diff --git a/main/hugectr_contributor_guide.html b/main/hugectr_contributor_guide.html index fea5d517cc..390475ce1d 100644 --- a/main/hugectr_contributor_guide.html +++ b/main/hugectr_contributor_guide.html @@ -196,10 +196,10 @@

Build HugeCTR Training Container from SourceBuild HugeCTR Inference Container from Source

-

  • ENABLE_HDFS: You can use this option to build HugeCTR together with HDFS to enable HDFS related functions. Permissible values are ON, MINIMAL and OFF (default). Setting this option to ON leads to building all necessary Hadoop modules that are required for building AND running both HugeCTR and HDFS. In contrast, MINIMAL restricts building only the minimum necessary set of components for building HugeCTR.

    • +

  • ENABLE_HDFS: You can use this option to build HugeCTR together with HDFS to enable HDFS related functions. Permissible values are ON and OFF (default). Setting this option to ON leads to building all necessary Hadoop modules that are required for building so that it can connect to HDFS deployments.

  • ENABLE_S3: You can use this option to build HugeCTR together with Amazon AWS S3 SDK to enable S3 related functions. Permissible values are ON and OFF (default). Setting this option to ON leads to building all necessary AWS SKKs and dependencies that are required for building AND running both HugeCTR and S3.

    • -

    Please note that setting DENABLE_HDFS=ON/MINIMAL or DENABLE_S3=ON requires root permission. So before using these two options to do the customized building, make sure you use -u root when you run the docker container.

    +

    Please note that setting DENABLE_HDFS=ON or DENABLE_S3=ON requires root permission. So before using these two options to do the customized building, make sure you use -u root when you run the docker container.

    Here are some examples of how you can build HugeCTR using these build options:

    $ mkdir -p build && cd build
 $ cmake -DCMAKE_BUILD_TYPE=Release -DSM=80 .. # Target is NVIDIA A100
@@ -212,7 +212,7 @@ 
    Build HugeCTR Training Container from Source
    $ mkdir -p build && cd build
-$ cmake -DCMAKE_BUILD_TYPE=Release -DSM="70;80" -DENABLE_HDFS=MINIMAL .. # Target is NVIDIA V100 / A100 with only minimum HDFS components mode on.
+$ cmake -DCMAKE_BUILD_TYPE=Release -DSM="70;80" -DENABLE_HDFS=ON .. # Target is NVIDIA V100 / A100 with HDFS components mode on.
 $ make -j && make install
    diff --git a/main/searchindex.js b/main/searchindex.js index 3a1fdc6ec4..c40c67685d 100644 --- a/main/searchindex.js +++ b/main/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["QAList", "additional_resources", "api/hugectr_layer_book", "api/index", "api/python_interface", "hierarchical_parameter_server/hps_database_backend", "hierarchical_parameter_server/hps_dlrm_benchmark", "hierarchical_parameter_server/hps_tf_api/index", "hierarchical_parameter_server/hps_tf_api/initialize", "hierarchical_parameter_server/hps_tf_api/layers", "hierarchical_parameter_server/hps_tf_user_guide", "hierarchical_parameter_server/hps_torch_api/index", "hierarchical_parameter_server/hps_torch_api/lookup_layer", "hierarchical_parameter_server/hps_torch_user_guide", "hierarchical_parameter_server/hps_trt_api/hps_plugin", "hierarchical_parameter_server/hps_trt_api/hps_plugin_creator", "hierarchical_parameter_server/hps_trt_api/index", "hierarchical_parameter_server/hps_trt_user_guide", 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Who are the target users of HugeCTR?": [[0, "who-are-the-target-users-of-hugectr"]], "2. Which models can be supported in HugeCTR?": [[0, "which-models-can-be-supported-in-hugectr"]], "3. Does HugeCTR support TensorFlow?": [[0, "does-hugectr-support-tensorflow"]], "4. Does HugeCTR support multiple nodes CTR training?": [[0, "does-hugectr-support-multiple-nodes-ctr-training"]], "5. How to deal with the huge embedding table that cannot be stored in a single GPU memory?": [[0, "how-to-deal-with-the-huge-embedding-table-that-cannot-be-stored-in-a-single-gpu-memory"]], "6. Which GPUs are supported in HugeCTR?": [[0, "which-gpus-are-supported-in-hugectr"]], "7. Must we use the DGX family such as DGX A100 to run HugeCTR?": [[0, "must-we-use-the-dgx-family-such-as-dgx-a100-to-run-hugectr"]], "8. Can HugeCTR run without InfiniBand?": [[0, "can-hugectr-run-without-infiniband"]], "9. Is there any requirement of CPU configuration for HugeCTR execution?": [[0, "is-there-any-requirement-of-cpu-configuration-for-hugectr-execution"]], "10. What is the specific format of files as input in HugeCTR?": [[0, "what-is-the-specific-format-of-files-as-input-in-hugectr"]], "11.\t Does HugeCTR support Python interface?": [[0, "does-hugectr-support-python-interface"]], "12. Does HugeCTR do synchronous training with multiple GPUs (and nodes)? Otherwise, does it do asynchronous training?": [[0, "does-hugectr-do-synchronous-training-with-multiple-gpus-and-nodes-otherwise-does-it-do-asynchronous-training"]], "13. Does HugeCTR support stream training?": [[0, "does-hugectr-support-stream-training"]], "14. What is a \u201cslot\u201d in HugeCTR?": [[0, "what-is-a-slot-in-hugectr"]], "15. What are the differences between LocalizedSlotEmbedding and DistributedSlotEmbedding?": [[0, "what-are-the-differences-between-localizedslotembedding-and-distributedslotembedding"]], "16. For multi-node\uff0cis DataReader required to read the same batch of data on each node for each step?": [[0, "for-multi-node-is-datareader-required-to-read-the-same-batch-of-data-on-each-node-for-each-step"]], "17. As model parallelism in embedding layers, how does it get all the embedding lookup features from multi-node / multi-gpu?": [[0, "as-model-parallelism-in-embedding-layers-how-does-it-get-all-the-embedding-lookup-features-from-multi-node-multi-gpu"]], "18. How to set data clauses, if there are two embeddings needed?": [[0, "how-to-set-data-clauses-if-there-are-two-embeddings-needed"]], "19. How to save and load models in HugeCTR?": [[0, "how-to-save-and-load-models-in-hugectr"]], "20. Could the post training model from HugeCTR be imported into other frameworks such as TensorFlow for inference deployment?": [[0, "could-the-post-training-model-from-hugectr-be-imported-into-other-frameworks-such-as-tensorflow-for-inference-deployment"]], "21. Does HugeCTR support overlap between different slots?": [[0, "does-hugectr-support-overlap-between-different-slots"]], "22. What if there\u2019s no value in a slot?": [[0, "what-if-there-s-no-value-in-a-slot"]], "23. How can I benchmark my network?": [[0, "how-can-i-benchmark-my-network"]], "24. How to set workspace_size_per_gpu_in_mb and slot_size_array?": [[0, "how-to-set-workspace-size-per-gpu-in-mb-and-slot-size-array"]], "25. Is nvlink required in HugeCTR?": [[0, "is-nvlink-required-in-hugectr"]], "26. Is DGX the only GPU server that is required in HugeCTR?": [[0, "is-dgx-the-only-gpu-server-that-is-required-in-hugectr"]], "27. Can HugeCTR run without InfiniBand?": [[0, "id1"]], "28. Does HugeCTR support loading pretrained embeddings in other formats?": [[0, "does-hugectr-support-loading-pretrained-embeddings-in-other-formats"]], "29. How to construct the model graph with branch topology in HugeCTR?": [[0, "how-to-construct-the-model-graph-with-branch-topology-in-hugectr"]], "30. What is the good practice of configuring the embedding vector size?": [[0, "what-is-the-good-practice-of-configuring-the-embedding-vector-size"]], "31. How to resolve the bus error when