What other 3 types of shapeless and fluid containers would be necessary for ai engineering provide examples and architecture overviews
In addition to the containers mentioned in the requirements, here are three more types of shapeless and fluid containers that are necessary for AI engineering:

ModuleDictList:

Overview: ModuleDictList is a combination of ModuleList and ModuleDict. It is a container that holds a collection of submodules, where each submodule can be accessed either by index or by name.
Example: A deep neural network that consists of multiple sequential layers, but also includes a few individual layers that are not sequential. In this case, ModuleDictList can be used to hold both the sequential layers (accessible by index) and the individual layers (accessible by name).
ParameterGroup:

Overview: ParameterGroup is a container specifically designed to group related parameters together. It allows for convenient management and access to a subset of parameters within a larger model.
Example: In a convolutional neural network, different layers may have distinct sets of parameters (e.g., weights, biases). ParameterGroup can be used to organize and manipulate the parameters within each layer separately, making it easier to apply specific optimization strategies to individual layers.
ModuleTree:

Overview: ModuleTree is a hierarchical container that represents a complex neural network architecture. It allows for a tree-like organization of submodules, where each node can have an arbitrary number of children nodes.
Example: An attention-based recurrent neural network, where each time step of the recurrent module is customized to have a different attention mechanism. ModuleTree can be used to organize the modules in a hierarchical structure, with the recurrent module at the root and each attention module as a child node, enabling flexible customization and management of the entire architecture.
These additional containers extend the functionality and flexibility of the AI engineering workflow by providing more versatile options for organizing, managing, and customizing neural network architectures.

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Zeta, Building Zetascale Transformers at lightspeed
Kye
OP
 — 08/22/2023 8:10 AM
Zeta is an all-new modular framework for building ultra-reliable and ultra-fast neural networks

https://github.com/kyegomez/zeta
GitHub
GitHub - kyegomez/zeta: Transformers at Zeta scale.
Transformers at Zeta scale. Contribute to kyegomez/zeta development by creating an account on GitHub.
GitHub - kyegomez/zeta: Transformers at Zeta scale.
Kye
OP
 — 08/22/2023 8:11 AM
Need to re-architecture the file structure tree and make it as theoretically simple as possible, using zeta.nn
Kye
OP
 — 08/22/2023 8:24 AM
Create documentation
Kye
OP
 — 08/22/2023 3:23 PM
I’ve been updating documentation
https://zetascale.readthedocs.io/en/latest/
Kye
OP
 — 08/22/2023 5:14 PM
Internets down at the office
Blackout
Kye
OP
 — 08/22/2023 6:06 PM
I'm back 👿
Kye
OP
 — 08/22/2023 8:14 PM
In due time Zeta will be the most used library to build the best models
It'll be the simplest, the fastest, and the most reliable and high performance ML library ever made
I'm seeking a team to grow Zeta, so if you like pytorch and want to make the best ML framework to build the best models join me and we'll set sail on this grand journey
Kye
OP
 — 08/23/2023 9:51 AM
multi-modal tokenizer
Language tokenizer
adding docs for them both now.
will be adding various variations of Vit from Lucidrain's repo soon, we need to create the best vit ever composed
Kye
OP
 — 08/23/2023 10:08 AM
solved 3 import errors
What is a transformer composed of?
architecture
Attention mechanism
Attention Layers
Decoder
Transformer Blocks

---
Embedding
Embedding algos

Biases
Biases, ALIBI

Submodules
FF
 
How can we modularize it even further to enable anyone to create the absolute BEST nets?
Kye
OP
 — 08/23/2023 12:10 PM
added Nebula, FAQ,
Kye
OP
 — 08/24/2023 12:10 PM
How to utilize the SOTA MultiQuery Attention Mechanism in less than 10 lines of code with zeta!

https://zeta.apac.ai/en/latest/zeta/nn/attention/multiquery/
Image
Kye
OP
 — 08/24/2023 12:52 PM
Introducing Zeta, the All-New AI Framework to Effortlessly Build the Best LLMS.

Learn more about the framework that will revolutionize AI engineering and enable developers to develop AI models 100x faster!

https://medium.com/@kyeg/introducing-zeta-the-all-new-ai-framework-to-effortlessly-build-the-best-llms-d874da65f275
Medium
Introducing Zeta, the All-New AI Framework to Effortlessly Build th...
Zeta, an all-new framework for Multi-Modality Models
Kye
OP
 — 08/26/2023 11:49 AM
Lora is now in Zeta, you can seamlessly add Lora into your LLMs and enjoy low cost finetuning!

