/Deep-Learning-Ultra

Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

Deep Learning Ultra

Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models with MLFlow in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS, running on CUDA 12.1. Tensorboard included for visualizations into model explainability and fine-tuning/understanding how your model learns.

Used and adopted by many!

Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs)
Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs)
Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs) Open source Deep Learning Containers (DLCs)

Ultra easy deployment

Edit line 18 in docker-compose.yaml for however many GPUs you have: count: 3 # num of gpus, then run:

docker compose up --build -d

  • Get security token to log into the notebook:

    token=$(docker exec -it ultra /bin/bash -c "jupyter notebook list") \
    echo ${token::-8}
    

Requirements

  • Linux configured with nvidia-container-toolkit found here, CUDA 12, NVIDIA Drivers v.525+
  • NVIDIA GPU -ARCH_7.5+

Deep Learning Container Services

  1. Deep Learning Notebook, or you can mount VSCode to /app | http://localhost:8888
  2. MLFlow is an open source platform for managing the end-to-end machine learning lifecycle, see more here | http://localhost:5000
  3. Tensorboard povides the visualization and tooling needed for machine learning experimentation | http://localhost:6006
  • Deep learning solution - all python bindings specifically compiled for c++/CUDA:

    • Pytorch 2
    • PyG (Graph Neural Networks)
    • NVIDIA RAPIDS
    • OpenCV v4.8
    • TensorFlow 2
  • CuPy, Anaconda Python v3.11.5, Captum, MLFlow and more!

  • Supports LLMs, HuggingFace, Computer Vision, Navigation, Physics Informed ML, and Graph Neural Networks


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