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很好
The AWS Deep Learning Containers for PyTorch include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL
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Working with AWS DLC significantly accelerates the ML deployment.
What do you like best about the product?
Frequently updating trained images for different frameworks reduced ML time to production.
What do you dislike about the product?
Customizing an AWS DLC still takes time to rebuild. The UI need to be improved.
What problems is the product solving and how is that benefiting you?
The ML Container idea solved many issues in ML deployment:
1) ML model portability
2) ML deployment speed
3) reduced ML production time
1) ML model portability
2) ML deployment speed
3) reduced ML production time
About Deep learning review.
What do you like best about the product?
As per me in AWS deep learning have some algorithms like RNN, CSS, LSTM and all these technology are good for me.
What do you dislike about the product?
AWS deel learning have some troubleshooting problems like setup error usage error and cleanup errros. So some one need to fix that as soon as possible.
What problems is the product solving and how is that benefiting you?
Sometimes its challenge to solve auto encoders error and they need massive dataset of training. So every one will understand better AWS deep learning.
Prebuilt Docker containers with DL full framework
What do you like best about the product?
We are working on an ML-based project and want to deploy that project in the docker container so we have used aws deep learning docker images so it can help us in many ways :
- less configuration is required to go live in minutes.
- easy to use images for your machine learning application.
- less configuration is required to go live in minutes.
- easy to use images for your machine learning application.
What do you dislike about the product?
All the features mentioned are well developed and as per the documentation nothing to dislike
- as this is managed service so there's a bit of a learning curve required by developers.
- as this is managed service so there's a bit of a learning curve required by developers.
What problems is the product solving and how is that benefiting you?
As we don't have to manage and create the docker images we use this in order to train our model in a faster way and achieve the accuracy also it can improve the speed of application to go live
Best Service Provide by AWS Easily Deploy Container
What do you like best about the product?
The fullness of the service offers really useful images for configuring contexts related to machine knowledge. The structure is authentically well regular and easy to learn. addict interface and stoner peace there was a smooth proficiency with AWS Deep knowledge. Provides us with roustabout pictures pre-installed for deep knowledge architecture.
What do you dislike about the product?
I discovered no downsides whilst the use of the AWS Deep getting to know gadget. The carrier is quickly learned thanks to the easy and intuitive interface. A guide is also available to solve any difficulties. Nothing extra to be advanced, however, yes it could be progressed via supplying diverse OS assistance for Deep gaining knowledge of pics so that improvement can work greater easily
What problems is the product solving and how is that benefiting you?
The hard component With Deep learning Dockerization is that it's complex for Dependency manipulation and AWS managed It properly in form. It helped me plenty in locating the essential pix to load inside the machine learning environment. This allowed me to noticeably lessen the time spent on the usually constructed deep learning framework.
Deep learning containers review
What do you like best about the product?
Easy to connect and work. Less configuration required to get things working
What do you dislike about the product?
Not so easy for practitioners. Needs a strong knowledge base
What problems is the product solving and how is that benefiting you?
We have hig data banks on loan credit checks that require some deep learning logic to be executed
Smooth and Fully Optimised DL Framework
What do you like best about the product?
I Used To Install and Setup My Machine Learning Project On to the DL Docker. it gives Me Really Smooth Experience and More than That It Consumes Less Time and Memories then Manual One
What do you dislike about the product?
Nothing more to Improve, But yes If It Support More Operating Systems and with Less memory Consumption then It's More than Improvement.
What problems is the product solving and how is that benefiting you?
Before Using Deep Learning I Use to have manual Image creation Which Giving Lots Of Build Issues, But with AWS DL it's Now Fast Forwarded
Exquisite
What do you like best about the product?
Very good environment with tensorflow and mxnet which is helped me a lot.
What do you dislike about the product?
Ahh no i don't have any dislikes about the same in my mind as of now i will let you know if i will found any
What problems is the product solving and how is that benefiting you?
This is ensuring reliable quick and i think most consistent deployments and actually it helped me a lot i mean i cannot describe here
Recommendations to others considering the product:
I will say go for it and help them to get introduced with this lovely platform
Very powerful tool
What do you like best about the product?
The completeness of the service that offers very useful images for configuring environments related to machine learning. The structure is very well organized and easy to learn.
What do you dislike about the product?
I found no downsides while using the AWS Deep Learning system. The service is easily learned thanks to the simple and intuitive interface. Support is also available to resolve any difficulties.
What problems is the product solving and how is that benefiting you?
The use of AWS Deep Learning Containers images designed to be used in a machine learning environment have in fact significantly reduced the workload and increased my productivity.
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