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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
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* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
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* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
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Refactor packages to fix split package issues (#411) * Refactor nd4j-common: org.nd4j.* -> org.nd4j.common.* Signed-off-by: Alex Black <blacka101@gmail.com> * Fix CUDA (missed nd4j-common package refactoring changes) Signed-off-by: Alex Black <blacka101@gmail.com> * nd4j-kryo: org.nd4j -> org.nd4j.kryo Signed-off-by: Alex Black <blacka101@gmail.com> * Fix nd4j-common for deeplearning4j-cuda Signed-off-by: Alex Black <blacka101@gmail.com> * nd4j-grppc-client: org.nd4j.graph -> org.nd4j.remote.grpc Signed-off-by: Alex Black <blacka101@gmail.com> * deeplearning4j-common: org.deeplearning4.* -> org.deeplearning4j.common.* Signed-off-by: Alex Black <blacka101@gmail.com> * deeplearning4j-core: org.deeplearning4j.* -> org.deeplearning.core.* Signed-off-by: Alex Black <blacka101@gmail.com> * deeplearning4j-cuda: org.deeplearning4j.nn.layers.* -> org.deeplearning4j.cuda.* Signed-off-by: Alex Black <blacka101@gmail.com> * Import fixes Signed-off-by: Alex Black <blacka101@gmail.com> * deeplearning4j-nlp-*: org.deeplearning4.text.* -> org.deeplearning4j.nlp.(language).* Signed-off-by: Alex Black <blacka101@gmail.com> * deeplearning4j-ui-model: org.deeplearning4j.ui -> org.deeplearning4j.ui.model Signed-off-by: Alex Black <blacka101@gmail.com> * datavec-spark-inference-{server/model/client}: org.datavec.spark.transform -> org.datavec.spark.inference.{server/model/client} Signed-off-by: Alex Black <blacka101@gmail.com> * datavec-jdbc: org.datavec.api -> org.datavec.jdbc Signed-off-by: Alex Black <blacka101@gmail.com> * Delete org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter in favor of (essentially identical) org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter Signed-off-by: Alex Black <blacka101@gmail.com> * ND4S fixes Signed-off-by: Alex Black <blacka101@gmail.com> * Fixes Signed-off-by: Alex Black <blacka101@gmail.com> * nd4j-common-tests: org.nd4j.* -> org.nd4j.common.tests Signed-off-by: Alex Black <blacka101@gmail.com> * Trigger CI Signed-off-by: Alex Black <blacka101@gmail.com> * Fixes Signed-off-by: Alex Black <blacka101@gmail.com> * #8878 Ignore CUDA tests on modules with 'nd4j-native under cuda' issue Signed-off-by: Alex Black <blacka101@gmail.com> * Fix bad imports in tests Signed-off-by: Alex Black <blacka101@gmail.com> * Add ignore on test (already failing) due to #8882 Signed-off-by: Alex Black <blacka101@gmail.com> * Import fixes Signed-off-by: Alex Black <blacka101@gmail.com> * Additional import fixes Signed-off-by: Alex Black <blacka101@gmail.com>
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package org.deeplearning4j.core.listener;
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import lombok.*;
import org.nd4j.linalg.api.environment.Nd4jEnvironment;
import org.nd4j.linalg.api.ops.performance.PerformanceTracker;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.api.memory.MemcpyDirection;
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import org.nd4j.shade.jackson.databind.ObjectMapper;
import org.nd4j.shade.jackson.dataformat.yaml.YAMLFactory;
import oshi.json.SystemInfo;
import oshi.json.hardware.CentralProcessor;
import oshi.json.hardware.GlobalMemory;
import oshi.json.hardware.HWDiskStore;
import oshi.json.software.os.NetworkParams;
import oshi.util.Util;
import java.io.IOException;
import java.io.Serializable;
import java.util.*;
@Builder
@Data
@AllArgsConstructor
public class HardwareMetric implements Serializable {
