Release Notes

Transfer Learning Toolkit V3.0

NVIDIA Transfer Learning Toolkit (TLT) is a Python package to enable NVIDIA customers the ability to fine-tune pretrained models with customer’s own data and export them for TensorRT based inference through an edge device.

Key Features

Features included in this release:

  • TLT Launcher:

    • Python3 pip package as a unified Command Line Interface (CLI)

  • TLT Resources:

    • Jupyter notebook examples showing how to use the pretrained models effectively.

  • TLT CV:

    • Pretrained models for several public architectures and reference applications serving computer vision related object classification, detection and segmentation use cases.

    • Support for YOLOv3, YOLOv4 FasterRCNN, SSD, RetinaNet and DSSD object detection models.

    • Support for MaskRCNN Instance segmentation model

    • Support for UNet Semantic segmentation model

    • Support to train highly accurate purpose-built models -

      PeopleNet, PeopleSegNet, TrafficCamNet, DashCamNet, FaceDetectIR, VehicleTypeNet, VehicleMakeNet, FpeNet, FaceDetect, GazeNet, GestureNet, EmotionNet

    • Quantization aware training for accurate INT8 models

    • Support for Automatic Mixed Precision (AMP) training

    • Offline augmentation tool for object detection datasets

    • Model adaptation and retraining that is easy to use in heterogeneous multiple GPU environments.

    • Pruning API that compresses the size of the model during training.

    • Model export API for integrating the model directly into the DeepStream or TLT CV Inference pipeline environment.

    • Converter utility to generate device specific optimized TensorRT engines.

    • TLT uses the CUDA multi-process service which helps in optimizing GPU utilization during multiple GPU training.

  • TLT Conversational AI:

    • Pretrained models for several public architectures and reference applications serving conversational AI related speech to text and natural language processing use cases, namely

      • Speech to Text

      • Text classification

      • Token classification

      • Punctuation and capitalization

      • Intent and slot classification

      • Question Answering

Contents

Components included in this release:

Software Requirements

  • Ubuntu 18.04 LTS

  • Docker API > 1.40

  • Docker-ce > 19.03

  • NVIDIA GPU Cloud

  • nvidia-docker2

  • Python > 3.6.9

  • Jupyter Notebook

  • NVIDIA GPU driver v455.xx or above

Note

DeepStream 5.0 for inference and deployment is recommended.

Hardware Requirements

The following system configuration is recommended to achieve reasonable training performance with the TLT and supported models provided:

  • 32 GB system RAM

  • 32 GB of GPU RAM

  • 8 core CPU

  • 1 NVIDIA GPU

  • 100 GB of SSD space

TLT is supported on A100, V100 and RTX 30x0 GPUs.

Known Issues

  • TLT CV

    • SSD, DSSD, YOLOv3, RetinaNet, FasterRCNN and MaskRCNN integration to DeepStream is a feature and requires custom plugins from the TensorRT Open Source Software (OSS) library. DeepStream 5.0 doesn’t natively support custom plugins from TensorRT OSS. Instructions to build TensorRT OSS and custom parsing code to run with Deepstream have been provided here.

    • Transfer Learning is not supported on pruned models across all applications.

    • When training with multiple GPUs, you might need to scale down the batch_size and/or scale up the learning rate to get the same accuracy seen in single GPU training.

    • When resuming a DetectNet_v2 training from checkpoint, please make sure to maintain the same number of GPUs and the same command line to restart the training.

    • When training DetectNet_v2 for object detection use-cases with more than 10 classes, you may need to either update the cost_weight parameter in the cost_function_config, or balance the number of samples per class in the dataset for better training.

    • When training a DetectNet_v2 network for datasets with less than 20,000 images, please use smaller batch-sizes (1, 2 or 4) to get better accuracy.

    • When using MaskRCNN, please make sure GPU 0 is free.

    • ResNet101 pre-trained weights from NGC is not supported on YOLOV3, SSD, DSSD and RetinaNet.

    • UNet is currently not supported for inference in DS 5.0. DeepStream 5.1 supports it.

    • When generating int8 engine with tlt-converter, please use -s if there is TensorRT error message saying weights are outside of fp16 range.

    • Yolov3/v4 models are compatible with only the following combination of DeepStream and TensorRT

Yolov3/Yolov4 Compatibility with DeepStream/TensorRT

Model

DeepStream

TensorRT

Compatible

YOLO v3/v4

5.0 / 5.1

7.0.x

Yes

YOLO v3/v4

5.0 / 5.1

7.1.x

Yes

YOLO v3/v4

5.0 / 5.1

7.2.1

Yes

YOLO v3/v4

5.0 / 5.1

7.2.2

No

  • TLT Conversational AI

    • In this beta release, Jarvis does not support fine-tuning of the QA model when using Megatron. It is recommended to use BERT for customized QA models.

    • Conversational AI models currently do not support resume from checkpoint for multiGPU training jobs. You will be able to resume resume training from a checkpoint for singleGPU training.

  • NGC CLI

    • When running ngc config set, the NGC CLI may not prompt the user to configure the team and org. In this case, users may run into an error when downloading models saying

      Missing org - If apikey is set, org is also required.
      

      Please maintain a back-up of your existing NGC API key from the ngc config at ~/.ngc/config and clear the ngc config by running the following command.

      ngc config clear
      

Resolved Issues

  • Loading pretrained weights when retraining a pruned model