FromConfig MlFlow
A fromconfig Launcher for MlFlow support.
Install
pip install fromconfig_mlflowQuickstart
To activate MlFlow login, simply add --launcher.log=mlflow to your command
fromconfig config.yaml params.yaml --launcher.log=mlflow - model - trainWith
model.py
"""Dummy Model."""
import mlflow
class Model:
def __init__(self, learning_rate: float):
self.learning_rate = learning_rate
def train(self):
print(f"Training model with learning_rate {self.learning_rate}")
if mlflow.active_run():
mlflow.log_metric("learning_rate", self.learning_rate)config.yaml
model:
_attr_: model.Model
learning_rate: "${params.learning_rate}"params.yaml
params:
learning_rate: 0.001It should print
Started run: http://127.0.0.1:5000/experiments/0/runs/7fe650dd99574784aec1e4b18fceb73f
Training model with learning_rate 0.001
If you navigate to http://127.0.0.1:5000/experiments/0/runs/7fe650dd99574784aec1e4b18fceb73f you should see your the logged learning_rate metric.
MlFlow server
To setup a local MlFlow tracking server, run
mlflow serverwhich should print
[INFO] Starting gunicorn 20.0.4
[INFO] Listening at: http://127.0.0.1:5000
We will assume that the tracking URI is http://127.0.0.1:5000 from now on.
Configure MlFlow
You can set the tracking URI either via an environment variable or via the config.
To set the MLFLOW_TRACKING_URI environment variable
export MLFLOW_TRACKING_URI=http://127.0.0.1:5000Alternatively, you can set the mlflow.tracking_uri config key either via command line with
fromconfig config.yaml params.yaml --launcher.log=mlflow --mlflow.tracking_uri="http://127.0.0.1:5000" - model - trainor in a config file with
launcher.yaml
# Configure mlflow
mlflow:
# tracking_uri: "http://127.0.0.1:5000" # Or set env variable MLFLOW_TRACKING_URI
# experiment_name: "test-experiment" # Which experiment to use
# run_id: 12345 # To restore a previous run
# run_name: test # To give a name to your new run
# artifact_location: "path/to/artifacts" # Used only when creating a new experiment
# Configure launcher
launcher:
log: mlflowand run
fromconfig config.yaml params.yaml launcher.yaml - model - trainArtifacts and Parameters
In this example, we add logging of the config and parameters.
Re-using the quickstart code, modify the launcher.yaml file
# Configure logging
logging:
level: 20
# Configure mlflow
mlflow:
# tracking_uri: "http://127.0.0.1:5000" # Or set env variable MLFLOW_TRACKING_URI
# experiment_name: "test-experiment" # Which experiment to use
# run_id: 12345 # To restore a previous run
# run_name: test # To give a name to your new run
# artifact_location: "path/to/artifacts" # Used only when creating a new experiment
# include_keys: # Only log params that match *model*
# - model
# Configure launcher
launcher:
log:
- logging
- mlflow
parse:
- mlflow.log_artifacts
- parser
- mlflow.log_paramsand run
fromconfig config.yaml params.yaml launcher.yaml - model - trainwhich prints
INFO:fromconfig_mlflow.launcher:Started run: http://127.0.0.1:5000/experiments/0/runs/<MLFLOW_RUN_ID>
Training model with learning_rate 0.001
If you navigate to the MlFlow run URL, you should see
- the parameters, a flattened version of the parsed config (
model.learning_rateis0.001and not${params.learning_rate}) - the original config, saved as
config.yaml - the parsed config, saved as
parsed.yaml
Usage-Reference
StartRunLauncher
To configure MlFlow, add a mlflow entry to your config and set the following parameters
-
run_id: if you wish to restart an existing run -
run_name: if you wish to give a name to your new run -
tracking_uri: to configure the tracking remote -
experiment_name: to use a different experiment than the custom experiment -
artifact_location: the location of the artifacts (config files)
Additionally, the launcher can be initialized with the following attributes
-
set_env_vars: if True (default isTrue), setMLFLOW_RUN_IDandMLFLOW_TRACKING_URI -
set_run_id: if True (default isFalse), setmlflow.run_idin config.
For example,
# Configure logging
logging:
level: 20
# Configure mlflow
mlflow:
# tracking_uri: "http://127.0.0.1:5000" # Or set env variable MLFLOW_TRACKING_URI
# experiment_name: "test-experiment" # Which experiment to use
# run_id: 12345 # To restore a previous run
# run_name: test # To give a name to your new run
# artifact_location: "path/to/artifacts" # Used only when creating a new experiment
# Configure Launcher
launcher:
log:
- logging
- _attr_: mlflow
set_env_vars: true
set_run_id: true
LogArtifactsLauncher
The launcher can be initialized with the following attributes
-
path_command: Name for the command file. IfNone, don't log the command. -
path_config: Name for the config file. IfNone, don't log the config.
For example,
# Configure logging
logging:
level: 20
# Configure mlflow
mlflow:
# tracking_uri: "http://127.0.0.1:5000" # Or set env variable MLFLOW_TRACKING_URI
# experiment_name: "test-experiment" # Which experiment to use
# run_id: 12345 # To restore a previous run
# run_name: test # To give a name to your new run
# artifact_location: "path/to/artifacts" # Used only when creating a new experiment
# Configure launcher
launcher:
log:
- logging
- mlflow
parse:
- _attr_: mlflow.log_artifacts
path_command: launch.sh
path_config: config.yaml
- parser
- _attr_: mlflow.log_artifacts
path_command: null
path_config: parsed.yaml
LogParamsLauncher
The launcher will use include_keys and ignore_keys if present in the config in the mlflow key.
-
ignore_keys: If given, don't log some parameters that have some substrings. -
include_keys: If given, only log some parameters that have some substrings. Also shorten the flattened parameter to start at the first match. For example, if the config is{"foo": {"bar": 1}}andinclude_keys=("bar",), then the logged parameter will be"bar".
For example,
# Configure logging
logging:
level: 20
# Configure mlflow
mlflow:
# tracking_uri: "http://127.0.0.1:5000" # Or set env variable MLFLOW_TRACKING_URI
# experiment_name: "test-experiment" # Which experiment to use
# run_id: 12345 # To restore a previous run
# run_name: test # To give a name to your new run
# artifact_location: "path/to/artifacts" # Used only when creating a new experiment
include_keys: # Only log params that match *model*
- model
# Configure launcher
launcher:
log:
- logging
- mlflow
parse:
- parser
- mlflow.log_params
