Merge pull request #210 from dule1322/CTX-5487

CTX-5487: Update README.md

README.md

@@ -1,88 +1,114 @@
+![](./docs_images/logo-dark.png)
 
-[![Linter code check](https://github.com/coretex-ai/coretexpylib/actions/workflows/linter-code-check.yml/badge.svg?branch=develop)](https://github.com/coretex-ai/coretexpylib/actions/workflows/linter-code-check.yml)
-
-![](https://coretex.ai/images/coretex_logo_new.svg)
-
-<h1 style="text-align: center;">Coretex.ai Python library</h1>
-
-Manage the complete lifecycle of your experiments and complex workloads, from project inception to production deployment and monitoring.
+<!-- <h1 style="text-align: center;">Coretex.ai Python library</h1> -->
+---
+<div align="center">
 
-## What is Coretex.ai?
+[Coretex AI](https://www.coretex.ai) - Manage the complete lifecycle of your experiments and complex workloads, from project inception to production deployment and monitoring.
+<br />
 
-Coretex.ai is a powerful MLOps platform designed to make AI experimentation fast and efficient. With Coretex.ai, data scientists, ML engineers, and less experienced users can easily:
-
-* Run their data processing experiments,
-* Build AI models,
-* Perform statistical data analysis,
-* Run computational simulations.
+[![Linter code check](https://github.com/coretex-ai/coretexpylib/actions/workflows/linter-code-check.yml/badge.svg?branch=develop)](https://github.com/coretex-ai/coretexpylib/actions/workflows/linter-code-check.yml)
+</div>
+
+
+## What is Coretex AI?
+
+Coretex.ai is a powerful MLOps platform designed to make AI experimentation fast and efficient. It contains multiple key features to help with that:
+- [MLOps Workflow Management]() - Use powerful yet simple tools to optimize, build and run your ML Workflows
+- [Model Deployment](https://docs.coretex.ai/v1/getting-started/learn-basics/deployment) - Deploy your Model to production efforlessly with full tracking capabilities
+- [Task Library](https://github.com/coretex-ai/coretex-jobs) - Out-of-the-box support for common ML Tasks:
+    - LLM (Llama3)
+    - RAG
+    - Text-to-image (Stable Diffusion)
+    - Object Detection (YOLOv10)
+    - BioInformatics (Qiime2)
+    - and many others...
+- [Multi-language Support]() - You are not limited to just Python, with Coretex we support all of these:
+    - Python (including Notebooks)
+    - R
+    - Bash
+    - Docker - Define a custom Dockerfile which should be executed
+- [Parameter Optimization](https://docs.coretex.ai/v1/getting-started/learn-basics/project-and-task#parameter-optimization) - Define multiple values for parameters and Coretex will magically take care of performing grid search using those parameters
+- [Team Collaboration](https://docs.coretex.ai/v1/getting-started/learn-basics/organizations#collaboration-and-sharing) - Invite other people to collaborate with you on a Project by using a role-based access control (RBAC) for your Project
+- [Dataset Management](https://docs.coretex.ai/v1/getting-started/learn-basics/dataset) - Manage your Datasets by using multitude of features provided by Coretex such as:
+    - Support for annotatin images and IMU data directly on the platform
+    - Combine and duplicate functionality for re-using or merging existing Datasets
+    - Automatic Dataset lineage tracking which offers insight into how the Dataset was created
+- [Real-time Experiment Tracking](#coretex-experiment-tracking) - Real-time tracking of Run metrics, Artifacts, stdout and stderr, etc...
+- [Infrastructure Setup](#infrastructure-setup) - Connect your own on-premise machines, or use dynamically scalable cloud machines
 
 Coretex.ai helps you iterate faster and with more confidence. You get reproducibility, scalability, transparency, and cost-effectiveness.
 
 ## Get started
 
-**Step 1:** [Sign up for a free account ->](https://coretex.ai/)
+**Step 1:** [Sign up for free](https://app.coretex.ai/register-organization)
 
-**Step 2:** Install coretex:
+**Step 2:** Install Coretex python library:
 
+```bash
+$ pip3 install coretex
+```
 ```bash
 $ pip install coretex
 ```
 
-**Step 3:** Migrate your project to coretex:
-
-```python
-from coretex import CustomDataset, ExecutingExperiment
-
+**Step 3:** Run your project on Coretex with <b><u>zero changes</b></u>:
 
