【Elasticsearch】-图片向量化存储
需要结合深度学习模型
1、pom依赖
注意结尾的webp-imageio 包,用于解决ImageIO.read读取部分图片返回为null的问题
<dependency>
<groupId>org.openpnp</groupId>
<artifactId>opencv</artifactId>
<version>4.7.0-0</version>
</dependency>
<dependency>
<groupId>com.microsoft.onnxruntime</groupId>
<artifactId>onnxruntime</artifactId>
<version>1.17.1</version>
</dependency>
<!-- 服务器端推理引擎 -->
<dependency>
<groupId>ai.djl</groupId>
<artifactId>api</artifactId>
<version>${djl.version}</version>
</dependency>
<dependency>
<groupId>ai.djl</groupId>
<artifactId>basicdataset</artifactId>
<version>${djl.version}</version>
</dependency>
<dependency>
<groupId>ai.djl</groupId>
<artifactId>model-zoo</artifactId>
<version>${djl.version}</version>
</dependency>
<!-- Pytorch -->
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-engine</artifactId>
<version>${djl.version}</version>
</dependency>
<dependency>
<groupId>ai.djl.pytorch</groupId>
<artifactId>pytorch-model-zoo</artifactId>
<version>${djl.version}</version>
</dependency>
<!-- ONNX -->
<dependency>
<groupId>ai.djl.onnxruntime</groupId>
<artifactId>onnxruntime-engine</artifactId>
<version>${djl.version}</version>
</dependency>
<!-- 解决ImageIO.read 读取为null -->
<dependency>
<groupId>org.sejda.imageio</groupId>
<artifactId>webp-imageio</artifactId>
<version>0.1.6</version>
</dependency>
2、加载模型
注意提前设置环境变量,pytorch依赖环境dll文件,如果不存在,则默认下载
System.setProperty("ENGINE_CACHE_DIR", modelPath);
import ai.djl.Device;
import ai.djl.modality.cv.Image;
import ai.djl.repository.zoo.Criteria;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.Translator;
public Criteria<Image, T> criteria() {
Translator<Image, T> translator = getTranslator(arguments);
try {
JarFileUtils.copyFileFromJar("/onnx/models/" + modelName, PathConstants.ONNX, null, false, true);
} catch (IOException e) {
throw new RuntimeException(e);
}
// String model_path = PathConstants.TEMP_DIR + PathConstants.ONNX + "/" + modelName;
String modelPath = PathConstants.TEMP_DIR + File.separator+PathConstants.ONNX_NAME+ File.separator + modelName;
log.info("路径修改前:{}",modelPath);
modelPath= DjlHandlerUtil.getFixedModelPath(modelPath);
log.info("路径修改后:{}",modelPath);
Criteria<Image, T> criteria =
Criteria.builder()
.setTypes(Image.class, getClassOfT())
.optModelUrls(modelPath)
.optTranslator(translator)
.optDevice(Device.cpu())
.optEngine(getEngine()) // Use PyTorch engine
.optProgress(new ProgressBar())
.build();
return criteria;
}
protected Translator<Image, float[]> getTranslator(Map<String, Object> arguments) {
BaseImageTranslator.BaseBuilder<?> builder=new BaseImageTranslator.BaseBuilder<BaseImageTranslator.BaseBuilder>() {
@Override
protected BaseImageTranslator.BaseBuilder self() {
return this;
}
};
return new BaseImageTranslator<float[]>(builder) {
@Override
public float[] processOutput(TranslatorContext translatorContext, NDList ndList) throws Exception {
return ndList.get(0).toFloatArray();
}
};
}
3、FaceFeatureTranslator
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.translate.Batchifier;
import ai.djl.translate.Pipeline;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
/**
* @author gc.x
* @date 2022-04
*/
public final class FaceFeatureTranslator implements Translator<Image, float[]> {
public FaceFeatureTranslator() {
}
@Override
public NDList processInput(TranslatorContext ctx, Image input) {
NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
Pipeline pipeline = new Pipeline();
pipeline
// .add(new Resize(160))
.add(new ToTensor())
.add(
new Normalize(
new float[]{127.5f / 255.0f, 127.5f / 255.0f, 127.5f / 255.0f},
new float[]{128.0f / 255.0f, 128.0f / 255.0f, 128.0f / 255.0f}));
return pipeline.transform(new NDList(array));
}
@Override
public float[] processOutput(TranslatorContext ctx, NDList list) {
NDList result = new NDList();
long numOutputs = list.singletonOrThrow().getShape().get(0);