private static ObjectMapper yamlMapper = new ObjectMapper(new YAMLFactory());
private Map<Integer,DeviceMetric> perCoreMetrics;
private long physicalProcessorCount,logicalProcessorCount;
private long currentMemoryUse;
private Map<Integer,DeviceMetric> gpuMetrics;
private String hostName;
private long ioWaitTime;
private long averagedCpuLoad;
private Map<Integer,DiskInfo> diskInfo;
private String name;
private HardwareMetric(){
//No-arg for JSON/YAML
}
/**
* Runs {@link #fromSystem(SystemInfo)}
* with a fresh {@link SystemInfo}
* @return the hardware metric based on
* the current snapshot of the system this
* runs on
*/
public static HardwareMetric fromSystem() {
return fromSystem(new SystemInfo());
}
/**
* Returns the relevant information
* needed for system diagnostics
* based on the {@link SystemInfo}
* @param systemInfo the system info to use
* @return the {@link HardwareMetric} for the
* system this process runs on
*/
public static HardwareMetric fromSystem(SystemInfo systemInfo) {
return fromSystem(systemInfo,UUID.randomUUID().toString());
}
/**
* Returns the relevant information
* needed for system diagnostics
* based on the {@link SystemInfo}
* @param systemInfo the system info to use
* @return the {@link HardwareMetric} for the
* system this process runs on
*/
public static HardwareMetric fromSystem(SystemInfo systemInfo,String name) {
HardwareMetricBuilder builder = HardwareMetric.builder();
CentralProcessor processor = systemInfo.getHardware().getProcessor();
long[] prevTicks = processor.getSystemCpuLoadTicks();
// Wait a second...
Util.sleep(1000);
long[] ticks = processor.getSystemCpuLoadTicks();
long iowait = ticks[oshi.hardware.CentralProcessor.TickType.IOWAIT.getIndex()] - prevTicks[oshi.hardware.CentralProcessor.TickType.IOWAIT.getIndex()];
GlobalMemory globalMemory = systemInfo.getHardware().getMemory();
NetworkParams networkParams = systemInfo.getOperatingSystem().getNetworkParams();
double[] processorCpuLoadBetweenTicks = processor.getProcessorCpuLoadBetweenTicks();
Map<Integer,DeviceMetric> cpuMetrics = new LinkedHashMap<>();
for(int i = 0; i < processorCpuLoadBetweenTicks.length; i++) {
cpuMetrics.put(i, DeviceMetric.builder()
.load(processorCpuLoadBetweenTicks[i]).
build());
}
Map<Integer,DiskInfo> diskInfoMap = new LinkedHashMap<>();
HWDiskStore[] diskStores = systemInfo.getHardware().getDiskStores();
for(int i = 0; i < diskStores.length; i++) {
HWDiskStore diskStore = diskStores[i];
DiskInfo diskInfo = DiskInfo.builder()
.bytesRead(diskStore.getReadBytes())
.bytesWritten(diskStore.getWriteBytes())
.name(diskStore.getName())
.modelName(diskStore.getModel())
.transferTime(diskStore.getTransferTime())
.build();
diskInfoMap.put(i,diskInfo);
}
Map<Integer,DeviceMetric> gpuMetric = new HashMap<>();
if(Nd4j.getBackend().getClass().getName().toLowerCase().contains("cublas")) {
Properties info = Nd4j.getExecutioner().getEnvironmentInformation();
/**
*
*/
List<Map<String, Object>> devicesList = (List<Map<String, Object>>) info.get(Nd4jEnvironment.CUDA_DEVICE_INFORMATION_KEY);
for(int i = 0; i < devicesList.size(); i++) {
double available = Double.parseDouble(devicesList.get(i).get(Nd4jEnvironment.CUDA_FREE_MEMORY_KEY).toString());
Map<MemcpyDirection, Long> memcpyDirectionLongMap = PerformanceTracker.getInstance().getCurrentBandwidth().get(i);
DeviceMetric deviceMetric = DeviceMetric.builder()
.bandwidthHostToDevice(memcpyDirectionLongMap.get(MemcpyDirection.HOST_TO_DEVICE))
.bandwidthDeviceToHost(memcpyDirectionLongMap.get(MemcpyDirection.DEVICE_TO_HOST))
.bandwidthDeviceToDevice(memcpyDirectionLongMap.get(MemcpyDirection.DEVICE_TO_DEVICE))
.memAvailable(available).totalMemory(Double.parseDouble(devicesList.get(i).get(Nd4jEnvironment.CUDA_TOTAL_MEMORY_KEY).toString()))
