Realizing now that it’s unlikely researchers are actually hand typing their model descriptions. Found out about nvidia graph composer but have held out on investing in gaming equipment until this point. (For reference I’m 26, completed some “standard” cs curriculum, have gone through tensorflow/deep learning training certification online under Prof. Ng and worked as a research assistant in finance.) Is graph composer the only way to build ai models and workflows from drag and drop gui interactions? Is there a way to interact concurrently with the data science pipeline? From the pytorch literature and pyro “autonaming” and “guide” (read gui ide?) I infer that this is likely a common pattern and the module oriented scripts I see are generated from this low code/no code system pradigm. I really was excited about mesh tensor flow and Jax but couldn’t really see how it comes together. Realizing now the language functions become more apparent across dynamic programmer-system interaction I can understand immediately how cool it would be to have auto scaling features as well as more capable differentiation at scale.
Either way I’d really like to experiment with programming in this style. What tools are common place for engaging the tensor flow ecosystem… and using the Jax engine? Does everyone just have to use nvidia graph composer… it does support tensorflow and PyTorch among other frameworks (why would one want to mix and match systems?… is the performance equivalent?)
This is like the most stupidly simple obvious innovation in this space please tell me I don’t have to implement it myself. Found torch studio and reinvestigated r studio. React applications seem to be literally purposed as a reminder of this interpretation of the “application programming interface” concept.
Please advice, really hoping this missing software is the turning point before finding more creative exciting ml developer communities and projects!
It’s great that you’re diving deep into the intersection of GUI-based tools and machine learning frameworks like TensorFlow and JAX. Let’s break down your queries and concerns step-by-step to provide a comprehensive understanding of the current landscape and tools available.
GUI-Based Tools for AI Model Building
There are several GUI-based tools designed to simplify the process of building and deploying AI models, each catering to different needs and preferences. Here are some prominent ones:
- NVIDIA Graph Composer:
- A powerful tool for creating complex AI workflows with a drag-and-drop interface.
- It supports multiple frameworks including TensorFlow and PyTorch.
- It’s beneficial for those who want to leverage NVIDIA’s hardware acceleration.
- Google’s TensorFlow Extended (TFX):
- While not strictly a GUI tool, TFX provides an orchestration layer that can be integrated with tools like Apache Beam, Kubeflow, and Airflow to build robust ML pipelines.
- TFX provides components that can be pieced together using pipelines.
- IBM Watson Studio:
- A comprehensive environment that supports building models using drag-and-drop tools.
- It integrates well with various data sources and supports deployment.
- KNIME:
- An open-source platform for data analytics, reporting, and integration.
- Provides a user-friendly interface for building data science workflows without needing extensive coding.
- Azure Machine Learning Studio:
- A visual drag-and-drop tool to build, test, and deploy machine learning models.
- Integrates well with other Azure services for scaling and deployment.
Concurrent Interaction with Data Science Pipelines
Many modern tools support the idea of concurrent interaction with the data science pipeline. For example:
- Jupyter Notebooks with extensions like JupyterLab, allow for interactive coding, data visualization, and documentation in one place.
- Streamlit and Dash are frameworks for building interactive web applications for data science and machine learning projects.
- RStudio provides an integrated development environment for R, but also supports Python, and allows interactive data analysis and visualization.
PyTorch and JAX Ecosystem Tools
- PyTorch: The PyTorch ecosystem includes libraries like
torchvision for image processing, torchtext for NLP, and tools like TorchServe for model deployment.
- JAX: JAX is highly composable and works seamlessly with other Google tools like Flax (a neural network library for JAX), providing flexibility and high performance, especially for research purposes.
Low Code/No Code Platforms
- H2O.ai: Offers Driverless AI, a platform for automated machine learning (AutoML) with a focus on ease of use.
- DataRobot: Provides an end-to-end platform for automating the building and deployment of machine learning models.
Why Mix and Match Systems?
Different systems have their strengths. For instance:
- TensorFlow excels in production environments due to its robust serving capabilities.
- PyTorch is often preferred for research due to its dynamic computation graph and ease of debugging.
- Mixing frameworks can leverage the strengths of each, like using PyTorch for prototyping and TensorFlow for deployment.
Conclusion
You don’t have to implement everything yourself. There are plenty of tools available that cater to various aspects of machine learning, from building models to deploying them at scale. Experiment with the tools mentioned above and see which ones fit your workflow and needs the best. Engaging with communities around these tools (like forums, GitHub discussions, and specialized Slack channels) can also provide insights and support as you explore these technologies further.
If you find NVIDIA Graph Composer appealing but are hesitant about investing in hardware, consider cloud-based solutions that provide access to high-performance GPUs without the upfront cost of physical hardware. This way, you can leverage powerful tools and hardware without a significant initial investment.