for (int i = 0; i < numOutputs; i++) {
result.add(list.singletonOrThrow().get(i));
}
float[][] embeddings = result.stream().map(NDArray::toFloatArray).toArray(float[][]::new);
float[] feature = new float[embeddings.length];
for (int i = 0; i < embeddings.length; i++) {
feature[i] = embeddings[i][0];
}
return feature;
}
@Override
public Batchifier getBatchifier() {
return Batchifier.STACK;
}
}
4、BaseImageTranslator
import ai.djl.Model;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.transform.CenterCrop;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.Resize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.modality.cv.util.NDImageUtils;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.translate.*;
import ai.djl.util.Utils;
import java.io.IOException;
import java.io.InputStream;
import java.net.MalformedURLException;
import java.net.URL;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
public abstract class BaseImageTranslator<T> implements Translator<Image, T> {
private static final float[] MEAN = {0.485f, 0.456f, 0.406f};
private static final float[] STD = {0.229f, 0.224f, 0.225f};
private Image.Flag flag;
private Pipeline pipeline;
private Batchifier batchifier;
/**
* Constructs an ImageTranslator with the provided builder.
*
* @param builder the data to build with
*/
public BaseImageTranslator(BaseBuilder<?> builder) {
flag = builder.flag;
pipeline = builder.pipeline;
batchifier = builder.batchifier;
}
/** {@inheritDoc} */
@Override
public Batchifier getBatchifier() {
return batchifier;
}
/**
* Processes the {@link Image} input and converts it to NDList.
*
* @param ctx the toolkit that helps create the input NDArray
* @param input the {@link Image} input
* @return a {@link NDList}
*/
@Override
public NDList processInput(TranslatorContext ctx, Image input) {
NDArray array = input.toNDArray(ctx.getNDManager(), flag);
array = NDImageUtils.resize(array, 640, 640);
array = array.transpose(2, 0, 1); // HWC -> CHW RGB -> BGR
// array = array.expandDims(0);
array = array.div(255f);
return new NDList(array);
// return pipeline.transform(new NDList(array));
}
protected static String getStringValue(Map<String, ?> arguments, String key, String def) {
Object value = arguments.get(key);
if (value == null) {
return def;
}
return value.toString();
}
protected static int getIntValue(Map<String, ?> arguments, String key, int def) {
Object value = arguments.get(key);
if (value == null) {
return def;
}
return (int) Double.parseDouble(value.toString());
}
protected static float getFloatValue(Map<String, ?> arguments, String key, float def) {
Object value = arguments.get(key);
if (value == null) {
return def;
}
return (float) Double.parseDouble(value.toString());
}
protected static boolean getBooleanValue(Map<String, ?> arguments, String key, boolean def) {
Object value = arguments.get(key);
if (value == null) {
return def;
}
return Boolean.parseBoolean(value.toString());
}
/**
* A builder to extend for all classes extending the {@link BaseImageTranslator}.
*
* @param <T> the concrete builder type
*/
@SuppressWarnings("rawtypes")
public abstract static class BaseBuilder<T extends BaseBuilder> {
protected int width = 224;
protected int height = 224;
protected Image.Flag flag = Image.Flag.COLOR;
protected Pipeline pipeline;
protected Batchifier batchifier = Batchifier.STACK;
/**
* Sets the optional {@link Image.Flag} (default is {@link
* Image.Flag#COLOR}).
*
* @param flag the color mode for the images
* @return this builder
*/
public T optFlag(Image.Flag flag) {
this.flag = flag;
return self();
}
/**
* Sets the {@link Pipeline} to use for pre-processing the image.
*
* @param pipeline the pre-processing pipeline
* @return this builder
*/
public T setPipeline(Pipeline pipeline) {
this.pipeline = pipeline;
return self();
}
/**
* Adds the {@link Transform} to the {@link Pipeline} use for pre-processing the image.
