V1.5.0

Here is where you should have the description of the project

1. Some things about the project

Use the Use this template button to create a new repository from this one

Tested on:

• Ubuntu 18.04 with Nvidia K80
  • Azure Data Science Virtual machine template
• Ubuntu 18.04 with Nvidia V100
• Ubuntu 20.04 with Nvidia GEFORCE RTX 2070 SUPER
• Ubuntu 18.04 with Nvidia GEFORCE 3070
• Ubuntu 20.04 with Nvidia GEFORCE 3070
• Macbook Pro 2015
• Macbook Pro 2017

NOTE: Both setups assume that your your CUDA and GPU drivers work if not check troubleshooting below

1. Python3
2. Miniconda

1. Initialize conda environment conda create -n <env name>
2. Activate conda environment conda activate <env name>
3. To install PyTorch for your OS follow instructions here (they keep changing it so it's hard to standardize)
4. Installing packages
   • conda install -c pytorch -c conda-forge -c bioconda --file=requirements.txt
   • If the above doesn't work then try: pip intall --user -r requirements.txt
5. Setup DVC and other libraries chmod 755 setup.sh; ./setup.sh

1. Install requirements pip install --user -r requirements.txt
2. Setup DVC and other libraries chmod 755 setup.sh; ./setup.sh

• Data versioning: DVC
• Model versioning + Training monitoring + Hyperparameter sweeps: Weights and Biases - Please signup first
• Distributed training: Weights and Biases
• Coding: JupyterLab or if you want free GPUs and data privacy isn't an issue Colab
• Training framework: Pytorch + PyTorchLightning
• Tabular data management: Pandas
• Plotting: Matplotlib + Plotly
• Deployment: FastAPI

Training models

1. The train.py script contains an outline to effectively use and train PyTorch models using PyTorch Lightning. Once you've customized the script you can run it by doing python3 main.py --train
2. You can find more tutorials for customizing stuff in the PyTorch lighting section below.

DVC

1. DVC will be initalized by default in the root directory via the setup.sh
2. Rest of the data versioning guide can be found here: https://dvc.org/doc/start/data-versioning

PyTorch Lightning

1. PyTorch Lightning will speed up a lot of your PyTorch workflow especially in the training phase.
2. Overview
3. Setting PyTorch Lightning to use in-line arguments e.g. python main.py --gpus 2 --max_steps 10 --limit_train_batches 10 --any_trainer_arg x
4. Turn your existing models into Lightning models
5. Setting up mid-training checkpoints
6. Auto Learning Rate Finder
7. Other advanced guides for things like multi-gpu training

Weights and Biases

1. To setup Weights and biases first sign up and create a project or get added to the project
2. Login via terminal and add Weights and Biases to start. Instructions here
3. How to log via PyTorch lightning to W&B?
4. How to integrate Ray Tune with Weights and Biases?
5. How to use built-in hyperparameter sweep functionality?

Jupyter Lab

1. Starting Jupyterlab jupyter lab
2. User guide, key mapping and shortcut customization

Deploying models with ease

1. In the deployment section there is a pre-built FastAPI template that will allow you to import a model, set the correct data type and return predictions
2. Once you've customized it acoording to your needs you can just run python3 main.py --deploy
3. There's also a customizable Dockerfile to make a Docker image for it

No Python3 Kernel in Jupyter Lab

Solve by doing the following:

1. python3 -m pip install ipykernel
2. python3 -m ipykernel install --user
3. Restart Jupyter Lab

DVC/Tensorboard/JupterLab command not found

1. python3 -m <command here>
2. Issue with python versions on the machine

Can't access jupyter from local machine after cloud deployment

1. Run Jupyter Lab jupyter lab --no-browser --ip=0.0.0.0 --port=8888

GPU driver/ tensorflow-gpu issues

1. Check which GPU you have on the machine and make sure it's supported
2. Install CUDA if you don't have it installed the best way to do this is to just do conda install tensorflow-gpu and it should take care of itself. 3.If the above doesn't work check if you have installed the correct version of tensorflow-gpu for installed CUDA drivers. nvidia-smi
3. Check which version of GPU drivers are needed for the GPU and the supported CUDA versions for the drivers from here.
4. When you have found the right version follow this guide and replace the CUDA driver files in the commands with the right versions.
5. If nothing works try a pre-built VM template on the cloud