running HugeCTR samples and notebooks?": [[0, "how-to-resolve-the-bus-error-when-running-hugectr-samples-and-notebooks"]], "32. What does the log \u201cmemory pool is empty\u201d imply for HugeCTR inference?": [[0, "what-does-the-log-memory-pool-is-empty-imply-for-hugectr-inference"]], "Additional Resources": [[1, "additional-resources"]], "HugeCTR Layer Classes and Methods": [[2, "hugectr-layer-classes-and-methods"]], "Input Layer": [[2, "input-layer"]], "Sparse Embedding": [[2, "sparse-embedding"]], "Embedding Types Detail": [[2, "embedding-types-detail"]], "DistributedSlotSparseEmbeddingHash Layer": [[2, "distributedslotsparseembeddinghash-layer"]], "LocalizedSlotSparseEmbeddingHash Layer": [[2, "localizedslotsparseembeddinghash-layer"]], "LocalizedSlotSparseEmbeddingOneHot Layer": [[2, "localizedslotsparseembeddingonehot-layer"]], "Dense Layers": [[2, "dense-layers"]], "Dense Layers Usage": [[2, "dense-layers-usage"]], "FullyConnected Layer": [[2, "fullyconnected-layer"]], "MLP Layer": [[2, "mlp-layer"]], "MultiCross Layer": [[2, "multicross-layer"]], "FmOrder2 Layer": [[2, "fmorder2-layer"]], 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Python Interface": [[4, "hugectr-python-interface"]], "About the HugeCTR Python Interface": [[4, "about-the-hugectr-python-interface"]], "High-level Training API": [[4, "high-level-training-api"]], "Solver": [[4, "solver"]], "CreateSolver method": [[4, "createsolver-method"]], "AsyncParam": [[4, "asyncparam"]], "AsyncParam class": [[4, "asyncparam-class"]], "HybridEmbeddingParam": [[4, "hybridembeddingparam"]], "HybridEmbeddingParam class": [[4, "hybridembeddingparam-class"]], "DataReaderParams": [[4, "datareaderparams"]], "DataReaderParams class": [[4, "datareaderparams-class"]], "Dataset formats": [[4, "dataset-formats"]], "Data Files": [[4, "data-files"]], "File List": [[4, "file-list"]], "Raw": [[4, "raw"]], "Parquet": [[4, "parquet"]], "OptParamsPy": [[4, "optparamspy"]], "CreateOptimizer method": [[4, "createoptimizer-method"]], "Layers": [[4, "layers"]], "Model": [[4, "model"], [4, "id2"]], "Model class": [[4, "model-class"]], "add method": [[4, "add-method"]], "compile method": 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"learningratescheduler"]], "get_next method": [[4, "get-next-method"]], "DataReader": [[4, "datareader"]], "is_eof method": [[4, "is-eof-method"]], "get_learning_rate_scheduler method": [[4, "get-learning-rate-scheduler-method"]], "get_data_reader_train method": [[4, "get-data-reader-train-method"]], "get_data_reader_eval method": [[4, "get-data-reader-eval-method"]], "start_data_reading method": [[4, "start-data-reading-method"]], "set_learning_rate method": [[4, "set-learning-rate-method"]], "train method": [[4, "train-method"]], "get_current_loss method": [[4, "get-current-loss-method"]], "eval method": [[4, "eval-method"]], "get_eval_metrics method": [[4, "get-eval-metrics-method"]], "save_params_to_files method": [[4, "save-params-to-files-method"]], "check_out_tensor method": [[4, "check-out-tensor-method"], [4, "id3"]], "Inference API": [[4, "inference-api"]], "InferenceParams": [[4, "inferenceparams"]], "InferenceParams class": [[4, "inferenceparams-class"]], "InferenceModel": [[4, "inferencemodel"]], "InferenceModel class": [[4, "inferencemodel-class"]], "predict method": [[4, "predict-method"]], "evaluate method": [[4, "evaluate-method"]], "Data Generator API": [[4, "data-generator-api"]], "DataGeneratorParams class": [[4, "datageneratorparams-class"]], "DataGenerator": [[4, "datagenerator"]], "DataGenerator class": [[4, "datagenerator-class"]], "generate method": [[4, "generate-method"]], "Data Source API": [[4, "data-source-api"]], "DataSourceParams class": [[4, "datasourceparams-class"]], "Hierarchical Parameter Server Database Backend": [[5, "hierarchical-parameter-server-database-backend"]], "Introduction to the HPS Database Backend": [[5, "introduction-to-the-hps-database-backend"]], "Background": [[5, "background"]], "Architecture": [[5, "architecture"]], "Training and Iterative Model Updates": [[5, "training-and-iterative-model-updates"]], "Execution": [[5, "execution"]], "Inference": [[5, "inference"]], "Training": [[5, "training"], [53, "training"], [53, "id3"]], "Lookup Optimization": [[5, "lookup-optimization"]], "Configuration": [[5, "configuration"]], "Inference Parameters and Embedding Cache Configuration": [[5, "inference-parameters-and-embedding-cache-configuration"]], "Inference Params Syntax": [[5, "inference-params-syntax"]], "Inference Parameters": [[5, "inference-parameters"]], "Parameter Server Configuration: Models": [[5, "parameter-server-configuration-models"]], "Volatile Database Configuration": [[5, "volatile-database-configuration"]], "Volatile Database Params Syntax": [[5, "volatile-database-params-syntax"]], "Parameter Server Configuration: Volatile Database": [[5, "parameter-server-configuration-volatile-database"]], "Volatile Database Parameters": [[5, "volatile-database-parameters"]], "Overflow Parameters": [[5, "overflow-parameters"]], "Common Volatile Database Parameters": [[5, "common-volatile-database-parameters"]], "Persistent Database Configuration": [[5, "persistent-database-configuration"]], "Persistent Database Params Syntax": [[5, "persistent-database-params-syntax"]], "Parameter Server Configuration: Persistent Database": [[5, "parameter-server-configuration-persistent-database"]], "Persistent Database Parameters": [[5, "persistent-database-parameters"]], "Update Source Configuration": [[5, "update-source-configuration"]], "Update Source Params Syntax": [[5, "update-source-params-syntax"]], "Parameter Server Configuration: Update Source": [[5, "parameter-server-configuration-update-source"]], "Update Source Parameters": [[5, "update-source-parameters"]], "Benchmark the DLRM Model with HPS": [[6, "benchmark-the-dlrm-model-with-hps"]], "Benchmark Setup": [[6, "benchmark-setup"]], "Results": [[6, "results"]], "Resources": [[6, "resources"]], "Hierarchical Parameter Server API": [[7, "hierarchical-parameter-server-api"]], "HPS Initialize": [[8, "hps-initialize"]], "HPS Layers": [[9, "hps-layers"]], "SparseLookupLayer": [[9, "sparselookuplayer"]], "LookupLayer": [[9, "lookuplayer"]], "Hierarchical Parameter Server Plugin for TensorFlow": [[10, "hierarchical-parameter-server-plugin-for-tensorflow"]], "Introduction to the HPS Plugin for TensorFlow": [[10, "introduction-to-the-hps-plugin-for-tensorflow"]], "Benefits of the Plugin for TensorFlow": [[10, "benefits-of-the-plugin-for-tensorflow"]], "Workflow": [[10, "workflow"], [17, "workflow"]], "Installation": [[10, "installation"], [13, "installation"], [17, "installation"], [20, "installation"], [21, "installation"], [22, "installation"], [23, "installation"], [24, "installation"], [26, "installation"], [27, "installation"], [28, "installation"], [30, "installation"], [31, "installation"], [32, "installation"], [33, "installation"]], "Compute Capability": [[10, "compute-capability"], [13, "compute-capability"], [17, "compute-capability"], [38, "compute-capability"]], "Installing HPS Using NGC Containers": [[10, "installing-hps-using-ngc-containers"], [13, "installing-hps-using-ngc-containers"], [17, "installing-hps-using-ngc-containers"]], "Example Notebooks": [[10, "example-notebooks"], [13, "example-notebooks"], [17, "example-notebooks"]], "Benchmark": [[10, "benchmark"], [17, "benchmark"]], "HPS Plugin for Torch API": [[11, "hps-plugin-for-torch-api"]], "HPS