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Catalyst
Learn how to log Catalyst runs to Neptune.
​Catalyst is a PyTorch framework for Deep Learning R&D. It is built with three main requirements in mind: reproducibility, rapid experimentation, and codebase reuse. Catalyst is a part of the PyTorch Ecosystem.
What will you get with this integration?
Neptune is integrated with Catalyst, so that you can automatically log:
- metrics,
- hparams (hyper-parameters),
- images,
- artifacts (videos, audio, model checkpoints, files, etc.),
- hardware consumption statistics,
stdoutandstderrlogs,- training code and git commit information.
TL;DR for the Catalyst users
This section is for Catalyst users who are familiar with loggers, like CSV logger or TensorBoard logger.
If you haven't worked with Catalyst loggers before, jump to the "Where to start?" section below.
Catalyst has a unified way of logging metadata, by using Loggers. NeptuneLogger is part of the Catalyst library.
So all you need to do to start logging is to create NeptuneLogger and pass it to the runner:
Create NeptuneLogger instance
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from catalyst import dl
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​
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# Create NeptuneLogger
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neptune_logger = dl.NeptuneLogger(
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project="common/catalyst-integration",
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tags=["pretraining", "retina"],
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)
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You need to pass project to
NeptuneLogger(), to inform logger where to log the metadata. There are more parameters to customize logger behavior, check Catalyst docs for more details.Pass neptune_logger to the Runner
Now, you can add
neptune_logger to the runner, like any other Catalyst logger:1
# You can pass it to the SupervisedRunner
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my_runner = dl.SupervisedRunner()
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​
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my_runner.train(
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loggers={"neptune": neptune_logger},
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...
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)
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​
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# You can pass it to the custom Runner
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class CustomRunner(dl.IRunner):
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...
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def get_loggers(self):
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return {
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"console": dl.ConsoleLogger(),
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"neptune": neptune_logger
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}
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​
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runner = CustomRunner().run()
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NeptuneLogger is now ready
Setup of the NeptuneLogger is done. You can run your scripts without additional changes and have all metadata logged in the single place for further analysis, comparison and sharing in the team.
Where to start?
If you want to try it out now you can either:
Quickstart
This quickstart will show you how to:
- Install required libraries,
- Analyse logged metadata and compare some runs.
At the end of this quickstart, you will be able to connect NeptuneLogger to your ML/RL scripts and use it in your experimentation.
Step 1: Install libraries
Before you start, make sure that:
- You have
Python 3.7+in your system,
Install neptune-client and catalyst
Depending on your operating system open a terminal or CMD and run this command:
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pip install neptune-client catalyst
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This integration is tested with
catalyst==21.5 and neptune-client==0.9.16.Step 2: Add NeptuneLogger to the Catalyst script
Catalyst has a unified way of logging metadata, by using Loggers. Neptune contributed NeptuneLogger which is now part of the Catalyst library.
So all you need to do to start logging is to create NeptuneLogger and pass it to the runner:
Create NeptuneLogger instance
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from catalyst import dl
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​
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# Create NeptuneLogger
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neptune_logger = dl.NeptuneLogger(
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project="common/catalyst-integration",
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)
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You need to pass project to
NeptuneLogger(), to inform it where to log metadata. There are more parameters to customize logger behavior, check Catalyst docs for more details.Once NeptuneLogger is created it will output a link to the run to the
stdout. It will look like this:
https://app.neptune.ai/common/catalyst-integration/e/CATALYST-1486​Pass neptune_logger to the Runner
Now, you can add
neptune_logger to the Runner. The example below uses SupervisedRunner as an example:1
from catalyst import dl
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​
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# Create runner
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my_runner = dl.SupervisedRunner()
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​
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my_runner.train(
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loggers={"neptune": neptune_logger},
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...
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)
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At this point
neptune_logger is ready.Remember that you can check the entire example:
Step 3: Run the script and analyze logged metadata in the Neptune UI
NeptuneLogger instance is ready. Run your script and have all metadata logged in the single place for further analysis, comparison, and sharing in the team.1
python main.py
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Go to the run link and explore metadata (metrics, hparams, model checkpoint) that were logged. Example run: https://app.neptune.ai/common/catalyst-integration/e/CATALYST-1486​
Example run
How to ask for help?
Please visit the Getting help page. Everything regarding support is there:
Other pages you may like
You may also find the following pages useful:
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