924 件 ・ 関連度順
概要と使いどころ
Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
日本語の概要は準備中です。原文の説明を表示しています。
Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.
日本語の概要は準備中です。原文の説明を表示しています。
Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
日本語の概要は準備中です。原文の説明を表示しています。
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
日本語の概要は準備中です。原文の説明を表示しています。
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
日本語の概要は準備中です。原文の説明を表示しています。
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
日本語の概要は準備中です。原文の説明を表示しています。
Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.
日本語の概要は準備中です。原文の説明を表示しています。
Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
日本語の概要は準備中です。原文の説明を表示しています。
Custom training plans by goal (strength, cardio, flexibility). Progressive overload programming, rest day optimization, home vs gym adaptations, deload weeks.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
日本語の概要は準備中です。原文の説明を表示しています。
Router for FunASR speech workflows: local transcription, subtitles, services, vLLM, normalization, and training/export.
日本語の概要は準備中です。原文の説明を表示しています。
Route GeoAI geospatial data, inference, training, foundation-model, and integration workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Routes official 3D Gaussian Splatting setup, data preparation, training, rendering, evaluation, and viewer workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle deployment.
日本語の概要は準備中です。原文の説明を表示しています。
Operate the legacy Frustum PointNets code release for KITTI or SUN RGB-D data preparation, TensorFlow runtime setup, training, inference, and 3D detection evaluation.
日本語の概要は準備中です。原文の説明を表示しています。
Repository operating skill for using Flow Forecast's flood_forecast package for time-series forecasting, data preparation, training, inference, evaluation, and hydrology-oriented advanced models.
日本語の概要は準備中です。原文の説明を表示しています。
Use Flair for NLP annotation, embeddings, text/sequence model prediction, corpus loading, training/fine-tuning, HunFlair biomedical NER/linking, and production troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Operate the Fengshenbang-LM Fengshen package for pipelines, model families, data/training utilities, examples, and conversion planning.
日本語の概要は準備中です。原文の説明を表示しています。
Use FedML for setup, CLI operations, MLOps launch, distributed and federated training, model serving, and workflow orchestration.
日本語の概要は準備中です。原文の説明を表示しています。
Routes FCOS object-detection repo tasks for inference demos, config/data setup, training/evaluation, ONNX export, and legacy PyTorch internals.
日本語の概要は準備中です。原文の説明を表示しています。
Guides FastVideo video, image, and audio generation, config-first inference, serving, training, distillation, evaluation, and performance workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guides TensorFlow Fast Style Transfer training, image stylization, and video stylization workflows from checkpoints, VGG/COCO assets, and script-oriented CLIs.
日本語の概要は準備中です。原文の説明を表示しています。