*
* @param transform the {@link Transform} to be added
* @return this builder
*/
public T addTransform(Transform transform) {
if (pipeline == null) {
pipeline = new Pipeline();
}
pipeline.add(transform);
return self();
}
/**
* Sets the {@link Batchifier} for the {@link Translator}.
*
* @param batchifier the {@link Batchifier} to be set
* @return this builder
*/
public T optBatchifier(Batchifier batchifier) {
this.batchifier = batchifier;
return self();
}
protected abstract T self();
protected void validate() {
if (pipeline == null) {
throw new IllegalArgumentException("pipeline is required.");
}
}
protected void configPreProcess(Map<String, ?> arguments) {
if (pipeline == null) {
pipeline = new Pipeline();
}
width = getIntValue(arguments, "width", 224);
height = getIntValue(arguments, "height", 224);
if (arguments.containsKey("flag")) {
flag = Image.Flag.valueOf(arguments.get("flag").toString());
}
if (getBooleanValue(arguments, "centerCrop", false)) {
addTransform(new CenterCrop());
}
if (getBooleanValue(arguments, "resize", false)) {
addTransform(new Resize(width, height));
}
if (getBooleanValue(arguments, "toTensor", true)) {
addTransform(new ToTensor());
}
String normalize = getStringValue(arguments, "normalize", "false");
if ("true".equals(normalize)) {
addTransform(new Normalize(MEAN, STD));
} else if (!"false".equals(normalize)) {
String[] tokens = normalize.split("\\s*,\\s*");
if (tokens.length != 6) {
throw new IllegalArgumentException("Invalid normalize value: " + normalize);
}
float[] mean = {
Float.parseFloat(tokens[0]),
Float.parseFloat(tokens[1]),
Float.parseFloat(tokens[2])
};
float[] std = {
Float.parseFloat(tokens[3]),
Float.parseFloat(tokens[4]),
Float.parseFloat(tokens[5])
};
addTransform(new Normalize(mean, std));
}
String range = (String) arguments.get("range");
if ("0,1".equals(range)) {
addTransform(a -> a.div(255f));
} else if ("-1,1".equals(range)) {
addTransform(a -> a.div(128f).sub(1));
}
if (arguments.containsKey("batchifier")) {
batchifier = Batchifier.fromString((String) arguments.get("batchifier"));
}
}
protected void configPostProcess(Map<String, ?> arguments) {}
}
/** A Builder to construct a {@code ImageClassificationTranslator}. */
@SuppressWarnings("rawtypes")
public abstract static class ClassificationBuilder<T extends BaseBuilder>
extends BaseBuilder<T> {
protected SynsetLoader synsetLoader;
/**
* Sets the name of the synset file listing the potential classes for an image.
*
* @param synsetArtifactName a file listing the potential classes for an image
* @return the builder
*/
public T optSynsetArtifactName(String synsetArtifactName) {
synsetLoader = new SynsetLoader(synsetArtifactName);
return self();
}
/**
* Sets the URL of the synset file.
*
* @param synsetUrl the URL of the synset file
* @return the builder
*/
public T optSynsetUrl(String synsetUrl) {
try {
this.synsetLoader = new SynsetLoader(new URL(synsetUrl));
} catch (MalformedURLException e) {
throw new IllegalArgumentException("Invalid synsetUrl: " + synsetUrl, e);
}
return self();
}
/**
* Sets the potential classes for an image.
