'gpu' 검색 결과

Diagnose slow loading, high memory use, and low frame rates in USD 3D scenes. It coordinates measurement, scene validation, and optimization, then produces reports comparing available before-and-after evidence and an optimized scene when changes are performed.

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• Investigate slow loading in USD scenes
• Diagnose low frame rates and high memory use
• Review repeated structures in large CAD scenes

Run a Kubernetes workflow that changes clothing in cropped person images, searches person attributes, and builds augmented datasets. The skill handles input preparation, run monitoring, and result retrieval for person re-identification data work.

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• Create clothing variations from cropped person images
• Prepare augmented datasets for person re-identification
• Search person attributes and inspect generated captions

Generate synthetic 3D brain MRI volumes with NV-Generate-CTMR MR-Brain v1, choosing scan types such as T1 or FLAIR. The wrapper records generation settings and output checks for experimental review, with restrictions on clinical and production use.

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• Generating synthetic T1 or T2 brain MRI volumes
• Trying FLAIR or SWI generation settings
• Specifying a random seed for generation

Search document collections with the NeMo Retriever CLI. Ingest files into a local search index or use a deployed service, then retrieve evidence for questions across PDFs, Office files, images, audio, and video.

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• Find evidence across internal documents
• Build a local search index from files
• Query an existing Retriever service

Guide repeated NeMo-RL or NeMo-gym experiments to test hypotheses and improve training settings. Establish a baseline, compare relevant metrics, and preserve reproducible records in git and TSV logs while keeping users involved in launch decisions.

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• Testing hypotheses about training settings
• Comparing accuracy, reward, or throughput
• Recording reproducible experiment histories

Deploy, run, and test NVIDIA speech recognition, speech synthesis, and translation services in the cloud or on your own infrastructure. Guidance covers model selection, environment checks, custom models, and adjustments to speech processing and pronunciation.

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• Deploy and test a speech recognition model
• Generate speech with Magpie
• Adjust pronunciation for specific words

Collect a short, read-only report from a running Jetson device, covering its model, L4T release, kernel, OS, power mode, and uptime. Use it to record the environment before performance, regression, or compatibility tests.

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• Record the device environment before performance tests
• Check the L4T release after flashing a Jetson
• Capture baseline information for compatibility tests

Provides instructions for serving language and vision-language models on Jetson with vLLM or SGLang. Selects a runtime for the device and JetPack version, supplies launch commands, and explains how to verify the OpenAI-compatible API.

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• Starting a language-model API on Jetson
• Connecting Open WebUI or an app to a model server
• Serving a vision-language model with vLLM

Prepare BOP datasets and run depth estimation, image segmentation, and FoundationPose to estimate object positions and orientations. Evaluate completed runs, compare accuracy and coverage, or change scoring settings without repeating inference.

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• Adapt a BOP dataset and test a single scene
• Estimate object poses without ground-truth annotations
• Re-score a completed run while preserving its original report

Build Earth2Studio wrappers for transformations at a single time point, from calculating wind speed to using pretrained or diffusion models. The workflow covers input and output coordinates, model loading, tests, and supporting documentation.

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• Calculate wind speed from u10m and v10m
• Add a pretrained precipitation estimator
• Wrap a diffusion model for higher-resolution output

Set up a NVIDIA Holoscan Sensor Bridge demo for the selected development kit. The workflow configures the host, builds and launches the demo container, checks board connectivity and FPGA version, and reports issues encountered.

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• Setting up an HSB demo on a supported development kit
• Building and launching the demo container
• Checking board connectivity and FPGA version

Measure throughput, latency, or maximum bandwidth for DOCA libraries using doca_bench on a host or BlueField Arm. Capture commands and environment details for repeatable comparisons, check run stability, and investigate configuration or measurement problems.

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• Measuring BlueField compression throughput
• Checking RDMA throughput or cryptographic latency
• Recording a baseline before driver or firmware upgrades

Predict the structure of a single protein chain from its amino acid sequence using OpenFold2 NIM. Run through NVIDIA's hosted API or local Docker, optionally add sequence alignments and structural templates, and save predictions with confidence information.

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• Predicting a single protein chain's structure from its sequence
• Adding A3M sequence alignments or mmCIF structural templates
• Comparing predictions from multiple model parameter sets

Extend an existing grover_base model with a new decoder that reconstructs molecular structure strings, then continue pretraining on your corpus. The workflow validates inputs, rebuilds vocabulary components, and records the conversion and training outputs.

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• Extend an existing grover_base model with cMIM
• Continue pretraining on a molecular corpus
• Trace checkpoint conversion and training records

Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.

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• Launching the AutoMagicCalib backend and web interface
• Configuring service ports and network access
• Adding the optional VGGT model during setup

Turn molecules listed in a SMILES CSV into numerical feature vectors using a KERMT model. The workflow checks the model and input, cleans molecular notation, and saves four sets of vectors with validity information.

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• Extract molecular feature vectors from a SMILES CSV
• Reuse a local KERMT checkpoint
• Extract features with a released pretrained model

Generate DNA sequences with NVIDIA Evo 2, check their length and alphabet, and save sequence files alongside probabilities and timing metrics. A local deployment also supports extracting numerical outputs from selected internal model layers.

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• Generate DNA from a starting sequence
• Save generated sequences and metrics
• Extract internal layer outputs from a local model

Predict biomolecular structures and estimate how strongly a ligand binds using Boltz2 NIM. Submit proteins, DNA, RNA, and ligand inputs through NVIDIA's hosted API or local Docker, then save structures and inspect confidence and affinity scores.

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• Predicting protein structures and DNA or RNA complexes
• Providing ligand inputs with SMILES or CCD identifiers
• Estimating binding affinity for one ligand

Find related protein sequences and generate multiple sequence alignments, which line up corresponding positions across sequences. Use NVIDIA's hosted API or a local Docker service for standard searches, paired complex alignments, and local structural template searches.

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• Generate protein alignments in A3M or FASTA format
• Create paired alignments for protein complexes
• Search PDB70 for structural templates locally

Design protein backbones, the structural framework of proteins, with RFDiffusion through NVIDIA NIM. Generate new structures, build around existing structural motifs, or design binders, then save PDB files for subsequent amino acid sequence design.

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• Generating a new protein backbone
• Building a backbone around an existing structural motif
• Designing a binder around specified target residues

Designs candidate protein sequences for a supplied PDB backbone using ProteinMPNN through NVIDIA NIM. Supports hosted or local processing, configurable design constraints, and saved FASTA sequence files with returned scores and a checked summary of results.

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• Generate sequence candidates for a protein backbone
• Design sequences while keeping selected chains or residues fixed
• Compare candidates across sampling temperatures

Monitor KERMT jobs running in the background by reading run records, Docker container status, and logs. Summarize training progress and validation metrics, stream logs, or report final test metrics when a finetune run completes successfully.

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• Check progress on a background pretraining run
• Watch training logs and validation metrics live
• Review test metrics after finetuning completes

Fine-tune Parakeet TDT v2 on clinical vocabulary using NeMo, then compare key-term error rates before and after training. The results guide whether to keep the model, expand the dataset, or prepare it for deployment.

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• Reducing transcription errors in medication names
• Checking whether training improved clinical term recognition
• Deciding whether to expand the training dataset

Generate drug-like molecule candidates with GenMol NIM through NVIDIA's hosted API or local Docker deployment. Expand molecular scaffolds, modify existing candidates, rank results by drug-likeness or lipophilicity, and save the ranked molecules to a file.

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• Generate new molecule candidates from scratch
• Add structures around a molecular scaffold
• Modify fragments of a lead molecule