Merge branch 'kyegomez:master' into master

README.md

@@ -336,6 +336,40 @@ niva(
 
 ```
 
+
+### ZetaCloud
+Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. 
+
+- Flexible Pricing with pooling from many clouds
+- Easy Deployment with 1 click
+- Various options for cloud providers!
+
+```bash
+Zetacloud CLI
+
+options:
+  -h, --help            show this help message and exit
+  -t TASK_NAME, --task_name TASK_NAME
+                        Task name
+  -c CLUSTER_NAME, --cluster_name CLUSTER_NAME
+                        Cluster name
+  -cl CLOUD, --cloud CLOUD
+                        Cloud provider
+  -g GPUS, --gpus GPUS  GPUs
+  -f FILENAME, --filename FILENAME
+                        Filename
+  -s, --stop            Stop flag
+  -d, --down            Down flag
+  -sr, --status_report  Status report flag
+
+```
+
+- A simple run example code would be like:
+
+```bash
+zeta -f train.py -g A100:8
+```
+
 # Documentation
 [Click here for the documentation, it's at zeta.apac.ai](https://zeta.apac.ai)
 

docs/corporate/zeta_cloud.md

@@ -60,3 +60,106 @@ The estimated timeline for shipping Zeta Cloud is as follows:
 | API Access for Third-Party Integrations | Providing API access for integration with other tools and services. | Developers, businesses needing integrations. | Monthly/Annual subscription or pay-per-use. |
 
 
+
+
+# GTM - Go To Market
+
+### **Contents**
+
+1. Positioning Statement
+2. Early Adopter Segments
+3. Branding
+4. Channel Strategy
+5. Initial Marketing Methods
+6. Testing Plan
+7. LTV/CAC
+
+---
+
+### **1. Positioning Statement**
+
+*For AI engineers and data scientists who struggle with the complexities of model training and deployment, Zeta Cloud is a new cloud-based AI service that simplifies these processes. Unlike traditional cloud services, we offer an automated, user-friendly platform with a strong focus on accessibility and efficiency.*
+
+---
+
+### **2. Early Adopter Segments**
+
+**Segment Characteristics:**
+- Demographics: AI engineers and data scientists in mid-sized tech companies and startups.
+- Unmet Needs: Simplification of AI model deployment, efficient resource management, cost-effective scaling.
+- Behaviors: Active users of cloud computing services, frequent participants in tech forums and communities.
+- Psychographics: Value innovation, efficiency, and user-friendly interfaces.
+- Multi-party Decision Making: End users (engineers and scientists), economic buyers (company executives), and key influencers (tech thought leaders and industry experts).
+
+**Implications for Targeted Marketing:**
+- Focused engagement in tech forums and communities.
+- Tailored content marketing addressing specific needs and pain points.
+- Leveraging influencers and thought leaders to reach decision-makers.
+
+---
+
+### **3. Branding**
+
+**Strengths of Product Name:**
+- 'Zeta Cloud' conveys a sense of technological advancement and cloud-based efficiency.
+
+**Brand Association Words:**
+- Innovative, Efficient, User-Friendly, Accessible, Empowering, Reliable.
+
+**Aspirational Brand Similarities:**
+- Brands like AWS, Google Cloud, and Azure for their technological prowess and market presence.
+
+---
+
+### **4. Channel Strategy**
+
+**Channels:**
+- Own Website: Primary channel for direct sales and customer engagement.
+- Sales Force: Blend of inside sales for smaller accounts and field sales for larger, enterprise-level deals.
+- Channel Partners: Collaborations with tech marketplaces and value-added resellers.
+
+**Partner Responsibilities and Margins:**
+- Education and initial engagement by Zeta Cloud, with partners focusing on closing sales and after-sales service.
+- Attractive margins to incentivize partner engagement and commitment.
+
+---
+
+### **5. Initial Marketing Methods**
+
+**Hypothesized Effective Methods:**
+1. **Content Marketing:** Strength - establishes thought leadership; Weakness - time-intensive.
+2. **Social Media and Community Engagement:** Strength - builds brand awareness; Weakness - requires consistent, high-quality engagement.
+3. **Paid Digital Advertising (e.g., Google Ads, LinkedIn):** Strength - targets specific segments; Weakness - can be costly.
+
+**Performance Metrics:**
+- Engagement rates, conversion rates, customer acquisition costs.
+
+**Secondary Marketing Methods:**
+- Email marketing, PR activities, and webinars; secondary due to longer lead times and higher resource requirements.
+
+---
+
+### **6. Testing Plan**
+
+**Completed Tests:**
+- Initial A/B testing on website messaging and layout.
+
+**Upcoming Tests:**
+- Content marketing effectiveness: Measuring engagement and conversion rates from different content types.
+- Social media ad campaigns: Assessing customer acquisition costs and conversion rates.
+- Budget for tests: Approximately $20,000 over three months.
+
+---
+
+### **7. LTV/CAC**
+
+**LTV Targets:**
+- Average annual revenue per customer: $5,000.
+- Variable contribution margin: 70%.
+- Retention rate: 85% annually.
+
+**CAC Projections:**
+- Mix of free and paid methods: 40% free methods (referrals), 60% paid methods.
+- Viral coefficient: 0.5.
+- CAC for paid methods: $500 - $1,000, varying by channel.
+

docs/zeta/cloud/main.md

@@ -0,0 +1,126 @@
+
+# ZetaCloud Documentation
+
+## Overview
+
+ZetaCloud is a versatile command-line tool that simplifies the process of training or fine-tuning machine learning models on remote GPU clusters. With just a few commands, you can effortlessly manage your tasks and harness the computational power of various GPUs. This comprehensive documentation will guide you through every aspect of the ZetaCloud CLI, from installation to advanced usage.
+
+## Table of Contents
+
+1. [Installation](#installation)
+2. [ZetaCloud CLI](#zetacloud-cli)
+   - [Options](#options)
+3. [Basic Usage](#basic-usage)
+   - [Example 1: Starting a Task](#example-1-starting-a-task)
+   - [Example 2: Stopping a Task](#example-2-stopping-a-task)
+   - [Example 3: Checking Task Status](#example-3-checking-task-status)
+4. [Advanced Usage](#advanced-usage)
+   - [Example 4: Cluster Selection](#example-4-cluster-selection)
+   - [Example 5: Choosing the Cloud Provider](#example-5-choosing-the-cloud-provider)
+5. [Additional Information](#additional-information)
+6. [References](#references)
+
+---
+
+## 1. Installation <a name="installation"></a>
+
+Getting started with ZetaCloud is quick and straightforward. Follow these steps to set up ZetaCloud on your machine:
+
+1. Open your terminal or command prompt.
+
+2. Install the `zetascale` package using `pip`:
+
+   ```bash
+   pip install zetascale
+   ```
+
+3. After a successful installation, you can access the ZetaCloud CLI by running the following command:
+
+   ```bash
+   zeta -h
+   ```
+
+   This command will display a list of available options and basic usage information for ZetaCloud.
+
+## 2. ZetaCloud CLI <a name="zetacloud-cli"></a>
+
+The ZetaCloud Command-Line Interface (CLI) provides a set of powerful options that enable you to manage tasks on GPU clusters effortlessly. Below are the available options:
+
+### Options <a name="options"></a>
+
+- `-h, --help`: Display the help message and exit.
+- `-t TASK_NAME, --task_name TASK_NAME`: Specify the name of your task.
+- `-c CLUSTER_NAME, --cluster_name CLUSTER_NAME`: Specify the name of the cluster you want to use.
+- `-cl CLOUD, --cloud CLOUD`: Choose the cloud provider (e.g., AWS, Google Cloud, Azure).
+- `-g GPUS, --gpus GPUS`: Specify the number and type of GPUs required for your task.
+- `-f FILENAME, --filename FILENAME`: Provide the filename of your Python script or code.
+- `-s, --stop`: Use this flag to stop a running task.
+- `-d, --down`: Use this flag to terminate a cluster.
+- `-sr, --status_report`: Check the status of your task.
+
+## 3. Basic Usage <a name="basic-usage"></a>
+
+ZetaCloud's basic usage covers essential tasks such as starting, stopping, and checking the status of your tasks. Let's explore these tasks with examples.
+
+### Example 1: Starting a Task <a name="example-1-starting-a-task"></a>
+
+To start a task, you need to specify the Python script you want to run and the GPU configuration. Here's an example command:
+
+```bash
+zeta -f train.py -g A100:8
+```
+
+In this example:
+- `-f train.py` indicates that you want to run the Python script named `train.py`.
+- `-g A100:8` specifies that you require 8 NVIDIA A100 GPUs for your task.
+
+### Example 2: Stopping a Task <a name="example-2-stopping-a-task"></a>
+
+If you need to stop a running task, you can use the following command:
+
+```bash
+zeta -s
+```
+
+This command will stop the currently running task.
+
+### Example 3: Checking Task Status <a name="example-3-checking-task-status"></a>
+
+To check the status of your task, use the following command:
+
+```bash
+zeta -sr
+```
+
+This command will provide you with a detailed status report for your active task.
+
+## 4. Advanced Usage <a name="advanced-usage"></a>
+
+ZetaCloud also offers advanced options that allow you to fine-tune your tasks according to your specific requirements.
+
+### Example 4: Cluster Selection <a name="example-4-cluster-selection"></a>
+
+You can select a specific cluster for your task by providing the cluster name with the `-c` option:
+
+```bash
+zeta -f train.py -g A100:8 -c my_cluster
+```
+
+This command will run your task on the cluster named `my_cluster`.
+
+### Example 5: Choosing the Cloud Provider <a name="example-5-choosing-the-cloud-provider"></a>
+
+ZetaCloud supports multiple cloud providers. You can specify your preferred cloud provider using the `-cl` option:
+
+```bash
+zeta -f train.py -g A100:8 -cl AWS
+```
+
+This command will execute your task on a cloud provider's infrastructure, such as AWS.
+
+## 5. Additional Information <a name="additional-information"></a>
+
+- ZetaCloud simplifies the process of utilizing GPU clusters, allowing you to focus on your machine learning tasks rather than infrastructure management.
+
+- You can easily adapt ZetaCloud to various cloud providers, making it a versatile tool for your machine learning needs.
+