*
* @param synset the potential classes for an image
* @return the builder
*/
public T optSynset(List<String> synset) {
synsetLoader = new SynsetLoader(synset);
return self();
}
/** {@inheritDoc} */
@Override
protected void validate() {
super.validate();
if (synsetLoader == null) {
synsetLoader = new SynsetLoader("synset.txt");
}
}
/** {@inheritDoc} */
@Override
protected void configPostProcess(Map<String, ?> arguments) {
String synset = (String) arguments.get("synset");
if (synset != null) {
optSynset(Arrays.asList(synset.split(",")));
}
String synsetUrl = (String) arguments.get("synsetUrl");
if (synsetUrl != null) {
optSynsetUrl(synsetUrl);
}
String synsetFileName = (String) arguments.get("synsetFileName");
if (synsetFileName != null) {
optSynsetArtifactName(synsetFileName);
}
}
}
protected static final class SynsetLoader {
private String synsetFileName;
private URL synsetUrl;
private List<String> synset;
public SynsetLoader(List<String> synset) {
this.synset = synset;
}
public SynsetLoader(URL synsetUrl) {
this.synsetUrl = synsetUrl;
}
public SynsetLoader(String synsetFileName) {
this.synsetFileName = synsetFileName;
}
public List<String> load(Model model) throws IOException {
if (synset != null) {
return synset;
} else if (synsetUrl != null) {
try (InputStream is = synsetUrl.openStream()) {
return Utils.readLines(is);
}
}
return model.getArtifact(synsetFileName, Utils::readLines);
}
}
}
5、创建向量索引字段
需要在索引库创建的时候,一并创建对应字段。
import co.elastic.clients.elasticsearch.ElasticsearchClient;
import co.elastic.clients.elasticsearch._types.mapping.Property;
import co.elastic.clients.elasticsearch._types.mapping.TypeMapping;
import co.elastic.clients.elasticsearch.indices.Alias;
import co.elastic.clients.elasticsearch.indices.CreateIndexRequest;
import co.elastic.clients.elasticsearch.indices.CreateIndexResponse;
import co.elastic.clients.elasticsearch.indices.ExistsRequest;
CreateIndexResponse response = null;
try {
TypeMapping.Builder tmBuilder = new TypeMapping.Builder();
// 图片相似检索,采用点积运算 文本相似采用余线相似
tmBuilder.properties('_img_vector', new Property.Builder().denseVector(builder -> builder.index(true).dims(1024).similarity("dot_product")
.indexOptions(opBuilder -> opBuilder.type("hnsw").m(12).efConstruction(100))).build());
TypeMapping typeMapping = tmBuilder.build();
CreateIndexRequest request = CreateIndexRequest.of(builder -> builder.index(indexName)
.aliases(indexName + "_alias", new Alias.Builder().isWriteIndex(true).build())
.mappings(typeMapping));
response = esClient.indices().create(request);
log.info("acknowledged: {}", response.acknowledged());
log.info("index: {}", response.index());
log.info("shardsAcknowledged: {}", response.shardsAcknowledged());
flag = response.acknowledged();
} catch (IOException e) {
e.printStackTrace();
}
创建后生成的结构数据如下
6、添加到ES
float[] feature;
// 自定义属性字段数据,构建文档
Map<String, Object> dataMap = req.getDataMap();
// 自定义内置参数
dataMap.put("_es_doc_type", "IMAGE");
dataMap.put("_img_vector", feature);
IndexRequest<Map> request = IndexRequest.of(i -> i
.index(req.getIndexLib())
.id(req.getDocId())
