NVIDIA Deep Learning GPU Training System (DIGITS)
Artificial neural network software
Image recognition software
Deep learning software
- Features
- Ease of use
- Ease of management
- Quality of support
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What is NVIDIA Deep Learning GPU Training System (DIGITS)
NVIDIA Deep Learning GPU Training System (DIGITS) is a desktop/server application that provides a web-based interface for training and evaluating deep learning models, primarily for computer vision tasks. It manages datasets, launches training jobs on NVIDIA GPUs, and visualizes training progress and results. DIGITS is commonly used by practitioners who want a GUI-driven workflow on NVIDIA hardware rather than building training pipelines entirely in code. It typically operates as a front end around NVIDIA-optimized deep learning frameworks and GPU drivers.
GUI-driven training workflow
DIGITS provides a browser-based UI for dataset management, model configuration, and job execution. This reduces the amount of scripting required for common image classification and segmentation experiments. It can be useful for teams that want repeatable training runs without building a full MLOps stack. The interface also supports monitoring and basic result inspection during training.
Tight NVIDIA GPU integration
DIGITS is designed to run on NVIDIA GPUs and aligns with NVIDIA’s CUDA software stack. It can simplify setup for GPU-accelerated training compared with assembling drivers, libraries, and framework configurations manually. For organizations standardizing on NVIDIA hardware, this can reduce variability across developer workstations and lab servers. Performance and compatibility generally depend on the underlying NVIDIA driver/CUDA versions in use.
Built-in experiment visualization
DIGITS surfaces training metrics and artifacts through the UI, such as loss/accuracy curves and sample predictions. This helps users quickly compare runs and identify issues like overfitting or data problems. The visualization is integrated into the training workflow rather than requiring separate notebooks or custom dashboards. It supports rapid iteration for common vision model development tasks.
Limited flexibility versus code
Compared with code-first deep learning frameworks, DIGITS can constrain model customization and training logic to what the UI and supported backends expose. Advanced workflows (custom layers, novel training loops, complex augmentation pipelines) often require dropping into the underlying framework directly. This can lead to a split workflow where DIGITS is used for some experiments and custom code for others. Teams with heavy research needs may outgrow the GUI approach.
Primarily vision-oriented scope
DIGITS focuses mainly on image-based deep learning use cases such as classification and segmentation. It is less suitable as a general-purpose platform for NLP, tabular ML, or multimodal pipelines without significant customization. Organizations seeking a single standard tool across many ML domains may need additional platforms. This can increase toolchain complexity across teams.
Lifecycle and support uncertainty
DIGITS has historically seen less active development than major mainstream deep learning frameworks and managed cloud images. Depending on the version in use, compatibility with newer CUDA/toolkit releases and modern framework versions may require careful validation. Enterprises may need to assess maintenance status, security patching expectations, and long-term viability before standardizing. This can affect production adoption and internal support burden.
Plan & Pricing
Pricing model: Free / Open-source (no charge) Distribution & access: Available for download via NVIDIA Docs (DIGITS GitHub download) and as an NGC / nvcr.io container (NGC registry). Notes: Distributed under the NVIDIA Software License Agreement (DIGITS SLA). NVIDIA documentation states DIGITS is not actively developed or supported (no obligation to provide updates/support).
Seller details
NVIDIA Corporation
Santa Clara, California, USA
1993
Public
https://www.nvidia.com/
https://x.com/nvidia
https://www.linkedin.com/company/nvidia/
