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fromconfig-mlflow-0.4.0
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# FromConfig MlFlow <!-- {docsify-ignore} -->
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fromconfig-mlflow-0.4.0 |
| نام | fromconfig-mlflow |
| نسخه کتابخانه | 0.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Criteo |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/criteo/fromconfig-mlflow |
| آدرس اینترنتی | https://pypi.org/project/fromconfig-mlflow/ |
| مجوز | - |
# FromConfig MlFlow <!-- {docsify-ignore} -->
[](https://pypi.python.org/pypi/fromconfig-mlflow)
[](https://github.com/criteo/fromconfig-mlflow/actions?query=workflow%3A%22Continuous+integration%22)
A [fromconfig](https://github.com/criteo/fromconfig) `Launcher` for [MlFlow](https://www.mlflow.org) support.
<!-- MarkdownTOC -->
- [Install](#install)
- [Quickstart](#quickstart)
- [MlFlow server](#mlflow-server)
- [Configure MlFlow](#configure-mlflow)
- [Artifacts and Parameters](#artifacts-and-parameters)
- [Usage-Reference](#usage-reference)
- [`StartRunLauncher`](#startrunlauncher)
- [`LogArtifactsLauncher`](#logartifactslauncher)
- [`LogParamsLauncher`](#logparamslauncher)
<!-- /MarkdownTOC -->
<a id="install"></a>
## Install
```bash
pip install fromconfig_mlflow
```
<a id="quickstart"></a>
## Quickstart
To activate `MlFlow` login, simply add `--launcher.log=mlflow` to your command
```bash
fromconfig config.yaml params.yaml --launcher.log=mlflow - model - train
```
With
`model.py`
```python
"""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`
```yaml
model:
_attr_: model.Model
learning_rate: "${params.learning_rate}"
```
`params.yaml`
```yaml
params:
learning_rate: 0.001
```
It 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.
<a id="mlflow-server"></a>
## MlFlow server
To setup a local MlFlow tracking server, run
```bash
mlflow server
```
which 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.
<a id="configure-mlflow"></a>
## 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
```bash
export MLFLOW_TRACKING_URI=http://127.0.0.1:5000
```
Alternatively, you can set the `mlflow.tracking_uri` config key either via command line with
```bash
fromconfig config.yaml params.yaml --launcher.log=mlflow --mlflow.tracking_uri="http://127.0.0.1:5000" - model - train
```
or in a config file with
`launcher.yaml`
```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: mlflow
```
and run
```bash
fromconfig config.yaml params.yaml launcher.yaml - model - train
```
<a id="artifacts-and-parameters"></a>
## Artifacts and Parameters
In this example, we add logging of the config and parameters.
Re-using the [quickstart](#quickstart) code, modify the `launcher.yaml` file
```yaml
# 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_params
```
and run
```bash
fromconfig config.yaml params.yaml launcher.yaml - model - train
```
which 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_rate` is `0.001` and not `${params.learning_rate}`)
- the original config, saved as `config.yaml`
- the parsed config, saved as `parsed.yaml`
<a id="usage-reference"></a>
## Usage-Reference
<a id="startrunlauncher"></a>
### `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 is `True`), set `MLFLOW_RUN_ID` and `MLFLOW_TRACKING_URI`
- `set_run_id`: if True (default is `False`), set `mlflow.run_id` in config.
For example,
```yaml
# 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
```
<a id="logartifactslauncher"></a>
### `LogArtifactsLauncher`
The launcher can be initialized with the following attributes
- `path_command`: Name for the command file. If `None`, don't log the command.
- `path_config`: Name for the config file. If `None`, don't log the config.
For example,
```yaml
# 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
```
<a id="logparamslauncher"></a>
### `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}}` and `include_keys=("bar",)`, then the logged parameter will be `"bar"`.
For example,
```yaml
# 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
```
نحوه نصب
نصب پکیج whl fromconfig-mlflow-0.4.0:
pip install fromconfig-mlflow-0.4.0.whl
نصب پکیج tar.gz fromconfig-mlflow-0.4.0:
pip install fromconfig-mlflow-0.4.0.tar.gz