Sacred
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​See code examples​ | ​See results in Neptune​ |
​Sacred is a tool to configure, organize, log, and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments.
With Neptune + Sacred integration the following metadata will be logged automatically for you:
Hyper-parameters
Losses & metrics
Training code(Python scripts or Jupyter notebooks) and git information
Dataset version
Model Configuration
You can also log other metadata types like interactive charts, video, audio, and more. See What can you log and display?​
You need to have Python 3.6+ and install the following libraries installed:
neptune-client[sacred]sacredtorchtorchvision
To install them just go to you console and run:
pip install neptune-client[sacred] sacred torch torchvision
This code example was tested using:
neptune-client[sacred]==0.9.19 sacred==0.8.2 torch==1.9.0 torchvision==0.10.0
Place this code snippet at the beginning of your script or notebook cell
import neptune.new as neptune​run = neptune.init(project = '<YOUR_WORKSPACE/YOUR_PROJECT>',api_token = '<YOURR_API_TOKEN>')
This opens a new Run in Neptune that allows you to log various objects.
You need to authenticate yourself and open an existing project. Here is how:
You can use the api_token='ANONYMOUS' and project='common/sacred-integration' to explore without having to create a Neptune account.
# Create sacred experimentex = Experiment('image_classification', interactive=True)​# Add NeptuneObserverex.observers.append(NeptuneObserver(run=neptune_run))
Using NeptuneObsever the following is automatically logged to Neptune UI for you:
Hyperparameters
Loss
Metrics
After you define your @ex.config(hyperparameters and configuration) and @ex.main(training loop) you can run your experiment as you normally would.
ex.run()
After running your script or notebook cell you will get a link similar to:
https://app.neptune.ai/o/common/org/sacred-integration/e/SAC-11/
with common/sacred-integration replaced by your project, and SAC-11 replaced by your run.
Click on the link to open the Run in Neptune to watch your model training live.
Initially, it may be empty but keep the tab with the Run open to see your experiment metadata update in real-time.
When sacred.Experiment.add_artifact() is called with a filename and optionally a name this will trigger an event in the Neptune0bserver to upload the file to Neptune.
ex.add_artifact(filename='model_weights', name=f'./model.pth')
The same goes for sacred resources.
run.stop()
Once you are done logging, you should stop tracking the Run using the stop() method. This is only needed when logging from a notebook or some other interactive environment.
​ | ​ | ​ |
​See code examples​ | ​See results in Neptune​ |
Please visit the Getting help page. Everything regarding support is there