Plugin for Torch": [[12, "hps-plugin-for-torch"]], "LookupLayer class": [[12, "lookuplayer-class"]], "Hierarchical Parameter Server Plugin for Torch": [[13, "hierarchical-parameter-server-plugin-for-torch"]], "Introduction to the HPS Plugin for Torch": [[13, "introduction-to-the-hps-plugin-for-torch"]], "HPS Plugin": [[14, "hps-plugin"]], "HPS Plugin Creator": [[15, "hps-plugin-creator"]], "HPS Plugin for TensorRT API": [[16, "hps-plugin-for-tensorrt-api"]], "Hierarchical Parameter Server Plugin for TensorRT": [[17, "hierarchical-parameter-server-plugin-for-tensorrt"]], "Introduction to the HPS Plugin for TensorRT": [[17, "introduction-to-the-hps-plugin-for-tensorrt"]], "Hierarchical Parameter Server": [[18, "hierarchical-parameter-server"], [36, "hierarchical-parameter-server"]], "Profiling HPS": [[19, "profiling-hps"]], "HPS profiler": [[19, "hps-profiler"]], "Build and install the HPS Profiler": [[19, "build-and-install-the-hps-profiler"]], "Create a synthetic embedding table": [[19, "create-a-synthetic-embedding-table"]], "Use the HPS Profiler to get the measurement results": [[19, "use-the-hps-profiler-to-get-the-measurement-results"]], "Profile HPS with Triton Perf Analyzer:": [[19, "profile-hps-with-triton-perf-analyzer"]], "HPS Profiler vs. Triton Perf Analyzer:": [[19, "hps-profiler-vs-triton-perf-analyzer"]], "Hierarchical Parameter Server Demo": [[20, "hierarchical-parameter-server-demo"], [41, "hierarchical-parameter-server-demo"]], "Overview": [[20, "overview"], [21, "overview"], [22, "overview"], [23, "overview"], [24, "overview"], [26, "overview"], [27, "overview"], [28, "overview"], [30, "overview"], [31, "overview"], [32, "overview"], [33, "overview"], [41, "overview"], [42, "overview"], [44, "overview"], [53, "overview"]], "Get HPS from NGC": [[20, "get-hps-from-ngc"], [21, "get-hps-from-ngc"], [22, "get-hps-from-ngc"], [23, "get-hps-from-ngc"], [24, "get-hps-from-ngc"], [28, "get-hps-from-ngc"]], "Configurations": [[20, "configurations"], [21, "configurations"], [22, "configurations"], [24, "configurations"], [26, "configurations"], [27, "configurations"], [32, "configurations"], [33, "configurations"]], "Train with native TF layers": [[20, "train-with-native-tf-layers"], [21, "train-with-native-tf-layers"], [22, "train-with-native-tf-layers"], [24, "train-with-native-tf-layers"], [33, "train-with-native-tf-layers"]], "Create the inference graph with HPS LookupLayer": [[20, "create-the-inference-graph-with-hps-lookuplayer"], [24, "create-the-inference-graph-with-hps-lookuplayer"]], "Inference with saved model graph": [[20, "inference-with-saved-model-graph"], [21, "inference-with-saved-model-graph"], [26, "inference-with-saved-model-graph"]], "HPS for Multiple Tables and Sparse Inputs": [[21, "hps-for-multiple-tables-and-sparse-inputs"]], "Create the inference graph with HPS SparseLookupLayer and LookupLayer": [[21, "create-the-inference-graph-with-hps-sparselookuplayer-and-lookuplayer"]], "HPS Pretrained Model Training Demo": [[22, "hps-pretrained-model-training-demo"]], "Load the pre-trained embeddings via HPS": [[22, "load-the-pre-trained-embeddings-via-hps"]], "HPS Table Fusion Demo": [[23, "hps-table-fusion-demo"]], "Create TF SavedModel": [[23, "create-tf-savedmodel"]], "Make inference with HPS table fusion": [[23, "make-inference-with-hps-table-fusion"]], "Deploy SavedModel using HPS with Triton TensorFlow Backend": [[24, "deploy-savedmodel-using-hps-with-triton-tensorflow-backend"], [24, "id1"]], "Deploy TF-TRT SavedModel using HPS with Triton TensorFlow Backend": [[24, "deploy-tf-trt-savedmodel-using-hps-with-triton-tensorflow-backend"]], "Hierarchical Parameter Server Notebooks": [[25, "hierarchical-parameter-server-notebooks"], [29, "hierarchical-parameter-server-notebooks"]], "Quickstart": [[25, "quickstart"], [29, "quickstart"], [34, "quickstart"]], "Pull the NGC Docker": [[25, "pull-the-ngc-docker"], [29, "pull-the-ngc-docker"]], "Clone the HugeCTR Repository": [[25, "clone-the-hugectr-repository"], [29, "clone-the-hugectr-repository"], [34, "clone-the-hugectr-repository"]], "Start the Jupyter Notebook": [[25, "start-the-jupyter-notebook"], [29, "start-the-jupyter-notebook"], [34, "start-the-jupyter-notebook"]], "Notebook List": [[25, "notebook-list"], [29, "notebook-list"], [34, "notebook-list"], [43, "notebook-list"]], "System Specifications": [[25, "system-specifications"], [29, "system-specifications"], [34, "system-specifications"], [43, "system-specifications"]], "SOK to HPS DLRM Demo": [[26, "sok-to-hps-dlrm-demo"]], "Get SOK from NGC": [[26, "get-sok-from-ngc"], [27, "get-sok-from-ngc"]], "Train with SOK embedding layers": [[26, "train-with-sok-embedding-layers"]], "Create the inference graph with HPS SparseLookupLayer": [[26, "create-the-inference-graph-with-hps-sparselookuplayer"]], "SOK Train DLRM Demo": [[27, "sok-train-dlrm-demo"]], "Build model with SOK embedding layers": [[27, "build-model-with-sok-embedding-layers"]], "Train with SOK models": [[27, "train-with-sok-models"]], "HPS Torch Demo": [[28, "hps-torch-demo"]], "Data Generation": [[28, "data-generation"], [31, "data-generation"], [41, "data-generation"]], "Lookup with Table Fusion": [[28, "lookup-with-table-fusion"]], "HPS TensorRT Plugin Benchmark for TensorFlow Large Model": [[30, "hps-tensorrt-plugin-benchmark-for-tensorflow-large-model"]], "Use NGC": [[30, "use-ngc"], [31, "use-ngc"], [32, "use-ngc"], [33, "use-ngc"]], "1. Create the TF model": [[30, "create-the-tf-model"]], "2. Build the HPS-integrated TensorRT engine": [[30, "build-the-hps-integrated-tensorrt-engine"]], "Step1: Prepare the 147GB embedding table": [[30, "step1-prepare-the-147gb-embedding-table"]], "1.1 Train a 147GB model from scratch": [[30, "train-a-147gb-model-from-scratch"]], "1.2 Get the embedding model file in hps format": [[30, "get-the-embedding-model-file-in-hps-format"]], "Step2: Prepare JSON configuration file for HPS": [[30, "step2-prepare-json-configuration-file-for-hps"]], "Step3: Convert to ONNX and do ONNX graph surgery": [[30, "step3-convert-to-onnx-and-do-onnx-graph-surgery"]], "Step4: Build the TensorRT engine": [[30, "step4-build-the-tensorrt-engine"]], "3. Benchmark HPS-integrated TensorRT engine on Triton": [[30, "benchmark-hps-integrated-tensorrt-engine-on-triton"]], "Step1: Create the model repository": [[30, "step1-create-the-model-repository"]], "Step2: Prepare the benchmark input data": [[30, "step2-prepare-the-benchmark-input-data"]], "Step3: Launch the Triton inference server": [[30, "step3-launch-the-triton-inference-server"]], "Step4: Run the benchmark": [[30, "step4-run-the-benchmark"]], "4. Benchmark for ARM64 or Grace + Hooper systems": [[30, "benchmark-for-arm64-or-grace-hooper-systems"]], "Step 1: Build the NVIDIA Merlin docker images": [[30, "step-1-build-the-nvidia-merlin-docker-images"]], "Step 2: Prepare host system for running the docker container": [[30, "step-2-prepare-host-system-for-running-the-docker-container"]], "Step 3: Create the model": [[30, "step-3-create-the-model"]], "Step 4: Prepare data": [[30, "step-4-prepare-data"]], "Step 5: Run benchmark": [[30, "step-5-run-benchmark"]], "HPS TensorRT Plugin Demo for HugeCTR Trained Model": [[31, "hps-tensorrt-plugin-demo-for-hugectr-trained-model"]], "Train with HugeCTR": [[31, "train-with-hugectr"]], "Build the HPS-integrated TensorRT engine": [[31, "build-the-hps-integrated-tensorrt-engine"], [32, "build-the-hps-integrated-tensorrt-engine"], [33, "build-the-hps-integrated-tensorrt-engine"]], "Step1: Prepare JSON configuration file for HPS": [[31, "step1-prepare-json-configuration-file-for-hps"]], "Step2: Convert to ONNX and do ONNX graph surgery": [[31, "step2-convert-to-onnx-and-do-onnx-graph-surgery"], [32, "step2-convert-to-onnx-and-do-onnx-graph-surgery"], [33, "step2-convert-to-onnx-and-do-onnx-graph-surgery"]], "Step3: Build the TensorRT engine": [[31, "step3-build-the-tensorrt-engine"], [32, "step3-build-the-tensorrt-engine"], [33, "step3-build-the-tensorrt-engine"]], "Deploy HPS-integrated TensorRT engine on Triton": [[31, "deploy-hps-integrated-tensorrt-engine-on-triton"], [32, "deploy-hps-integrated-tensorrt-engine-on-triton"]], "HPS TensorRT Plugin Demo for PyTorch Trained Model": [[32, "hps-tensorrt-plugin-demo-for-pytorch-trained-model"]], "Train with PyTorch": [[32, "train-with-pytorch"]], "Step1: Prepare sparse model and JSON configuration file for HPS": [[32, "step1-prepare-sparse-model-and-json-configuration-file-for-hps"], [33, "step1-prepare-sparse-model-and-json-configuration-file-for-hps"]], "HPS TensorRT Plugin Demo for TensorFlow Trained Model": [[33, "hps-tensorrt-plugin-demo-for-tensorflow-trained-model"]], "Deploy HPS-integrated TensorRT engine with Triton on multiple GPUs": [[33, "deploy-hps-integrated-tensorrt-engine-with-triton-on-multiple-gpus"]], "HPS Plugin for TensorRT Notebooks": [[34, "hps-plugin-for-tensorrt-notebooks"]], "Pull the Container from NGC": [[34, "pull-the-container-from-ngc"]], "Contributing to HugeCTR": [[35, "contributing-to-hugectr"]], "Overview of Contributing to HugeCTR": [[35, "overview-of-contributing-to-hugectr"]], "Contribute New Code": [[35, "contribute-new-code"]], "How to Start your Development": [[35, "how-to-start-your-development"]], "Set Up the Development Environment With Merlin Containers": [[35, "set-up-the-development-environment-with-merlin-containers"]], "Build HugeCTR Training Container from Source": [[35, "build-hugectr-training-container-from-source"]], "Build HugeCTR Inference Container from Source": [[35, "build-hugectr-inference-container-from-source"]], "Build Sparse Operation Kit (SOK) from Source": [[35, "build-sparse-operation-kit-sok-from-source"]], "HugeCTR Core Features": [[36, "hugectr-core-features"]], "Summary of Core Features": [[36, "summary-of-core-features"]], "Model Parallel Training": [[36, "model-parallel-training"]], "Multi-Node Training": [[36, "multi-node-training"]], "Mixed Precision Training": [[36, "mixed-precision-training"]], "SGD Optimizer and Learning Rate Scheduling": [[36, "sgd-optimizer-and-learning-rate-scheduling"]], "HugeCTR to ONNX Converter": [[36, "hugectr-to-onnx-converter"]], "HDFS Support": [[36, "hdfs-support"]], "Sparse Operation Kit": [[36, "sparse-operation-kit"], [56, "sparse-operation-kit"]], "HugeCTR Talks and Blogs": [[37, "hugectr-talks-and-blogs"]], "Talks": [[37, "talks"]], "Blogs": [[37, "blogs"]], "Introduction to HugeCTR": [[38, "introduction-to-hugectr"]], "About HugeCTR": [[38, "about-hugectr"]], "Installing and Building HugeCTR": [[38, "installing-and-building-hugectr"]], "Installing HugeCTR Using NGC Containers": [[38, "installing-hugectr-using-ngc-containers"]], "Building HugeCTR from Scratch": [[38, "building-hugectr-from-scratch"]], "Tools": [[38, "tools"]], "Generating Synthetic Data and Benchmarks": [[38, "generating-synthetic-data-and-benchmarks"]], "Downloading and Preprocessing Datasets": [[38, "downloading-and-preprocessing-datasets"]], "Merlin HugeCTR": [[39, "merlin-hugectr"]], "Related Resources": [[39, "related-resources"]], "HugeCTR Embedding Collection": [[40, "hugectr-embedding-collection"]], "About this Notebook": [[40, "about-this-notebook"]], "Concepts and API Reference": [[40, "concepts-and-api-reference"]], "Setup": [[40, "setup"], [41, "setup"], [42, "setup"]], "Use an Embedding Collection with a DLRM Model": [[40, "use-an-embedding-collection-with-a-dlrm-model"]], "Data Preparation": [[40, "data-preparation"], [42, "data-preparation"], [53, "data-preparation"]], "Run the following commands on the terminal to prepare the data for this notebook": [[40, "run-the-following-commands-on-the-terminal-to-prepare-the-data-for-this-notebook"]], "Prepare the Training Script": [[40, "prepare-the-training-script"]], "Embedding Table Placement Strategy: Round Robin": [[40, "embedding-table-placement-strategy-round-robin"]], "Embedding Table Placement Strategy: Uniform": [[40, "embedding-table-placement-strategy-uniform"]], "Embedding Table Placement Strategy: Hybrid": [[40, "embedding-table-placement-strategy-hybrid"]], "Use Dynamic Hash Table with Round Robin Table Placement Strategy": [[40, "use-dynamic-hash-table-with-round-robin-table-placement-strategy"]], "Train from Scratch": [[41, "train-from-scratch"]], "Convert HugeCTR to ONNX": [[41, "convert-hugectr-to-onnx"]], "1. Inference with HPS & ONNX": [[41, "inference-with-hps-onnx"]], "2. Lookup the Embedding Vector from DLPack": [[41, "lookup-the-embedding-vector-from-dlpack"]], "3. Multi-process inference": [[41, "multi-process-inference"]], "4. Redis Cluster deployment (without TLS/SSL)": [[41, "redis-cluster-deployment-without-tls-ssl"]], "5. Redis Cluster deployment (with TLS/SSL)": [[41, "redis-cluster-deployment-with-tls-ssl"]], "HugeCTR End-end Example with NVTabular": [[42, "hugectr-end-end-example-with-nvtabular"]], "Data Preprocessing using NVTabular": [[42, "data-preprocessing-using-nvtabular"]], "Training a WDL model with HugeCTR": [[42, "training-a-wdl-model-with-hugectr"]], "HugeCTR Example Notebooks": [[43, "hugectr-example-notebooks"]], "1. Clone the HugeCTR Repository": [[43, "clone-the-hugectr-repository"]], "2. Pull the NGC Docker and run it": [[43, "pull-the-ngc-docker-and-run-it"]], "3. Customized Building (Optional)": [[43, "customized-building-optional"]], "4. Start the Jupyter Notebook": [[43, "start-the-jupyter-notebook"]], "Training Recommender Systems on Multi-modal Data": [[44, "training-recommender-systems-on-multi-modal-data"]], "MovieLens-25M: Download and Convert": [[45, "movielens-25m-download-and-convert"]], "Getting Started": [[45, "getting-started"]], "Convert the dataset": [[45, "convert-the-dataset"]], "Splitting into train and validation dataset": [[45, "splitting-into-train-and-validation-dataset"]], "Next steps": [[45, "next-steps"]], "MovieLens Data Enrichment": [[46, "movielens-data-enrichment"]], "Scraping data from IMDB": [[46, "scraping-data-from-imdb"]], "Collect synopsis for all movies": [[46, "collect-synopsis-for-all-movies"]], "Scraping movie posters": [[46, "scraping-movie-posters"]], "Movie Poster Feature Extraction with ResNet": [[47, "movie-poster-feature-extraction-with-resnet"]], "Download a pretrained ResNet-50 from NVIDIA GPU cloud": [[47, "download-a-pretrained-resnet-50-from-nvidia-gpu-cloud"]], "Extract 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Who are the target users of HugeCTR?": [[0, "who-are-the-target-users-of-hugectr"]], "2. Which models can be supported in HugeCTR?": [[0, "which-models-can-be-supported-in-hugectr"]], "3. Does HugeCTR support TensorFlow?": [[0, "does-hugectr-support-tensorflow"]], "4. Does HugeCTR support multiple nodes CTR training?": [[0, "does-hugectr-support-multiple-nodes-ctr-training"]], "5. How to deal with the huge embedding table that cannot be stored in a single GPU memory?": [[0, "how-to-deal-with-the-huge-embedding-table-that-cannot-be-stored-in-a-single-gpu-memory"]], "6. Which GPUs are supported in HugeCTR?": [[0, "which-gpus-are-supported-in-hugectr"]], "7. Must we use the DGX family such as DGX A100 to run HugeCTR?": [[0, "must-we-use-the-dgx-family-such-as-dgx-a100-to-run-hugectr"]], "8. Can HugeCTR run without InfiniBand?": [[0, "can-hugectr-run-without-infiniband"]], "9. Is there any requirement of CPU configuration for HugeCTR execution?": [[0, "is-there-any-requirement-of-cpu-configuration-for-hugectr-execution"]], "10. What is the specific format of files as input in HugeCTR?": [[0, "what-is-the-specific-format-of-files-as-input-in-hugectr"]], "11.