deep-diver
commented
2 years ago @osanseviero
I think 4 blog posts are too long, so I think it would be better to compress the whole thing into 1 blog post with HuggingFace related stuffs in TFX pipeline:
- How to preprocess the dataset appropriately: use feature extractor, re-ordering the indicies
- How to train ViT model from HuggingFace's Transformers: explaning project strucutre, KerasTuner for hyperparameter Tuning
- How to write a custom component: HFPusher to push trained model and Space application to the Hub
What do you think?
osanseviero
Hi folks!
@sayakpaul and I have written three parts series blog posts about ViT model deployment on local, kubernetes(GKE), and GCP Vertex AI at HF blog platform a while ago.
At this time, we want to propose additional blog post series to help ML folks to expand their knowledge beyond ML deployment. Basically, we are going to show how to use sevenn major tools from TensorFlow ecosystem that are
TensorFlow Extended(TFX),TensorFlow,TensorFlow Data Validation,TensorFlow Model Analysis,TensorFlow Serving, andKerasTuner. Additionally, all of the steps will be demonstrated to work on local and cloud environment(GCP Vertex AI and Dataflow) It is going to four blog posts 👇🏼 :Basic: as the first step, we show how to build ML pipeline with the most basic components, which are
ExampleGen,Trainer, andPusher. These components are responsible for injecting raw dataset into the ML pipeline, training a TensorFlow model, and deploying a trained model.Intermediate: as the second step, we show how to extend the ML pipeline from the first step by adding more components, which are
SchemaGen,StatisticsGen, andTransform. These components are responsible for analyzing the structures of the dataset, analyzing the statistical traits of the features in the dataset, and data pre-processing.Advanced Part1: as the third step, we show how to extend the ML pipeline from the second step by adding more components, which are
ResolverandEvaluator. These components are responsible for importing existing Artifacts (such as previously trained model) and comparing the performance between two models (one from the Resolver and one from the current pipeline run).Advanced Part2: as the fourth step, we show how to extend the ML pipeline from the third step by adding one more additional component,
Tuner. This component is responsible for running a set of experiments with different sets of hyperparameters with fewer epochs, and the found best hyperparameter combination will be passed to theTrainer, andTrainerwill train the model longer time with that hyperparameter combinations as the starting point.Also, we will show how to put 🤗 related custom components in the pipeline. That is to push trained model to the 🤗 Model Hub, to push (create) Gradio application to the 🤗 Space Hub automatically.
All the above steps are done implemented, so if this proposal gets accepted, we can start writing about it. The URL of the on-going project repository: https://github.com/deep-diver/mlops-hf-tf-vision-models
@osanseviero maybe you could give some opinions about this?