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
No
API Access
Has API
Yes
Integrations
AWS Lambda
Yes
Amazon CloudWatch
Yes
Amazon SageMaker
Yes
Amazon SageMaker Studio
Yes
Amazon SageMaker Unified Studio
Yes
Amazon Web Services (AWS)
Yes
Change Healthcare Data & Analytics
Yes
Keras
Yes
MXNet
Yes
PyTorch
Yes
Integrations
AWS Lambda
No
Amazon CloudWatch
No
Amazon SageMaker
No
Amazon SageMaker Studio
No
Amazon SageMaker Unified Studio
No
Amazon Web Services (AWS)
No
Change Healthcare Data & Analytics
No
Keras
No
MXNet
No
PyTorch
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
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)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
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
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No