.deviceName(devicesList.get(i).get(Nd4jEnvironment.CUDA_DEVICE_NAME_KEY).toString())
.build();
gpuMetric.put(i,deviceMetric);
}
}
return builder.logicalProcessorCount(processor.getLogicalProcessorCount())
.physicalProcessorCount(processor.getPhysicalProcessorCount())
.name(name)
Merge master to upstream (#7945) * Shugeo strided slice zeros (#14) * Modified strided_slice op to properly work with empty-like shapes. * Fixed test for reduce_mean with empty-like input. * [WIP] Last merge (#15) * correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks * [WIP] Fixing outstanding issues for NLP (#9) * Avoid using not-inited objects * Test fixed. * Redundant method avoided for models like FastText * KMeans++ implementation * KMeans++ implementation * Disable parallel execution * KMeans++ * Tests * Dev branch merge (#16) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Fix some issues on master (#17) * Fix DataVec test issue * Fix issue with dl4j SameDiff output layer * Dtype fix for lambda layers * #7912 BertIterator dtype fix (use float32 not global default) * [WIP] Next set of CUDA stuff (#7) New CUDA implementations and improvements * bad file * Dev branch master merge (#23) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * SameDiff ops, TF import and fixes (#24) * CheckNumerics tests + fixes + misc fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fake quant Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * FakeQuantWithMinMaxArgs Signed-off-by: AlexDBlack <blacka101@gmail.com> * CheckNumerics fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Javadoc Signed-off-by: AlexDBlack <blacka101@gmail.com> * Exception tweak Signed-off-by: AlexDBlack <blacka101@gmail.com> * fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix for out of scope stack allocated var use Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignores Signed-off-by: AlexDBlack <blacka101@gmail.com> * Ignore for known failing test (already logged issue) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Merge upstream to fork (#25) * Add thousand-separator commas to TotalParams (#7915) * Add thousand-separator commas to TotalParams The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them. * Add thousand-separator commas to MultiLayerNetwork Corresponding change to MultiLayerNetwork Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com> * Update contributing and issue/PR templates (#7934) Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix link to AdaDelta paper (#7942) Fix link to AdaDelta paper hosted on matthewzeiler.com Signed-off-by: Jxtps * Fixes, and ignores for known/logged failing issues (#7943) Signed-off-by: AlexDBlack <blacka101@gmail.com> * SameDiff + DL4J/SameDiff: Multiple fixes (#28) * #7919 HDF5 attribute buffer length fix Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7909 Arbiter constructor exception ux improvements Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7925 RNN output layer length checks Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Add listener for validating inputs are not incorrectly modified Signed-off-by: AlexDBlack <blacka101@gmail.com> * #7939 Integrate NonInplaceValidationListener into tests * #7844 DL4J SameDiff fixes for variable minibatch size * DL4J SameDiff fixes - ensure gradient for input placeholder is available Signed-off-by: AlexDBlack <blacka101@gmail.com> * Tweaks to ExternalErrorsFunction - use placeholders, make more robust * Another fix * More fixes * More SameDiff/DL4J fixes * Scope out scalar array creation in BaseScalarOp * Remove debug code Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] Final dev branch merge (#29) * SameDiff: convertDataType and gradient check util improvements (#12) * GradCheck util improvements * StopGradient constructor + test * SameDiff: Add datatype conversion * Javadoc and add DataType.isNumerical() * Small fix * Fix SameDiff TF import test cases intermediate naming (workaround for bad default) * TFGraphTestAllHelper: check intermediates in execution order * Add missing debug listener * [WIP] lstmBlock fix + other changes (#13) - fixes lstmBlock issue - changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer - CheckNumerics op - fixes for ReduceBool IsInfOrNan & IsFinite * Small test fix * CheckNumerics op wrapper * Compatibility of deserialization (#18) Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> * SameDiff: add activation gradient checking support for debugging (#19) * SameDiff gradient checker: first pass on activation gradient checks * Fixes + tests for activation gradient checking * Javadoc * [WIP] Some nd4j data type corrections (#20) * Adjust data type * Set correct Data type. * Size of proper data type. * fix averaged cpu load (#22) * [WIP] Multiple dataset iterators (#27) * Splitting dataset into arbitrary number * Fixes * Multiple split of iterator * Test * Test * Some fixes * signature change * one more tweak Signed-off-by: raver119 <raver119@gmail.com> * one more test for sequential use of DataSetIteratorSplitter Signed-off-by: raver119 <raver119@gmail.com> * Fixes * Fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * one more test for Alexander Signed-off-by: raver119 <raver119@gmail.com> * minor test fix Signed-off-by: raver119 <raver119@gmail.com> * Some fixes * Some fixes * couple of assertions tweaked Signed-off-by: raver119 <raver119@gmail.com> * MDS splitter test :/ Signed-off-by: raver119 <raver119@gmail.com> * Minor refactoring * Multi dataset * Some fixes * More tests * Small number of test fixes/improvements (failures on CI) (#31) Signed-off-by: AlexDBlack <blacka101@gmail.com> * [WIP] More CUDA stuff (#26) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * LRN BP CUDA Signed-off-by: raver119 <raver119@gmail.com> * less memory Signed-off-by: raver119 <raver119@gmail.com> * Fixed bug with crop_and_resize op helper. * get rid of unnecessary index-calculation dunction Signed-off-by: Yurii <yurii@skymind.io> * Fixed sort with nth_element cuda-based helper. * Refactored nth_element. * Refactored nth_element op and tests. * Modified usage of dim array with sortTad routine. * Refactored main routine of helper for non_max_image_suppression op. * non_max_image_suppression op helper with cuda kernel implementation. Initial revision. * fix vol2col cuda kernel * meh Signed-off-by: raver119 <raver119@gmail.com> * topK concept Signed-off-by: raver119 <raver119@gmail.com> * unsorted topK with scanWitdh of 1 Signed-off-by: raver119 <raver119@gmail.com> * correct vol2col tests * sorted/unsorted topK Signed-off-by: raver119 <raver119@gmail.com> * implementation and fixing col2im/col2vol * Corrected usage flags with input/output with reverse op. * dup is const now Signed-off-by: raver119 <raver119@gmail.com> * percentile op Signed-off-by: raver119 <raver119@gmail.com> * group tests for mapool2d Signed-off-by: Yurii <yurii@skymind.io> * special test for george Signed-off-by: raver119 <raver119@gmail.com> * less threads for sortTad Signed-off-by: raver119 <raver119@gmail.com> * provide conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * remove auther in sort tad kernel code Signed-off-by: Yurii <yurii@skymind.io> * provide depthwise_conv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * - max_pooling_with_argmax - null check for special use Signed-off-by: raver119 <raver119@gmail.com> * dts cuda Signed-off-by: raver119 <raver119@gmail.com> * provide sconv2d for cuda Signed-off-by: Yurii <yurii@skymind.io> * std cuda Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op to conform TF implementation. * Improved suppression helper. * provide