The Deep Learning Research Infrastructure provides a versatile, high-performance computing environment for designing, training, evaluating, and deploying deep learning models across a broad range of architectures, including convolutional neural networks (CNNs), transformer-based models, recurrent networks, and hybrid approaches. Built on a combination of NVIDIA RTX 6000 ADA and H100 NVL GPUs, the testbed offers researchers and industry partners scalable GPU compute suited to experiments ranging from lightweight prototyping to large-scale model training. DEEPLAB supports the full deep learning research lifecycle, from dataset preparation and model architecture design through training, hyperparameter optimization, and performance benchmarking. The environment accommodates frameworks such as PyTorch and TensorFlow, and is well-suited to applications across domains including predictive analytics, signal processing, time-series forecasting, and representation learning. This testbed contributes to the CITADELS Framework by providing accessible, institution-grade deep learning infrastructure that bridges the gap between academic research and industrial-scale DeepTech deployment.