Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
No
API Access
Has API
Yes
Integrations
Amazon EC2 Trn2 Instances
No
Amazon EKS
No
Amazon SageMaker
No
Anyscale
No
Apache Airflow
No
Azure Kubernetes Service (AKS)
No
Dask
No
Databricks
No
Eclipse IDE
Yes
Feast
No
Integrations
Amazon EC2 Trn2 Instances
Yes
Amazon EKS
Yes
Amazon SageMaker
Yes
Anyscale
Yes
Apache Airflow
Yes
Azure Kubernetes Service (AKS)
Yes
Dask
Yes
Databricks
Yes
Eclipse IDE
No
Feast
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Open source. Consumption-based.
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
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
No
Document Classification
No
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No