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pww.comNVIDIA Corporation - Special Call - NasdaqGS:NVDA

NasdaqGS:NVDA

Debraj Sinha [Executives] 💬

Debraj Sinha, an executive at NVIDIA Corporation, moderated a webinar focused on their product TAO (Train, Adapt, and Optimize). Here's a summary of his key points and interactions during the session:

  1. Introduction: Debraj welcomed attendees to the webinar and introduced himself as part of NVIDIA's Metropolis Marketing team. He outlined the interactive nature of the webinar and encouraged participants to use the Q&A window for queries.

  2. Speaker Introduction: He then introduced Chintan Shah, the Product Manager for TAO at NVIDIA, who was set to present on TAO 4.0, highlighting its features, particularly AutoML, which automates model tuning for datasets. A demo showcasing AutoML deployment with TAO was also announced.

  3. Q&A Moderation: Throughout the Q&A session, Debraj fielded and redirected questions to Chintan Shah and Varun Praveen (Engineering Lead for TAO), covering various aspects of TAO:

    • Jetson AI Platform Support: Asked if TAO models are compatible with NVIDIA's Jetson AI and Edge AI platforms.
    • Training Visualization: Inquired about TAO's support for visualizing the training process.
    • AutoML Training Digitalization: Clarified if TAO's AutoML supports training digitalization.
    • Custom Model Architectures: Varun confirmed the ability to add custom architectures in TAO.
    • Pre-Trained Models with AutoML: Discussed whether AutoML could work with pre-trained models such as PeopleNet.
    • DeepStream Integration: Addressed compatibility between DeepStream and TAO.
    • Installation and Multi-GPU Usage: Explained how to install TAO on a PC and if TAO can utilize multiple GPUs.
    • TAO vs Other DL Applications: Highlighted TAO's advantages over other deep learning applications.
    • Difference Between TAO and TensorRT: Clarified the distinction between TAO and TensorRT.
    • Minimum Hardware/Software Requirements: Mentioned the need for information on the minimum setup for TAO.
    • Optimizing for Jetson Xavier: Asked about necessary configurations for running models on Jetson Xavier.
    • Multiple Validation Sets: Varun addressed the capability to handle multiple validation sets and saving the best models.
    • Expanding Models with New Data: Chintan discussed tools for expanding models with additional data.
    • Customizing Training Loop: Asked if users can customize training loops akin to Keras callbacks.
    • TensorRT Input Types: Varun was queried about TensorRT's support for streaming data types as input.
    • Future YOLO Versions Availability: Varun was asked about upcoming YOLO versions for TAO.

  4. Webinar Wrap-up: Debraj concluded the Q&A by thanking Chintan, Varun, and the audience. He reminded attendees about the upcoming GTC Conference and teased new features for TAO's version 5.0. He also mentioned that an on-demand version of the webinar would be available soon.

Throughout the session, Debraj facilitated the flow of information, ensuring questions were directed to the appropriate speakers and acknowledged the responses given.

Chintan Shah [Executives] 💬

Chintan Shah, the Product Manager at NVIDIA responsible for TAO (Train, Adapt, Optimize), discussed several key aspects of TAO and its integration with AutoML during a special call. Here's a detailed summary of the points he covered:

  1. Challenges in AI Development: Shah highlighted common challenges developers face when building AI applications, including the multitude of training frameworks (TensorFlow, PyTorch, MXNet), numerous model architectures to choose from (CNNs, transformers), and the necessity to work with large datasets for accurate training. He also mentioned the complexity involved in deploying models optimized for different platforms, such as edge devices or large data centers.

  2. TAO Toolkit Overview: TAO is introduced as a low-code toolkit built atop TensorFlow and PyTorch that simplifies AI and deep learning complexities. It offers a starting point with NVIDIA-provided pre-trained models across various architectures and use cases, such as object detection, segmentation, keypoint estimation, and more. Users can customize these models to their specific datasets.