-def main(experiment: ExecutingExperiment[CustomDataset]):
-    # Remove "pass" and start task execution from here
-    pass
-
-
-if __name__ == "__main__":
-    main()
+```bash
+$ coretex run main.py
 ```
 
-Read the documentation and learn how you can migrate your project to the Coretex platform -> [Migrate your project to Coretex](https://app.gitbook.com/o/6QxmEiF5ygi67vFH3kV1/s/YoN0XCeop3vrJ0hyRKxx/getting-started/demo-experiments/migrate-your-project-to-coretex)
+## Infrastructure Setup
 
-## Key Features
-
-Coretex.ai offers a range of features to support users in their AI experimentation, including:
-
-* **Task Templates:** Battle-tested templates that make training ML models and processing data simple,
-
-* **Machine Learning Model Creation:** Quick and easy creation of machine learning models, with less friction and more stability,
-
-* **Optimized Pipeline Execution:** Execution optimization of any computational pipeline, including large-scale statistical analysis and various simulations,
-
-* **Team Collaboration:** The whole workflow in Coretex is centered around this concept to help centralize user management and enable transparent monitoring of storage and compute resources for administrators,
-
-* **Dataset Management and Annotation Tools:** Powerful tools for managing and annotating datasets,
-
-* **Run Orchestration and Result Analysis:** Detailed management of runs, ensuring reproducibility and easy comparison of results,
-
-* **IT Infrastructure Setup:** Easy setup of IT infrastructure, whether connecting self-managed computers or using paid, dynamically scalable cloud computers,
-
-* **Live Metrics Tracking:** Real-time tracking of run metrics during execution,
-
-* **Artifact Upload and Management:** Easy upload and management of run artifacts, including models and results.
-
-## Guaranteeing Reproducibility
+Connecting your own on-premise machines or your cloud machines to an MLOps platform has never been easier. This can be achieved by running one simple command:
+```bash
+$ coretex node start
+```
 
-One of the key benefits of Coretex.ai is its ability to guarantee reproducibility. The platform keeps track of all configurations and parameters between runs, ensuring that users never lose track of their work.
+## Coretex Experiment Tracking
+
+Coretex will automatically track:
+- Source code and parameters
+- Artifacts - files which are generated as a result of execution
+- Console output - stdout and stderr
+- Resouce usage (CPU, GPU, RAM, Swap, IO, network, etc...)
+
+<table>
+<tbody>
+  <tr>
+    <td>Metrics</td>
+    <td>Artifacts</td>
+  </tr>
+  <tr>
+    <td><img src="./docs_images/metrics_preview.png" width="100%"></td>
+    <td><img src="./docs_images/artifacts_preview.png" width="100%"></td>
+  </tr>
+  <tr>
+    <td>Console</td>
+    <td>Source code</td>
+  </tr>
+  <tr>
+    <td><img src="./docs_images/console_preview.png" width="100%"></td>
+    <td><img src="./docs_images/snapshot_preview.png" width="100%"></td>
+  </tr>
+</tbody>
+</table>
+
+One of the key benefits of Coretex is its ability to guarantee reproducibility. Since the platform keeps track of code, all configurations and parameters between runs, this ensures that you can run the same identical Workflow over and over again.
 
 ## Supported Use Cases
 
 Coretex.ai is a versatile platform that can be used for a variety of use cases, including:
 
-* Training ML models,
-* Large-scale statistical analysis,
-* Simulations (physics, molecular dynamics, population dynamics, econometrics, and more).
+- Training ML models
+- Large-scale statistical analysis
+- Simulations (physics, molecular dynamics, population dynamics, econometrics, and more)
+- Deploying all kinds of ML models (including LLMs)
 
 ## Compatibility with other libraries
 
-Coretex is compatible with all ML libraries such as Wandb, Tensorboard, PyTorch, and etc. There are no limits when it comes to Coretex integration with other libraries.
+Coretex is compatible with all existing Python ML frameworks (PyTorch, Tensorflow, Keras, XGBoost, Scikit-Learn, and many others). We also support using other libraries like Tensorboard, Weights & Biases, and others for tracking the experiments.
 
 ## Support
 
-If you require any assistance or have any questions, our support team is available to help. Please feel free to reach out to us through our contact page or via email [email protected]. We will be happy to assist you with any inquiries or issues you may have. Check out the Coretex platform overview at [coretex.ai](https://www.coretex.ai) for more information, tutorials, and documentation.
+If you require any assistance or have any questions feel free to join our [Discord server](https://discord.gg/zm7PAtKZkn). You can also reach out to us through via email [email protected]. We will be happy to assist you with any inquiries or issues you may have. Check out the Coretex platform overview at [coretex.ai](https://www.coretex.ai) for more information, tutorials, and documentation.