sadhaklal

Multi-layer perceptron (MLP) trained on CIFAR-2 (a subset of CIFAR-10 for classifying 'airplane' vs. 'bird'). This project pertains to Exercise 2 of Chapter 7 of the book "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann. Code: https://github.com/sambitmukherjee/dlwpt-exercises/blob/main/chapter_7/exercise_2.ipynb Model: https://huggingface.co/sadhaklal/mlp-cifar2-v2

Project visibility

Public

Last active

2/1/2024, 10:20:22 AM

Owner

Sambit Mukherjee

Contributors

1 user

Total runs

Total compute

12 minutes

Export & update data

Use our Public API to export data or update data, such as editing the config of existing runs. Learn more in the docs →

Find the run path

To use the public API, you'll often need the run path which is <entity>/<project>/<run_id>. In the app UI, open a run page, then click on the Overview tab to see the run path.

Update config for an existing run

This example updates one of your configuration settings.

import wandb api = wandb.Api() run = api.run("sadhaklal/mlp-cifar2-v2/<run_id>") run.config["key"] = updated_value run.update()

Export metrics from a single run to a CSV file

This snippet finds all the metrics saved for a single run and saves them to a CSV file.

import wandb api = wandb.Api() # run is specified by <entity>/<project>/<run_id> run = api.run("sadhaklal/mlp-cifar2-v2/<run_id>") # save the metrics for the run to a csv file metrics_dataframe = run.history() metrics_dataframe.to_csv("metrics.csv")

Read metrics for a run

Pull down the accuracy and timestamps for logged metric data. In this example, data was logged with wandb.log({"accuracy": acc}) to a run with the run path <entity>/<project>/<run_id>.

import wandb api = wandb.Api() run = api.run("sadhaklal/mlp-cifar2-v2/<run_id>") if run.state == "finished": for i, row in run.history().iterrows(): print(row["_timestamp"], row["accuracy"])

Get unsampled metric data

When you pull data from history, by default it's sampled to 500 points. Get all the logged data points using run.scan_history(). Here's an example downloading all the loss data points logged in history.

import wandb api = wandb.Api() run = api.run("sadhaklal/mlp-cifar2-v2/<run_id>") history = run.scan_history() losses = [row["loss"] for row in history]

Download the best model file from a sweep

This snippet downloads the model file with the highest validation accuracy from a sweep with runs that saved model files to model.h5.

import wandb api = wandb.Api() sweep = api.sweep("sadhaklal/mlp-cifar2-v2/<sweep_id>") runs = sorted(sweep.runs, key=lambda run: run.summary.get("val_acc", 0), reverse=True) val_acc = runs[0].summary.get("val_acc", 0) print(f"Best run {runs[0].name} with {val_acc}% validation accuracy") runs[0].file("model.h5").download(replace=True) print("Best model saved to model-best.h5")