Metaflow

Overview

Metaflow is a framework created by Netflix for creating and running ML workflows.

This integration lets users apply decorators to Metaflow steps and flows to automatically log parameters and artifacts to W&B.

• Decorating a step will enable or disable logging for certain types within that step.
• Decorating the flow will enable or disable logging for every step in the flow.

Quickstart

Install W&B and login

Notebook

!pip install -Uqqq metaflow fastcore wandb

import wandb
wandb.login()

Command Line

pip install -Uqqq metaflow fastcore wandb
wandb login

Decorate your flows and steps

Step

Decorating a step will enable or disable logging for certain types within that Step.

In this example, all datasets and models in start will be logged

from wandb.integration.metaflow import wandb_log

class WandbExampleFlow(FlowSpec):
    @wandb_log(datasets=True, models=True, settings=wandb.Settings(...))
    @step
    def start(self):
        self.raw_df = pd.read_csv(...).    # pd.DataFrame -> upload as dataset
        self.model_file = torch.load(...)  # nn.Module    -> upload as model
        self.next(self.transform)

Flow

Decorating a flow is equivalent to decorating all the constituent steps with a default.

In this case, all steps in WandbExampleFlow will log datasets and models by default -- the same as decorating each step with @wandb_log(datasets=True, models=True)

from wandb.integration.metaflow import wandb_log

@wandb_log(datasets=True, models=True)  # decorate all @step 
class WandbExampleFlow(FlowSpec):
    @step
    def start(self):
        self.raw_df = pd.read_csv(...).    # pd.DataFrame -> upload as dataset
        self.model_file = torch.load(...)  # nn.Module    -> upload as model
        self.next(self.transform)

Flow and Steps

Decorating the flow is equivalent to decorating all steps with a default. That means if you later decorate a Step with another @wandb_log, you will override the flow-level decoration.

In the example below:

• start and mid will log datasets and models, but
• end will not log datasets or models.

from wandb.integration.metaflow import wandb_log

@wandb_log(datasets=True, models=True)  # same as decorating start and mid
class WandbExampleFlow(FlowSpec):
  # this step will log datasets and models
  @step
  def start(self):
    self.raw_df = pd.read_csv(...).    # pd.DataFrame -> upload as dataset
    self.model_file = torch.load(...)  # nn.Module    -> upload as model
    self.next(self.mid)

  # this step will also log datasets and models
  @step
  def mid(self):
    self.raw_df = pd.read_csv(...).    # pd.DataFrame -> upload as dataset
    self.model_file = torch.load(...)  # nn.Module    -> upload as model
    self.next(self.end)

  # this step is overwritten and will NOT log datasets OR models
  @wandb_log(datasets=False, models=False)
  @step
  def end(self):
    self.raw_df = pd.read_csv(...).    
    self.model_file = torch.load(...)

Where is my data? Can I access it programmatically?

You can access the information we've captured in three ways: inside the original Python process being logged using the wandb client library, via the web app UI, or programmatically using our Public API. Parameters are saved to W&B's config and can be found in the Overview tab. datasets, models, and others are saved to W&B Artifacts and can be found in the Artifacts tab. Base python types are saved to W&B's summary dict and can be found in the Overview tab. See our guide to the Public API for details on using the API to get this information programmatically from outside .

Here's a cheatsheet:

Data	Client library	UI
Parameter(...)	wandb.config	Overview tab, Config
datasets, models, others	wandb.use_artifact("{var_name}:latest")	Artifacts tab
Base Python types (dict, list, str, etc.)	wandb.summary	Overview tab, Summary

wandb_log kwargs

kwarg	Options
datasets	True: Log instance variables that are a dataset False
models	True: Log instance variables that are a model False
others	True: Log anything else that is serializable as a pickle False
settings	wandb.Settings(...): Specify your own wandb settings for this step or flow None: Equivalent to passing wandb.Settings() By default, if: settings.run_group is None, it will be set to {flow_name}/{run_id} settings.run_job_type is None, it will be set to {run_job_type}/{step_name}

Frequently Asked Questions

What exactly do you log? Do you log all instance and local variables?

wandb_log only logs instance variables. Local variables are NEVER logged. This is useful to avoid logging unnecessary data.

Which data types get logged?

We currently support these types:

Logging Setting	Type
default (always on)	dict, list, set, str, int, float, bool
datasets	pd.DataFrame pathlib.Path
models	nn.Module sklearn.base.BaseEstimator
others	Anything that is pickle-able and JSON serializable

Examples of logging behavior

Kind of Variable	behavior	Example	Data Type
Instance	Auto-logged	self.accuracy	float
Instance	Logged if datasets=True	self.df	pd.DataFrame
Instance	Not logged if datasets=False	self.df	pd.DataFrame
Local	Never logged	accuracy	float
Local	Never logged	df	pd.DataFrame

Does this track artifact lineage?

Yes! If you have an artifact that is an output of step A and an input to step B, we automatically construct the lineage DAG for you.

For an example of this behavior, please see this notebook and its corresponding W&B Artifacts page