https://github.com/interactivetech/pdk-obj-det-demo-sil

https://github.com/interactivetech/pdk-obj-det-demo-sil

Last synced: about 1 year ago
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• Host: GitHub
• URL: https://github.com/interactivetech/pdk-obj-det-demo-sil
• Owner: interactivetech
• Created: 2023-10-10T16:20:14.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-18T03:19:18.000Z (over 2 years ago)
• Last Synced: 2025-02-10T05:25:05.563Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 72.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# pdk-obj-det-demo

Demo running End to End MLOPS using Determined, Pachyderm, and Kserve. Demo runs Object Detection on Xview Dataset

# Step 1: Prepare Environment 

* Create Jupyterlab instance on MLDS cluster with resource pool T4 or A100

* Select one GPU in jupyterlab environment

* Use docker container: `mendeza/breck:nb_0.0.5`

See screenshot below:



# Step 2: Setup and install

* Open Terminal

* `git clone https://github.com/interactivetech/pdk-obj-det-demo-sil.git`

* `cd pdk-obj-det-demo/`

* `git clone http://github.com/interactivetech/pdk-use-cases-dev-sil.git`

* Go to MLDM: http://mldm-pachyderm.us.rdlabs.hpecorp.net/ , make sure there is a project called object-detection-demo

* open the object-detection-demo project in MLDM and make sure all the pipelines (and repos) are deleted. We will create the data repo and the pachyderm pipelines in a jupyter notebook using the Pachyderm Python Client

* Main notebook will be using to complete the demo Demo_PDK.ipynb

* Notebook use to run predictions from served KServe model: Demo_Request_Prediction.ipynb

# Running the notebook

* Run all the cells before hand, some cells will take >5 min because one of the cells trains a FasterRCNN model inside the notebook, and another cell trains a model using MLDE.

* Walk through the demo cell by cell to demonstrate MLDE and MLDM

# Note:

The pachyderm train.py and container will check if there have been previous experiments ran:

```python

try:

    if checkpoint is None:

        parent_id = None

        configfile["data"]["pachyderm"]["previous_commit"] = None

        exp = client.create_experiment(configfile, code_path)

    else:

        parent_id = checkpoint.training.experiment_id

        configfile["data"]["pachyderm"]["previous_commit"] = pach_version

        exp = client.continue_experiment(

            configfile, parent_id, checkpoint.uuid

        )

```

If you want to retrain your model from scratch, be aware of this.