/wandb_tutorial

This is a gentle introductin on how to start using an awesome library called Weights and Biases.

Primary LanguagePython

🪄 W&B Minimal PyTorch Tutorial

This tutorial is also accompanied with a PyTorch source code, it can be found in src folder. Furthermore, all plots and metrics that I mentioned here can be found here in this link.

You can also run the code with wandb. First you shoule go to a src directory, and run the following command:

python main.py

0. About W&B.

Machine learning experiment tracking, dataset versioning, and model evaluation.

1. Setting up.

  1. Create an account on wandb.ai.
  2. Install wandb.
pip install wandb
  1. Link your machine with your account. When logging in you should enter your private API key from wandb.ai.
wandb login

2. Start a new run.

import wandb
wandb.init(project="my-funny-project")

wandb.init(·) starts the tracking system metrics and console logs.

3. Start to track metrics.

Different metrics like loss, accuracy can be easily done with wandb.log() command. For example,

wandb.log({'accuracy': train_acc, 'loss': train_loss})

By default, wandb plots all metrics in one section. If you want to divide sections as for a training, validation, etc. You can just simply add a section name to the metric name by slash.

For example, if you had two losses, training and validation losses. You can split sections as follows:

wandb.log({'train/loss': train_loss, 'val/loss': val_loss})

4. Track hyperparameters.

When using argparse, you can use the command below and easily track hyperparameters you have used.

wandb.config.update(args) # adds all of the arguments as config variables

There are also other ways to save configuration values. For example, you can save configurationsa as a dictionary and pass it. Check more details here.

5. Track and visualise your weights and gradients.

Add wandb.watch(model, log = 'all' ) to track gradients and parameters weights.

Visualisation of weights:

Weights Visualisation

Visualisation of gradients:

Gradients Visualisation

6. Tune hyperparameters.

  1. Create a sweep configuration file, sweep.yaml.

For example it may look like this:

program: train.py
method: bayes
metric:
  name: validation_loss
  goal: minimize
parameters:
  learning_rate:
    min: 0.0001
    max: 0.1
  optimizer:
    values: ["adam", "sgd"]
  1. Initialize a sweep.

Run the following command:

wandb sweep sweep.yaml
  1. Launch agent(s)
wandb agent your-sweep-id

W&B will present some cool visualisations like this: Sweep Example