.document(dataMap)
);
IndexResponse response = esClient.index(request);
boolean flag = response.result() == Result.Created;
log.info("添加文档id={},结果={}", req.getDocId(), flag);
实际存储的数据结构如下图
7、pytorch环境依赖
cpu/linux-x86_64/native/lib/libc10.so.gz
cpu/linux-x86_64/native/lib/libtorch_cpu.so.gz
cpu/linux-x86_64/native/lib/libtorch.so.gz
cpu/linux-x86_64/native/lib/libgomp-52f2fd74.so.1.gz
cpu/osx-aarch64/native/lib/libtorch_cpu.dylib.gz
cpu/osx-aarch64/native/lib/libtorch.dylib.gz
cpu/osx-aarch64/native/lib/libc10.dylib.gz
cpu/osx-x86_64/native/lib/libtorch_cpu.dylib.gz
cpu/osx-x86_64/native/lib/libiomp5.dylib.gz
cpu/osx-x86_64/native/lib/libtorch.dylib.gz
cpu/osx-x86_64/native/lib/libc10.dylib.gz
cpu/win-x86_64/native/lib/torch.dll.gz
cpu/win-x86_64/native/lib/uv.dll.gz
cpu/win-x86_64/native/lib/torch_cpu.dll.gz
cpu/win-x86_64/native/lib/c10.dll.gz
cpu/win-x86_64/native/lib/fbgemm.dll.gz
cpu/win-x86_64/native/lib/libiomp5md.dll.gz
cpu/win-x86_64/native/lib/asmjit.dll.gz
cpu/win-x86_64/native/lib/libiompstubs5md.dll.gz
cpu-precxx11/linux-aarch64/native/lib/libc10.so.gz
cpu-precxx11/linux-aarch64/native/lib/libtorch_cpu.so.gz
cpu-precxx11/linux-aarch64/native/lib/libarm_compute-973e5a6b.so.gz
cpu-precxx11/linux-aarch64/native/lib/libopenblasp-r0-56e95da7.3.24.so.gz
cpu-precxx11/linux-aarch64/native/lib/libtorch.so.gz
cpu-precxx11/linux-aarch64/native/lib/libarm_compute_graph-6990f339.so.gz
cpu-precxx11/linux-aarch64/native/lib/libstdc%2B%2B.so.6.gz
cpu-precxx11/linux-aarch64/native/lib/libarm_compute_core-0793f69d.so.gz
cpu-precxx11/linux-aarch64/native/lib/libgfortran-b6d57c85.so.5.0.0.gz
cpu-precxx11/linux-aarch64/native/lib/libgomp-6e1a1d1b.so.1.0.0.gz
cpu-precxx11/linux-x86_64/native/lib/libgomp-a34b3233.so.1.gz
cpu-precxx11/linux-x86_64/native/lib/libc10.so.gz
cpu-precxx11/linux-x86_64/native/lib/libtorch_cpu.so.gz
cpu-precxx11/linux-x86_64/native/lib/libtorch.so.gz
cpu-precxx11/linux-x86_64/native/lib/libstdc%2B%2B.so.6.gz
cu121/linux-x86_64/native/lib/libc10_cuda.so.gz
cu121/linux-x86_64/native/lib/libcudnn.so.8.gz
cu121/linux-x86_64/native/lib/libnvfuser_codegen.so.gz
cu121/linux-x86_64/native/lib/libc10.so.gz
cu121/linux-x86_64/native/lib/libtorch_cpu.so.gz
cu121/linux-x86_64/native/lib/libcaffe2_nvrtc.so.gz
cu121/linux-x86_64/native/lib/libcudnn_adv_infer.so.8.gz
cu121/linux-x86_64/native/lib/libcudnn_cnn_train.so.8.gz
cu121/linux-x86_64/native/lib/libcudnn_ops_infer.so.8.gz
cu121/linux-x86_64/native/lib/libnvrtc-builtins-6c5639ce.so.12.1.gz
cu121/linux-x86_64/native/lib/libnvrtc-b51b459d.so.12.gz
cu121/linux-x86_64/native/lib/libtorch.so.gz
cu121/linux-x86_64/native/lib/libtorch_cuda_linalg.so.gz
cu121/linux-x86_64/native/lib/libcublas-37d11411.so.12.gz
cu121/linux-x86_64/native/lib/libtorch_cuda.so.gz
cu121/linux-x86_64/native/lib/libcudnn_adv_train.so.8.gz
cu121/linux-x86_64/native/lib/libcublasLt-f97bfc2c.so.12.gz
cu121/linux-x86_64/native/lib/libnvToolsExt-847d78f2.so.1.gz
cu121/linux-x86_64/native/lib/libcudnn_ops_train.so.8.gz
cu121/linux-x86_64/native/lib/libcudnn_cnn_infer.so.8.gz
