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https://github.com/yotamaflalo/mlflow_try
https://github.com/yotamaflalo/mlflow_try
Last synced: 27 days ago
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- Host: GitHub
- URL: https://github.com/yotamaflalo/mlflow_try
- Owner: YotamAflalo
- Created: 2024-07-10T15:28:51.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-10T17:31:30.000Z (4 months ago)
- Last Synced: 2024-07-11T19:14:57.631Z (4 months ago)
- Language: Python
- Size: 46.9 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Virtual Environment
Create Virutal Environment with Conda
```python
conda create -n mlflow-venv python=3.10
```
```python
conda activate mlflow-venv
```
```python
pip install mlflow
```
# Notes on MLflow Tracking
`mlflow.set_tracking_uri()` connects to a tracking URI. You can also set the MLFLOW_TRACKING_URI environment variable to have MLflow find a URI from there. In both cases, the URI can either be a HTTP/HTTPS URI for a remote server, a database connection string, or a local path to log data to a directory. The URI defaults to mlruns.
`mlflow.get_tracking_uri()` returns the current tracking URI.
`mlflow.create_experiment()` creates a new experiment and returns its ID. Runs can be launched under the experiment by passing the experiment ID to mlflow.start_run.
`mlflow.set_experiment()` sets an experiment as active. If the experiment does not exist, creates a new experiment. If you do not specify an experiment in mlflow.start_run(), new runs are launched under this experiment.
`mlflow.start_run()` returns the currently active run (if one exists), or starts a new run and returns a mlflow.ActiveRun object usable as a context manager for the current run. You do not need to call start_run explicitly: calling one of the logging functions with no active run automatically starts a new one.
`mlflow.end_run()` ends the currently active run, if any, taking an optional run status.
`mlflow.log_param()` logs a single key-value param in the currently active run. The key and value are both strings. Use mlflow.log_params() to log multiple params at once.
`mlflow.log_metric()` logs a single key-value metric. The value must always be a number. MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log multiple metrics at once.
`mlflow.set_tag()` sets a single key-value tag in the currently active run. The key and value are both strings. Use mlflow.set_tags() to set multiple tags at once.
`mlflow.log_artifact()` logs a local file or directory as an artifact, optionally taking an artifact_path to place it in within the run’s artifact URI. Run artifacts can be organized into directories, so you can place the artifact in a directory this way.
`mlflow.log_artifacts()` logs all the files in a given directory as artifacts, again taking an optional artifact_path.
#### If you get Port Already in use error while using mlflow
- Get list of Services & PID running
`sudo lsof -i tcp:5000 `
- Kill them
`kill -15 `
# MLFlow Projects
- Create Run using MLFlow project file
`mlflow run . --experiment-name Loan_prediction` # run from folder where `MLProject` file is present
- Run from git repository
`mlflow run https://github.com/manifoldailearning/ml-flow-project --experiment-name Loan_prediction`
# MLFlow Models
- install virtualenv
`pip install virtualenv`
- install chardet
`pip install chardet`
- Serve the Models with Local REST server
`mlflow models serve -m runs://model --port 9000`
`mlflow models serve -m /Users/nachiketh/Desktop/author-repo/Complete-MLOps-BootCamp/MLFlow-Manage-ML-Experiments/mlruns/636758781795674813/91ef1ea3f63d40a7a33c4251dd088618/artifacts/RandomForestClassifier --port 9000`
# Generate Predictions
- http://127.0.0.1:9000/invocations
```json
{
"dataframe_split": {
"columns": [
"Gender",
"Married",
"Dependents",
"Education",
"Self_Employed",
"LoanAmount",
"Loan_Amount_Term",
"Credit_History",
"Property_Area",
"TotalIncome"
],
"data": [
[
1.0,
0.0,
0.0,
0.0,
0.0,
4.98745,
360.0,
1.0,
2.0,
8.698
]
]
}
}
```
# Curl
http://localhost:9000
```bash
curl --location 'http://127.0.0.1:9000/invocations' \
--header 'Content-Type: application/json' \
--data '{
"dataframe_split": {
"columns": [
"Gender",
"Married",
"Dependents",
"Education",
"Self_Employed",
"LoanAmount",
"Loan_Amount_Term",
"Credit_History",
"Property_Area",
"TotalIncome"
],
"data": [
[
1.0,
0.0,
0.0,
0.0,
0.0,
4.98745,
360.0,
1.0,
2.0,
8.698
]
]
}
}'
```
```yaml
LogisticRegression/
- conda.yaml
- MLmodel
- model.pkl
- requriments.txt
```
- Installation
`pip install mysqlclient`
-port 5001
`mlflow server --host 0.0.0.0 --port 5001 --backend-store-uri mysql://root:admin123@localhost/db_mlflow --default-artifact-root $PWD/mlruns`
- port 5000
`mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri mysql://root:admin123@localhost/db_mlflow --default-artifact-root $PWD/mlruns`
`export MLFLOW_TRACKING_URI=http://0.0.0.0:5001`
`mlflow models serve -m "models:/Prediction_Model_RF/Production" -p 1234`