# FromConfig MlFlow
Project description
FromConfig MlFlow
A fromconfig Launcher
for MlFlow support.
Install
pip install fromconfig_mlflow
Quickstart
Once installed, the launcher is available with the name mlflow
.
Start a local MlFlow server with
mlflow server
You should see
[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.
Set the MLFLOW_TRACKING_URI
environment variable
export MLFLOW_TRACKING_URI=http://127.0.0.1:5000
Given the following module
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)
and config files
config.yaml
model:
_attr_: foo.Model
learning_rate: "${params.learning_rate}"
params.yaml
params:
learning_rate: 0.001
Run
fromconfig config.yaml params.yaml --launcher.log=mlflow - model - train
which prints
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 the logged metric learning_rate
.
You can also use a launcher.yaml
file
# 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
launcher:
log: mlflow # Start run
by running
fromconfig config.yaml params.yaml launcher.yaml - model - train
This example can be found in docs/examples/quickstart
.
Artifacts 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 # Start run
parse:
- mlflow_log_artifacts # Log config.yaml and launch.sh
- parser # Parse config
- mlflow_log_params # Log flattened config as run parameters
and run
fromconfig config.yaml params.yaml launcher.yaml - model - train
If you navigate to the MlFlow run, you should see
- the original config (before parsing), saved as
config.yaml
, logged bymlflow_log_artifacts
- the parameters, a flattened version of the parsed config (
model.learning_rate
is0.001
and not${params.learning_rate}
) logged bymlflow_log_params
.
This example can be found in docs/examples/artifacts-params
.
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 runrun_name
: if you wish to give a name to your new runtracking_uri
: to configure the tracking remoteexperiment_name
: to use a different experiment than the custom experimentartifact_location
: the location of the artifacts (config files)
Additionally, the launcher can be initialized with the following attributes
set_env_vars
: if True (default), setMLFLOW_RUN_ID
andMLFLOW_TRACKING_URI
set_run_id
: if True (default), setmlflow.run_id
in config.
For example
# 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
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,
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"
.
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