  3. Customization and Transfer Learning: Shah emphasized TAO's capability to enable users to customize models with their own data. This includes using pre-trained, task-specific models or general-purpose architectures for transfer learning, adapting them to unique datasets.

  4. Deployment Flexibility: He clarified that models trained using TAO can be deployed on Jetson Xavier and that there are no specific configurations limiting training to Jetson or any other deployment platform. Once trained, models can be exported to generate an engine suitable for deployment.

  5. Licensing and Model Addition Challenges: Shah addressed the absence of YOLO models (specifically YOLO 5 and 7) in TAO due to licensing issues. However, he mentioned plans to introduce new transformer models in upcoming releases, which are expected to offer comparable or better accuracy and performance than YOLO models.

  6. Integration with TensorBoard and AutoML: AutoML within TAO integrates with TensorBoard, allowing users to view training logs for better insights and transparency during the model tuning process. Users can enable TensorBoard logging during AutoML runs for detailed monitoring.

  7. Fine-Tuning Pre-Trained Models: Pre-trained models like PeopleNet and TrafficNet, based on TechNet and similar architectures, can be fine-tuned using AutoML as long as they align with the architectures supported for fine-tuning.

  8. DeepStream Compatibility: Shah confirmed that TAO supports DeepStream, NVIDIA's streaming analytics SDK. All TAO models can be integrated into DeepStream applications, with a turnkey example available on GitHub to facilitate this process.

  9. TAO vs. TensorRT: Shah differentiated between TAO, which is primarily a training toolkit, and TensorRT, which focuses on optimizing models for inference runtime on NVIDIA GPUs. TAO leverages TensorRT to generate optimized inference engines, but users must then deploy these engines on their inference platforms.

  10. Enterprise Support and Upcoming Events: Shah invited attendees interested in enterprise support to reach out. He also announced the upcoming GTC event (March 20-23), where TAO 5.0, featuring new transformer models and enhancements, would be discussed. Attendees were encouraged to register for the event.

Varun Praveen [Executive] 💬

Varun Praveen from NVIDIA Corporation provided several key points during the special call:

  1. Custom Model Support in TAO:

    • Users cannot import entire custom model architectures into the TAO (Train, Adapt, Optimize) platform at present.
    • Pre-trained model weights can be imported for image classification and semantic segmentation tasks via a bring-your-own-model-weights converter.
    • Support for bringing in custom model architectures for object detection and other segmentation tasks is currently unavailable.

  2. Pre-Trained Models Availability:

    • All pre-trained models released with TAO have trainable versions available on the NVIDIA GPU Cloud (NGC). These can be used in AutoML jobs.

  3. Minimum Hardware Requirements:

    • The minimum hardware setup includes an 8-core CPU and at least one NVIDIA GPU from the Pascal generation or newer.
    • GeForce or RTX machines require driver version 520 or higher.
    • For DGX machines or Tesla GPUs, the minimum supported driver version is 470.

  4. Deployment on Jetson Xavier:

    • Almost all models can be deployed on Jetson Xavier for inference after training.
    • A trained model can be exported as an [.EPRT] file, with no special configurations required specifically for Jetson or other deployment platforms.

  5. Validation Data Sets Limitation:

    • The training pipeline evaluation does not currently support multiple validation datasets or selecting fine-grained models based on mean average precision (MAP) across different datasets.
    • Users can combine datasets into a single validation set, but separate validation and saving per dataset is not supported.

  6. Custom Callbacks:

    • TAO offers various customization options as configurable parameters, including different learning rate schedules, optimizers, and callbacks such as exponential moving average.
    • However, users cannot currently implement their own custom callbacks.

  7. Input Data Types:

    • While not specified in detail, Varun acknowledges that certain stream data types are allowed as inputs.

In summary, Varun Praveen outlined limitations regarding custom model architecture integration in TAO, highlighted the availability of trainable pre-trained models, specified hardware and software requirements, discussed deployment capabilities on Jetson Xavier, mentioned validation dataset handling limitations, and clarified the extent of customization options available for users, including the current lack of support for user-defined callbacks.