NVML_ERROR_LIBRARY_NOT_FOUND);
}
return (nvmlInit());
}
// Shuts down NVML and decrements the reference count on the dynamically loaded
// "libnvidia-ml.so.1" library.
// Call this once NVML is no longer being used.
nvmlReturn_t nvmlShutdown_dl(void) {
if (nvmlHandle == NULL) {
return NVML_SUCCESS;
}
nvmlReturn_t r = nvmlShutdown();
if (r != NVML_SUCCESS) {
return (r);
}
return (dlclose(nvmlHandle) ? NVML_ERROR_UNKNOWN : NVML_SUCCESS);
}
// This function is here because the API provided by NVML is not very user
// friendly. This function can be used to get average utilization.gpu or
// power.draw.
//
// `device`: The identifier of the target device.
// `type`: Type of sampling event. Only NVML_TOTAL_POWER_SAMPLES and NVML_GPU_UTILIZATION_SAMPLES are supported.
// `lastSeenTimeStamp`: Return average using samples with timestamp greather than this timestamp. Unix epoch in micro seconds.
// `averageUsage`: Reference in which average is returned.
//
// In my experiments, I found that NVML_GPU_UTILIZATION_SAMPLES buffer stores
// 100 samples that are uniformly spread with ~6 samples per second. So the
// buffer stores last ~16s of data.
// NVML_TOTAL_POWER_SAMPLES buffer stores 120 samples, but in different runs I
// noticed them to be non-uniformly separated. Sometimes 120 samples only
// consisted of 10s of data and sometimes they were spread over 60s.
//
nvmlReturn_t nvmlDeviceGetAverageUsage(nvmlDevice_t device, nvmlSamplingType_t type, unsigned long long lastSeenTimeStamp, unsigned int* averageUsage) {
if (nvmlHandle == NULL) {
return (NVML_ERROR_LIBRARY_NOT_FOUND);
}
// We don't really use this because both the metrics we support
// averagePowerUsage and averageGPUUtilization are unsigned int.
nvmlValueType_t sampleValType;
// This will be set to the number of samples that can be queried. We would
// need to allocate an array of this size to store the samples.
unsigned int sampleCount;
// Invoking this method with `samples` set to NULL sets the sampleCount.
nvmlReturn_t r = nvmlDeviceGetSamples(device, type, lastSeenTimeStamp, &sampleValType, &sampleCount, NULL);
if (r != NVML_SUCCESS) {
return (r);
}
// Allocate memory to store sampleCount samples.
// In my experiments, the sampleCount at this stage was always 120 for
// NVML_TOTAL_POWER_SAMPLES and 100 for NVML_GPU_UTILIZATION_SAMPLES
nvmlSample_t* samples = (nvmlSample_t*) malloc(sampleCount * sizeof(nvmlSample_t));
r = nvmlDeviceGetSamples(device, type, lastSeenTimeStamp, &sampleValType, &sampleCount, samples);
if (r != NVML_SUCCESS) {
free(samples);
return (r);
}
int i = 0;
unsigned int sum = 0;
for (; i < sampleCount; i++) {
sum += samples[i].sampleValue.uiVal;
}
*averageUsage = sum/sampleCount;
free(samples);
return (r);
}
*/
import "C"
import (
"errors"
"fmt"
"time"
)
const (
szDriver = C.NVML_SYSTEM_DRIVER_VERSION_BUFFER_SIZE
szName = C.NVML_DEVICE_NAME_BUFFER_SIZE
szUUID = C.NVML_DEVICE_UUID_BUFFER_SIZE
)
var errLibraryNotLoaded = errors.New("could not load NVML library")
// Initialize initializes NVML.
// Call this before calling any other methods.
func Initialize() error {
return errorString(C.nvmlInit_dl())
}
// Shutdown shuts down NVML.
// Call this once NVML is no longer being used.
func Shutdown() error {
return errorString(C.nvmlShutdown_dl())
}
// errorString takes a nvmlReturn_t and converts it into a golang error.
// It uses a nvml method to convert to a user friendly error message.
func errorString(ret C.nvmlReturn_t) error {
if ret == C.NVML_SUCCESS {
return nil
}
// We need to special case this because if nvml library is not found
// nvmlErrorString() method will not work.
if ret == C.NVML_ERROR_LIBRARY_NOT_FOUND || C.nvmlHandle == nil {
return errLibraryNotLoaded
}
err := C.GoString(C.nvmlErrorString(ret))
return fmt.Errorf("nvml: %v", err)
}
// SystemDriverVersion returns the the driver version on the system.
func SystemDriverVersion() (string, error) {
if C.nvmlHandle == nil {
return "", errLibraryNotLoaded
}
var driver [szDriver]C.char
r := C.nvmlSystemGetDriverVersion(&driver[0], szDriver)
return C.GoString(&driver[0]), errorString(r)
}
// DeviceCount returns the number of nvidia devices on the system.
func DeviceCount() (uint, error) {
if C.nvmlHandle == nil {
return 0, errLibraryNotLoaded
}
var n C.uint
r := C.nvmlDeviceGetCount(&n)
return uint(n), errorString(r)
}
// Device is the handle for the device.