pooling3d for cuda Signed-off-by: Yurii <yurii@skymind.io> * minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * more of minor lstm rearrangements Signed-off-by: raver119 <raver119@gmail.com> * (bi)dynamic_rnn Signed-off-by: raver119 <raver119@gmail.com> * templates init order Signed-off-by: raver119 <raver119@gmail.com> * Refactored non_max_suppression op. * Added cuda kernel for non_max_suppression. * CPU sort by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value Signed-off-by: raver119 <raver119@gmail.com> * CPU sort TAD by key/value tests Signed-off-by: raver119 <raver119@gmail.com> * Eliminate compiler error with cuda implementation. * - repaired gradCheck in cuda - provide conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * missed signature Signed-off-by: raver119 <raver119@gmail.com> * provide depthwise_conv2d_bp for cuda Signed-off-by: Yurii <yurii@skymind.io> * Implementation of lup helper with cuda kernel. Initial commit. * further work on backprops for convolutions Signed-off-by: Yurii <yurii@skymind.io> * CUDA linear sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * CUDA tad sort by key/val Signed-off-by: raver119 <raver119@gmail.com> * start providing of backprop for pooling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * Added atomicAdd for bool datatype. * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic partition scalar CUDA Signed-off-by: raver119 <raver119@gmail.com> * important comment Signed-off-by: raver119 <raver119@gmail.com> * fix pooling2d/3d backprop helpers Signed-off-by: Yurii <yurii@skymind.io> * Added non-linear test with dynamic_partition. * Improved test for dynamic_partition. * dynamic_partition TAD concept Signed-off-by: raver119 <raver119@gmail.com> * - dynamic_partition TAD CUDA impl - dynamic_partition TAD CPU fix Signed-off-by: raver119 <raver119@gmail.com> * - rewrite cpu code for usampling2d/3d - write cuda code for usampling2d/3d Signed-off-by: Yurii <yurii@skymind.io> * dynamic_stitch CUDA vector case Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case concept Signed-off-by: raver119 <raver119@gmail.com> * dynamic_stitch CUDA TAD case impl Signed-off-by: raver119 <raver119@gmail.com> * Added tests for dynamic_stitch 3D-4D cases. * minor tests tweaks Signed-off-by: raver119 <raver119@gmail.com> * Fixed type check for dynamic stitch. * min/max bp Signed-off-by: raver119 <raver119@gmail.com> * rewrite code for upsampling2d/3d cpu Signed-off-by: Yurii <yurii@skymind.io> * reduce min/max/norm_max bp Signed-off-by: raver119 <raver119@gmail.com> * lup implementation. Additional enhancements. * provide code for upsamling2d/3d backprop Signed-off-by: Yurii <yurii@skymind.io> * weightedCrossEntropyWithLogits Signed-off-by: raver119 <raver119@gmail.com> * Fixed template math atomicMul for 64bit ints. * Refactored dynamic_partition_bp op. * inverseBroadcast fix Signed-off-by: raver119 <raver119@gmail.com> * DynamicPartitionBP test datatype fixed. * - nd4j_atomicMul Windows fix - cpu/NDArrayLambda.hpp excluded from CUDA Signed-off-by: raver119 <raver119@gmail.com>
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.averagedCpuLoad((long)(processor.getSystemCpuLoad() * 100))
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.ioWaitTime(iowait).gpuMetrics(gpuMetric)
.hostName(networkParams.getHostName()).diskInfo(diskInfoMap)
.currentMemoryUse(globalMemory.getTotal() - globalMemory.getAvailable())
.perCoreMetrics(cpuMetrics)
.build();
}
public String toYaml(){
try {
return yamlMapper.writeValueAsString(this);
} catch (Exception e){
throw new RuntimeException(e);
}
}
public static HardwareMetric fromYaml(@NonNull String yaml){
try {
return yamlMapper.readValue(yaml, HardwareMetric.class);
} catch (IOException e){
throw new RuntimeException(e);
}
}
}