Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Enhance machine learning model performance by capturing real-time training metrics and issuing alerts for any detected anomalies. To minimize both time and expenses associated with the training of ML models, the training processes can be automatically halted upon reaching the desired accuracy. Furthermore, continuous monitoring and profiling of system resource usage can trigger alerts when bottlenecks arise, leading to better resource management. The Amazon SageMaker Debugger significantly cuts down troubleshooting time during training, reducing it from days to mere minutes by automatically identifying and notifying users about common training issues, such as excessively large or small gradient values. Users can access alerts through Amazon SageMaker Studio or set them up via Amazon CloudWatch. Moreover, the SageMaker Debugger SDK further enhances model monitoring by allowing for the automatic detection of novel categories of model-specific errors, including issues related to data sampling, hyperparameter settings, and out-of-range values. This comprehensive approach not only streamlines the training process but also ensures that models are optimized for efficiency and accuracy.
Description
GDB, or the GNU Project debugger, enables users to observe the internal workings of a program during its execution or determine what the program was doing at the time of a crash. To get started, launch your application while taking into account any factors that could influence its performance. Once your program halts, analyze the events that transpired up to that point. You can modify elements within your program to test fixes for one issue and subsequently explore additional problems. These programs may be run on the same device as GDB (native), on a separate machine (remote), or through a simulator. GDB is compatible with most well-known UNIX systems, Microsoft Windows editions, and Mac OS X. Additionally, inferior objects now feature a read-only attribute called 'connection_num', which displays the connection number as seen in the 'info connections' and 'info inferiors' commands. Furthermore, a new method named gdb.Frame.level() has been introduced, providing the stack level associated with the frame object, thereby enhancing the debugging experience significantly.
API Access
Has API
API Access
Has API
Integrations
AWS Lambda
Amazon CloudWatch
Amazon SageMaker
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
Integrations
AWS Lambda
Amazon CloudWatch
Amazon SageMaker
Amazon SageMaker Studio
Amazon SageMaker Unified Studio
Amazon Web Services (AWS)
Change Healthcare Data & Analytics
Keras
MXNet
PyTorch
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/sagemaker/debugger/
Vendor Details
Company Name
GDB
Website
www.sourceware.org/gdb/
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization