diff --git a/audio/speech_recognition/conformer/paddlepaddle/README.md b/audio/speech_recognition/conformer/paddlepaddle/README.md index 05356b0f8a10250b4295f852bdf583de7f32c76b..adebd5755b84fced6e00a31f16fe30d599b167a3 100644 --- a/audio/speech_recognition/conformer/paddlepaddle/README.md +++ b/audio/speech_recognition/conformer/paddlepaddle/README.md @@ -12,6 +12,12 @@ CNN based models achieving state-of-the-art accuracies. On the widely used Libri of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_recognition/conformer_wenet/pytorch/README.md b/audio/speech_recognition/conformer_wenet/pytorch/README.md index 6d39b05d487ec8794a1422845a6711a9de5969b6..04a07b4b5bb1ac15e15b6bb176ad59a32c7f02c8 100755 --- a/audio/speech_recognition/conformer_wenet/pytorch/README.md +++ b/audio/speech_recognition/conformer_wenet/pytorch/README.md @@ -7,6 +7,12 @@ convolutional neural networks (CNNs) and transformers. It employs CNNs for local capture long-range dependencies in data. This combination allows the Conformer to efficiently handle both local patterns and global relationships, making it particularly effective for audio and speech tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Install Dependencies diff --git a/audio/speech_recognition/efficient_conformer_v2_wenet/pytorch/README.md b/audio/speech_recognition/efficient_conformer_v2_wenet/pytorch/README.md index 1ac4b0f625d4585ea363b4dc25b24bd6442d6059..a159c2eb5622ed21559e2eb6e821596bc7d894e0 100644 --- a/audio/speech_recognition/efficient_conformer_v2_wenet/pytorch/README.md +++ b/audio/speech_recognition/efficient_conformer_v2_wenet/pytorch/README.md @@ -7,6 +7,12 @@ offering transformers a series of designs and optimizations for mobile accelerat The number of parameters and latency of the model are critical for resource-constrained hardware, so EfficientFormerV2 combines a fine-grained joint search strategy to propose an efficient network with low latency and size. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Install Dependencies diff --git a/audio/speech_recognition/rnnt/pytorch/README.md b/audio/speech_recognition/rnnt/pytorch/README.md index 17dff16a085af1a0d1eec2c4bc9d7341b3a57fe1..c0352bf7e57a82cf2f937ca9d73719499424d867 100644 --- a/audio/speech_recognition/rnnt/pytorch/README.md +++ b/audio/speech_recognition/rnnt/pytorch/README.md @@ -10,6 +10,12 @@ combines these representations. RNN-T handles variable-length input/output seque during training. It's particularly effective for speech recognition as it can process continuous audio streams and output text in real-time, achieving state-of-the-art performance on various benchmarks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_recognition/transformer_wenet/pytorch/README.md b/audio/speech_recognition/transformer_wenet/pytorch/README.md index 3138a024404046440d2709def38bc3656b646349..b9e8f209094e1498c20b4188c851a730679ec6ff 100755 --- a/audio/speech_recognition/transformer_wenet/pytorch/README.md +++ b/audio/speech_recognition/transformer_wenet/pytorch/README.md @@ -8,6 +8,12 @@ parallel (as opposed to sequentially) and capture complex dependencies in data, sequence. Transformers have since become the foundation for state-of-the-art models in various tasks, especially in natural language processing, such as the BERT and GPT series. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Install Dependencies diff --git a/audio/speech_recognition/u2++_conformer_wenet/pytorch/README.md b/audio/speech_recognition/u2++_conformer_wenet/pytorch/README.md index 555bc945f4b35431a5401fec4413835fddac43c1..ef640d1911b6e8089c76e8409e6492d9e5d331f3 100755 --- a/audio/speech_recognition/u2++_conformer_wenet/pytorch/README.md +++ b/audio/speech_recognition/u2++_conformer_wenet/pytorch/README.md @@ -6,6 +6,12 @@ U2++, an enhanced version of U2 to further improve the accuracy. The core idea o backward information of the labeling sequences at the same time at training to learn richer information, and combine the forward and backward prediction at decoding to give more accurate recognition results. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Install Dependencies diff --git a/audio/speech_recognition/unified_conformer_wenet/pytorch/README.md b/audio/speech_recognition/unified_conformer_wenet/pytorch/README.md index 6a2eb85f0bce056a3d704ff3d41ea8949e9dfc0b..420fa9f8ef99fd2cfcbbd3c46092df0f550154c0 100755 --- a/audio/speech_recognition/unified_conformer_wenet/pytorch/README.md +++ b/audio/speech_recognition/unified_conformer_wenet/pytorch/README.md @@ -7,6 +7,12 @@ Unified Conformer is an architecture that has become state-of-the-art in the fie Processing and Computer Vision tasks thanks to its powerful self-attention mechanism¹. The Conformer architecture has been modified from ASR to Automatic Speaker Verification (ASV) with very minor changes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Install Dependencies diff --git a/audio/speech_synthesis/fastspeech2/paddlepaddle/README.md b/audio/speech_synthesis/fastspeech2/paddlepaddle/README.md index bce73c30e70d5eb04c73c330ecc70a542fe70d34..eaf897aca1cd2a39e2484f3e2a56093cd4fc671c 100644 --- a/audio/speech_synthesis/fastspeech2/paddlepaddle/README.md +++ b/audio/speech_synthesis/fastspeech2/paddlepaddle/README.md @@ -8,6 +8,12 @@ waveform from text in parallel, enjoying the benefit of fully end-to-end inferen FastSpeech 2 achieves a 3x training speed-up over FastSpeech, and FastSpeech 2s enjoys even faster inference speed; 2) FastSpeech 2 and 2s outperform FastSpeech in voice quality, and FastSpeech 2 can even surpass autoregressive models. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_synthesis/hifigan/paddlepaddle/README.md b/audio/speech_synthesis/hifigan/paddlepaddle/README.md index d9e0063f482f48e5f096a1ba0a2d2622776e0b8d..525f154c73c1be82f02c8fac8a85b3dea6d08dae 100644 --- a/audio/speech_synthesis/hifigan/paddlepaddle/README.md +++ b/audio/speech_synthesis/hifigan/paddlepaddle/README.md @@ -6,6 +6,12 @@ HiFiGAN is a commonly used vocoder in academia and industry in recent years, whi generated by acoustic models into high-quality audio. This vocoder uses generative adversarial networks as the basis for generating models. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_synthesis/tacotron2/pytorch/README.md b/audio/speech_synthesis/tacotron2/pytorch/README.md index b5bc8ef8fa0d1be86699164d4602dcea3929ea87..e39cc657caeed5608368bfaadddb386fd53decd7 100644 --- a/audio/speech_synthesis/tacotron2/pytorch/README.md +++ b/audio/speech_synthesis/tacotron2/pytorch/README.md @@ -9,6 +9,12 @@ vocoder to produce high-quality audio. The model achieves near-human speech qual learned acoustic representations, enabling more natural prosody and articulation while maintaining computational efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_synthesis/vqmivc/pytorch/README.md b/audio/speech_synthesis/vqmivc/pytorch/README.md index 4afac17dc0640709e62203c6a7a5cfdc896b256d..10c70335d102b79665f7c51f5cbc3382b7bc71c3 100644 --- a/audio/speech_synthesis/vqmivc/pytorch/README.md +++ b/audio/speech_synthesis/vqmivc/pytorch/README.md @@ -9,6 +9,12 @@ inter-dependencies between speech components, VQMIVC achieves superior naturalne traditional methods. This unsupervised approach is particularly effective for retaining source linguistic content while accurately capturing target speaker characteristics. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/audio/speech_synthesis/waveglow/pytorch/README.md b/audio/speech_synthesis/waveglow/pytorch/README.md index 00058b66e68d102aa4947a59ff0195393580a612..8db58ca884aa94c085784cc981517ea37c689751 100644 --- a/audio/speech_synthesis/waveglow/pytorch/README.md +++ b/audio/speech_synthesis/waveglow/pytorch/README.md @@ -8,6 +8,12 @@ audio synthesis, without the need for auto-regression. WaveGlow is implemented u using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d-reconstruction/hashnerf/pytorch/README.md b/cv/3d-reconstruction/hashnerf/pytorch/README.md index 3b904466862a16b7178bafd4743b5e0cdeb5dfb8..90ccd5de9a859feaeba7505baec0b5d96c7a0421 100644 --- a/cv/3d-reconstruction/hashnerf/pytorch/README.md +++ b/cv/3d-reconstruction/hashnerf/pytorch/README.md @@ -8,6 +8,12 @@ efficiency. Based on instant-ngp's approach, HashNeRF employs a grid encoder and high-quality rendering results. The model supports various datasets and custom scenes, making it suitable for applications in computer graphics, virtual reality, and 3D reconstruction tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/bevformer/pytorch/README.md b/cv/3d_detection/bevformer/pytorch/README.md index 7783512ddcc08462dc19b2a3145f7f3b4ea1a49d..b08399ed67398757b126ddd692f87dd838157800 100755 --- a/cv/3d_detection/bevformer/pytorch/README.md +++ b/cv/3d_detection/bevformer/pytorch/README.md @@ -9,6 +9,12 @@ approach achieves state-of-the-art performance on nuScenes dataset, matching LiD multiple perception tasks simultaneously, making it a versatile solution for comprehensive scene understanding in autonomous driving applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/centerpoint/pytorch/README.md b/cv/3d_detection/centerpoint/pytorch/README.md index 27a9d5d67d8dee70b3c4e7aaa75c560cd0851803..ae2cc6a34c8a36afaac3f4f41772dfc6d3a23d6b 100644 --- a/cv/3d_detection/centerpoint/pytorch/README.md +++ b/cv/3d_detection/centerpoint/pytorch/README.md @@ -8,6 +8,12 @@ size, orientation, and velocity. A second stage refines these estimates using ad simplifies 3D tracking to greedy closest-point matching, achieving top performance on nuScenes and Waymo datasets while maintaining efficiency and simplicity in implementation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/paconv/pytorch/README.md b/cv/3d_detection/paconv/pytorch/README.md index 69a10d0901ba4d30c719bd3e67c20368bf63ab18..8987c32225d8156a991647d38e64e4f44f9404a8 100644 --- a/cv/3d_detection/paconv/pytorch/README.md +++ b/cv/3d_detection/paconv/pytorch/README.md @@ -8,6 +8,12 @@ Bank, with coefficients learned from point positions through ScoreNet. This data handle irregular point cloud data efficiently. PAConv integrates seamlessly with existing MLP-based pipelines, achieving state-of-the-art performance in classification and segmentation tasks while maintaining computational efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/part_a2_anchor/pytorch/README.md b/cv/3d_detection/part_a2_anchor/pytorch/README.md index 3e45faedcbde89a30c6a219fb93bf4e77c044b11..2a2f106f2632221d6bd812d0362f09fab3637ef5 100644 --- a/cv/3d_detection/part_a2_anchor/pytorch/README.md +++ b/cv/3d_detection/part_a2_anchor/pytorch/README.md @@ -8,6 +8,12 @@ part locations using free part supervisions; second, it aggregates these parts t approach effectively captures object geometry, achieving state-of-the-art performance on the KITTI dataset while maintaining computational efficiency for practical applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/part_a2_free/pytorch/README.md b/cv/3d_detection/part_a2_free/pytorch/README.md index 189ba0b7e988602302be37588f0cfa3dfa66ce58..8cafdce0acc394fd5cc89be445bde211d60ce713 100644 --- a/cv/3d_detection/part_a2_free/pytorch/README.md +++ b/cv/3d_detection/part_a2_free/pytorch/README.md @@ -8,6 +8,12 @@ supervisions, then aggregating these parts to refine box scores and locations. T geometry through a novel RoI-aware point cloud pooling module, achieving state-of-the-art performance on the KITTI dataset while maintaining computational efficiency for practical applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/pointnet2/pytorch/README.md b/cv/3d_detection/pointnet2/pytorch/README.md index bd3547177d56425eabe102dd35768eec5848fbe4..5c351c022d79664e9331f1aba8beda892beee351 100644 --- a/cv/3d_detection/pointnet2/pytorch/README.md +++ b/cv/3d_detection/pointnet2/pytorch/README.md @@ -8,6 +8,12 @@ multiple scales. The network adapts to varying point densities through novel set on complex scenes. PointNet++ excels in tasks like 3D object classification and segmentation by effectively capturing fine-grained geometric patterns in point clouds. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/pointpillars/pytorch/README.md b/cv/3d_detection/pointpillars/pytorch/README.md index ce5c69321f080213507cdce0f47e51a182cfc200..4d1533aa925308caf115f219d1528c71846dd4eb 100755 --- a/cv/3d_detection/pointpillars/pytorch/README.md +++ b/cv/3d_detection/pointpillars/pytorch/README.md @@ -8,6 +8,12 @@ networks for processing. This approach balances accuracy and speed, making it su autonomous driving. PointPillars achieves state-of-the-art performance on the KITTI dataset while maintaining computational efficiency through its pillar-based encoding and simplified network architecture. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/pointrcnn/pytorch/README.md b/cv/3d_detection/pointrcnn/pytorch/README.md index 42d36c3dd3a5238facb375d46d0fdf2afa4846cf..11bfb3a8b48284d43155a75a51e054f1854b71bb 100644 --- a/cv/3d_detection/pointrcnn/pytorch/README.md +++ b/cv/3d_detection/pointrcnn/pytorch/README.md @@ -8,6 +8,12 @@ it generates accurate 3D box proposals in a bottom-up manner. The second stage r achieves state-of-the-art performance on the KITTI dataset, demonstrating superior accuracy in 3D object detection tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/3d_detection/pointrcnn_iou/pytorch/README.md b/cv/3d_detection/pointrcnn_iou/pytorch/README.md index fe3b2e578d6b33a17ad1f46da061b3fedf388cc4..20005746ba7659b66ef112afd5cf30fc18d60732 100644 --- a/cv/3d_detection/pointrcnn_iou/pytorch/README.md +++ b/cv/3d_detection/pointrcnn_iou/pytorch/README.md @@ -1,75 +1,81 @@ -# PointRCNN-IoU - -## Model Description - -PointRCNN-IoU is an enhanced version of the PointRCNN framework that incorporates Intersection over Union (IoU) -optimization for 3D object detection. It processes raw point cloud data in two stages: first generating 3D proposals, -then refining them with IoU-aware regression. This approach improves bounding box accuracy by directly optimizing the -overlap between predicted and ground truth boxes. PointRCNN-IoU maintains the efficiency of its predecessor while -achieving higher precision in 3D object detection tasks. - -## Model Preparation - -### Prepare Resources - -Download the kitti dataset from - -Download the "planes" subdataset from - -```bash -OpenPCDet -├── data -│ ├── kitti -│ │ │── ImageSets -│ │ │── training -│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) -│ │ │── testing -│ │ │ ├──calib & velodyne & image_2 -├── pcdet -├── tools -``` - -```bash -# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own -cd /toolbox/openpcdet -python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml -``` - -### Install Dependencies - -```bash -## install libGL and libboost -yum install mesa-libGL -yum install boost-devel - -# Install numba -pushd /toolbox/numba -python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log -pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl -popd - -# Install spconv -pushd /toolbox/spconv -bash clean_spconv.sh -bash build_spconv.sh -bash install_spconv.sh -popd - -# Install openpcdet -pushd /toolbox/openpcdet -pip3 install -r requirements.txt -bash build_openpcdet.sh -bash install_openpcdet.sh -popd -``` - -## Model Training - -```bash -# Single GPU training -cd tools/ -python3 train.py --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml - -# Multiple GPU training -bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml -``` +# PointRCNN-IoU + +## Model Description + +PointRCNN-IoU is an enhanced version of the PointRCNN framework that incorporates Intersection over Union (IoU) +optimization for 3D object detection. It processes raw point cloud data in two stages: first generating 3D proposals, +then refining them with IoU-aware regression. This approach improves bounding box accuracy by directly optimizing the +overlap between predicted and ground truth boxes. PointRCNN-IoU maintains the efficiency of its predecessor while +achieving higher precision in 3D object detection tasks. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.06 | + +## Model Preparation + +### Prepare Resources + +Download the kitti dataset from + +Download the "planes" subdataset from + +```bash +OpenPCDet +├── data +│ ├── kitti +│ │ │── ImageSets +│ │ │── training +│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) +│ │ │── testing +│ │ │ ├──calib & velodyne & image_2 +├── pcdet +├── tools +``` + +```bash +# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own +cd /toolbox/openpcdet +python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml +``` + +### Install Dependencies + +```bash +## install libGL and libboost +yum install mesa-libGL +yum install boost-devel + +# Install numba +pushd /toolbox/numba +python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log +pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl +popd + +# Install spconv +pushd /toolbox/spconv +bash clean_spconv.sh +bash build_spconv.sh +bash install_spconv.sh +popd + +# Install openpcdet +pushd /toolbox/openpcdet +pip3 install -r requirements.txt +bash build_openpcdet.sh +bash install_openpcdet.sh +popd +``` + +## Model Training + +```bash +# Single GPU training +cd tools/ +python3 train.py --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml + +# Multiple GPU training +bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/pointrcnn_iou.yaml +``` diff --git a/cv/3d_detection/second/pytorch/README.md b/cv/3d_detection/second/pytorch/README.md index e28fe30e969bb25f85812f254940a005a0d1a4ff..b41d98adec2b8f2c9431339bda00824866edc0db 100644 --- a/cv/3d_detection/second/pytorch/README.md +++ b/cv/3d_detection/second/pytorch/README.md @@ -1,75 +1,81 @@ -# SECOND - -## Model Description - -SECOND is an efficient 3D object detection framework for LiDAR point cloud data, utilizing sparse convolutional networks -to enhance information retention. It introduces improved sparse convolution methods for faster training and inference, -along with novel angle loss regression for better orientation estimation. The framework also incorporates a unique data -augmentation approach to boost convergence speed and performance. SECOND achieves state-of-the-art results on the KITTI -benchmark while maintaining rapid inference, making it suitable for real-time applications like autonomous driving. - -## Model Preparation - -### Prepare Resources - -Download the kitti dataset from - -Download the "planes" subdataset from - -```bash -OpenPCDet -├── data -│ ├── kitti -│ │ │── ImageSets -│ │ │── training -│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) -│ │ │── testing -│ │ │ ├──calib & velodyne & image_2 -├── pcdet -├── tools -``` - -```bash -# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own -cd /toolbox/openpcdet -python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml -``` - -### Install Dependencies - -```bash -## install libGL and libboost -yum install mesa-libGL -yum install boost-devel - -# Install numba -pushd /toolbox/numba -python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log -pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl -popd - -# Install spconv -pushd /toolbox/spconv -bash clean_spconv.sh -bash build_spconv.sh -bash install_spconv.sh -popd - -# Install openpcdet -pushd /toolbox/openpcdet -pip3 install -r requirements.txt -bash build_openpcdet.sh -bash install_openpcdet.sh -popd -``` - -## Model Training - -```bash -# Single GPU training -cd tools/ -python3 train.py --cfg_file cfgs/kitti_models/second.yaml - -# Multiple GPU training -bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/second.yaml -``` +# SECOND + +## Model Description + +SECOND is an efficient 3D object detection framework for LiDAR point cloud data, utilizing sparse convolutional networks +to enhance information retention. It introduces improved sparse convolution methods for faster training and inference, +along with novel angle loss regression for better orientation estimation. The framework also incorporates a unique data +augmentation approach to boost convergence speed and performance. SECOND achieves state-of-the-art results on the KITTI +benchmark while maintaining rapid inference, making it suitable for real-time applications like autonomous driving. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.06 | + +## Model Preparation + +### Prepare Resources + +Download the kitti dataset from + +Download the "planes" subdataset from + +```bash +OpenPCDet +├── data +│ ├── kitti +│ │ │── ImageSets +│ │ │── training +│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) +│ │ │── testing +│ │ │ ├──calib & velodyne & image_2 +├── pcdet +├── tools +``` + +```bash +# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own +cd /toolbox/openpcdet +python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml +``` + +### Install Dependencies + +```bash +## install libGL and libboost +yum install mesa-libGL +yum install boost-devel + +# Install numba +pushd /toolbox/numba +python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log +pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl +popd + +# Install spconv +pushd /toolbox/spconv +bash clean_spconv.sh +bash build_spconv.sh +bash install_spconv.sh +popd + +# Install openpcdet +pushd /toolbox/openpcdet +pip3 install -r requirements.txt +bash build_openpcdet.sh +bash install_openpcdet.sh +popd +``` + +## Model Training + +```bash +# Single GPU training +cd tools/ +python3 train.py --cfg_file cfgs/kitti_models/second.yaml + +# Multiple GPU training +bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/second.yaml +``` diff --git a/cv/3d_detection/second_iou/pytorch/README.md b/cv/3d_detection/second_iou/pytorch/README.md index a36cabb15c85ba7a820e14bf535891d8c1535997..766aa11ae64d0413f9d219ca86f1b664c3482617 100644 --- a/cv/3d_detection/second_iou/pytorch/README.md +++ b/cv/3d_detection/second_iou/pytorch/README.md @@ -1,76 +1,82 @@ -# SECOND-IoU - -## Model Description - -SECOND-IoU is an enhanced version of the SECOND framework that incorporates Intersection over Union (IoU) optimization -for 3D object detection from LiDAR point clouds. It leverages sparse convolutional networks to efficiently process 3D -data while maintaining spatial information. The model introduces IoU-aware regression to improve bounding box accuracy -and orientation estimation. SECOND-IoU achieves state-of-the-art performance on 3D detection benchmarks, offering faster -inference speeds and better precision than traditional methods, making it suitable for real-time applications like -autonomous driving. - -## Model Preparation - -### Prepare Resources - -Download the kitti dataset from - -Download the "planes" subdataset from - -```bash -OpenPCDet -├── data -│ ├── kitti -│ │ │── ImageSets -│ │ │── training -│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) -│ │ │── testing -│ │ │ ├──calib & velodyne & image_2 -├── pcdet -├── tools -``` - -```bash -# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own -cd /toolbox/openpcdet -python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml -``` - -### Install Dependencies - -```bash -## install libGL and libboost -yum install mesa-libGL -yum install boost-devel - -# Install numba -pushd /toolbox/numba -python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log -pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl -popd - -# Install spconv -pushd /toolbox/spconv -bash clean_spconv.sh -bash build_spconv.sh -bash install_spconv.sh -popd - -# Install openpcdet -pushd /toolbox/openpcdet -pip3 install -r requirements.txt -bash build_openpcdet.sh -bash install_openpcdet.sh -popd -``` - -## Model Training - -```bash -# Single GPU training -cd tools/ -python3 train.py --cfg_file cfgs/kitti_models/second_iou.yaml - -# Multiple GPU training -bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/second_iou.yaml -``` +# SECOND-IoU + +## Model Description + +SECOND-IoU is an enhanced version of the SECOND framework that incorporates Intersection over Union (IoU) optimization +for 3D object detection from LiDAR point clouds. It leverages sparse convolutional networks to efficiently process 3D +data while maintaining spatial information. The model introduces IoU-aware regression to improve bounding box accuracy +and orientation estimation. SECOND-IoU achieves state-of-the-art performance on 3D detection benchmarks, offering faster +inference speeds and better precision than traditional methods, making it suitable for real-time applications like +autonomous driving. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.06 | + +## Model Preparation + +### Prepare Resources + +Download the kitti dataset from + +Download the "planes" subdataset from + +```bash +OpenPCDet +├── data +│ ├── kitti +│ │ │── ImageSets +│ │ │── training +│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2) +│ │ │── testing +│ │ │ ├──calib & velodyne & image_2 +├── pcdet +├── tools +``` + +```bash +# Modify the `DATA_PATH` in the kitti_dataset.yaml to your own +cd /toolbox/openpcdet +python3 -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml +``` + +### Install Dependencies + +```bash +## install libGL and libboost +yum install mesa-libGL +yum install boost-devel + +# Install numba +pushd /toolbox/numba +python3 setup.py bdist_wheel -d build_pip 2>&1 | tee compile.log +pip3 install build_pip/numba-0.56.4-cp310-cp310-linux_x86_64.whl +popd + +# Install spconv +pushd /toolbox/spconv +bash clean_spconv.sh +bash build_spconv.sh +bash install_spconv.sh +popd + +# Install openpcdet +pushd /toolbox/openpcdet +pip3 install -r requirements.txt +bash build_openpcdet.sh +bash install_openpcdet.sh +popd +``` + +## Model Training + +```bash +# Single GPU training +cd tools/ +python3 train.py --cfg_file cfgs/kitti_models/second_iou.yaml + +# Multiple GPU training +bash scripts/dist_train.sh 16 --cfg_file cfgs/kitti_models/second_iou.yaml +``` diff --git a/cv/distiller/cwd/pytorch/README.md b/cv/distiller/cwd/pytorch/README.md index db68c39443dab2c1ab8a582392594fd542519b4d..20fee28a48c5916f4b08c38d93e24270225f0838 100644 --- a/cv/distiller/cwd/pytorch/README.md +++ b/cv/distiller/cwd/pytorch/README.md @@ -8,6 +8,12 @@ student networks by transforming each channel's feature map into a probability m This approach focuses on the most salient regions of channel-wise maps, improving distillation efficiency and accuracy. CWD outperforms spatial distillation methods while requiring less computational cost during training. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/distiller/rkd/pytorch/README.md b/cv/distiller/rkd/pytorch/README.md index 3bd0e331f7905056c8b40ecf7e0bb8baffec4a8f..5887a8e4f007a8f6729e79173b61d300741ec6b8 100755 --- a/cv/distiller/rkd/pytorch/README.md +++ b/cv/distiller/rkd/pytorch/README.md @@ -8,6 +8,12 @@ preserving the relationships (distance and angle) between embeddings. This appro learning tasks, where maintaining the relative structure of the embedding space is crucial. RKD enhances student model performance by capturing higher-order relational knowledge from the teacher. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Install Dependencies diff --git a/cv/distiller/wsld/pytorch/README.md b/cv/distiller/wsld/pytorch/README.md index d3c2b3b26ca2283622aaa1ba84a89a1fe324ceef..a623d08e5ed13fb2c593eecff1c13b424e2b0148 100644 --- a/cv/distiller/wsld/pytorch/README.md +++ b/cv/distiller/wsld/pytorch/README.md @@ -8,6 +8,12 @@ WSLD assigns different weights to each class based on their importance or diffic on challenging or critical classes. This approach improves the student model's performance, particularly in imbalanced datasets or tasks where certain classes require more attention. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_detection/retinaface/pytorch/README.md b/cv/face_detection/retinaface/pytorch/README.md index e220ac6f4aea6935aee6e2df3094806c5a0a3658..320c603857d58fe6ba0aee10c913a466da45e06c 100644 --- a/cv/face_detection/retinaface/pytorch/README.md +++ b/cv/face_detection/retinaface/pytorch/README.md @@ -14,6 +14,12 @@ On the IJB-C test set, RetinaFace enables state of the art methods (ArcFace) to verification (TAR=89.59% for FAR=1e-6). (5) By employing light-weight backbone networks, RetinaFace can run real-time on a single CPU core for a VGA-resolution image. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_recognition/arcface/pytorch/README.md b/cv/face_recognition/arcface/pytorch/README.md index d11fd055c50594ecfe1b75b578ba1e8ce53f51c6..c0567028bc9ba063c5e415e4c95cad5f8eda2d3a 100644 --- a/cv/face_recognition/arcface/pytorch/README.md +++ b/cv/face_recognition/arcface/pytorch/README.md @@ -7,6 +7,12 @@ discriminative features for face recognition. The proposed ArcFace has a clear g correspondence to the geodesic distance on the hypersphere. ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_recognition/blazeface/paddlepaddle/README.md b/cv/face_recognition/blazeface/paddlepaddle/README.md index 60772cbb5ae19e7fac62af45904ddce47d8c45b2..d7bf66467974e22bccf8ea13b51ce0904eecdcba 100644 --- a/cv/face_recognition/blazeface/paddlepaddle/README.md +++ b/cv/face_recognition/blazeface/paddlepaddle/README.md @@ -5,6 +5,12 @@ BlazeFace is Google Research published face detection model. It's lightweight but good performance, and tailored for mobile GPU inference. It runs at a speed of 200-1000+ FPS on flagship devices. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_recognition/cosface/pytorch/README.md b/cv/face_recognition/cosface/pytorch/README.md index 3ac73556bbf35ffbb94aff8d30667643d426ff48..1c66b61820be822ac2bb408aa6e1e8974bf5d568 100644 --- a/cv/face_recognition/cosface/pytorch/README.md +++ b/cv/face_recognition/cosface/pytorch/README.md @@ -6,6 +6,12 @@ CosFace is a face recognition model that achieves state-of-the-art results by in loss function when training the neural network, which learns highly discriminative facial embeddings by maximizing inter-class differences and minimizing intra-class variations. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_recognition/facenet/pytorch/README.md b/cv/face_recognition/facenet/pytorch/README.md index 9d0d66112c05d4cf78409dbc8f1df202dea6e00b..d9ffe00051c825ad2f166d6160ebd4252ac3bbd7 100644 --- a/cv/face_recognition/facenet/pytorch/README.md +++ b/cv/face_recognition/facenet/pytorch/README.md @@ -8,6 +8,12 @@ closer together than those of different individuals. Facenet excels in tasks lik clustering, offering high accuracy and efficiency. Its compact embeddings make it scalable for large-scale applications in security and identity verification. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/face_recognition/facenet/tensorflow/README.md b/cv/face_recognition/facenet/tensorflow/README.md index 16dee24005fafb68527bd524c245f789158a103b..09e84498e06c3c98c793e8e834a3d188b832a02d 100644 --- a/cv/face_recognition/facenet/tensorflow/README.md +++ b/cv/face_recognition/facenet/tensorflow/README.md @@ -8,6 +8,12 @@ closer together than those of different individuals. Facenet excels in tasks lik clustering, offering high accuracy and efficiency. Its compact embeddings make it scalable for large-scale applications in security and identity verification. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/gnn/gat/paddlepaddle/README.md b/cv/gnn/gat/paddlepaddle/README.md index 6429faf2ad6a2060bdda3a882acde097949d860f..e000ccd6ec74da38bd39cc464dc7028e8e16b127 100644 --- a/cv/gnn/gat/paddlepaddle/README.md +++ b/cv/gnn/gat/paddlepaddle/README.md @@ -8,6 +8,12 @@ to neighboring nodes through attention coefficients, allowing for more flexible approach enables the model to handle varying neighborhood sizes and capture complex relationships in graph data, making it particularly effective for tasks like node classification and graph-based prediction problems. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/gnn/gcn/mindspore/README.md b/cv/gnn/gcn/mindspore/README.md index 8e48864cde91b3278b96cf4f970f4fea767e9d7f..403e86fce9b55883232d798aea097a80d257b681 100755 --- a/cv/gnn/gcn/mindspore/README.md +++ b/cv/gnn/gcn/mindspore/README.md @@ -7,6 +7,12 @@ data. A scalable approach based on an efficient variant of convolutional neural graphs was presented. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/gnn/gcn/paddlepaddle/README.md b/cv/gnn/gcn/paddlepaddle/README.md index 63cb50c181eb3fe23a001f761e32a4b288c91cd5..2eb4ee3d1a1ba5a7809c7564cab24bc69b96cbb5 100644 --- a/cv/gnn/gcn/paddlepaddle/README.md +++ b/cv/gnn/gcn/paddlepaddle/README.md @@ -7,6 +7,12 @@ data. A scalable approach based on an efficient variant of convolutional neural graphs was presented. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/gnn/graphsage/paddlepaddle/README.md b/cv/gnn/graphsage/paddlepaddle/README.md index fe7a86ba67f99563d2c0fe77346bfa810a34635d..c8e2e25c802ff30a6d1373bdace9526348eed80a 100644 --- a/cv/gnn/graphsage/paddlepaddle/README.md +++ b/cv/gnn/graphsage/paddlepaddle/README.md @@ -8,6 +8,12 @@ sampling and aggregating features from a node's local neighborhood. This approac unseen nodes and graphs, making it particularly effective for dynamic graphs and large-scale applications like social network analysis and recommendation systems. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/image_generation/dcgan/mindspore/README.md b/cv/image_generation/dcgan/mindspore/README.md index e5c2825da1a5dea12627b89ed4d6e516dd5c58dc..7f365275e62f319e9b87381c14f11ee7bbd14460 100644 --- a/cv/image_generation/dcgan/mindspore/README.md +++ b/cv/image_generation/dcgan/mindspore/README.md @@ -5,6 +5,12 @@ The deep convolutional generative adversarial networks (DCGANs) first introduced CNN into the GAN structure, and the strong feature extraction ability of convolution layer was used to improve the generation effect of GAN. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/image_generation/pix2pix/paddlepaddle/README.md b/cv/image_generation/pix2pix/paddlepaddle/README.md index 684ed993aab710258dc8538d9e735a14d3cadfa3..84cfbc0437b06419f9935959143fda042548a1c6 100755 --- a/cv/image_generation/pix2pix/paddlepaddle/README.md +++ b/cv/image_generation/pix2pix/paddlepaddle/README.md @@ -8,6 +8,12 @@ information to the generation network, Pix2pix uses another style of image as th generation network, so the fake image is related to another style of image which is input as supervision information, thus realizing the process of image translation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/instance_segmentation/solo/pytorch/README.md b/cv/instance_segmentation/solo/pytorch/README.md index 237fd3b69fdc1eb070aaf31b4b85100fab825fc2..41a1ce85a50a5283688389323c80f753f1d05f2f 100644 --- a/cv/instance_segmentation/solo/pytorch/README.md +++ b/cv/instance_segmentation/solo/pytorch/README.md @@ -8,6 +8,12 @@ categories to each pixel based on an object's location and size. Unlike traditio instance masks without complex post-processing or region proposals. This approach achieves competitive accuracy with Mask R-CNN while offering a simpler and more flexible framework for instance-level recognition tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/instance_segmentation/solov2/pytorch/README.md b/cv/instance_segmentation/solov2/pytorch/README.md index 01939217a7c64fa62e20b94ce011b25b351d7cea..e6fbc79242541fbfcb63cdac761a8d3dbf482998 100644 --- a/cv/instance_segmentation/solov2/pytorch/README.md +++ b/cv/instance_segmentation/solov2/pytorch/README.md @@ -8,6 +8,12 @@ SOLOv2 introduces Matrix NMS, a faster non-maximum suppression technique that pr architecture achieves state-of-the-art performance in both speed and accuracy, with a lightweight version running at 31.3 FPS. It serves as a strong baseline for various instance-level recognition tasks beyond segmentation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/instance_segmentation/yolact/pytorch/README.md b/cv/instance_segmentation/yolact/pytorch/README.md index c06cee1a593e21cd715d7cc7478a37701e4336d0..43d1d57e97d495c49c4b4a6186ca39d1e5d99edb 100644 --- a/cv/instance_segmentation/yolact/pytorch/README.md +++ b/cv/instance_segmentation/yolact/pytorch/README.md @@ -8,6 +8,12 @@ instance masks. This approach enables fast processing while maintaining competit performance with deformable convolutions and optimized prediction heads. The model achieves real-time speeds on single GPUs, making it suitable for applications requiring quick instance segmentation in video streams or interactive systems. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/multi_object_tracking/bytetrack/paddlepaddle/README.md b/cv/multi_object_tracking/bytetrack/paddlepaddle/README.md index 4e7ef4662f09c3366029f99f09574c806ea66637..a49ce47f937f33f27bb4910bd45e3fdd1ca2829b 100644 --- a/cv/multi_object_tracking/bytetrack/paddlepaddle/README.md +++ b/cv/multi_object_tracking/bytetrack/paddlepaddle/README.md @@ -9,6 +9,12 @@ performance on benchmarks like MOT17, with high MOTA, IDF1, and HOTA scores whil speeds. Its simple yet effective design makes it a robust solution for various object tracking applications in video analysis. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/multi_object_tracking/deep_sort/pytorch/README.md b/cv/multi_object_tracking/deep_sort/pytorch/README.md index bf4debfe1be16a18ff1db923b97a9b4c335c8a38..2026a08598e4649bfba62d9bc8c02127b2a063cd 100644 --- a/cv/multi_object_tracking/deep_sort/pytorch/README.md +++ b/cv/multi_object_tracking/deep_sort/pytorch/README.md @@ -8,6 +8,12 @@ especially in complex scenarios with occlusions. DeepSORT uses a Kalman filter f detections using both motion and appearance cues. This approach improves tracking consistency and reduces identity switches, making it particularly effective for person tracking in crowded scenes and video surveillance applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/multi_object_tracking/fairmot/pytorch/README.md b/cv/multi_object_tracking/fairmot/pytorch/README.md index e4167dd92b813a464d11f7d0f41e8bb1a79fcaa6..b852dbd065154db540ed9ba203e84a51c3a1479e 100644 --- a/cv/multi_object_tracking/fairmot/pytorch/README.md +++ b/cv/multi_object_tracking/fairmot/pytorch/README.md @@ -8,6 +8,12 @@ Operating on high-resolution feature maps, FairMOT achieves fairness between det improved tracking accuracy. Its joint learning approach eliminates the need for cascaded processing, making it more efficient and effective for complex tracking scenarios in crowded environments. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/crnn/mindspore/README.md b/cv/ocr/crnn/mindspore/README.md index 649384425934370ae4c64da2667093292258146b..8cbaaade3227dbd8e2dc9b726dbe46d9368b0878 100644 --- a/cv/ocr/crnn/mindspore/README.md +++ b/cv/ocr/crnn/mindspore/README.md @@ -9,6 +9,12 @@ without character segmentation or horizontal scaling, making it versatile for bo recognition tasks. Its compact architecture and unified framework make it practical for real-world applications like document analysis and OCR. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/crnn/paddlepaddle/README.md b/cv/ocr/crnn/paddlepaddle/README.md index 6c9b8b1672fdd715d5780fc8c4698d167b953ec5..b7e6372166ee930c087fbddd310b8000e76b4ad4 100644 --- a/cv/ocr/crnn/paddlepaddle/README.md +++ b/cv/ocr/crnn/paddlepaddle/README.md @@ -9,6 +9,12 @@ without character segmentation or horizontal scaling, making it versatile for bo recognition tasks. Its compact architecture and unified framework make it practical for real-world applications like document analysis and OCR. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/dbnet/pytorch/README.md b/cv/ocr/dbnet/pytorch/README.md index 8202dc43e0b1bb84eb7f01680c0c5109818cbd96..9ba1bd78164463fe96438a601761a1cd531aa881 100755 --- a/cv/ocr/dbnet/pytorch/README.md +++ b/cv/ocr/dbnet/pytorch/README.md @@ -13,6 +13,12 @@ five benchmark datasets, which consistently achieves state-of-the-art results, i speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/dbnetpp/paddlepaddle/README.md b/cv/ocr/dbnetpp/paddlepaddle/README.md index ad038cda186a460b65fede14cd222b810bb350a2..1491fbba364b53af4987e60edd8cc4c5b05b7c0c 100644 --- a/cv/ocr/dbnetpp/paddlepaddle/README.md +++ b/cv/ocr/dbnetpp/paddlepaddle/README.md @@ -8,6 +8,12 @@ simplifying post-processing and improving accuracy. The ASF module enhances scal multi-scale features. This architecture enables DBNet++ to detect text of arbitrary shapes and extreme aspect ratios efficiently, achieving state-of-the-art performance in both accuracy and speed across various text detection benchmarks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/dbnetpp/pytorch/README.md b/cv/ocr/dbnetpp/pytorch/README.md index ece02b7e88eaded5b5c7ec03817bc0173d2fba6f..c5cb0eca82da8dab70162888ce7c942e86958ff7 100644 --- a/cv/ocr/dbnetpp/pytorch/README.md +++ b/cv/ocr/dbnetpp/pytorch/README.md @@ -8,6 +8,12 @@ simplifying post-processing and improving accuracy. The ASF module enhances scal multi-scale features. This architecture enables DBNet++ to detect text of arbitrary shapes and extreme aspect ratios efficiently, achieving state-of-the-art performance in both accuracy and speed across various text detection benchmarks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/pp-ocr-db/paddlepaddle/README.md b/cv/ocr/pp-ocr-db/paddlepaddle/README.md index 0d1b59ca9712204feaa14d23c45e3ab64325ba32..765aefdba1bf6323109ccd4e71b243f68ba000d9 100644 --- a/cv/ocr/pp-ocr-db/paddlepaddle/README.md +++ b/cv/ocr/pp-ocr-db/paddlepaddle/README.md @@ -8,6 +8,12 @@ model is optimized for real-time performance and can handle diverse text layouts particularly effective in document analysis and scene text recognition tasks, offering a balance between accuracy and computational efficiency for practical OCR applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/pp-ocr-east/paddlepaddle/README.md b/cv/ocr/pp-ocr-east/paddlepaddle/README.md index d842f83086cce888f662d6880aeb702fd7e301a8..371fee6de48e8556891361dee36aa30eb3752e7f 100644 --- a/cv/ocr/pp-ocr-east/paddlepaddle/README.md +++ b/cv/ocr/pp-ocr-east/paddlepaddle/README.md @@ -8,6 +8,12 @@ scene images. The model is designed for real-time performance and can handle tex PP-OCR-EAST is particularly effective in complex scenarios, offering a balance between detection accuracy and computational efficiency for practical OCR applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/pse/paddlepaddle/README.md b/cv/ocr/pse/paddlepaddle/README.md index 413c7f0871b4dc5d418f07d24dce76bd22be192e..d4d4f5552fcdf7334535ff9e6c38ce12dc26054c 100644 --- a/cv/ocr/pse/paddlepaddle/README.md +++ b/cv/ocr/pse/paddlepaddle/README.md @@ -8,6 +8,12 @@ expansion algorithm. PSE effectively handles complex scenarios like curved text architecture combines feature pyramid networks with a novel post-processing method, making it particularly suitable for detecting text in diverse orientations and layouts with high accuracy. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/sar/pytorch/README.md b/cv/ocr/sar/pytorch/README.md index cee369f630e8d350ba736f254faa7fe70085b115..e0e9fd31293d1eab1c5f92d7b08b8c56a9f4c1ef 100755 --- a/cv/ocr/sar/pytorch/README.md +++ b/cv/ocr/sar/pytorch/README.md @@ -10,6 +10,12 @@ off-the-shelf neural network components and only word-level annotations. It is c LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/sast/paddlepaddle/README.md b/cv/ocr/sast/paddlepaddle/README.md index f98f954b79c9409b2679c0ed059b47836be6dc23..8fb092421482cbb96a773e20d62451c2a3946acb 100644 --- a/cv/ocr/sast/paddlepaddle/README.md +++ b/cv/ocr/sast/paddlepaddle/README.md @@ -11,6 +11,12 @@ be highly effective across several benchmarks like ICDAR2015 and SCUT-CTW1500, S also operates efficiently, achieving significant performance metrics such as running at 27.63 FPS on a NVIDIA Titan Xp with a high detection accuracy, making it a notable solution for arbitrary-shaped text detection challenges. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/ocr/satrn/pytorch/base/README.md b/cv/ocr/satrn/pytorch/base/README.md index f3ce80cdde0f4700ba417619430b38f5864b8a67..34d69f3b7d153f1b4b8526faac64e5511b5275ae 100755 --- a/cv/ocr/satrn/pytorch/base/README.md +++ b/cv/ocr/satrn/pytorch/base/README.md @@ -9,6 +9,12 @@ enables it to handle complex text arrangements and large inter-character spacing outperforms traditional methods in recognizing irregular texts, making it valuable for real-world applications like sign and logo recognition. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/point_cloud/point-bert/pytorch/README.md b/cv/point_cloud/point-bert/pytorch/README.md index 71231e2c1cdbc1e336661617ebe88ba4f04cb759..0dc7a5c9b01374f50701b5782e60837a18eec71f 100644 --- a/cv/point_cloud/point-bert/pytorch/README.md +++ b/cv/point_cloud/point-bert/pytorch/README.md @@ -9,6 +9,12 @@ AutoEncoder (dVAE) to generate discrete point tokens containing meaningful local some patches of input point clouds and feed them into the backbone Transformer. The pre-training objective is to recover the original point tokens at the masked locations under the supervision of point tokens obtained by the Tokenizer. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/pose/alphapose/pytorch/README.md b/cv/pose/alphapose/pytorch/README.md index 20ebc61f8b3cf28c1c73ccf9f22e5ce3e5825b59..a4dae3c4ea72de34bc3d98aa521efdbc976670be 100755 --- a/cv/pose/alphapose/pytorch/README.md +++ b/cv/pose/alphapose/pytorch/README.md @@ -8,6 +8,12 @@ mAP) on COCO dataset and 80+ mAP (82.1 mAP) on MPII dataset. To match poses that frames, we also provide an efficient online pose tracker called Pose Flow. It is the first open-source online pose tracker that achieves both 60+ mAP (66.5 mAP) and 50+ MOTA (58.3 MOTA) on PoseTrack Challenge dataset. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/pose/hrnet/paddlepaddle/README.md b/cv/pose/hrnet/paddlepaddle/README.md index 5bb76c392b91ec3416d8255f2ea25f410a3e99c7..edca0d096cf816d35e66c7baa9a4f558a3f270b3 100644 --- a/cv/pose/hrnet/paddlepaddle/README.md +++ b/cv/pose/hrnet/paddlepaddle/README.md @@ -9,6 +9,12 @@ one by one, and connect the multi-resolution streams in parallel. The resulting the nth stage contains n streams corresponding to n resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/pose/hrnet/pytorch/README.md b/cv/pose/hrnet/pytorch/README.md index 02a1f0b93cca4c9bf424ab19cab8b0285265f4f5..ecd707118f13e4c929a2646bd5e87b1a70a5d1e2 100644 --- a/cv/pose/hrnet/pytorch/README.md +++ b/cv/pose/hrnet/pytorch/README.md @@ -9,6 +9,12 @@ one by one, and connect the multi-resolution streams in parallel. The resulting the nth stage contains n streams corresponding to n resolutions. The authors conduct repeated multi-resolution fusions by exchanging the information across the parallel streams over and over. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/pose/openpose/mindspore/README.md b/cv/pose/openpose/mindspore/README.md index ed061672468b123f76d4f9b2dc53225332e38cdf..3cebd1c32173d79bb4e4541e83230fec9643c3f1 100644 --- a/cv/pose/openpose/mindspore/README.md +++ b/cv/pose/openpose/mindspore/README.md @@ -8,6 +8,12 @@ efficiency remains stable regardless of the number of people in an image. It sim associates them to individuals, making it particularly effective for scenarios with multiple people, such as crowd analysis and human-computer interaction applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/self_supervised_learning/mae/pytorch/README.md b/cv/self_supervised_learning/mae/pytorch/README.md index 685f2d1b2b242a4ed5675925eb4a4e4a4052c90f..8963e7636d5ee3cb9623581ba6ef6fe816767d42 100644 --- a/cv/self_supervised_learning/mae/pytorch/README.md +++ b/cv/self_supervised_learning/mae/pytorch/README.md @@ -8,6 +8,12 @@ the encoder processes only the visible patches, and the lightweight decoder reco latent representation and mask tokens. MAE demonstrates that high masking ratios (e.g., 75%) can lead to robust feature learning, making it scalable and effective for various downstream vision tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/apcnet/pytorch/README.md b/cv/semantic_segmentation/apcnet/pytorch/README.md index d139ec3328ab777797b2ae6708b0306ca8e6e1fb..0138351af69cad397d083a0fbe9b2a777cc356a8 100644 --- a/cv/semantic_segmentation/apcnet/pytorch/README.md +++ b/cv/semantic_segmentation/apcnet/pytorch/README.md @@ -7,6 +7,12 @@ representations with multiple well-designed Adaptive Context Modules (ACMs). Spe image representation as a guidance to estimate the local affinity coefficients for each sub-region. And then calculates a context vector with these affinities. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/att_unet/pytorch/README.md b/cv/semantic_segmentation/att_unet/pytorch/README.md index 2469abaf1920ecb0444c16b8a17371c2726dd876..5442e4c9f939ce4e155805dcac0784fce78d9a5b 100644 --- a/cv/semantic_segmentation/att_unet/pytorch/README.md +++ b/cv/semantic_segmentation/att_unet/pytorch/README.md @@ -9,6 +9,12 @@ modules, improving model sensitivity and accuracy. Attention U-Net efficiently p computational overhead, making it particularly effective for tasks requiring precise segmentation of complex anatomical structures. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/bisenet/pytorch/README.md b/cv/semantic_segmentation/bisenet/pytorch/README.md index 0641e9fadc187e4e31a6de22c9ca00694f977393..ae8489e290adc680642e0306239beac17019819f 100644 --- a/cv/semantic_segmentation/bisenet/pytorch/README.md +++ b/cv/semantic_segmentation/bisenet/pytorch/README.md @@ -7,6 +7,12 @@ spatial information and generate high-resolution features. Meanwhile, a Context is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/bisenetv2/paddlepaddle/README.md b/cv/semantic_segmentation/bisenetv2/paddlepaddle/README.md index aa8feb46cbf9dd361712b573a580f07256ef1683..f5f85d6cad3c815ed9d841337a1123c5045bd5d5 100644 --- a/cv/semantic_segmentation/bisenetv2/paddlepaddle/README.md +++ b/cv/semantic_segmentation/bisenetv2/paddlepaddle/README.md @@ -10,6 +10,12 @@ supplied by the Detail Branch. Therefore, the Semantic Branch can be made very l fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/bisenetv2/pytorch/README.md b/cv/semantic_segmentation/bisenetv2/pytorch/README.md index 2b316072f8b3286cf8515f31bc22c413b065dedd..7e77b6844331d68acfd5ee3f5bea21e2e1119e0b 100644 --- a/cv/semantic_segmentation/bisenetv2/pytorch/README.md +++ b/cv/semantic_segmentation/bisenetv2/pytorch/README.md @@ -10,6 +10,12 @@ supplied by the Detail Branch. Therefore, the Semantic Branch can be made very l fast-downsampling strategy. Both types of feature representation are merged to construct a stronger and more comprehensive feature representation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/cgnet/pytorch/README.md b/cv/semantic_segmentation/cgnet/pytorch/README.md index afceb7958d530dd334072efc6aed8e6d7ba1de84..b85fa82ddd93defef7241bd8b3b062cb1a87ad8d 100644 --- a/cv/semantic_segmentation/cgnet/pytorch/README.md +++ b/cv/semantic_segmentation/cgnet/pytorch/README.md @@ -6,6 +6,12 @@ A novel Context Guided Network (CGNet), which is a light-weight and efficient ne Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing segmentation networks. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/contextnet/pytorch/README.md b/cv/semantic_segmentation/contextnet/pytorch/README.md index 07f315f23057e48326d734fa6085dc167772eb42..683be8dc75ed2bced24fd83bdf373bcfdc3c56f0 100644 --- a/cv/semantic_segmentation/contextnet/pytorch/README.md +++ b/cv/semantic_segmentation/contextnet/pytorch/README.md @@ -7,6 +7,12 @@ pyramid representation to produce competitive semantic segmentation in real-time combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/dabnet/pytorch/README.md b/cv/semantic_segmentation/dabnet/pytorch/README.md index 40235ce2a0cb5a6d1d2fde45a5fc846506247092..8dadbeb8fb88e88425a5ed4cbc81ced81aa7f28c 100644 --- a/cv/semantic_segmentation/dabnet/pytorch/README.md +++ b/cv/semantic_segmentation/dabnet/pytorch/README.md @@ -6,6 +6,12 @@ A novel Depthwise Asymmetric Bottleneck (DAB) module, which efficiently adopts d Based on the DAB module, design a Depth-wise Asymmetric Bottleneck Network (DABNet) especially for real-time semantic segmentation. It creates sufficient receptive field and densely utilizes the contextual information. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/danet/pytorch/README.md b/cv/semantic_segmentation/danet/pytorch/README.md index c0badffef3337ea76e838f6938fdd61935e59ff5..e00e50d7fb97c3a64d44655cb0dfd3b6b883ef84 100644 --- a/cv/semantic_segmentation/danet/pytorch/README.md +++ b/cv/semantic_segmentation/danet/pytorch/README.md @@ -7,6 +7,12 @@ mechanism instead of simply stacking convolutions to compute the spatial attenti capture global information directly. DANet uses in parallel a position attention module and a channel attention module to capture feature dependencies in spatial and channel domains. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/ddrnet/pytorch/README.md b/cv/semantic_segmentation/ddrnet/pytorch/README.md index cee8e72aba0d4a72793d336fff9b098baa1046ed..0f89b4eaf6237c18a32dcfc01492ca1a8e7c3bb9 100644 --- a/cv/semantic_segmentation/ddrnet/pytorch/README.md +++ b/cv/semantic_segmentation/ddrnet/pytorch/README.md @@ -8,6 +8,12 @@ performed. Additionally, we design a new contextual information extractor named (DAPPM) to enlarge effective receptive fields and fuse multi-scale context based on low-resolution feature maps. Our method achieves a new state-of-the-art trade-off between accuracy and speed on both Cityscapes and CamVid dataset. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/deeplabv3/mindspore/README.md b/cv/semantic_segmentation/deeplabv3/mindspore/README.md index c6c406a91e0c678d087e212f509c91dfe3400527..f8df4709f3aafaef6c4c61b8f2109cd703471264 100755 --- a/cv/semantic_segmentation/deeplabv3/mindspore/README.md +++ b/cv/semantic_segmentation/deeplabv3/mindspore/README.md @@ -7,6 +7,12 @@ keypoints of DeepLabV3: Its multi-grid atrous convolution makes it better to dea scales, and augmented ASPP makes image-level features available to capture long range information. This repository provides a script and recipe to DeepLabV3 model and achieve state-of-the-art performance. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/deeplabv3/paddlepaddle/README.md b/cv/semantic_segmentation/deeplabv3/paddlepaddle/README.md index 6fa1239d14b27c66594d81f3ed0c910d2a5b7290..d4bb648689968da5d09211bfb8eebc8cfeeaa512 100644 --- a/cv/semantic_segmentation/deeplabv3/paddlepaddle/README.md +++ b/cv/semantic_segmentation/deeplabv3/paddlepaddle/README.md @@ -6,6 +6,12 @@ DeepLabV3 is a semantic segmentation architecture that improves upon DeepLabV2 w problem of segmenting objects at multiple scales, modules are designed which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/deeplabv3/pytorch/README.md b/cv/semantic_segmentation/deeplabv3/pytorch/README.md index 0774e19207e496f551990fe3dd196fe2affd4c97..890a17b15f30dead6f3afc564ba17034b45b280a 100644 --- a/cv/semantic_segmentation/deeplabv3/pytorch/README.md +++ b/cv/semantic_segmentation/deeplabv3/pytorch/README.md @@ -6,6 +6,12 @@ DeepLabV3 is a semantic segmentation architecture that improves upon DeepLabV2 w problem of segmenting objects at multiple scales, modules are designed which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/deeplabv3plus/paddlepaddle/README.md b/cv/semantic_segmentation/deeplabv3plus/paddlepaddle/README.md index fe11612e9aab5ca136235401e5cd17512b080392..a0b03d49d64f65e1aee6d56bf9d2c281019d8ba8 100644 --- a/cv/semantic_segmentation/deeplabv3plus/paddlepaddle/README.md +++ b/cv/semantic_segmentation/deeplabv3plus/paddlepaddle/README.md @@ -6,6 +6,12 @@ DeepLabv3 is a semantic segmentation architecture that improves upon DeepLabv2 w problem of segmenting objects at multiple scales, modules are designed which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/deeplabv3plus/tensorflow/README.md b/cv/semantic_segmentation/deeplabv3plus/tensorflow/README.md index afaa6bf653a3821e6a5873669f60a85f009cbe4b..2d76dfb198350dfff48032ca58c438e6650d7f30 100644 --- a/cv/semantic_segmentation/deeplabv3plus/tensorflow/README.md +++ b/cv/semantic_segmentation/deeplabv3plus/tensorflow/README.md @@ -7,6 +7,12 @@ encoder-decoder architecture. The network employs atrous convolution to capture effectively. It introduces a novel feature called the "ASPP" module, which utilizes parallel atrous convolutions to capture fine-grained details and global context simultaneously. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/denseaspp/pytorch/README.md b/cv/semantic_segmentation/denseaspp/pytorch/README.md index a17fa3df1eaccda95685f65448ea7f2b8008426e..219bfdc598ca7034445451101505ccdf2bbd03e7 100644 --- a/cv/semantic_segmentation/denseaspp/pytorch/README.md +++ b/cv/semantic_segmentation/denseaspp/pytorch/README.md @@ -6,6 +6,12 @@ Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), which connects a s dense way. Such that it generates multi-scale features that not only cover a larger scale range, but also cover that scale range densely, without significantly increasing the model size. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/dfanet/pytorch/README.md b/cv/semantic_segmentation/dfanet/pytorch/README.md index c898bd2b7d1fcac32445b0c963d074aa560f72bb..b607aa74aa519778e11333f841d6a2b558f7f25b 100644 --- a/cv/semantic_segmentation/dfanet/pytorch/README.md +++ b/cv/semantic_segmentation/dfanet/pytorch/README.md @@ -8,6 +8,12 @@ respectively. Based on the multi-scale feature propagation, DFANet substantially it still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and segmentation performance. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/dnlnet/paddlepaddle/README.md b/cv/semantic_segmentation/dnlnet/paddlepaddle/README.md index 4722e6a23f4468966b11c9e7dcb8a6232aaf18ef..a53cfbb72d959633c39afbe93f409f2b19888130 100644 --- a/cv/semantic_segmentation/dnlnet/paddlepaddle/README.md +++ b/cv/semantic_segmentation/dnlnet/paddlepaddle/README.md @@ -9,6 +9,12 @@ network's ability to capture contextual information while maintaining computatio superior performance in tasks requiring precise spatial understanding, such as urban scene segmentation, by effectively aggregating both local and global features. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/dunet/pytorch/README.md b/cv/semantic_segmentation/dunet/pytorch/README.md index 42ec2f9cf86fb01fa9ad0ccf06912870db9a1a68..e47370fd885be9a5d977af7258bc0726a53f80d0 100644 --- a/cv/semantic_segmentation/dunet/pytorch/README.md +++ b/cv/semantic_segmentation/dunet/pytorch/README.md @@ -8,6 +8,12 @@ designed to extract context information and enable precise localization by combi high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels' scales and shapes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/encnet/pytorch/README.md b/cv/semantic_segmentation/encnet/pytorch/README.md index 3e59f3b5561a1dae30b7be6ee4960f809ae4e0a9..08e58f82245f8e197412bbf518fbc9c13eb8aa56 100644 --- a/cv/semantic_segmentation/encnet/pytorch/README.md +++ b/cv/semantic_segmentation/encnet/pytorch/README.md @@ -5,6 +5,12 @@ The Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/enet/pytorch/README.md b/cv/semantic_segmentation/enet/pytorch/README.md index 3ca9866932b2759715628c18276a354f0424e7a5..304caf27c0efc84f9746f3f5ebeac0cda5e820e8 100644 --- a/cv/semantic_segmentation/enet/pytorch/README.md +++ b/cv/semantic_segmentation/enet/pytorch/README.md @@ -9,6 +9,12 @@ dilated convolutions, and spatial dropout. ENet's lightweight design makes it pa requiring fast inference on resource-constrained devices, such as mobile platforms or real-time video processing systems, without compromising segmentation quality. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/erfnet/pytorch/README.md b/cv/semantic_segmentation/erfnet/pytorch/README.md index a8dc933457c903670cbdf523a40c40085a97b40d..56502973f9411d2c7162416f71800ae724742bfc 100644 --- a/cv/semantic_segmentation/erfnet/pytorch/README.md +++ b/cv/semantic_segmentation/erfnet/pytorch/README.md @@ -6,6 +6,12 @@ A deep architecture that is able to run in real-time while providing accurate se architecture is a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/espnet/pytorch/README.md b/cv/semantic_segmentation/espnet/pytorch/README.md index d8b603aea05010f79d4c2504e8af09be393efd47..69d4525bf6b062df9aaea2d7536dfc1357f4c6ee 100644 --- a/cv/semantic_segmentation/espnet/pytorch/README.md +++ b/cv/semantic_segmentation/espnet/pytorch/README.md @@ -6,6 +6,12 @@ ESPNet is a convolutional neural network for semantic segmentation of high resol ESPNet is based on a convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/fastfcn/paddlepaddle/README.md b/cv/semantic_segmentation/fastfcn/paddlepaddle/README.md index 0e8b399ba2f27206c77ba5a21346f63dc4905b7d..072b05030bad87a86df5d269c90444f80cb95449 100644 --- a/cv/semantic_segmentation/fastfcn/paddlepaddle/README.md +++ b/cv/semantic_segmentation/fastfcn/paddlepaddle/README.md @@ -7,6 +7,12 @@ uses an efficient encoder-decoder architecture and depthwise separable convoluti simplified design allows FastFCN to run much faster than prior FCNs while maintaining good segmentation quality. FastFCN demonstrates real-time segmentation is possible with a carefully designed lightweight CNN architecture. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/fastscnn/pytorch/README.md b/cv/semantic_segmentation/fastscnn/pytorch/README.md index b8b5c4a63f4d49859c58934aa65e161066f52886..3ba25090bbae51d90535fa8915045c398e5c733b 100644 --- a/cv/semantic_segmentation/fastscnn/pytorch/README.md +++ b/cv/semantic_segmentation/fastscnn/pytorch/README.md @@ -7,6 +7,12 @@ resolution image data (1024x2048px) suited to efficient computation on embedded to downsample' module which computes low-level features for multiple resolution branches simultaneously. The network combines spatial detail at high resolution with deep features extracted at lower resolution. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/fcn/pytorch/README.md b/cv/semantic_segmentation/fcn/pytorch/README.md index 4ae8295cab7b02b8fd562190aa662e82d035bd74..fb145f4002d917dc23a9b64cd9d7dabf8f4ed288 100644 --- a/cv/semantic_segmentation/fcn/pytorch/README.md +++ b/cv/semantic_segmentation/fcn/pytorch/README.md @@ -9,6 +9,12 @@ connections are local. The network consists of a downsampling path, used to extr upsampling path, which allows for localization. FCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/fpenet/pytorch/README.md b/cv/semantic_segmentation/fpenet/pytorch/README.md index b6c5c7795f32d0c68196579499f51d447bee6e77..f306b4ef3bd095c9ae48d37badc8038c2aa88432 100644 --- a/cv/semantic_segmentation/fpenet/pytorch/README.md +++ b/cv/semantic_segmentation/fpenet/pytorch/README.md @@ -7,6 +7,12 @@ Specifically, use a feature pyramid encoding block to encode multi-scale context convolutions in all stages of the encoder. A mutual embedding upsample module is introduced in the decoder to aggregate the high-level semantic features and low-level spatial details efficiently. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/gcnet/pytorch/README.md b/cv/semantic_segmentation/gcnet/pytorch/README.md index 4e2ba81626a95530c20bd174f85bd5f1626aaea9..7f8016a71f177f019af3d33940c6231068db1bef 100755 --- a/cv/semantic_segmentation/gcnet/pytorch/README.md +++ b/cv/semantic_segmentation/gcnet/pytorch/README.md @@ -6,6 +6,12 @@ A Global Context Network, or GCNet, utilises global context blocks to model long based on the Non-Local Network, but it modifies the architecture so less computation is required. Global context blocks are applied to multiple layers in a backbone network to construct the GCNet. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/hardnet/pytorch/README.md b/cv/semantic_segmentation/hardnet/pytorch/README.md index 687683692eb780b8061910efe3c43fc4dcc4549d..89dfb428b85490a53922d4b672bd0238a97cb751 100644 --- a/cv/semantic_segmentation/hardnet/pytorch/README.md +++ b/cv/semantic_segmentation/hardnet/pytorch/README.md @@ -6,6 +6,12 @@ The Harmonic Densely Connected Network to achieve high efficiency in terms of bo network achieves 35%, 36%, 30%, 32%, and 45% inference time reduction compared with FC-DenseNet-103, DenseNet-264, ResNet-50, ResNet-152, and SSD-VGG, respectively. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/icnet/pytorch/README.md b/cv/semantic_segmentation/icnet/pytorch/README.md index fb48a24a68546f34624a586e93dfbd24c6d431a5..2713420219b0c3f3c2d5fa3f2f10aadc07b3b438 100644 --- a/cv/semantic_segmentation/icnet/pytorch/README.md +++ b/cv/semantic_segmentation/icnet/pytorch/README.md @@ -6,6 +6,12 @@ An image cascade network (ICNet) that incorporates multi-resolution branches und in-depth analysis of the framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/lednet/pytorch/README.md b/cv/semantic_segmentation/lednet/pytorch/README.md index 06b5817cadf6680838d861454cf9d7b2ea2aef80..c120ef48f701da93635341339ee1b298a3f26241 100644 --- a/cv/semantic_segmentation/lednet/pytorch/README.md +++ b/cv/semantic_segmentation/lednet/pytorch/README.md @@ -8,6 +8,12 @@ where two new operations, channel split and shuffle, are utilized in each residu cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/linknet/pytorch/README.md b/cv/semantic_segmentation/linknet/pytorch/README.md index 73bc80fec9440022de2616673773ac67f98218f6..157ce7746bd0e6dffefb3c2a1d2f54be51a84325 100644 --- a/cv/semantic_segmentation/linknet/pytorch/README.md +++ b/cv/semantic_segmentation/linknet/pytorch/README.md @@ -6,6 +6,12 @@ A novel deep neural network architecture which allows it to learn without any si parameters. The network uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of resolution 3x640x360. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/mask2former/pytorch/README.md b/cv/semantic_segmentation/mask2former/pytorch/README.md index e3493e747e2bb4bc930346a3f10c9b97fea156cc..3992bfeb6d6a11f6be49d39ec6bca3ebb6db89f2 100644 --- a/cv/semantic_segmentation/mask2former/pytorch/README.md +++ b/cv/semantic_segmentation/mask2former/pytorch/README.md @@ -10,6 +10,12 @@ utilize high-resolution features. It feeds successive feature maps from the pixe successive Transformer decoder layers in a round-robin fashion. Finally, we incorporate optimization improvements that boost model performance without introducing additional computation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/mobileseg/paddlepaddle/README.md b/cv/semantic_segmentation/mobileseg/paddlepaddle/README.md index 6074932be2ee46e2df3988dc3ec316b9484aa001..f11679ed2fbf18c3386cc09cdb8ec61af765c913 100644 --- a/cv/semantic_segmentation/mobileseg/paddlepaddle/README.md +++ b/cv/semantic_segmentation/mobileseg/paddlepaddle/README.md @@ -5,6 +5,12 @@ MobileSeg models adopt encoder-decoder architecture and use lightweight models as encoder. These semantic segmentation models are designed for mobile and edge devices. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/ocnet/pytorch/README.md b/cv/semantic_segmentation/ocnet/pytorch/README.md index 564782d9826a341cd9c3ae0bc45224a9b93884fb..5175c7ddad7c220f16337e06c6c1098e0a9f9d73 100644 --- a/cv/semantic_segmentation/ocnet/pytorch/README.md +++ b/cv/semantic_segmentation/ocnet/pytorch/README.md @@ -9,6 +9,12 @@ model dense relations between pixels, focusing on object boundaries and structur leveraging object context information makes it particularly effective for complex scene understanding tasks in computer vision applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/ocrnet/paddlepaddle/README.md b/cv/semantic_segmentation/ocrnet/paddlepaddle/README.md index 70f3ed8407e96578be467fa634989a9d4405d4e9..0071f2d32199476a99ed5a12779ed1794596db37 100644 --- a/cv/semantic_segmentation/ocrnet/paddlepaddle/README.md +++ b/cv/semantic_segmentation/ocrnet/paddlepaddle/README.md @@ -9,6 +9,12 @@ object regions, OCRNet augments each pixel's representation with contextual info approach improves segmentation accuracy, particularly in complex scenes, by better capturing object boundaries and contextual relationships between different image elements. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/ocrnet/pytorch/README.md b/cv/semantic_segmentation/ocrnet/pytorch/README.md index 5aad45ce4157b1243ddf9f998d0de7c93f469279..22bfe5541c140cd52a89c3a5c6d2d9f8147b858c 100644 --- a/cv/semantic_segmentation/ocrnet/pytorch/README.md +++ b/cv/semantic_segmentation/ocrnet/pytorch/README.md @@ -9,6 +9,12 @@ object regions, OCRNet augments each pixel's representation with contextual info approach improves segmentation accuracy, particularly in complex scenes, by better capturing object boundaries and contextual relationships between different image elements. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/pp_humansegv1/paddlepaddle/README.md b/cv/semantic_segmentation/pp_humansegv1/paddlepaddle/README.md index 415bb4b008eccfd3d2fa6505bcb92af47fef14b9..c5cd3fd9a59cdbb60b49d035125b988bef36ff1c 100644 --- a/cv/semantic_segmentation/pp_humansegv1/paddlepaddle/README.md +++ b/cv/semantic_segmentation/pp_humansegv1/paddlepaddle/README.md @@ -9,6 +9,12 @@ fine-tuning for enhanced performance. PP-HumanSegV1 is particularly valuable for replacement, portrait snapshot, and barrage penetration, providing high-quality segmentation results with minimal computational requirements. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/pp_humansegv2/paddlepaddle/README.md b/cv/semantic_segmentation/pp_humansegv2/paddlepaddle/README.md index 1d26fd47c62acc222c94bd7e095842e6568afd3c..cd2d3400d6c587a68e6772ddb40672ea6f844547 100644 --- a/cv/semantic_segmentation/pp_humansegv2/paddlepaddle/README.md +++ b/cv/semantic_segmentation/pp_humansegv2/paddlepaddle/README.md @@ -8,6 +8,12 @@ compared to its predecessor. The model supports zero-cost deployment for immedia fine-tuning for better performance. PP-HumanSegV2 is particularly effective for applications like video background replacement and portrait segmentation, delivering high-quality results with optimized computational efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/pp_liteseg/paddlepaddle/README.md b/cv/semantic_segmentation/pp_liteseg/paddlepaddle/README.md index 463d8e4abc088a1d0ba75c75b84575643f5f41e1..59c3cf7fc4f5ccca1aec656ee632286cc6e1102b 100644 --- a/cv/semantic_segmentation/pp_liteseg/paddlepaddle/README.md +++ b/cv/semantic_segmentation/pp_liteseg/paddlepaddle/README.md @@ -8,6 +8,12 @@ representations, this model proposes a Unified Attention Fusion Module (UAFM), w channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/psanet/pytorch/README.md b/cv/semantic_segmentation/psanet/pytorch/README.md index 18c73228f6afc5cec07056590c5ae50b3c1e0985..6d13e56c0974d55e59d7d623812155e80d783e35 100644 --- a/cv/semantic_segmentation/psanet/pytorch/README.md +++ b/cv/semantic_segmentation/psanet/pytorch/README.md @@ -8,6 +8,12 @@ propagation in bi-direction for scene parsing is enabled. Information at other p prediction of the current position and vice versa, information at the current position can be distributed to assist the prediction of other ones. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/pspnet/pytorch/README.md b/cv/semantic_segmentation/pspnet/pytorch/README.md index 7606a31ad0f5ad9abb1a886b41f8e12afc36e317..d1f5b6ddba9f708ad166bece0c7dbe6cdb81fab3 100644 --- a/cv/semantic_segmentation/pspnet/pytorch/README.md +++ b/cv/semantic_segmentation/pspnet/pytorch/README.md @@ -8,6 +8,12 @@ achieves state-ofthe-art performance on various datasets. It came first in Image VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields the new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/refinenet/pytorch/README.md b/cv/semantic_segmentation/refinenet/pytorch/README.md index 164645f25dc57d78cf2cadc645797ae1a801f5be..472c9d92c8167d70691ef67de39b55361d95c6e6 100644 --- a/cv/semantic_segmentation/refinenet/pytorch/README.md +++ b/cv/semantic_segmentation/refinenet/pytorch/README.md @@ -9,6 +9,12 @@ convolutions. The individual components of RefineNet employ residual connections which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/segnet/pytorch/README.md b/cv/semantic_segmentation/segnet/pytorch/README.md index b6bf7f1cdc446b3b59dc9ba13ae96e2f19a820f8..c1d703d6dafe9696302cdc602c54d3205770f92f 100644 --- a/cv/semantic_segmentation/segnet/pytorch/README.md +++ b/cv/semantic_segmentation/segnet/pytorch/README.md @@ -10,6 +10,12 @@ of SegNet lies is in the manner in which the decoder upsamples its lower resolut the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/stdc/paddlepaddle/README.md b/cv/semantic_segmentation/stdc/paddlepaddle/README.md index 134b1de2b12ecb31864f720904c6554250f13f78..fc533b5c45249c7bb0a373e92b55152d3e529e53 100644 --- a/cv/semantic_segmentation/stdc/paddlepaddle/README.md +++ b/cv/semantic_segmentation/stdc/paddlepaddle/README.md @@ -9,6 +9,12 @@ information learning in low-level layers. By fusing both low-level and deep feat segmentation results with optimized computational efficiency, making it particularly suitable for real-time applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/stdc/pytorch/README.md b/cv/semantic_segmentation/stdc/pytorch/README.md index 6343c3bba3dcda3580b126f3bd66b8178b07558d..1edb2fea5c4827434659424617451f6260e3ebc8 100644 --- a/cv/semantic_segmentation/stdc/pytorch/README.md +++ b/cv/semantic_segmentation/stdc/pytorch/README.md @@ -9,6 +9,12 @@ information learning in low-level layers. By fusing both low-level and deep feat segmentation results with optimized computational efficiency, making it particularly suitable for real-time applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/unet++/pytorch/README.md b/cv/semantic_segmentation/unet++/pytorch/README.md index 07989de113adda04cdf81b025726eca4e3e03e8c..53bdf0337a2f48f1aed5fa56835043fc2180abe9 100644 --- a/cv/semantic_segmentation/unet++/pytorch/README.md +++ b/cv/semantic_segmentation/unet++/pytorch/README.md @@ -8,6 +8,12 @@ encoder and decoder feature maps, making the optimization task easier. By enhanc network levels, UNet++ improves segmentation accuracy, particularly in complex medical imaging tasks. Its architecture effectively handles the challenges of precise boundary detection and small object segmentation in medical images. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/unet/paddlepaddle/README.md b/cv/semantic_segmentation/unet/paddlepaddle/README.md index bb2d220141da62c4202982fa1494eafb485d8923..4df09a4fd67c46f3a8d8afa31fa6471853111aaf 100644 --- a/cv/semantic_segmentation/unet/paddlepaddle/README.md +++ b/cv/semantic_segmentation/unet/paddlepaddle/README.md @@ -6,6 +6,12 @@ A network and training strategy that relies on the strong use of data augmentati samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/unet/pytorch/README.md b/cv/semantic_segmentation/unet/pytorch/README.md index eb00fc6cb6c064d59cacff61f121ff01316a6b62..fde9e8b87a3eb12e3f3777e8d45cc87e42dc2291 100644 --- a/cv/semantic_segmentation/unet/pytorch/README.md +++ b/cv/semantic_segmentation/unet/pytorch/README.md @@ -6,6 +6,12 @@ A network and training strategy that relies on the strong use of data augmentati samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/unet3d/pytorch/README.md b/cv/semantic_segmentation/unet3d/pytorch/README.md index 88f1f3bd8bdfcb9b21bb57f63fb8333b9942c883..5821ae23168116976bd64fa05370e2da44f737d3 100644 --- a/cv/semantic_segmentation/unet3d/pytorch/README.md +++ b/cv/semantic_segmentation/unet3d/pytorch/README.md @@ -8,6 +8,12 @@ sparsely annotated volumes to produce dense 3D segmentations. The model supports segmentation workflows, incorporating on-the-fly elastic deformations for efficient data augmentation. 3D-UNet is particularly valuable in medical imaging for tasks requiring precise 3D anatomical structure delineation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/semantic_segmentation/vnet/tensorflow/README.md b/cv/semantic_segmentation/vnet/tensorflow/README.md index e719fb8490376c974ff8b26a60e3eadfd2ab1ff4..56e9be097e49596be9012d8fa9385c89f85de724 100644 --- a/cv/semantic_segmentation/vnet/tensorflow/README.md +++ b/cv/semantic_segmentation/vnet/tensorflow/README.md @@ -8,6 +8,12 @@ architecture incorporates residual connections and volumetric convolutions to ca dimensions. VNet's innovative design enables precise segmentation of complex anatomical structures, making it particularly valuable in medical imaging tasks such as organ segmentation and tumor detection in volumetric datasets. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/basicvsr++/pytorch/README.md b/cv/super_resolution/basicvsr++/pytorch/README.md index 2674fbf04825fc28f49f0987ee3cb75042319ef5..b2b68ba7714ba91aea682b45ce33a9ea88b3c339 100755 --- a/cv/super_resolution/basicvsr++/pytorch/README.md +++ b/cv/super_resolution/basicvsr++/pytorch/README.md @@ -9,6 +9,12 @@ including compressed video enhancement, and achieved top results in NTIRE 2021 c structure effectively processes entire video sequences, making it a state-of-the-art solution for high-quality video upscaling and restoration. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/basicvsr/pytorch/README.md b/cv/super_resolution/basicvsr/pytorch/README.md index 5c30134bff22bf46c01b9749fadb483ca0b9851e..e33589a615d9581c046d8d2c9826d72508ee477a 100755 --- a/cv/super_resolution/basicvsr/pytorch/README.md +++ b/cv/super_resolution/basicvsr/pytorch/README.md @@ -8,6 +8,12 @@ Figure, red and blue colors represent the backward and forward propagations, res contain only generic components. S, W and R refer to the flow estimation module, spatial warping module, and residual blocks, respectively. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/esrgan/pytorch/README.md b/cv/super_resolution/esrgan/pytorch/README.md index 957c5b687f22df188cab5b1b8c52fcfafd20b95d..1f25e8647c244a0f66b7f0433da19f88059545ec 100755 --- a/cv/super_resolution/esrgan/pytorch/README.md +++ b/cv/super_resolution/esrgan/pytorch/README.md @@ -1,51 +1,57 @@ -# ESRGAN - -## Model Description - -ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) is an advanced deep learning model for single image -super-resolution. It improves upon SRGAN by introducing Residual-in-Residual Dense Blocks (RRDB) without batch -normalization, relativistic GAN for the discriminator, and enhanced perceptual loss using pre-activation features. These -innovations enable ESRGAN to generate more realistic textures with fewer artifacts, producing higher-quality upscaled -images. It achieved first place in the PIRM2018-SR Challenge, demonstrating superior visual quality and more natural -textures compared to its predecessor. - -## Model Preparation - -### Prepare Resources - -```shell -# Download DIV2K: https://data.vision.ee.ethz.ch/cvl/DIV2K/ or you can follow this tools/dataset_converters/div2k/README.md -$ mkdir -p data/DIV2K -``` - -### Install Dependencies - -```shell -# Install libGL -## CentOS -yum install -y mesa-libGL -## Ubuntu -apt install -y libgl1-mesa-glx - -git clone https://github.com/open-mmlab/mmagic.git -b v1.2.0 --depth=1 -cd mmagic/ -pip3 install -e . -v - -sed -i 's/diffusers.models.unet_2d_condition/diffusers.models.unets.unet_2d_condition/g' mmagic/models/editors/vico/vico_utils.py -pip install albumentations -``` - -## Model Training - -```shell -# One single GPU -python3 tools/train.py configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py - -# Mutiple GPUs on one machine -sed -i 's/python /python3 /g' tools/dist_train.sh -bash tools/dist_train.sh configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py 8 -``` - -## References - -- [mmagic](https://github.com/open-mmlab/mmagic) +# ESRGAN + +## Model Description + +ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) is an advanced deep learning model for single image +super-resolution. It improves upon SRGAN by introducing Residual-in-Residual Dense Blocks (RRDB) without batch +normalization, relativistic GAN for the discriminator, and enhanced perceptual loss using pre-activation features. These +innovations enable ESRGAN to generate more realistic textures with fewer artifacts, producing higher-quality upscaled +images. It achieved first place in the PIRM2018-SR Challenge, demonstrating superior visual quality and more natural +textures compared to its predecessor. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + +## Model Preparation + +### Prepare Resources + +```shell +# Download DIV2K: https://data.vision.ee.ethz.ch/cvl/DIV2K/ or you can follow this tools/dataset_converters/div2k/README.md +$ mkdir -p data/DIV2K +``` + +### Install Dependencies + +```shell +# Install libGL +## CentOS +yum install -y mesa-libGL +## Ubuntu +apt install -y libgl1-mesa-glx + +git clone https://github.com/open-mmlab/mmagic.git -b v1.2.0 --depth=1 +cd mmagic/ +pip3 install -e . -v + +sed -i 's/diffusers.models.unet_2d_condition/diffusers.models.unets.unet_2d_condition/g' mmagic/models/editors/vico/vico_utils.py +pip install albumentations +``` + +## Model Training + +```shell +# One single GPU +python3 tools/train.py configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py + +# Mutiple GPUs on one machine +sed -i 's/python /python3 /g' tools/dist_train.sh +bash tools/dist_train.sh configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py 8 +``` + +## References + +- [mmagic](https://github.com/open-mmlab/mmagic) diff --git a/cv/super_resolution/liif/pytorch/README.md b/cv/super_resolution/liif/pytorch/README.md index f0106f01ac3bd29325054e6ed315c5346210d36a..fddaf48fbfa5f10f9c0c847857da95e64fe03ef3 100755 --- a/cv/super_resolution/liif/pytorch/README.md +++ b/cv/super_resolution/liif/pytorch/README.md @@ -8,6 +8,12 @@ arbitrary resolution representation. LIIF combines 2D deep features with coordin images, even at resolutions 30x higher than training data. This approach bridges discrete and continuous image representations, outperforming traditional resizing methods and supporting tasks with varying image sizes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/real_basicvsr/pytorch/README.md b/cv/super_resolution/real_basicvsr/pytorch/README.md index d45dab40b5d5462d4d9bb063dfd22166de1585d7..590aabb4a207bfe71e0ea2b9de1a3a0f0952ba46 100755 --- a/cv/super_resolution/real_basicvsr/pytorch/README.md +++ b/cv/super_resolution/real_basicvsr/pytorch/README.md @@ -8,6 +8,12 @@ and artifact suppression. The model introduces a stochastic degradation scheme t performance, and emphasizes the use of longer sequences over larger batches for more effective temporal information utilization. RealBasicVSR demonstrates superior quality and efficiency in video enhancement tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/ttsr/pytorch/README.md b/cv/super_resolution/ttsr/pytorch/README.md index 9bc1e608eecc374599c6c935b53fdb65d2d47b93..809d051633f991028b48953b5380e8ede08e84b8 100755 --- a/cv/super_resolution/ttsr/pytorch/README.md +++ b/cv/super_resolution/ttsr/pytorch/README.md @@ -9,6 +9,12 @@ hard-attention for texture transfer, and soft-attention for texture synthesis. T transfer through attention mechanisms, allowing for high-quality image reconstruction at various magnification levels (1x to 4x). +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/cv/super_resolution/ttvsr/pytorch/README.md b/cv/super_resolution/ttvsr/pytorch/README.md index bafacc6c3dc2e36045b37a94240f3dd7a996f45f..10e7403f10a2c6ae6ec4a9ad1c4749862155edeb 100755 --- a/cv/super_resolution/ttvsr/pytorch/README.md +++ b/cv/super_resolution/ttvsr/pytorch/README.md @@ -9,6 +9,12 @@ approach improves video super-resolution by better utilizing temporal informatio quality upscaled videos. TTVSR demonstrates superior performance in handling complex video sequences while maintaining efficient processing capabilities. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/docs/MODEL_TEMPLATE.md b/docs/MODEL_TEMPLATE.md index 45c8266462a90d5276164f3c7f0b1bcb212a46a4..dde05d2a5b6589bb9b5441ce38edc1135fcaf766 100644 --- a/docs/MODEL_TEMPLATE.md +++ b/docs/MODEL_TEMPLATE.md @@ -8,10 +8,10 @@ A brief introduction about this model. ## Supported Environments -| Iluvatar GPU | IXUCA Version | -|--------------|---------------| -| BI-V100 | 3.0.0 | -| BI-V150 | 4.2.0 | +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.2.0 | 25.03 | +| BI-V100 | 3.2.0 | 23.03 | ## Model Preparation diff --git a/hpc/molecular_dynamics/water_se_e2_a/tensorflow/README.md b/hpc/molecular_dynamics/water_se_e2_a/tensorflow/README.md index 7109b71e3b21b2091d37e6215f96d301729be0e5..fdeb13b34b7c12c1d7ee41208fde114e0085f416 100644 --- a/hpc/molecular_dynamics/water_se_e2_a/tensorflow/README.md +++ b/hpc/molecular_dynamics/water_se_e2_a/tensorflow/README.md @@ -4,7 +4,17 @@ The notation of se_e2_a is short for the Deep Potential Smooth Edition (DeepPot- Note that it is sometimes called a “two-atom embedding descriptor” which means the input of the embedding net is atomic distances. The descriptor does encode multi-body information (both angular and radial information of neighboring atoms). In this example, we will train a DeepPot-SE model for a water system. A complete training input script of this example can be found in the directory. -## Step 1: Installation +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.09 | + +## Model Preparation + +### Prepare Resources + +### Install Dependencies ``` apt install git git clone --recursive -b v2.2.2 https://github.com/deepmodeling/deepmd-kit.git deepmd-kit @@ -12,7 +22,7 @@ pip3 install numpy==1.22.3 pip3 install deepmd-kit[gpu,cu10,lmp,ipi]==2.2.2 ``` -### Install the DeePMD-kit’s Python interface +#### Install the DeePMD-kit’s Python interface Visit Iluvatar Corex official website - Resource Center page (https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=381380977957597184) to obtain the Linux version software stack offline installation package. If you already have an account, click the "Login" button at the upper right corner. If you do not have an account, click the "Login" button and select "Register" to apply for an account. And then download BI_SDK3.1.0 (4.57GB). ``` pip3 install tensorflow-2.6.5+corex.3.1.0-cp38-cp38-linux_x86_64.whl @@ -20,7 +30,7 @@ sed -i '473s/np.float64/np.float32/' deepmd/env.py pip3 install . ``` -### Install the DeePMD-kit’s C++ interface +#### Install the DeePMD-kit’s C++ interface ``` deepmd_source_dir=`pwd` cd $deepmd_source_dir/source @@ -31,14 +41,14 @@ make -j4 make install ``` -### Install from pre-compiled C library +#### Install from pre-compiled C library ``` cmake -DDEEPMD_C_ROOT=./libdeepmd_c -DCMAKE_INSTALL_PREFIX=$deepmd_root .. make -j8 make install ``` -### Install LAMMPS +#### Install LAMMPS ``` cd $deepmd_source_dir wget https://github.com/lammps/lammps/archive/stable_23Jun2022_update4.tar.gz @@ -53,14 +63,14 @@ make -j4 make install ``` -### Install i-PI +#### Install i-PI ``` cd ../.. pip3 install -U i-PI pip3 install pytest ``` -## Step 2: Training +## Model Training ### One single GPU ``` @@ -70,12 +80,12 @@ export TF_ENABLE_DEPRECATION_WARNINGS=1 DP_INTERFACE_PREC=low dp train input.json ``` -## Results +## Model Results | GPU | average training | | ----------- | -------------------- | | 1 card | 0.0325 s/batch | -## Reference +## References https://github.com/deepmodeling/deepmd-kit#about-deepmd-kit diff --git a/multimodal/contrastive_learning/clip/pytorch/README.md b/multimodal/contrastive_learning/clip/pytorch/README.md index 330516e0d692e27309cb86ed23211e498152827e..5c26ef895fda1056490202eb57818063c0992e30 100644 --- a/multimodal/contrastive_learning/clip/pytorch/README.md +++ b/multimodal/contrastive_learning/clip/pytorch/README.md @@ -8,6 +8,12 @@ encoder and a text encoder to predict the correct pairings of a batch of (image, the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/controlnet/pytorch/README.md b/multimodal/diffusion_model/controlnet/pytorch/README.md index d422502289c80a87a136ddd316fa3e60d62a8c9e..a040b3d8e18947e187a7217220570f86ee81ed47 100644 --- a/multimodal/diffusion_model/controlnet/pytorch/README.md +++ b/multimodal/diffusion_model/controlnet/pytorch/README.md @@ -12,6 +12,12 @@ Stable diffusion is trained on billions of images, and it already knows what is But it does not know the meaning of that "Control Image (Source Image)". Our target is to let it know. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/ddpm/pytorch/README.md b/multimodal/diffusion_model/ddpm/pytorch/README.md index 6fdf2649c741d98eef90ddfe8b71bc105fd33207..e7a3bd2726f6ed013fb6d617bf48aa36367ec388 100644 --- a/multimodal/diffusion_model/ddpm/pytorch/README.md +++ b/multimodal/diffusion_model/ddpm/pytorch/README.md @@ -8,6 +8,12 @@ adding Gaussian noise to data during training and then learning to reverse this to generate high-quality samples by starting from random noise and iteratively refining it. DDPMs have shown impressive results in image generation, offering stable training and producing diverse, realistic outputs. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/stable-diffusion-1.4/pytorch/README.md b/multimodal/diffusion_model/stable-diffusion-1.4/pytorch/README.md index 10af10f452a8b98a71ba174de38ef5fcdfb48e71..4c4d39faa3873130cd638a84d5e8d45eefff80f0 100644 --- a/multimodal/diffusion_model/stable-diffusion-1.4/pytorch/README.md +++ b/multimodal/diffusion_model/stable-diffusion-1.4/pytorch/README.md @@ -8,6 +8,12 @@ images by operating in a compressed latent space. The model leverages a frozen C and process input prompts. With its 860M UNet and 123M text encoder, Stable Diffusion achieves remarkable results while maintaining computational efficiency, making it accessible for users with GPUs having at least 4GB VRAM. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Install Dependencies diff --git a/multimodal/diffusion_model/stable-diffusion-1.5/pytorch/README.md b/multimodal/diffusion_model/stable-diffusion-1.5/pytorch/README.md index 19099d741272a74670cdd15c2ce62855bb699b77..9704f14f18f33d9899d20fb30b0340a694c78059 100644 --- a/multimodal/diffusion_model/stable-diffusion-1.5/pytorch/README.md +++ b/multimodal/diffusion_model/stable-diffusion-1.5/pytorch/README.md @@ -9,6 +9,12 @@ while maintaining exceptional visual quality. With its ability to interpret dive corresponding images, Stable Diffusion 1.5 has become a powerful tool for creative applications, AI-assisted design, and visual content generation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.09 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/stable-diffusion-2.1/pytorch/README.md b/multimodal/diffusion_model/stable-diffusion-2.1/pytorch/README.md index a2b3c90938dd87a492e7c4b46d6912b0ee19d2aa..63ddc390bcf041349e4a79b19dd3c67b573750c0 100644 --- a/multimodal/diffusion_model/stable-diffusion-2.1/pytorch/README.md +++ b/multimodal/diffusion_model/stable-diffusion-2.1/pytorch/README.md @@ -9,6 +9,12 @@ understanding compared to earlier versions. The model operates efficiently in a accessible for various applications while maintaining exceptional visual fidelity. Stable Diffusion 2.1 has become a powerful tool for creative professionals and AI enthusiasts alike. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.09 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/stable-diffusion-3/pytorch/README.md b/multimodal/diffusion_model/stable-diffusion-3/pytorch/README.md index 6148fcc52280d6d135bc893e24ac130ddcc152dd..7606bac7f81270f486c2f37b64ec0aa9bfe8e247 100644 --- a/multimodal/diffusion_model/stable-diffusion-3/pytorch/README.md +++ b/multimodal/diffusion_model/stable-diffusion-3/pytorch/README.md @@ -9,6 +9,12 @@ prompt comprehension. With its ability to generate highly detailed and contextua pushes the boundaries of AI-assisted creativity. The model maintains efficient processing through its latent space operations while delivering state-of-the-art results in image synthesis and generation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.12 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/diffusion_model/stable-diffusion-xl/pytorch/README.md b/multimodal/diffusion_model/stable-diffusion-xl/pytorch/README.md index 76a3fe59d925090fed2a26da34fa85fcf367560e..cab188b629eec72309e906da4b0a60c784cf17b2 100644 --- a/multimodal/diffusion_model/stable-diffusion-xl/pytorch/README.md +++ b/multimodal/diffusion_model/stable-diffusion-xl/pytorch/README.md @@ -9,6 +9,12 @@ techniques, Stable Diffusion XL excels at producing photorealistic and artistic diversity. The model's ability to interpret complex prompts and generate corresponding images makes it a valuable tool for creative professionals, designers, and AI enthusiasts. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.12 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/vision-language_model/blip/pytorch/README.md b/multimodal/vision-language_model/blip/pytorch/README.md index b00ddc0719e36601e84d11ce8ecd79ec0ec74821..bc829e71233f83a5fa5505517209bc640b3669a4 100755 --- a/multimodal/vision-language_model/blip/pytorch/README.md +++ b/multimodal/vision-language_model/blip/pytorch/README.md @@ -8,6 +8,12 @@ visual comprehension and text generation. It employs a unique bootstrapping mech web-sourced image-text pairs, improving the quality of training data. This approach enables BLIP to achieve superior performance in tasks like image captioning, visual question answering, and multimodal understanding. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.1 | 24.03 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/vision-language_model/l-verse/pytorch/README.md b/multimodal/vision-language_model/l-verse/pytorch/README.md index 0b2191f9e695b096d468265c7a689dfe6b511644..af09c96c8eda37683085d89887760d5b56a33340 100644 --- a/multimodal/vision-language_model/l-verse/pytorch/README.md +++ b/multimodal/vision-language_model/l-verse/pytorch/README.md @@ -9,6 +9,12 @@ without requiring fine-tuning or additional frameworks. Its AugVAE component ach reconstruction, while BiART effectively distinguishes between conditional references and generation targets. L-Verse demonstrates impressive results in multimodal tasks, particularly on MS-COCO Captions dataset. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/vision-language_model/llava-1.5/pytorch/README.md b/multimodal/vision-language_model/llava-1.5/pytorch/README.md index 3cb47c183bb2097df17a2fdeb5f5d4b17db44ce9..df97f7e8798b2feb645654fc36ad25fcc4412ec1 100644 --- a/multimodal/vision-language_model/llava-1.5/pytorch/README.md +++ b/multimodal/vision-language_model/llava-1.5/pytorch/README.md @@ -6,6 +6,12 @@ LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-gener instruction-following data. It is an auto-regressive language model, based on the transformer architecture. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.12 | + ## Model Preparation ### Install Dependencies diff --git a/multimodal/vision-language_model/moe-llava-phi2-2.7b/pytorch/README.md b/multimodal/vision-language_model/moe-llava-phi2-2.7b/pytorch/README.md index 0a98afec10f36396d6a6d4cfcc3d6bb512d5361d..7272152bb9d3e15512f427bb29f9c3bef8ca6ee8 100644 --- a/multimodal/vision-language_model/moe-llava-phi2-2.7b/pytorch/README.md +++ b/multimodal/vision-language_model/moe-llava-phi2-2.7b/pytorch/README.md @@ -8,6 +8,12 @@ information. The model leverages expert networks to specialize in different aspe enabling more accurate and context-aware responses. MoE-LLaVA is particularly effective in applications requiring complex reasoning across visual and linguistic domains, such as image captioning and visual question answering. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.2.0 | 25.03 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/vision-language_model/moe-llava-qwen-1.8b/pytorch/README.md b/multimodal/vision-language_model/moe-llava-qwen-1.8b/pytorch/README.md index bf216ff0382fc357bc43d1629857c4602fb41b58..36ef33bc75a208ad8b1746914d30ae96697f51ae 100644 --- a/multimodal/vision-language_model/moe-llava-qwen-1.8b/pytorch/README.md +++ b/multimodal/vision-language_model/moe-llava-qwen-1.8b/pytorch/README.md @@ -9,6 +9,12 @@ information. The model leverages expert networks to specialize in different aspe enabling more accurate and context-aware responses. MoE-LLaVA is particularly effective in applications requiring complex reasoning across visual and linguistic domains, such as image captioning and visual question answering. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.2.0 | 25.03 | + ## Model Preparation ### Prepare Resources diff --git a/multimodal/vision-language_model/moe-llava-stablelm-1.6b/pytorch/README.md b/multimodal/vision-language_model/moe-llava-stablelm-1.6b/pytorch/README.md index 4c34f8dcfc5c1876a32861073ac39902898902fd..a40dd6207f13ae03d05454d76efe1f0885929a32 100644 --- a/multimodal/vision-language_model/moe-llava-stablelm-1.6b/pytorch/README.md +++ b/multimodal/vision-language_model/moe-llava-stablelm-1.6b/pytorch/README.md @@ -8,6 +8,12 @@ information. The model leverages expert networks to specialize in different aspe enabling more accurate and context-aware responses. MoE-LLaVA is particularly effective in applications requiring complex reasoning across visual and linguistic domains, such as image captioning and visual question answering. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.2.0 | 25.03 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md b/nlp/cloze_test/glm/pytorch/README.md similarity index 85% rename from nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md rename to nlp/cloze_test/glm/pytorch/README.md index 40a178be7dacf42ed5072c552c4b739729ae8256..63bfb2b48bfe3ca4ffd0e9e9ef4e3b8ded206ca3 100644 --- a/nlp/cloze_test/glm/pytorch/GLMForMultiTokenCloze/README.md +++ b/nlp/cloze_test/glm/pytorch/README.md @@ -9,6 +9,12 @@ including NLU, conditional generation, and unconditional generation. With its ab tasks through adjustable blank configurations, GLM outperforms specialized models like BERT, T5, and GPT while maintaining efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/dialogue_generation/cpm/pytorch/README.md b/nlp/dialogue_generation/cpm/pytorch/README.md index 29ab6f0b98c3e0f8124a6896788771585d52d1be..d2951b139425685a1601bf07ca307d29ab462ac2 100644 --- a/nlp/dialogue_generation/cpm/pytorch/README.md +++ b/nlp/dialogue_generation/cpm/pytorch/README.md @@ -9,6 +9,12 @@ enables effective few-shot and zero-shot learning capabilities, making it partic processing. As one of the largest Chinese language models, CPM significantly advances the state of Chinese NLP research and applications.s +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/bart_fairseq/pytorch/README.md b/nlp/language_model/bart_fairseq/pytorch/README.md index 41c1943c590f78ed595ae67c4f0b2e7286c73216..3c0a77698a458d1c62128f4276581a0bb7e0208a 100644 --- a/nlp/language_model/bart_fairseq/pytorch/README.md +++ b/nlp/language_model/bart_fairseq/pytorch/README.md @@ -9,6 +9,12 @@ allows it to effectively handle both understanding and generation tasks, making applications. BART has demonstrated state-of-the-art performance on benchmarks like XSum, CNN/Daily Mail, and GLUE, showcasing its robust capabilities in text transformation and comprehension. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/bert/mindspore/README.md b/nlp/language_model/bert/mindspore/README.md index 3b6bbd94056022e70352b76bf81da1f3c3093f59..e13c25aefe8367945ddb2143bea50957adb521c3 100644 --- a/nlp/language_model/bert/mindspore/README.md +++ b/nlp/language_model/bert/mindspore/README.md @@ -8,6 +8,12 @@ context from both directions in text. Pretrained using Masked Language Modeling tasks, BERT achieves state-of-the-art results across various NLP tasks through fine-tuning. Its ability to understand deep contextual relationships in text has made it a fundamental model in modern NLP research and applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/bert/paddlepaddle/README.md b/nlp/language_model/bert/paddlepaddle/README.md index 271858a9131b371ab670c3e87c08da37729c5325..f852aed62c09a23d312a0967037a13a6ff0999d7 100644 --- a/nlp/language_model/bert/paddlepaddle/README.md +++ b/nlp/language_model/bert/paddlepaddle/README.md @@ -8,6 +8,12 @@ context from both directions in text. Pretrained using Masked Language Modeling tasks, BERT achieves state-of-the-art results across various NLP tasks through fine-tuning. Its ability to understand deep contextual relationships in text has made it a fundamental model in modern NLP research and applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/bert/pytorch/README.md b/nlp/language_model/bert/pytorch/README.md index bec27fdfdf000bd62b4d2428ef7b63426e0c6419..771aa96f96b86fab25591364fa4209e3fb10be9f 100644 --- a/nlp/language_model/bert/pytorch/README.md +++ b/nlp/language_model/bert/pytorch/README.md @@ -8,6 +8,12 @@ context from both directions in text. Pretrained using Masked Language Modeling tasks, BERT achieves state-of-the-art results across various NLP tasks through fine-tuning. Its ability to understand deep contextual relationships in text has made it a fundamental model in modern NLP research and applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/bert/tensorflow/base/README.md b/nlp/language_model/bert/tensorflow/README.md similarity index 92% rename from nlp/language_model/bert/tensorflow/base/README.md rename to nlp/language_model/bert/tensorflow/README.md index c487108de50be2cd34bdf5f16932cae5fd11493c..e2c7074f7e4f6c7f83280db133196c8566147ddb 100644 --- a/nlp/language_model/bert/tensorflow/base/README.md +++ b/nlp/language_model/bert/tensorflow/README.md @@ -1,68 +1,74 @@ -# BERT Pretraining - -## Model Description - -BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking language model that revolutionized -natural language processing. It employs a transformer architecture with bidirectional attention, enabling it to capture -context from both directions in text. Pretrained using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) -tasks, BERT achieves state-of-the-art results across various NLP tasks through fine-tuning. Its ability to understand -deep contextual relationships in text has made it a fundamental model in modern NLP research and applications. - -## Model Preparation - -### Prepare Resources - -This [Google Drive location](https://drive.google.com/drive/folders/1oQF4diVHNPCclykwdvQJw8n_VIWwV0PT) contains the -following. -You need to download tf1_ckpt folde , vocab.txt and bert_config.json into one file named bert_pretrain_ckpt_tf - -```sh -bert_pretrain_ckpt_tf: contains checkpoint files - model.ckpt-28252.data-00000-of-00001 - model.ckpt-28252.index - model.ckpt-28252.meta - vocab.txt - bert_config.json -``` - -[Download and preprocess datasets](https://github.com/mlcommons/training/tree/master/language_model/tensorflow/bert#generate-the-tfrecords-for-wiki-dataset) -You need to make a file named bert_pretrain_tf_records and store the results above. -tips: you can git clone this repo in other place ,we need the bert_pretrain_tf_records results here. - -### Install Dependencies - -```shell -bash init_tf.sh -wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.7.tar.gz -tar xf openmpi-4.0.7.tar.gz -cd openmpi-4.0.7/ -./configure --prefix=/usr/local/bin --with-orte -make -j4 && make install -export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH -``` - -## Model Training - -```shell -# Training on single card -bash run_1card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/train_data \ - --init_checkpoint=/path/to/bert_pretrain_ckpt_tf/model.ckpt-28252 \ - --eval_files_dir=/path/to/bert_pretrain_tf_records/eval_data \ - --train_batch_size=6 \ - --bert_config_file=/path/to/bert_pretrain_ckpt_tf/bert_config.json - -# Training on mutil-cards -export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 -export IX_NUM_CUDA_VISIBLE_DEVICES=8 -bash run_multi_card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/train_data \ - --init_checkpoint=/path/to/bert_pretrain_ckpt_tf/model.ckpt-28252 \ - --eval_files_dir=/path/to/bert_pretrain_tf_records/eval_data \ - --train_batch_size=6 \ - --bert_config_file=/path/to/bert_pretrain_ckpt_tf/bert_config.json -``` - -## Model Results - -| Model | GPUs | acc | fps | -|------------------|------------|----------|----------| -| BERT Pretraining | BI-V100 x8 | 0.424126 | 0.267241 | +# BERT Pretraining + +## Model Description + +BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking language model that revolutionized +natural language processing. It employs a transformer architecture with bidirectional attention, enabling it to capture +context from both directions in text. Pretrained using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) +tasks, BERT achieves state-of-the-art results across various NLP tasks through fine-tuning. Its ability to understand +deep contextual relationships in text has made it a fundamental model in modern NLP research and applications. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + +## Model Preparation + +### Prepare Resources + +This [Google Drive location](https://drive.google.com/drive/folders/1oQF4diVHNPCclykwdvQJw8n_VIWwV0PT) contains the +following. +You need to download tf1_ckpt folde , vocab.txt and bert_config.json into one file named bert_pretrain_ckpt_tf + +```sh +bert_pretrain_ckpt_tf: contains checkpoint files + model.ckpt-28252.data-00000-of-00001 + model.ckpt-28252.index + model.ckpt-28252.meta + vocab.txt + bert_config.json +``` + +[Download and preprocess datasets](https://github.com/mlcommons/training/tree/master/language_model/tensorflow/bert#generate-the-tfrecords-for-wiki-dataset) +You need to make a file named bert_pretrain_tf_records and store the results above. +tips: you can git clone this repo in other place ,we need the bert_pretrain_tf_records results here. + +### Install Dependencies + +```shell +bash init_tf.sh +wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.7.tar.gz +tar xf openmpi-4.0.7.tar.gz +cd openmpi-4.0.7/ +./configure --prefix=/usr/local/bin --with-orte +make -j4 && make install +export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH +``` + +## Model Training + +```shell +# Training on single card +bash run_1card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/train_data \ + --init_checkpoint=/path/to/bert_pretrain_ckpt_tf/model.ckpt-28252 \ + --eval_files_dir=/path/to/bert_pretrain_tf_records/eval_data \ + --train_batch_size=6 \ + --bert_config_file=/path/to/bert_pretrain_ckpt_tf/bert_config.json + +# Training on mutil-cards +export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +export IX_NUM_CUDA_VISIBLE_DEVICES=8 +bash run_multi_card_FPS.sh --input_files_dir=/path/to/bert_pretrain_tf_records/train_data \ + --init_checkpoint=/path/to/bert_pretrain_ckpt_tf/model.ckpt-28252 \ + --eval_files_dir=/path/to/bert_pretrain_tf_records/eval_data \ + --train_batch_size=6 \ + --bert_config_file=/path/to/bert_pretrain_ckpt_tf/bert_config.json +``` + +## Model Results + +| Model | GPUs | acc | fps | +|------------------|------------|----------|----------| +| BERT Pretraining | BI-V100 x8 | 0.424126 | 0.267241 | diff --git a/nlp/language_model/roberta_fairseq/pytorch/README.md b/nlp/language_model/roberta_fairseq/pytorch/README.md index c1cf78eccee7e795758368de2baa51d3ba8ab12b..96c23ba70717566344f92072e1112f621add6f3a 100644 --- a/nlp/language_model/roberta_fairseq/pytorch/README.md +++ b/nlp/language_model/roberta_fairseq/pytorch/README.md @@ -9,6 +9,12 @@ tasks. By training on longer sequences and optimizing the training procedure, Ro understanding capabilities compared to its predecessor, making it a powerful tool for natural language processing applications. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/language_model/xlnet/paddlepaddle/README.md b/nlp/language_model/xlnet/paddlepaddle/README.md index fc3f7755978998124fc48a7189e1d2ada0541a6f..ab195253a700fe06926905cef3acf3eb8c460b01 100644 --- a/nlp/language_model/xlnet/paddlepaddle/README.md +++ b/nlp/language_model/xlnet/paddlepaddle/README.md @@ -9,6 +9,12 @@ Additionally, it incorporates Transformer-XL architecture, which handles long-ra recurrence and relative positional encoding. XLNet achieves state-of-the-art performance across various NLP tasks by leveraging these innovative techniques. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/ner/bert/pytorch/README.md b/nlp/ner/bert/pytorch/README.md index 2a26ac4ac4e9aa643b6d30e852dde206d7071d95..558111d5cec2ba663a535ec169246babac267fb1 100644 --- a/nlp/ner/bert/pytorch/README.md +++ b/nlp/ner/bert/pytorch/README.md @@ -9,6 +9,12 @@ recognition accuracy compared to traditional methods. BERT NER's ability to unde makes it particularly effective for complex text analysis tasks in various domains, including information extraction and text mining. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/question_answering/bert/pytorch/README.md b/nlp/question_answering/bert/pytorch/README.md index 69be503c1b1abc3b3af223f7a11f9d50049f618f..23b31fa41968dc1db979f3115cb690440f2b9ca7 100644 --- a/nlp/question_answering/bert/pytorch/README.md +++ b/nlp/question_answering/bert/pytorch/README.md @@ -8,6 +8,12 @@ attention mechanism. The model is trained to predict the start and end positions demonstrating exceptional performance in comprehension tasks. BERT SQuAD's ability to understand context and relationships between words makes it particularly effective for complex question answering scenarios in various domains. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/text_classification/bert/pytorch/README.md b/nlp/text_classification/bert/pytorch/README.md index 3d114e84bd60f5cc3551cb7477ef5d5e02b304af..1caa9b248e502b0a89418d1d47f1b4c420e1dfc8 100644 --- a/nlp/text_classification/bert/pytorch/README.md +++ b/nlp/text_classification/bert/pytorch/README.md @@ -8,6 +8,12 @@ understanding context. By leveraging BERT's bidirectional attention mechanism, i linguistic nuances and relationships between text segments. This makes BERT WNLI particularly valuable for tasks requiring deep comprehension of sentence structure and meaning, such as coreference resolution and textual entailment. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Install Dependencies diff --git a/nlp/text_correction/ernie/paddlepaddle/README.md b/nlp/text_correction/ernie/paddlepaddle/README.md index c88e03717d28906099ab1290ec269bc599d87836..df22f35b1c2db2b51d6ed0be99f15e2acf08fad5 100644 --- a/nlp/text_correction/ernie/paddlepaddle/README.md +++ b/nlp/text_correction/ernie/paddlepaddle/README.md @@ -8,6 +8,12 @@ sources, such as structured knowledge graphs, and by integrating multiple lingui semantics, and common sense. The model achieves this by using a knowledge-enhanced pre-training approach, which helps ERNIE better understand and generate more accurate and contextually aware language representations. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/nlp/text_summarisation/bert/pytorch/README.md b/nlp/text_summarisation/bert/pytorch/README.md index 273952a7bf0707b62659c2115ccd6ef795ec1049..3965ea12b8a2beb5068cab1549929460fefd1fca 100644 --- a/nlp/text_summarisation/bert/pytorch/README.md +++ b/nlp/text_summarisation/bert/pytorch/README.md @@ -9,6 +9,12 @@ informative summaries while preserving the original meaning. BERT summarization applications requiring efficient information extraction and condensation, such as news aggregation, document analysis, and content curation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.03 | + ## Model Preparation ### Install Dependencies diff --git a/nlp/translation/convolutional_fairseq/pytorch/README.md b/nlp/translation/convolutional_fairseq/pytorch/README.md index f5678448629086016245c53d7b3707f3528b7868..c3d1f55557aaf03aca2eab7ee59b1d736fdd74f3 100644 --- a/nlp/translation/convolutional_fairseq/pytorch/README.md +++ b/nlp/translation/convolutional_fairseq/pytorch/README.md @@ -1,69 +1,75 @@ -# Convolutional - -## Model Description - -Convolutional translation models leverage convolutional neural networks (CNNs) for machine translation tasks, offering -an alternative to traditional RNN-based approaches. These models process input sequences through multiple convolutional -layers, capturing local patterns and hierarchical features in the text. By using stacked convolutions with gated linear -units, they effectively model long-range dependencies while maintaining computational efficiency. Convolutional -translation models are particularly advantageous for parallel processing and handling large-scale translation tasks, -demonstrating competitive performance in sequence-to-sequence learning scenarios with reduced training time compared to -recurrent architectures. - -## Model Preparation - -### Prepare Resources - -```bash -# Go to "toolbox/Fairseq" directory in root path -cd ../../../../toolbox/Fairseq/ - -cd fairseq/examples/translation/ -bash prepare-wmt14en2de.sh -cd ../.. - -TEXT=examples/translation/wmt17_en_de -fairseq-preprocess \ - --source-lang en --target-lang de \ - --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ - --destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \ - --workers 20 -``` - -### Install Dependencies - -Convolutional model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. - -```bash -bash install_toolbox_fairseq.sh -``` - -## Model Training - -```bash -# Train -mkdir -p checkpoints/fconv_wmt_en_de -fairseq-train data-bin/wmt17_en_de --arch fconv_wmt_en_de \ - --max-epoch 100 \ - --dropout 0.2 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer nag --clip-norm 0.1 \ - --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ - --max-tokens 4000 \ - --no-epoch-checkpoints \ - --save-dir checkpoints/fconv_wmt_en_de - -# Evaluate -fairseq-generate data-bin/wmt17_en_de --path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \ - --beam 5 --remove-bpe -``` - -## Model Results - -| Model | GPUs | QPS | Train Epochs | Evaluate_Bleu | -|---------------|------------|---------|--------------|---------------| -| Convolutional | BI-V100 x8 | 1650.49 | 100 | 25.55 | - -## References - -- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) +# Convolutional + +## Model Description + +Convolutional translation models leverage convolutional neural networks (CNNs) for machine translation tasks, offering +an alternative to traditional RNN-based approaches. These models process input sequences through multiple convolutional +layers, capturing local patterns and hierarchical features in the text. By using stacked convolutions with gated linear +units, they effectively model long-range dependencies while maintaining computational efficiency. Convolutional +translation models are particularly advantageous for parallel processing and handling large-scale translation tasks, +demonstrating competitive performance in sequence-to-sequence learning scenarios with reduced training time compared to +recurrent architectures. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + +## Model Preparation + +### Prepare Resources + +```bash +# Go to "toolbox/Fairseq" directory in root path +cd ../../../../toolbox/Fairseq/ + +cd fairseq/examples/translation/ +bash prepare-wmt14en2de.sh +cd ../.. + +TEXT=examples/translation/wmt17_en_de +fairseq-preprocess \ + --source-lang en --target-lang de \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/wmt17_en_de --thresholdtgt 0 --thresholdsrc 0 \ + --workers 20 +``` + +### Install Dependencies + +Convolutional model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. + +```bash +bash install_toolbox_fairseq.sh +``` + +## Model Training + +```bash +# Train +mkdir -p checkpoints/fconv_wmt_en_de +fairseq-train data-bin/wmt17_en_de --arch fconv_wmt_en_de \ + --max-epoch 100 \ + --dropout 0.2 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --optimizer nag --clip-norm 0.1 \ + --lr 0.5 --lr-scheduler fixed --force-anneal 50 \ + --max-tokens 4000 \ + --no-epoch-checkpoints \ + --save-dir checkpoints/fconv_wmt_en_de + +# Evaluate +fairseq-generate data-bin/wmt17_en_de --path checkpoints/fconv_wmt_en_de/checkpoint_best.pt \ + --beam 5 --remove-bpe +``` + +## Model Results + +| Model | GPUs | QPS | Train Epochs | Evaluate_Bleu | +|---------------|------------|---------|--------------|---------------| +| Convolutional | BI-V100 x8 | 1650.49 | 100 | 25.55 | + +## References + +- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) diff --git a/nlp/translation/t5/pytorch/README.md b/nlp/translation/t5/pytorch/README.md index b752f2303ba49c03c11464f113c4707fc5dfe0b5..336eb168455f542c2a5b9926bacec3b75172b786 100644 --- a/nlp/translation/t5/pytorch/README.md +++ b/nlp/translation/t5/pytorch/README.md @@ -8,6 +8,12 @@ text generation problem. This allows T5 to use the same architecture and trainin By converting inputs and outputs into text sequences, T5 demonstrates strong performance across multiple benchmarks while maintaining a consistent and scalable approach to natural language processing tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Install Dependencies diff --git a/nlp/translation/transformer/paddlepaddle/README.md b/nlp/translation/transformer/paddlepaddle/README.md index 93ae1df259b8ffa2b81a968239d2f8f6038c0e96..e2df96e620ed3597dd77f32f7553fcfb4e0670ce 100644 --- a/nlp/translation/transformer/paddlepaddle/README.md +++ b/nlp/translation/transformer/paddlepaddle/README.md @@ -10,6 +10,12 @@ multi-head attention and position-wise feed-forward networks. Transformers have state-of-the-art models like BERT, GPT, and T5, driving advancements in machine translation, text generation, and other NLP tasks. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Install Dependencies diff --git a/nlp/translation/transformer_fairseq/pytorch/README.md b/nlp/translation/transformer_fairseq/pytorch/README.md index b7a3f7da039dc917505ddf47f7897db7cd5448ca..94ecbd4f4e63c5162e0981ffb85990d14f773375 100644 --- a/nlp/translation/transformer_fairseq/pytorch/README.md +++ b/nlp/translation/transformer_fairseq/pytorch/README.md @@ -1,75 +1,81 @@ -# Transformer - -## Model Description - -The Transformer model revolutionized natural language processing with its attention-based architecture, eliminating the -need for recurrent connections. It employs self-attention mechanisms to process input sequences in parallel, capturing -long-range dependencies more effectively than previous models. Transformers excel in tasks like translation, text -generation, and summarization by dynamically weighting the importance of different words in a sequence. Their parallel -processing capability enables faster training and better scalability, making them the foundation for state-of-the-art -language models like BERT and GPT. - -## Model Preparation - -### Prepare Resources - -```bash -# Go to "toolbox/Fairseq" directory in root path -cd ../../../../toolbox/Fairseq/ - -cd fairseq/examples/translation/ -bash prepare-iwslt14.sh -cd ../.. - -TEXT=examples/translation/iwslt14.tokenized.de-en -fairseq-preprocess --source-lang de --target-lang en \ - --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ - --destdir data-bin/iwslt14.tokenized.de-en \ - --workers 20 -``` - -### Install Dependencies - -Transformer model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. - -```bash -bash install_toolbox_fairseq.sh -``` - -## Model Training - -```bash -# Train -mkdir -p checkpoints/transformer -fairseq-train data-bin/iwslt14.tokenized.de-en \ - --arch transformer_iwslt_de_en --share-decoder-input-output-embed \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --dropout 0.3 --weight-decay 0.0001 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --max-tokens 4096 \ - --max-epoch 100 \ - --eval-bleu \ - --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \ - --eval-bleu-detok moses \ - --eval-bleu-remove-bpe \ - --eval-bleu-print-samples \ - --save-dir checkpoints/transformer \ - --no-epoch-checkpoints \ - --best-checkpoint-metric bleu --maximize-best-checkpoint-metric - -# Evaluate -fairseq-generate data-bin/iwslt14.tokenized.de-en \ - --path checkpoints/transformer/checkpoint_best.pt \ - --batch-size 128 --beam 5 --remove-bpe -``` - -## Model Results - -| Model | GPUs | QPS | Train Epochs | Bleu | -|-------------|------------|---------|--------------|-------|--| -| Transformer | BI-V100 x8 | 3204.78 | 100 | 35.07 | - -## References - -- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) +# Transformer + +## Model Description + +The Transformer model revolutionized natural language processing with its attention-based architecture, eliminating the +need for recurrent connections. It employs self-attention mechanisms to process input sequences in parallel, capturing +long-range dependencies more effectively than previous models. Transformers excel in tasks like translation, text +generation, and summarization by dynamically weighting the importance of different words in a sequence. Their parallel +processing capability enables faster training and better scalability, making them the foundation for state-of-the-art +language models like BERT and GPT. + +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + +## Model Preparation + +### Prepare Resources + +```bash +# Go to "toolbox/Fairseq" directory in root path +cd ../../../../toolbox/Fairseq/ + +cd fairseq/examples/translation/ +bash prepare-iwslt14.sh +cd ../.. + +TEXT=examples/translation/iwslt14.tokenized.de-en +fairseq-preprocess --source-lang de --target-lang en \ + --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ + --destdir data-bin/iwslt14.tokenized.de-en \ + --workers 20 +``` + +### Install Dependencies + +Transformer model is using Fairseq toolbox. Before you run this model, you need to setup Fairseq first. + +```bash +bash install_toolbox_fairseq.sh +``` + +## Model Training + +```bash +# Train +mkdir -p checkpoints/transformer +fairseq-train data-bin/iwslt14.tokenized.de-en \ + --arch transformer_iwslt_de_en --share-decoder-input-output-embed \ + --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ + --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ + --dropout 0.3 --weight-decay 0.0001 \ + --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ + --max-tokens 4096 \ + --max-epoch 100 \ + --eval-bleu \ + --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \ + --eval-bleu-detok moses \ + --eval-bleu-remove-bpe \ + --eval-bleu-print-samples \ + --save-dir checkpoints/transformer \ + --no-epoch-checkpoints \ + --best-checkpoint-metric bleu --maximize-best-checkpoint-metric + +# Evaluate +fairseq-generate data-bin/iwslt14.tokenized.de-en \ + --path checkpoints/transformer/checkpoint_best.pt \ + --batch-size 128 --beam 5 --remove-bpe +``` + +## Model Results + +| Model | GPUs | QPS | Train Epochs | Bleu | +|-------------|------------|---------|--------------|-------|--| +| Transformer | BI-V100 x8 | 3204.78 | 100 | 35.07 | + +## References + +- [Fairseq](https://github.com/facebookresearch/fairseq/tree/v0.10.2) diff --git a/others/graph_machine_learning/graph_wavenet/pytorch/README.md b/others/graph_machine_learning/graph_wavenet/pytorch/README.md index 58085b4987738e47c90506d83b4342a3c3f18390..4194b5237ab5e924fef8260df2011c9840082773 100644 --- a/others/graph_machine_learning/graph_wavenet/pytorch/README.md +++ b/others/graph_machine_learning/graph_wavenet/pytorch/README.md @@ -9,6 +9,12 @@ effectively handles complex, large-scale datasets, demonstrating superior perfor WaveNet's innovative approach to modeling both spatial and temporal dependencies makes it a powerful tool for analyzing and predicting patterns in dynamic, interconnected systems. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/others/kolmogorov_arnold_networks/kan/pytorch/README.md b/others/kolmogorov_arnold_networks/kan/pytorch/README.md index e5b38e96263945ccc32165568a168813cc12c959..a8d2b5514295d82f1823a72b8109ce79059dd841 100644 --- a/others/kolmogorov_arnold_networks/kan/pytorch/README.md +++ b/others/kolmogorov_arnold_networks/kan/pytorch/README.md @@ -8,6 +8,12 @@ Kolmogorov-Arnold representation theorem. KANs and MLPs are dual: KANs have acti have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.1.1 | 24.12 | + ## Model Preparation ### Install Dependencies diff --git a/others/model_pruning/network-slimming/pytorch/README.md b/others/model_pruning/network-slimming/pytorch/README.md index 460b0cf14fff62f340f6a42872f5e276cbf085bb..61c009e83748600b862b2ad02729366165fea111 100755 --- a/others/model_pruning/network-slimming/pytorch/README.md +++ b/others/model_pruning/network-slimming/pytorch/README.md @@ -9,6 +9,12 @@ computational costs without sacrificing accuracy. Network Slimming is architectu VGG, ResNet, and DenseNet. It's particularly useful for deploying deep learning models on resource-constrained devices, offering a balance between model efficiency and predictive performance. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.0.0 | 23.06 | + ## Model Preparation ### Install Dependencies diff --git a/others/recommendation_systems/deepfm/paddlepaddle/README.md b/others/recommendation_systems/deepfm/paddlepaddle/README.md index 39f79e812c186968ecc29d24a0318eff9fcbd06b..de0173daf5c56f1e903c9ad19cd6b10ec901c27a 100644 --- a/others/recommendation_systems/deepfm/paddlepaddle/README.md +++ b/others/recommendation_systems/deepfm/paddlepaddle/README.md @@ -8,6 +8,12 @@ The model is end-to-end trainable and excels in tasks like click-through rate (C recommendations. By integrating both FM and DNN, DeepFM efficiently handles sparse data, offering better performance compared to traditional methods, especially in large-scale applications such as advertising and product recommendations. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/dlrm/paddlepaddle/README.md b/others/recommendation_systems/dlrm/paddlepaddle/README.md index bc2bf436d4052085c71ea7d382d21d82b588acd1..bb51d79232dc6187ab6066d27abea2c039b11531 100644 --- a/others/recommendation_systems/dlrm/paddlepaddle/README.md +++ b/others/recommendation_systems/dlrm/paddlepaddle/README.md @@ -9,6 +9,12 @@ fully-connected layers for numerical features. Its specialized parallelization s embedding tables and data parallelism for dense layers, optimizing memory usage and computational efficiency. DLRM serves as a benchmark for recommendation system development and performance evaluation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/dlrm/pytorch/README.md b/others/recommendation_systems/dlrm/pytorch/README.md index 293e9a88135e930074f24133045540e6eca889b9..1c0c4ee42936555ceebeebc545d2cbf07661c57b 100644 --- a/others/recommendation_systems/dlrm/pytorch/README.md +++ b/others/recommendation_systems/dlrm/pytorch/README.md @@ -9,6 +9,12 @@ fully-connected layers for numerical features. Its specialized parallelization s embedding tables and data parallelism for dense layers, optimizing memory usage and computational efficiency. DLRM serves as a benchmark for recommendation system development and performance evaluation. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/ffm/paddlepaddle/README.md b/others/recommendation_systems/ffm/paddlepaddle/README.md index a4139f3f642866b03613a98fcfca04d3d9c79566..870881755dc4b24e40afa08260ea741961f622e7 100644 --- a/others/recommendation_systems/ffm/paddlepaddle/README.md +++ b/others/recommendation_systems/ffm/paddlepaddle/README.md @@ -7,6 +7,12 @@ features of the same field are one-hot separately, so in FFM, each one-dimension each field of the other features, which is not only related to the feature, but also to the field. By introducing the concept of field, FFM attributes features of the same nature to the same field. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/ncf/pytorch/README.md b/others/recommendation_systems/ncf/pytorch/README.md index 2461c2f2a24595483ec4511646bb9b41b9ffca77..347258681176c05f0e1a9d8231b1ae64ffd7c013 100644 --- a/others/recommendation_systems/ncf/pytorch/README.md +++ b/others/recommendation_systems/ncf/pytorch/README.md @@ -9,6 +9,12 @@ performance. It significantly improves recommendation accuracy by leveraging dee particularly effective for collaborative filtering tasks, demonstrating superior results on real-world datasets compared to traditional methods. NCF's architecture makes it a powerful tool for personalized recommendation systems. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.2.0 | 22.09 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/wide_deep/paddlepaddle/README.md b/others/recommendation_systems/wide_deep/paddlepaddle/README.md index 36cf8d96758c10a5302b446554afe1425c95b326..2ce44043acd96e4594202208d4b3d013a6106c20 100644 --- a/others/recommendation_systems/wide_deep/paddlepaddle/README.md +++ b/others/recommendation_systems/wide_deep/paddlepaddle/README.md @@ -8,6 +8,12 @@ for learning complex patterns. This architecture effectively balances precise me ability to generalize to unseen combinations. Wide&Deep has proven particularly effective in large-scale recommendation systems, offering improved performance in tasks like app recommendation while maintaining computational efficiency. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 2.3.0 | 22.12 | + ## Model Preparation ### Prepare Resources diff --git a/others/recommendation_systems/xdeepfm/paddlepaddle/README.md b/others/recommendation_systems/xdeepfm/paddlepaddle/README.md index 642c1b70120b5e55ce54a99240ea1f422c34bd59..536f79b9555f2be76b22b44276a60f63419d420f 100644 --- a/others/recommendation_systems/xdeepfm/paddlepaddle/README.md +++ b/others/recommendation_systems/xdeepfm/paddlepaddle/README.md @@ -9,6 +9,12 @@ networks, enabling both explicit and implicit feature learning. This architectur engineering while improving recommendation accuracy. Particularly effective for sparse data, xDeepFM excels in tasks like click-through rate prediction, offering enhanced performance in large-scale recommendation scenarios. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V100 | 3.1.0 | 23.12 | + ## Model Preparation ### Prepare Resources diff --git a/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md b/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md index f8b45af44ff62b3f687c2a314d497f55cb714b04..d5953d1246e28d433344654cbcdbb40df5a217ca 100644 --- a/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md +++ b/reinforcement_learning/q-learning-networks/dqn/paddlepaddle/README.md @@ -8,6 +8,12 @@ DQN introduces experience replay and target network stabilization to enable stab revolutionized AI capabilities in complex environments, achieving human-level performance in Atari games and forming the basis for advanced decision-making systems in robotics and game AI. +## Supported Environments + +| GPU | [IXUCA SDK](https://gitee.com/deep-spark/deepspark#%E5%A4%A9%E6%95%B0%E6%99%BA%E7%AE%97%E8%BD%AF%E4%BB%B6%E6%A0%88-ixuca) | Release | +|--------|-----------|---------| +| BI-V150 | 4.2.0 | 25.03 | + ## Model Preparation ### Install Dependencies