\t Does HugeCTR support Python interface?": [[0, "does-hugectr-support-python-interface"]], "12. Does HugeCTR do synchronous training with multiple GPUs (and nodes)? Otherwise, does it do asynchronous training?": [[0, "does-hugectr-do-synchronous-training-with-multiple-gpus-and-nodes-otherwise-does-it-do-asynchronous-training"]], "13. Does HugeCTR support stream training?": [[0, "does-hugectr-support-stream-training"]], "14. What is a \u201cslot\u201d in HugeCTR?": [[0, "what-is-a-slot-in-hugectr"]], "15. What are the differences between LocalizedSlotEmbedding and DistributedSlotEmbedding?": [[0, "what-are-the-differences-between-localizedslotembedding-and-distributedslotembedding"]], "16. For multi-node\uff0cis DataReader required to read the same batch of data on each node for each step?": [[0, "for-multi-node-is-datareader-required-to-read-the-same-batch-of-data-on-each-node-for-each-step"]], "17. As model parallelism in embedding layers, how does it get all the embedding lookup features from multi-node / multi-gpu?": [[0, "as-model-parallelism-in-embedding-layers-how-does-it-get-all-the-embedding-lookup-features-from-multi-node-multi-gpu"]], "18. How to set data clauses, if there are two embeddings needed?": [[0, "how-to-set-data-clauses-if-there-are-two-embeddings-needed"]], "19. How to save and load models in HugeCTR?": [[0, "how-to-save-and-load-models-in-hugectr"]], "20. Could the post training model from HugeCTR be imported into other frameworks such as TensorFlow for inference deployment?": [[0, "could-the-post-training-model-from-hugectr-be-imported-into-other-frameworks-such-as-tensorflow-for-inference-deployment"]], "21. Does HugeCTR support overlap between different slots?": [[0, "does-hugectr-support-overlap-between-different-slots"]], "22. What if there\u2019s no value in a slot?": [[0, "what-if-there-s-no-value-in-a-slot"]], "23. How can I benchmark my network?": [[0, "how-can-i-benchmark-my-network"]], "24. How to set workspace_size_per_gpu_in_mb and slot_size_array?": [[0, "how-to-set-workspace-size-per-gpu-in-mb-and-slot-size-array"]], "25. Is nvlink required in HugeCTR?": [[0, "is-nvlink-required-in-hugectr"]], "26. Is DGX the only GPU server that is required in HugeCTR?": [[0, "is-dgx-the-only-gpu-server-that-is-required-in-hugectr"]], "27. Can HugeCTR run without InfiniBand?": [[0, "id1"]], "28. Does HugeCTR support loading pretrained embeddings in other formats?": [[0, "does-hugectr-support-loading-pretrained-embeddings-in-other-formats"]], "29. How to construct the model graph with branch topology in HugeCTR?": [[0, "how-to-construct-the-model-graph-with-branch-topology-in-hugectr"]], "30. What is the good practice of configuring the embedding vector size?": [[0, "what-is-the-good-practice-of-configuring-the-embedding-vector-size"]], "31. How to resolve the bus error when running HugeCTR samples and notebooks?": [[0, "how-to-resolve-the-bus-error-when-running-hugectr-samples-and-notebooks"]], "32. What does the log \u201cmemory pool is empty\u201d imply for HugeCTR inference?": [[0, "what-does-the-log-memory-pool-is-empty-imply-for-hugectr-inference"]], "Additional Resources": [[1, "additional-resources"]], "HugeCTR Layer Classes and Methods": [[2, "hugectr-layer-classes-and-methods"]], "Input Layer": [[2, "input-layer"]], "Sparse Embedding": [[2, "sparse-embedding"]], "Embedding Types Detail": [[2, "embedding-types-detail"]], "DistributedSlotSparseEmbeddingHash Layer": [[2, "distributedslotsparseembeddinghash-layer"]], "LocalizedSlotSparseEmbeddingHash Layer": [[2, "localizedslotsparseembeddinghash-layer"]], "LocalizedSlotSparseEmbeddingOneHot Layer": [[2, "localizedslotsparseembeddingonehot-layer"]], "Dense Layers": [[2, "dense-layers"]], "Dense Layers Usage": [[2, "dense-layers-usage"]], "FullyConnected Layer": [[2, "fullyconnected-layer"]], "MLP Layer": [[2, "mlp-layer"]], "MultiCross Layer": [[2, "multicross-layer"]], "FmOrder2 Layer": [[2, "fmorder2-layer"]], 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[[4, "compile-method"]], "fit method": [[4, "fit-method"]], "summary method": [[4, "summary-method"]], "graph_to_json method": [[4, "graph-to-json-method"]], "construct_from_json method": [[4, "construct-from-json-method"]], "load_dense_weights method": [[4, "load-dense-weights-method"]], "load_dense_optimizer_states method": [[4, "load-dense-optimizer-states-method"]], "load_sparse_weights method": [[4, "load-sparse-weights-method"]], "load_sparse_optimizer_states method": [[4, "load-sparse-optimizer-states-method"]], "freeze_dense method": [[4, "freeze-dense-method"]], "freeze_embedding method": [[4, "freeze-embedding-method"]], "unfreeze_dense method": [[4, "unfreeze-dense-method"]], "unfreeze_embedding method": [[4, "unfreeze-embedding-method"]], "reset_learning_rate_scheduler method": [[4, "reset-learning-rate-scheduler-method"]], "set_source method": [[4, "set-source-method"], [4, "id1"]], "Low-level Training API": [[4, "low-level-training-api"]], "LearningRateScheduler": [[4, "learningratescheduler"]], "get_next method": [[4, "get-next-method"]], "DataReader": [[4, "datareader"]], "is_eof method": [[4, "is-eof-method"]], "get_learning_rate_scheduler method": [[4, "get-learning-rate-scheduler-method"]], "get_data_reader_train method": [[4, "get-data-reader-train-method"]], "get_data_reader_eval method": [[4, "get-data-reader-eval-method"]], "start_data_reading method": [[4, "start-data-reading-method"]], "set_learning_rate method": [[4, "set-learning-rate-method"]], "train method": [[4, "train-method"]], "get_current_loss method": [[4, "get-current-loss-method"]], "eval method": [[4, "eval-method"]], "get_eval_metrics method": [[4, "get-eval-metrics-method"]], "save_params_to_files method": [[4, "save-params-to-files-method"]], "check_out_tensor method": [[4, "check-out-tensor-method"], [4, "id3"]], "Inference API": [[4, "inference-api"]], "InferenceParams": [[4, "inferenceparams"]], "InferenceParams class": [[4, "inferenceparams-class"]], "InferenceModel": [[4, "inferencemodel"]], "InferenceModel class": [[4, "inferencemodel-class"]], "predict method": [[4, "predict-method"]], "evaluate method": [[4, "evaluate-method"]], "Data Generator API": [[4, "data-generator-api"]], "DataGeneratorParams class": [[4, "datageneratorparams-class"]], "DataGenerator": [[4, "datagenerator"]], "DataGenerator class": [[4, "datagenerator-class"]], "generate method": [[4, "generate-method"]], "Data Source API": [[4, "data-source-api"]], "DataSourceParams class": [[4, "datasourceparams-class"]], "Hierarchical Parameter Server Database Backend": [[5, "hierarchical-parameter-server-database-backend"]], "Introduction to the HPS Database Backend": [[5, "introduction-to-the-hps-database-backend"]], "Background": [[5, "background"]], "Architecture": [[5, "architecture"]], "Training and Iterative Model