mlopscli - pypi Package Compare versions

Comparing version 0.1.3 to 0.1.4

PKG-INFO

 Metadata-Version: 2.3
 Name: mlopscli
-Version: 0.1.3
+Version: 0.1.4
 Summary: CLI to turn DS scripts to composable pipelines.
@@ -28,6 +28,53 @@ Author: Himanshu Bajpai
 
-1. Write your scripts (`data_prep.py`, `train_model.py`, `evaluate_model.py`)
+1. Write your scripts focussing on different steps in ML Lifecyle (`data_prep.py`, `train_model.py`, `evaluate_model.py`)
 2. Define them in a `job_order.yaml` with the dependencies.
 3. Install the mlops cli : `pip install mlopscli`
-4. Run the command: `mlopscli execute --job prepare_train_pipeline --job_config job_order.yaml`
+4. Run the CLI command.
 
+#### Commands Available
+
+1. Spin up a streamlit dashboard where the metadata of all the runs will be available.
+
+```bash
+mlopscli dashboard
+```
+
+2. Dry Run the pipeline to ensure Job Config is valid. Validations like dependencies, file paths are checked during dry run
+
+```bash
+mlopscli dry-run --job prepare_train_pipeline --job_config job_order.yaml
+```
+
+3. Execute the pipeline and get results
+
+```bash
+mlopscli execute --job prepare_train_pipeline --job_config job_order.yaml --observe
+```
+
+`--observe` : If passed, the resource consumption like CPU/Memory usage will be calculated for each step.
+
+
+> ⚠️ **NOTE** : 
+>  - If the environments already exists, it is not recreated.
+>  - Once the DAG is prepared, the steps in the same level are ran in parallel.
+
+4. Clean up Environments
+
+```bash
+mlopscli cleanup --step-name train
+```
+
+`--step-name` : If passed, the environment associated with the input step is deleted.
+
+```bash
+mlopscli cleanup --all
+```
+
+`--all` : If passed, all the environments are cleaned up.
+
+
+**Quick Demo**
+
+[![Watch the demo](https://img.youtube.com/vi/MFBbSA-SHFU/hqdefault.jpg)](https://www.youtube.com/watch?v=MFBbSA-SHFU)
+
+

pyproject.toml

 [project]
 name = "mlopscli"
-version = "0.1.3"
+version = "0.1.4"
 description = "CLI to turn DS scripts to composable pipelines."
@@ -5,0 +5,0 @@ authors = [

README.md

@@ -7,5 +7,52 @@ # mlopscli 🚀
 
-1. Write your scripts (`data_prep.py`, `train_model.py`, `evaluate_model.py`)
+1. Write your scripts focussing on different steps in ML Lifecyle (`data_prep.py`, `train_model.py`, `evaluate_model.py`)
 2. Define them in a `job_order.yaml` with the dependencies.
 3. Install the mlops cli : `pip install mlopscli`
-4. Run the command: `mlopscli execute --job prepare_train_pipeline --job_config job_order.yaml`
+4. Run the CLI command.
+
+#### Commands Available
+
+1. Spin up a streamlit dashboard where the metadata of all the runs will be available.
+
+```bash
+mlopscli dashboard
+```
+
+2. Dry Run the pipeline to ensure Job Config is valid. Validations like dependencies, file paths are checked during dry run
+
+```bash
+mlopscli dry-run --job prepare_train_pipeline --job_config job_order.yaml
+```
+
+3. Execute the pipeline and get results
+
+```bash
+mlopscli execute --job prepare_train_pipeline --job_config job_order.yaml --observe
+```
+
+`--observe` : If passed, the resource consumption like CPU/Memory usage will be calculated for each step.
+
+
+> ⚠️ **NOTE** : 
+>  - If the environments already exists, it is not recreated.
+>  - Once the DAG is prepared, the steps in the same level are ran in parallel.
+
+4. Clean up Environments
+
+```bash
+mlopscli cleanup --step-name train
+```
+
+`--step-name` : If passed, the environment associated with the input step is deleted.
+
+```bash
+mlopscli cleanup --all
+```
+
+`--all` : If passed, all the environments are cleaned up.
+
+
+**Quick Demo**
+
+[![Watch the demo](https://img.youtube.com/vi/MFBbSA-SHFU/hqdefault.jpg)](https://www.youtube.com/watch?v=MFBbSA-SHFU)
+

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