Check out the docs

zeta.apac.ai
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Kye
OP
 — 08/26/2023 12:01 PM
Just added TokenLearner to Zeta, it's learns tokens from inputs and was made for robotic models,


Download zeta with pip install zetascale and check it out!

https://zeta.apac.ai/en/latest/zeta/nn/modules/token_learner/

And ⭐️ the repo here https://github.com/kyegomez/zeta
Image
GitHub
GitHub - kyegomez/zeta: Transformers at Zeta scale.
Transformers at Zeta scale. Contribute to kyegomez/zeta development by creating an account on GitHub.
GitHub - kyegomez/zeta: Transformers at Zeta scale.
Kye
OP
 — 08/28/2023 10:16 AM
build UI drag and drop graph platform like flowwise for building the neural nets
https://github.com/mert-kurttutan/torchview
https://github.com/szagoruyko/pytorchviz

make it free or charge a subscription
GitHub
GitHub - mert-kurttutan/torchview: torchview: visualize pytorch models
torchview: visualize pytorch models. Contribute to mert-kurttutan/torchview development by creating an account on GitHub.
GitHub - mert-kurttutan/torchview: torchview: visualize pytorch models
GitHub
GitHub - szagoruyko/pytorchviz: A small package to create visualiza...
A small package to create visualizations of PyTorch execution graphs - GitHub - szagoruyko/pytorchviz: A small package to create visualizations of PyTorch execution graphs
GitHub - szagoruyko/pytorchviz: A small package to create visualiza...
Kye
OP
 — 08/29/2023 6:46 PM
SentencePiece Tokenizer is coming up in Zeta

https://github.com/kyegomez/zeta/blob/main/zeta/tokenizers/sentence_piece.py
GitHub
zeta/zeta/tokenizers/sentence_piece.py at main · kyegomez/zeta
Transformers at Zeta scale. Contribute to kyegomez/zeta development by creating an account on GitHub.
zeta/zeta/tokenizers/sentence_piece.py at main · kyegomez/zeta
Kye
OP
 — 08/29/2023 7:21 PM
@Researcher 

100+ Multi-Modal AI papers on zeta!

https://zeta.apac.ai/en/latest/research/ 
Kye
OP
 — 08/30/2023 11:40 PM
Create a masterclass on building models with zeta
make youtube videos on zeta courses
Kye
OP
 — 08/31/2023 11:15 AM
zeta.rl need a maintainer
need help with unit tests
need help with docs
Kye
OP
 — 08/31/2023 3:26 PM
added rotary embeddings
added truncatedrotary embddings from: https://arxiv.org/pdf/2308.10882.pdf
Kye
OP
 — 09/01/2023 1:04 PM
New submodules
quantization, make it super easy to quantize models

Bitsandbytes
4bit
pradeep1148 — 09/01/2023 1:04 PM
https://colab.research.google.com/drive/1Vvju5kOyBsDr7RX_YAvp6ZsSOoSMjhKD?usp=sharing#scrollTo=rusMOmw-rbvp
Google Colaboratory
Image
Kye
OP
 — 09/07/2023 11:19 AM
Updates
added mixture of attention not experts
added local attention
solved alot of dependency/circular import errors
Kye
OP
 — Today at 9:14 AM
What other 3 types of shapeless and fluid containers would be necessary for ai engineering provide examples and architecture overviews
In addition to the containers mentioned in the requirements, here are three more types of shapeless and fluid containers that are necessary for AI engineering:

ModuleDictList:

Overview: ModuleDictList is a combination of ModuleList and ModuleDict. It is a container that holds a collection of submodules, where each submodule can be accessed either by index or by name.
Example: A deep neural network that consists of multiple sequential layers, but also includes a few individual layers that are not sequential. In this case, ModuleDictList can be used to hold both the sequential layers (accessible by index) and the individual layers (accessible by name).
ParameterGroup:

Overview: ParameterGroup is a container specifically designed to group related parameters together. It allows for convenient management and access to a subset of parameters within a larger model.
Example: In a convolutional neural network, different layers may have distinct sets of parameters (e.g., weights, biases). ParameterGroup can be used to organize and manipulate the parameters within each layer separately, making it easier to apply specific optimization strategies to individual layers.
ModuleTree:

Overview: ModuleTree is a hierarchical container that represents a complex neural network architecture. It allows for a tree-like organization of submodules, where each node can have an arbitrary number of children nodes.
Example: An attention-based recurrent neural network, where each time step of the recurrent module is customized to have a different attention mechanism. ModuleTree can be used to organize the modules in a hierarchical structure, with the recurrent module at the root and each attention module as a child node, enabling flexible customization and management of the entire architecture.
These additional containers extend the functionality and flexibility of the AI engineering workflow by providing more versatile options for organizing, managing, and customizing neural network architectures.
To make AI engineering seamless, we can provide three additional types of shapeless and fluid containers for AI engineering. These containers help organize and manage different components of AI models and algorithms. Let's explore these containers along with examples and architecture overviews:

DataLoader:
The DataLoader container is responsible for managing and loading datasets for training or testing AI models. It provides utilities for efficient data loading, shuffling, batch creation, and parallel processing. By using DataLoader, seamless integration of diverse datasets can be achieved, resulting in improved training efficiency.

Architecture Overview:
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To make AI engineering seamless, we can provide three additional types of shapeless and fluid containers for AI engineering. These containers help organize and manage different components of AI models and algorithms. Let's explore these containers along with examples and architecture overviews:

DataLoader:
The DataLoader container is responsible for managing and loading datasets for training or testing AI models. It provides utilities for efficient data loading, shuffling, batch creation, and parallel processing. By using DataLoader, seamless integration of diverse datasets can be achieved, resulting in improved training efficiency.

Architecture Overview:

The DataLoader container takes input datasets and organizes them into batches.
It can perform shuffling of data for better training convergence.
Parallel processing techniques like multithreading or multiprocessing can be utilized for faster data loading.
DataLoader efficiently feeds the data to the model during training or testing phases.
Example: PyTorch provides a DataLoader class that enables seamless loading of various datasets, such as images, text, or time series data. Here's an example of how DataLoader can be used:

from torch.utils.data import DataLoader, Dataset

class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data
        
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, index):
        return self.data[index]
        
# Create an instance of MyDataset
my_dataset = MyDataset([1, 2, 3, 4, 5])

# Create a DataLoader
data_loader = DataLoader(my_dataset, batch_size=2, shuffle=True)

# Iterate over the data batches
for batch in data_loader:
    print(batch)
Pipeline:
The Pipeline container provides a streamlined way to define and execute a sequence of data processing steps or algorithms. It enables easy integration of different transformation or feature extraction techniques in AI pipelines. By using a pipeline, AI engineering becomes more modular and customizable.

Architecture Overview:

A pipeline consists of multiple stages, each performing a specific data processing or algorithmic operation.
The stages can be organized and interconnected in a sequential manner, allowing data to flow through each stage.
Each stage can have configurable parameters, which can be fine-tuned during AI model development.
The pipeline can handle both offline and online data processing, enabling real-time AI applications.
Example: Scikit-learn provides a Pipeline class that simplifies the creation of data processing pipelines. Here's an example of how Pipeline can be used:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# Define stages of the pipeline
preprocessing_stage = ('scaler', StandardScaler())
classification_stage = ('classifier', LogisticRegression())

# Create the pipeline
pipeline = Pipeline([preprocessing_stage, classification_stage])

# Fit the pipeline on training data
pipeline.fit(X_train, y_train)

# Predict using the fitted pipeline
y_pred = pipeline.predict(X_test)
ModelRegistry:
The ModelRegistry container acts as a centralized repository for storing and managing trained AI models. It provides functionalities to register, retrieve, update, and delete AI models. By using a ModelRegistry, AI engineering becomes more scalable, as multiple models can be tracked and versioned easily.

Architecture Overview:

The ModelRegistry stores trained AI models along with their associated metadata like version, hyperparameters, and performance metrics.
It enables easy registration of new models and maintains a history of model versions.
The registered models can be retrieved for inference or further training purposes.
The ModelRegistry can integrate with deployment infrastructure to make models accessible as APIs or services.
Example: MLflow is an open-source platform that provides model management capabilities, including a ModelRegistry. Here's an example of how ModelRegistry can be used with MLflow:

import mlflow
from mlflow.register_model import register_model
from mlflow.models import Model

# Log a trained model to MLflow
with mlflow.start_run() as run:
    # Train and save the model
    model = train_model()

    # Log the model
    mlflow.pytorch.log_model(model, "model")

# Register the model in the ModelRegistry
model_name = "my_model"
model_path = f"runs:/{run.info.run_id}/model"
version = register_model(model_uri=model_path, name=model_name)

# Load the registered model for inference
loadedim = Model.load(model_uri=f"models:/{model_name}/{version}")
By incorporating these additional shapeless and fluid containers, AI engineering can be made more seamless, efficient, and modular, ultimately leading to improved development and deployment of AI models and algorithms.
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