Updates": [[5, "training-and-iterative-model-updates"]], "Execution": [[5, "execution"]], "Inference": [[5, "inference"]], "Training": [[5, "training"], [53, "training"], [53, "id3"]], "Lookup Optimization": [[5, "lookup-optimization"]], "Configuration": [[5, "configuration"]], "Inference Parameters and Embedding Cache Configuration": [[5, "inference-parameters-and-embedding-cache-configuration"]], "Inference Params Syntax": [[5, "inference-params-syntax"]], "Inference Parameters": [[5, "inference-parameters"]], "Parameter Server Configuration: Models": [[5, "parameter-server-configuration-models"]], "Volatile Database Configuration": [[5, "volatile-database-configuration"]], "Volatile Database Params Syntax": [[5, "volatile-database-params-syntax"]], "Parameter Server Configuration: Volatile Database": [[5, "parameter-server-configuration-volatile-database"]], "Volatile Database Parameters": [[5, "volatile-database-parameters"]], "Overflow Parameters": [[5, "overflow-parameters"]], "Common Volatile Database Parameters": [[5, "common-volatile-database-parameters"]], "Persistent Database Configuration": [[5, "persistent-database-configuration"]], "Persistent Database Params Syntax": [[5, "persistent-database-params-syntax"]], "Parameter Server Configuration: Persistent Database": [[5, "parameter-server-configuration-persistent-database"]], "Persistent Database Parameters": [[5, "persistent-database-parameters"]], "Update Source Configuration": [[5, "update-source-configuration"]], "Update Source Params Syntax": [[5, "update-source-params-syntax"]], "Parameter Server Configuration: Update Source": [[5, "parameter-server-configuration-update-source"]], "Update Source Parameters": [[5, "update-source-parameters"]], "Benchmark the DLRM Model with HPS": [[6, "benchmark-the-dlrm-model-with-hps"]], "Benchmark Setup": [[6, "benchmark-setup"]], "Results": [[6, "results"]], "Resources": [[6, "resources"]], "Hierarchical Parameter Server API": [[7, "hierarchical-parameter-server-api"]], "HPS Initialize": [[8, "hps-initialize"]], "HPS Layers": [[9, "hps-layers"]], "SparseLookupLayer": [[9, "sparselookuplayer"]], "LookupLayer": [[9, "lookuplayer"]], "Hierarchical Parameter Server Plugin for TensorFlow": [[10, "hierarchical-parameter-server-plugin-for-tensorflow"]], "Introduction to the HPS Plugin for TensorFlow": [[10, "introduction-to-the-hps-plugin-for-tensorflow"]], "Benefits of the Plugin for TensorFlow": [[10, "benefits-of-the-plugin-for-tensorflow"]], "Workflow": [[10, "workflow"], [17, "workflow"]], "Installation": [[10, "installation"], [13, "installation"], [17, "installation"], [20, "installation"], [21, "installation"], [22, "installation"], [23, "installation"], [24, "installation"], [26, "installation"], [27, "installation"], [28, "installation"], [30, "installation"], [31, "installation"], [32, "installation"], [33, "installation"]], "Compute Capability": [[10, "compute-capability"], [13, "compute-capability"], [17, "compute-capability"], [38, "compute-capability"]], "Installing HPS Using NGC Containers": [[10, "installing-hps-using-ngc-containers"], [13, "installing-hps-using-ngc-containers"], [17, "installing-hps-using-ngc-containers"]], "Example Notebooks": [[10, "example-notebooks"], [13, "example-notebooks"], [17, "example-notebooks"]], "Benchmark": [[10, "benchmark"], [17, "benchmark"]], "HPS Plugin for Torch API": [[11, "hps-plugin-for-torch-api"]], "HPS Plugin for Torch": [[12, "hps-plugin-for-torch"]], "LookupLayer class": [[12, "lookuplayer-class"]], "Hierarchical Parameter Server Plugin for Torch": [[13, "hierarchical-parameter-server-plugin-for-torch"]], "Introduction to the HPS Plugin for Torch": [[13, "introduction-to-the-hps-plugin-for-torch"]], "HPS Plugin": [[14, "hps-plugin"]], "HPS Plugin Creator": [[15, "hps-plugin-creator"]], "HPS Plugin for TensorRT API": [[16, "hps-plugin-for-tensorrt-api"]], "Hierarchical Parameter Server Plugin for TensorRT": [[17, "hierarchical-parameter-server-plugin-for-tensorrt"]], "Introduction to the HPS Plugin for TensorRT": [[17, "introduction-to-the-hps-plugin-for-tensorrt"]], "Hierarchical Parameter Server": [[18, "hierarchical-parameter-server"], [36, "hierarchical-parameter-server"]], "Profiling HPS": [[19, "profiling-hps"]], "HPS profiler": [[19, "hps-profiler"]], "Build and install the HPS Profiler": [[19, "build-and-install-the-hps-profiler"]], "Create a synthetic embedding table": [[19, "create-a-synthetic-embedding-table"]], "Use the HPS Profiler to get the measurement results": [[19, "use-the-hps-profiler-to-get-the-measurement-results"]], "Profile HPS with Triton Perf Analyzer:": [[19, "profile-hps-with-triton-perf-analyzer"]], "HPS Profiler vs. Triton Perf Analyzer:": [[19, "hps-profiler-vs-triton-perf-analyzer"]], "Hierarchical Parameter Server Demo": [[20, "hierarchical-parameter-server-demo"], [41, "hierarchical-parameter-server-demo"]], "Overview": [[20, "overview"], [21, "overview"], [22, "overview"], [23, "overview"], [24, "overview"], [26, "overview"], [27, "overview"], [28, "overview"], [30, "overview"], [31, "overview"], [32, "overview"], [33, "overview"], [41, "overview"], [42, "overview"], [44, "overview"], [53, "overview"]], "Get HPS from NGC": [[20, "get-hps-from-ngc"], [21, "get-hps-from-ngc"], [22, "get-hps-from-ngc"], [23, "get-hps-from-ngc"], [24, "get-hps-from-ngc"], [28, "get-hps-from-ngc"]], "Configurations": [[20, "configurations"], [21, "configurations"], [22, "configurations"], [24, "configurations"], [26, "configurations"], [27, "configurations"], [32, "configurations"], [33, "configurations"]], "Train with native TF layers": [[20, "train-with-native-tf-layers"], [21, "train-with-native-tf-layers"], [22, "train-with-native-tf-layers"], [24, "train-with-native-tf-layers"], [33, "train-with-native-tf-layers"]], "Create the inference graph with HPS LookupLayer": [[20, "create-the-inference-graph-with-hps-lookuplayer"], [24, "create-the-inference-graph-with-hps-lookuplayer"]], "Inference with saved model graph": [[20, "inference-with-saved-model-graph"], [21, "inference-with-saved-model-graph"], [26, "inference-with-saved-model-graph"]], "HPS for Multiple Tables and Sparse Inputs": [[21, "hps-for-multiple-tables-and-sparse-inputs"]], "Create the inference graph with HPS SparseLookupLayer and LookupLayer": [[21, "create-the-inference-graph-with-hps-sparselookuplayer-and-lookuplayer"]], "HPS Pretrained Model Training Demo": [[22, "hps-pretrained-model-training-demo"]], "Load the pre-trained embeddings via HPS": [[22, "load-the-pre-trained-embeddings-via-hps"]], "HPS Table Fusion Demo": [[23, "hps-table-fusion-demo"]], "Create TF SavedModel": [[23, "create-tf-savedmodel"]], "Make inference with HPS table fusion": [[23, "make-inference-with-hps-table-fusion"]], "Deploy SavedModel using HPS with Triton TensorFlow Backend": [[24, "deploy-savedmodel-using-hps-with-triton-tensorflow-backend"], [24, "id1"]], "Deploy TF-TRT SavedModel using HPS with Triton TensorFlow Backend": [[24, "deploy-tf-trt-savedmodel-using-hps-with-triton-tensorflow-backend"]], "Hierarchical Parameter Server Notebooks": [[25, "hierarchical-parameter-server-notebooks"], [29, "hierarchical-parameter-server-notebooks"]], "Quickstart": [[25, "quickstart"], [29, "quickstart"], [34, "quickstart"]], "Pull the NGC Docker": [[25, "pull-the-ngc-docker"], [29, "pull-the-ngc-docker"]], "Clone the HugeCTR Repository": [[25, "clone-the-hugectr-repository"], [29, "clone-the-hugectr-repository"], [34, "clone-the-hugectr-repository"]], "Start