// This handle is obtained by calling DeviceHandleByIndex().
type Device struct {
dev C.nvmlDevice_t
}
// DeviceHandleByIndex returns the device handle for a particular index.
// The indices range from 0 to DeviceCount()-1. The order in which NVML
// enumerates devices has no guarantees of consistency between reboots.
func DeviceHandleByIndex(idx uint) (Device, error) {
if C.nvmlHandle == nil {
return Device{}, errLibraryNotLoaded
}
var dev C.nvmlDevice_t
r := C.nvmlDeviceGetHandleByIndex(C.uint(idx), &dev)
return Device{dev}, errorString(r)
}
// MinorNumber returns the minor number for the device.
// The minor number for the device is such that the Nvidia device node
// file for each GPU will have the form /dev/nvidia[minor number].
func (d Device) MinorNumber() (uint, error) {
if C.nvmlHandle == nil {
return 0, errLibraryNotLoaded
}
var n C.uint
r := C.nvmlDeviceGetMinorNumber(d.dev, &n)
return uint(n), errorString(r)
}
// UUID returns the globally unique immutable UUID associated with this device.
func (d Device) UUID() (string, error) {
if C.nvmlHandle == nil {
return "", errLibraryNotLoaded
}
var uuid [szUUID]C.char
r := C.nvmlDeviceGetUUID(d.dev, &uuid[0], szUUID)
return C.GoString(&uuid[0]), errorString(r)
}
// Name returns the product name of the device.
func (d Device) Name() (string, error) {
if C.nvmlHandle == nil {
return "", errLibraryNotLoaded
}
var name [szName]C.char
r := C.nvmlDeviceGetName(d.dev, &name[0], szName)
return C.GoString(&name[0]), errorString(r)
}
// MemoryInfo returns the total and used memory (in bytes) of the device.
func (d Device) MemoryInfo() (uint64, uint64, error) {
if C.nvmlHandle == nil {
return 0, 0, errLibraryNotLoaded
}
var memory C.nvmlMemory_t
r := C.nvmlDeviceGetMemoryInfo(d.dev, &memory)
return uint64(memory.total), uint64(memory.used), errorString(r)
}
// UtilizationRates returns the percent of time over the past sample period during which:
// utilization.gpu: one or more kernels were executing on the GPU.
// utilizatoin.memory: global (device) memory was being read or written.
func (d Device) UtilizationRates() (uint, uint, error) {
if C.nvmlHandle == nil {
return 0, 0, errLibraryNotLoaded
}
var utilization C.nvmlUtilization_t
r := C.nvmlDeviceGetUtilizationRates(d.dev, &utilization)
return uint(utilization.gpu), uint(utilization.memory), errorString(r)
}
// PowerUsage returns the power usage for this GPU and its associated circuitry
// in milliwatts. The reading is accurate to within +/- 5% of current power draw.
func (d Device) PowerUsage() (uint, error) {
if C.nvmlHandle == nil {
return 0, errLibraryNotLoaded
}
var n C.uint
r := C.nvmlDeviceGetPowerUsage(d.dev, &n)
return uint(n), errorString(r)
}
// AveragePowerUsage returns the power usage for this GPU and its associated circuitry
// in milliwatts averaged over the samples collected in the last `since` duration.
func (d Device) AveragePowerUsage(since time.Duration) (uint, error) {
if C.nvmlHandle == nil {
return 0, errLibraryNotLoaded
}
lastTs := C.ulonglong(time.Now().Add(-1*since).UnixNano() / 1000)
var n C.uint
r := C.nvmlDeviceGetAverageUsage(d.dev, C.NVML_TOTAL_POWER_SAMPLES, lastTs, &n)
return uint(n), errorString(r)
}
// AverageGPUUtilization returns the utilization.gpu metric (percent of time
// one of more kernels were executing on the GPU) averaged over the samples
// collected in the last `since` duration.
func (d Device) AverageGPUUtilization(since time.Duration) (uint, error) {
if C.nvmlHandle == nil {
return 0, errLibraryNotLoaded
}
lastTs := C.ulonglong(time.Now().Add(-1*since).UnixNano() / 1000)
var n C.uint
r := C.nvmlDeviceGetAverageUsage(d.dev, C.NVML_GPU_UTILIZATION_SAMPLES, lastTs, &n)
return uint(n), errorString(r)
}

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