cu121/linux-x86_64/native/lib/libgomp-52f2fd74.so.1.gz
cu121/linux-x86_64/native/lib/libcudart-9335f6a2.so.12.gz
cu121/win-x86_64/native/lib/zlibwapi.dll.gz
cu121/win-x86_64/native/lib/cudnn_ops_train64_8.dll.gz
cu121/win-x86_64/native/lib/torch.dll.gz
cu121/win-x86_64/native/lib/nvrtc-builtins64_121.dll.gz
cu121/win-x86_64/native/lib/cufftw64_11.dll.gz
cu121/win-x86_64/native/lib/cudnn_adv_infer64_8.dll.gz
cu121/win-x86_64/native/lib/nvrtc64_120_0.dll.gz
cu121/win-x86_64/native/lib/cusolverMg64_11.dll.gz
cu121/win-x86_64/native/lib/torch_cuda.dll.gz
cu121/win-x86_64/native/lib/cufft64_11.dll.gz
cu121/win-x86_64/native/lib/cublas64_12.dll.gz
cu121/win-x86_64/native/lib/cudnn64_8.dll.gz
cu121/win-x86_64/native/lib/uv.dll.gz
cu121/win-x86_64/native/lib/cudnn_cnn_train64_8.dll.gz
cu121/win-x86_64/native/lib/caffe2_nvrtc.dll.gz
cu121/win-x86_64/native/lib/torch_cpu.dll.gz
cu121/win-x86_64/native/lib/c10.dll.gz
cu121/win-x86_64/native/lib/cudnn_cnn_infer64_8.dll.gz
cu121/win-x86_64/native/lib/c10_cuda.dll.gz
cu121/win-x86_64/native/lib/cudart64_12.dll.gz
cu121/win-x86_64/native/lib/nvfuser_codegen.dll.gz
cu121/win-x86_64/native/lib/fbgemm.dll.gz
cu121/win-x86_64/native/lib/curand64_10.dll.gz
cu121/win-x86_64/native/lib/libiomp5md.dll.gz
cu121/win-x86_64/native/lib/cusolver64_11.dll.gz
cu121/win-x86_64/native/lib/cudnn_adv_train64_8.dll.gz
cu121/win-x86_64/native/lib/cublasLt64_12.dll.gz
cu121/win-x86_64/native/lib/nvToolsExt64_1.dll.gz
cu121/win-x86_64/native/lib/nvJitLink_120_0.dll.gz
cu121/win-x86_64/native/lib/cusparse64_12.dll.gz
cu121/win-x86_64/native/lib/asmjit.dll.gz
cu121/win-x86_64/native/lib/cudnn_ops_infer64_8.dll.gz
cu121/win-x86_64/native/lib/libiompstubs5md.dll.gz
cu121/win-x86_64/native/lib/cupti64_2023.1.1.dll.gz
cu121-precxx11/linux-x86_64/native/lib/libgomp-a34b3233.so.1.gz
cu121-precxx11/linux-x86_64/native/lib/libc10_cuda.so.gz
cu121-precxx11/linux-x86_64/native/lib/libcudnn.so.8.gz
cu121-precxx11/linux-x86_64/native/lib/libnvfuser_codegen.so.gz
cu121-precxx11/linux-x86_64/native/lib/libc10.so.gz
cu121-precxx11/linux-x86_64/native/lib/libtorch_cpu.so.gz
cu121-precxx11/linux-x86_64/native/lib/libcaffe2_nvrtc.so.gz
cu121-precxx11/linux-x86_64/native/lib/libcudnn_adv_infer.so.8.gz
cu121-precxx11/linux-x86_64/native/lib/libcudnn_cnn_train.so.8.gz
cu121-precxx11/linux-x86_64/native/lib/libcudnn_ops_infer.so.8.gz
cu121-precxx11/linux-x86_64/native/lib/libnvrtc-builtins-6c5639ce.so.12.1.gz
cu121-precxx11/linux-x86_64/native/lib/libnvrtc-b51b459d.so.12.gz
cu121-precxx11/linux-x86_64/native/lib/libtorch.so.gz
cu121-precxx11/linux-x86_64/native/lib/libtorch_cuda_linalg.so.gz
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cu121-precxx11/linux-x86_64/native/lib/libcudnn_adv_train.so.8.gz
cu121-precxx11/linux-x86_64/native/lib/libcublasLt-f97bfc2c.so.12.gz
cu121-precxx11/linux-x86_64/native/lib/libnvToolsExt-847d78f2.so.1.gz
cu121-precxx11/linux-x86_64/native/lib/libcudnn_ops_train.so.8.gz
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cu121-precxx11/linux-x86_64/native/lib/libcudart-9335f6a2.so.12.gz