the Jupyter Notebook": [[25, "start-the-jupyter-notebook"], [29, "start-the-jupyter-notebook"], [34, "start-the-jupyter-notebook"]], "Notebook List": [[25, "notebook-list"], [29, "notebook-list"], [34, "notebook-list"], [43, "notebook-list"]], "System Specifications": [[25, "system-specifications"], [29, "system-specifications"], [34, "system-specifications"], [43, "system-specifications"]], "SOK to HPS DLRM Demo": [[26, "sok-to-hps-dlrm-demo"]], "Get SOK from NGC": [[26, "get-sok-from-ngc"], [27, "get-sok-from-ngc"]], "Train with SOK embedding layers": [[26, "train-with-sok-embedding-layers"]], "Create the inference graph with HPS SparseLookupLayer": [[26, "create-the-inference-graph-with-hps-sparselookuplayer"]], "SOK Train DLRM Demo": [[27, "sok-train-dlrm-demo"]], "Build model with SOK embedding layers": [[27, "build-model-with-sok-embedding-layers"]], "Train with SOK models": [[27, "train-with-sok-models"]], "HPS Torch Demo": [[28, "hps-torch-demo"]], "Data Generation": [[28, "data-generation"], [31, "data-generation"], [41, "data-generation"]], "Lookup with Table Fusion": [[28, "lookup-with-table-fusion"]], "HPS TensorRT Plugin Benchmark for TensorFlow Large Model": [[30, "hps-tensorrt-plugin-benchmark-for-tensorflow-large-model"]], "Use NGC": [[30, "use-ngc"], [31, "use-ngc"], [32, "use-ngc"], [33, "use-ngc"]], "1. Create the TF model": [[30, "create-the-tf-model"]], "2. Build the HPS-integrated TensorRT engine": [[30, "build-the-hps-integrated-tensorrt-engine"]], "Step1: Prepare the 147GB embedding table": [[30, "step1-prepare-the-147gb-embedding-table"]], "1.1 Train a 147GB model from scratch": [[30, "train-a-147gb-model-from-scratch"]], "1.2 Get the embedding model file in hps format": [[30, "get-the-embedding-model-file-in-hps-format"]], "Step2: Prepare JSON configuration file for HPS": [[30, "step2-prepare-json-configuration-file-for-hps"]], "Step3: Convert to ONNX and do ONNX graph surgery": [[30, "step3-convert-to-onnx-and-do-onnx-graph-surgery"]], "Step4: Build the TensorRT engine": [[30, "step4-build-the-tensorrt-engine"]], "3. Benchmark HPS-integrated TensorRT engine on Triton": [[30, "benchmark-hps-integrated-tensorrt-engine-on-triton"]], "Step1: Create the model repository": [[30, "step1-create-the-model-repository"]], "Step2: Prepare the benchmark input data": [[30, "step2-prepare-the-benchmark-input-data"]], "Step3: Launch the Triton inference server": [[30, "step3-launch-the-triton-inference-server"]], "Step4: Run the benchmark": [[30, "step4-run-the-benchmark"]], "4. Benchmark for ARM64 or Grace + Hooper systems": [[30, "benchmark-for-arm64-or-grace-hooper-systems"]], "Step 1: Build the NVIDIA Merlin docker images": [[30, "step-1-build-the-nvidia-merlin-docker-images"]], "Step 2: Prepare host system for running the docker container": [[30, "step-2-prepare-host-system-for-running-the-docker-container"]], "Step 3: Create the model": [[30, "step-3-create-the-model"]], "Step 4: Prepare data": [[30, "step-4-prepare-data"]], "Step 5: Run benchmark": [[30, "step-5-run-benchmark"]], "HPS TensorRT Plugin Demo for HugeCTR Trained Model": [[31, "hps-tensorrt-plugin-demo-for-hugectr-trained-model"]], "Train with HugeCTR": [[31, "train-with-hugectr"]], "Build the HPS-integrated TensorRT engine": [[31, "build-the-hps-integrated-tensorrt-engine"], [32, "build-the-hps-integrated-tensorrt-engine"], [33, "build-the-hps-integrated-tensorrt-engine"]], "Step1: Prepare JSON configuration file for HPS": [[31, "step1-prepare-json-configuration-file-for-hps"]], "Step2: Convert to ONNX and do ONNX graph surgery": [[31, "step2-convert-to-onnx-and-do-onnx-graph-surgery"], [32, "step2-convert-to-onnx-and-do-onnx-graph-surgery"], [33, "step2-convert-to-onnx-and-do-onnx-graph-surgery"]], "Step3: Build the TensorRT engine": [[31, "step3-build-the-tensorrt-engine"], [32, "step3-build-the-tensorrt-engine"], [33, "step3-build-the-tensorrt-engine"]], "Deploy HPS-integrated TensorRT engine on Triton": [[31, "deploy-hps-integrated-tensorrt-engine-on-triton"], [32, "deploy-hps-integrated-tensorrt-engine-on-triton"]], "HPS TensorRT Plugin Demo for PyTorch Trained Model": [[32, "hps-tensorrt-plugin-demo-for-pytorch-trained-model"]], "Train with PyTorch": [[32, "train-with-pytorch"]], "Step1: Prepare sparse model and JSON configuration file for HPS": [[32, "step1-prepare-sparse-model-and-json-configuration-file-for-hps"], [33, "step1-prepare-sparse-model-and-json-configuration-file-for-hps"]], "HPS TensorRT Plugin Demo for TensorFlow Trained Model": [[33, "hps-tensorrt-plugin-demo-for-tensorflow-trained-model"]], "Deploy HPS-integrated TensorRT engine with Triton on multiple GPUs": [[33, "deploy-hps-integrated-tensorrt-engine-with-triton-on-multiple-gpus"]], "HPS Plugin for TensorRT Notebooks": [[34, "hps-plugin-for-tensorrt-notebooks"]], "Pull the Container from NGC": [[34, "pull-the-container-from-ngc"]], "Contributing to HugeCTR": [[35, "contributing-to-hugectr"]], "Overview of Contributing to HugeCTR": [[35, "overview-of-contributing-to-hugectr"]], "Contribute New Code": [[35, "contribute-new-code"]], "How to Start your Development": [[35, "how-to-start-your-development"]], "Set Up the Development Environment With Merlin Containers": [[35, "set-up-the-development-environment-with-merlin-containers"]], "Build HugeCTR Training Container from Source": [[35, "build-hugectr-training-container-from-source"]], "Build HugeCTR Inference Container from Source": [[35, "build-hugectr-inference-container-from-source"]], "Build Sparse Operation Kit (SOK) from Source": [[35, "build-sparse-operation-kit-sok-from-source"]], "HugeCTR Core Features": [[36, "hugectr-core-features"]], "Summary of Core Features": [[36, "summary-of-core-features"]], "Model Parallel Training": [[36, "model-parallel-training"]], "Multi-Node Training": [[36, "multi-node-training"]], "Mixed Precision Training": [[36, "mixed-precision-training"]], "SGD Optimizer and Learning Rate Scheduling": [[36, "sgd-optimizer-and-learning-rate-scheduling"]], "HugeCTR to ONNX Converter": [[36, "hugectr-to-onnx-converter"]], "HDFS Support": [[36, "hdfs-support"]], "Sparse Operation Kit": [[36, "sparse-operation-kit"], [56, "sparse-operation-kit"]], "HugeCTR Talks and Blogs": [[37, "hugectr-talks-and-blogs"]], "Talks": [[37, "talks"]], "Blogs": [[37, "blogs"]], "Introduction to HugeCTR": [[38, "introduction-to-hugectr"]], "About HugeCTR": [[38, "about-hugectr"]], "Installing and Building HugeCTR": [[38, "installing-and-building-hugectr"]], "Installing HugeCTR Using NGC Containers": [[38, "installing-hugectr-using-ngc-containers"]], "Building HugeCTR from Scratch": [[38, "building-hugectr-from-scratch"]], "Tools": [[38, "tools"]], "Generating Synthetic Data and Benchmarks": [[38, "generating-synthetic-data-and-benchmarks"]], "Downloading and Preprocessing Datasets": [[38, "downloading-and-preprocessing-datasets"]], "Merlin HugeCTR": [[39, "merlin-hugectr"]], "Related Resources": [[39, "related-resources"]], "HugeCTR Embedding Collection": [[40, "hugectr-embedding-collection"]], "About this Notebook": [[40, "about-this-notebook"]], "Concepts and API Reference": [[40, "concepts-and-api-reference"]], "Setup": [[40, "setup"], [41, "setup"], [42, "setup"]], "Use an Embedding Collection with a DLRM Model": [[40, "use-an-embedding-collection-with-a-dlrm-model"]], "Data Preparation": [[40, "data-preparation"], [42, "data-preparation"], [53, "data-preparation"]], "Run the following