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Visualization with TensorBoard
Interactive mode
Get a node for interactive use:
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Train on MNIST with TensorFlow summary and go back to login node:
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python ./mnist-with-summaries.py
exit |
Batch mode
The same can be accomplished in batch mode using the following tfbd_sample.sb script:
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module load wmlce tensorboard --logdir ~/tensorflow/mnist/logs/ --port [user<user_pick_port]port> # please use random number within [6500-6999] |
Forward the [userthe <user_pick_port] on port> on remote machine to the port 16006 port <user_pick_port> on local machine:
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ssh -N -f -L localhost:16006:localhost:[user<user_pick_port>:<node_name>:<user_pick_port]port> your<user_user_name@halname>@hal.ncsa.illinois.edu |
Paste the follow following address into a web browser to start the TensorBoard session:
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localhost:16006<user_pick_port> |
Simple Example with Pytorch
Interactive mode
Get a node for interactive use:
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| Name | Version | Description | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| caffe | 1.0 | Caffe is a deep learning framework made with expression, speed, and modularity in mind. | ||||||||
| cudatoolkit | 10.1.105168 | The NVIDIA® CUDA® Toolkit provides a development environment for creating high-performance GPU-accelerated applications. | ||||||||
| cudnn | 7.5.01+10.1 | The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. | ||||||||
| h5py | 2.8.0 | The h5py package is a Pythonic interface to the HDF5 binary data format. | ||||||||
| jupyter | 1.0.0 | Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. | ||||||||
| matplotlib | 2.2.3 | Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms . | ||||||||
| nccl | 2.4.27 | The NVIDIA Collective Communications Library (NCCL) implements multi-GPU and multi-node collective communication primitives that are performance-optimized for NVIDIA GPUs.numpy | ||||||||
| 1.14.5 | NumPy is the fundamental package for scientific computing with Python. | opencv | 3.4.2 | OpenCV was designed for computational efficiency and with a strong focus on real-time applications. | ||||||
| pytablespytorch | 3.4.4 | PyTables is a package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data. | pytorch | 1.1.0.1 | PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries. | scikit-learn | 0.19.1 | Simple and efficient tools for data mining and data analysis.scipy | 1.1.0 | SciPy is a Python-based ecosystem of open-source software for mathematics, science, and engineering |
| tensorboard | 1.1314.0 | To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. | ||||||||
| tensorflow-gpu | 1.1314.10 | The core open-source library to help you develop and train ML models. | torchvision | 0.2.1 | The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. |