Why Is the Training Speed Similar When Different Notebook Flavors Are Used?
If your training job is single-process in code, the training speed is basically the same no matter when the notebook flavor of 8 vCPUs and 64 GB of memory or the flavor of 72 vCPUs and 512 GB of memory is used. For example, if your training job uses 2 vCPUs and 4 GB of memory, the training speed is similar no matter when you use the notebook flavor of 4 vCPUs and 8 GB of memory or the flavor of 8 vCPUs and 64 GB of memory.
If your training job is multi-process in code, the training speed backed by the notebook flavor of 72 vCPUs and 512 GB of memory is higher than that backed by the notebook flavor of 8 vCPUs and 64 GB of memory.
Others FAQs
- How Do I Use Multiple Ascend Cards for Debugging in a Notebook Instance?
- Why Is the Training Speed Similar When Different Notebook Flavors Are Used?
- How Do I Perform Incremental Training When Using MoXing?
- How Do I View GPU Usage on the Notebook?
- How Can I Obtain GPU Usage Through Code?
- Which Real-Time Performance Indicators of an Ascend Chip Can I View?
- Does the System Automatically Stop or Delete a Notebook Instance If I Do Not Enable Automatic Stop?
- What Are the Relationships Between Files Stored in JupyterLab, Terminal, and OBS?
- How Do I Migrate Data from an Old-Version Notebook Instance to a New-Version One?
- How Do I Use the Datasets Created on ModelArts in a Notebook Instance?
- pip and Common Commands
- What Are Sizes of the /cache Directories for Different Notebook Specifications in DevEnviron?
- How Do I Isolate IAM Users for Using Development Environments?
- What Is the Impact of Resource Overcommitment on Notebook Instances?
Feedback
Was this page helpful?
Provide feedbackThank you very much for your feedback. We will continue working to improve the documentation.See the reply and handling status in My Cloud VOC.
For any further questions, feel free to contact us through the chatbot.
Chatbotmore