commands on the terminal to prepare the data for this notebook": [[40, "run-the-following-commands-on-the-terminal-to-prepare-the-data-for-this-notebook"]], "Prepare the Training Script": [[40, "prepare-the-training-script"]], "Embedding Table Placement Strategy: Round Robin": [[40, "embedding-table-placement-strategy-round-robin"]], "Embedding Table Placement Strategy: Uniform": [[40, "embedding-table-placement-strategy-uniform"]], "Embedding Table Placement Strategy: Hybrid": [[40, "embedding-table-placement-strategy-hybrid"]], "Use Dynamic Hash Table with Round Robin Table Placement Strategy": [[40, "use-dynamic-hash-table-with-round-robin-table-placement-strategy"]], "Train from Scratch": [[41, "train-from-scratch"]], "Convert HugeCTR to ONNX": [[41, "convert-hugectr-to-onnx"]], "1. Inference with HPS & ONNX": [[41, "inference-with-hps-onnx"]], "2. Lookup the Embedding Vector from DLPack": [[41, "lookup-the-embedding-vector-from-dlpack"]], "3. Multi-process inference": [[41, "multi-process-inference"]], "4. Redis Cluster deployment (without TLS/SSL)": [[41, "redis-cluster-deployment-without-tls-ssl"]], "5. Redis Cluster deployment (with TLS/SSL)": [[41, "redis-cluster-deployment-with-tls-ssl"]], "HugeCTR End-end Example with NVTabular": [[42, "hugectr-end-end-example-with-nvtabular"]], "Data Preprocessing using NVTabular": [[42, "data-preprocessing-using-nvtabular"]], "Training a WDL model with HugeCTR": [[42, "training-a-wdl-model-with-hugectr"]], "HugeCTR Example Notebooks": [[43, "hugectr-example-notebooks"]], "1. Clone the HugeCTR Repository": [[43, "clone-the-hugectr-repository"]], "2. Pull the NGC Docker and run it": [[43, "pull-the-ngc-docker-and-run-it"]], "3. Customized Building (Optional)": [[43, "customized-building-optional"]], "4. Start the Jupyter Notebook": [[43, "start-the-jupyter-notebook"]], "Training Recommender Systems on Multi-modal Data": [[44, "training-recommender-systems-on-multi-modal-data"]], "MovieLens-25M: Download and Convert": [[45, "movielens-25m-download-and-convert"]], "Getting Started": [[45, "getting-started"]], "Convert the dataset": [[45, "convert-the-dataset"]], "Splitting into train and validation dataset": [[45, "splitting-into-train-and-validation-dataset"]], "Next steps": [[45, "next-steps"]], "MovieLens Data Enrichment": [[46, "movielens-data-enrichment"]], "Scraping data from IMDB": [[46, "scraping-data-from-imdb"]], "Collect synopsis for all movies": [[46, "collect-synopsis-for-all-movies"]], "Scraping movie posters": [[46, "scraping-movie-posters"]], "Movie Poster Feature Extraction with ResNet": [[47, "movie-poster-feature-extraction-with-resnet"]], "Download a pretrained ResNet-50 from NVIDIA GPU cloud": [[47, "download-a-pretrained-resnet-50-from-nvidia-gpu-cloud"]], "Extract features for all movies": [[47, "extract-features-for-all-movies"]], "Movie Synopsis Feature Extraction with Bart text summarization": [[48, "movie-synopsis-feature-extraction-with-bart-text-summarization"]], "Download pretrained BART model": [[48, "download-pretrained-bart-model"]], "Extracting embeddings for all movie\u2019s synopsis": [[48, "extracting-embeddings-for-all-movie-s-synopsis"]], "Creating Multi-Modal Movie Feature Store": [[49, "creating-multi-modal-movie-feature-store"]], "Real data": [[49, "real-data"]], "Synthetic data": [[49, "synthetic-data"]], "ETL with NVTabular": [[50, "etl-with-nvtabular"]], "Defining our Preprocessing Pipeline": [[50, "defining-our-preprocessing-pipeline"]], "Running the pipeline": [[50, "running-the-pipeline"]], "Checking the pre-processing outputs": [[50, "checking-the-pre-processing-outputs"]], "Training HugeCTR Model with Pre-trained Embeddings": [[51, "training-hugectr-model-with-pre-trained-embeddings"]], "Loading pretrained movie features into non-trainable embedding layer": [[51, "loading-pretrained-movie-features-into-non-trainable-embedding-layer"]], "Define and train model": [[51, "define-and-train-model"]], "Multi-modal Example Notebooks": [[52, "multi-modal-example-notebooks"]], "HugeCTR Training with Remote File System Example": [[53, "hugectr-training-with-remote-file-system-example"]], "Setup HugeCTR": [[53, "setup-hugectr"]], "Training with HDFS Example": [[53, "training-with-hdfs-example"]], "Training a DLRM model": [[53, "training-a-dlrm-model"]], "Training a DCN model with AWS S3": [[53, "training-a-dcn-model-with-aws-s3"]], "Data preparation": [[53, "id1"], [53, "id2"]], "Training a DCN model with Google Cloud Storage": [[53, "training-a-dcn-model-with-google-cloud-storage"]], "Performance": [[54, "performance"]], "MLPerf on DGX-2 and DGX A100": [[54, "mlperf-on-dgx-2-and-dgx-a100"]], "Evaluating HugeCTR\u2019s Performance on the DGX-1": [[54, "evaluating-hugectr-s-performance-on-the-dgx-1"]], "Evaluating HugeCTR\u2019s Performance on TensorFlow": [[54, "evaluating-hugectr-s-performance-on-tensorflow"]], "Release Notes": [[55, "release-notes"]], "What\u2019s New in Version 24.06": [[55, "what-s-new-in-version-24-06"]], "What\u2019s New in Version 23.12": [[55, "what-s-new-in-version-23-12"]], "What\u2019s New in Version 23.11": [[55, "what-s-new-in-version-23-11"]], "What\u2019s New in Version 23.08": [[55, "what-s-new-in-version-23-08"]], "What\u2019s New in Version 23.06": [[55, "what-s-new-in-version-23-06"]], "What\u2019s New in Version 23.04": [[55, "what-s-new-in-version-23-04"]], "What\u2019s New in Version 23.02": [[55, "what-s-new-in-version-23-02"]], "What\u2019s New in Version 4.3": [[55, "what-s-new-in-version-4-3"]], "What\u2019s New in Version 4.2": [[55, "what-s-new-in-version-4-2"]], "What\u2019s New in Version 4.1": [[55, "what-s-new-in-version-4-1"]], "What\u2019s New in Version 4.0": [[55, "what-s-new-in-version-4-0"]], "What\u2019s New in Version 3.9": [[55, "what-s-new-in-version-3-9"]], "What\u2019s New in Version 3.8": [[55, "what-s-new-in-version-3-8"]], "What\u2019s New in Version 3.7": [[55, "what-s-new-in-version-3-7"]], "What\u2019s New in Version 3.6": [[55, "what-s-new-in-version-3-6"]], "What\u2019s New in Version 3.5": [[55, "what-s-new-in-version-3-5"]], "What\u2019s New in Version 3.4.1": [[55, "what-s-new-in-version-3-4-1"]], "What\u2019s New in Version 3.4": [[55, "what-s-new-in-version-3-4"]], "What\u2019s New in Version 3.3.1": [[55, "what-s-new-in-version-3-3-1"]], "What\u2019s New in Version 3.3": [[55, "what-s-new-in-version-3-3"]], "What\u2019s New in Version 3.2.1": [[55, "what-s-new-in-version-3-2-1"]], "What\u2019s New in Version 3.2": [[55, "what-s-new-in-version-3-2"]], "What\u2019s New in Version 3.1": [[55, "what-s-new-in-version-3-1"]], "What\u2019s New in Version 3.0.1": [[55, "what-s-new-in-version-3-0-1"]], "What\u2019s New in Version 3.0": [[55, "whats-new-in-version-3-0"]], "What\u2019s New in Version 2.3": [[55, "what-s-new-in-version-2-3"]], "Known Issues": [[55, "known-issues"]]}, "indexentries": {"init() (in module hierarchical_parameter_server)": [[8, "hierarchical_parameter_server.Init"]], "lookuplayer (class in hierarchical_parameter_server)": [[9, "hierarchical_parameter_server.LookupLayer"]], "sparselookuplayer (class in hierarchical_parameter_server)": [[9, "hierarchical_parameter_server.SparseLookupLayer"]], "call() (hierarchical_parameter_server.lookuplayer method)": [[9, "hierarchical_parameter_server.LookupLayer.call"]], "call() (hierarchical_parameter_server.sparselookuplayer method)": [[9, "hierarchical_parameter_server.SparseLookupLayer.call"]]}}) \ No newline at end of file