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Description
DeepGit provides a superior way to address the question "why is this code there?" compared to traditional Git clients by facilitating a thorough exploration of source code history. This innovative tool builds upon the git blame feature, enabling users to easily track modifications made to specific lines or segments of code. Notably, DeepGit excels in recognizing code movements, even when lines have undergone changes that render them non-identical. Furthermore, it is available for free, making it accessible for use in both personal and commercial settings. Users can seamlessly integrate DeepGit with various IDEs that support external tools, including popular platforms like Eclipse, Visual Studio, and IntelliJ IDEA, as well as robust text editors like Sublime. For those interested in mastering its functionalities, a tour is available to demonstrate how DeepGit operates effectively. Compatible with Windows, macOS, and Linux, DeepGit generates a blame report for the chosen file and conducts an analysis of the selected line and its surrounding context to trace its origin. It's important to note that the origin identified by DeepGit may not directly align with the corresponding left counterpart. Additionally, even when focusing on a single line, DeepGit often identifies a block of lines that serves as the best match for further investigation, enhancing the user's understanding of code evolution. This capability not only clarifies the rationale behind code changes but also aids developers in maintaining better code comprehension over time.
Description
You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.
API Access
Has API
API Access
Has API
Integrations
Amazon EC2 Trn2 Instances
Amazon EKS
Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Dask
Databricks
Eclipse IDE
Integrations
Amazon EC2 Trn2 Instances
Amazon EKS
Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Dask
Databricks
Eclipse IDE
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Syntevo
Country
Germany
Website
www.syntevo.com/deepgit/
Vendor Details
Company Name
Anyscale
Founded
2019
Country
United States
Website
ray.io
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization