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Figure 14 RoCE test result (receive end) Figure 15 RoCE test result (server) If the RoCE bandwidth test has been started for a NIC, the following error message is displayed when the task is started again.
3 Preparing an Image Server Obtain a Linux x86_64 server running Ubuntu 18.04.
torch.cuda.set_device(hvd.local_rank()) cudnn.benchmark = True # Set up standard model. model = getattr(models, args.model)() # By default, Adasum doesn't need scaling up learning rate. lr_scaler = hvd.size() if not args.use_adasum else 1 if args.cuda: # Move model to GPU.
Figure 1 Run logs of training jobs with GPU specifications (one compute node) Figure 2 Run logs of training jobs with GPU specifications (two compute nodes) Parent topic: Example: Creating a Custom Image for Training
3 Preparing an Image Server Obtain a Linux x86_64 server running Ubuntu 18.04.
Users cannot add pay-per-use nodes (including AutoScaler scenarios) in a yearly/monthly resource pool.
Creating a Notebook Instance (Default Page) Before developing a model, create a notebook instance and access it for coding. Context Notebook is billed as follows: A running notebook instance will be billed based on used resources.
ip, port, body): infer_url = "{}://{}:{}" url = infer_url.format(schema, ip, port) response = requests.post(url, data=body) print(response.content) High-speed access does not support load balancing.
mm:ss (UTC) node_label String Node label os_type String OS type of a node name String Name of an edge node os_name String OS name of a node arch String Node architecture id String Edge node ID instance_status String Running status of a model instance on the node.
The current version supports modelarts.vm.cpu.2u, modelarts.vm.gpu.pnt004 (must be requested), modelarts.vm.ai1.snt3 (must be requested), and custom (available only when the service is deployed in a dedicated resource pool).
For details, see (Optional) Selecting a Training Mode. Add tags if you want to manage training jobs by group. For details, see (Optional) Adding Tags. Perform follow-up procedure. For details, see Follow-Up Operations.
Using PyTorch to Create a Training Job (New-Version Training) This section describes how to train a model by calling ModelArts APIs.
The notebook instances with remote SSH enabled have VS Code plug-ins (such as Python and Jupyter) and the VS Code server package pre-installed, which occupy about 1 GB persistent storage space. Key Pair Set a key pair after remote SSH is enabled.
) MaaS console UI CN-Hong Kong ModelArts Standard ModelArts console UI All Huawei Cloud regions ModelArts Lite Server ModelArts console Create a Lite Server node through the UI or API.
Letters, digits, hyphens (-), and underscores (_) are allowed. Description (Optional) Job description, which helps you learn about the job information in the training job list. Experiment Specifies whether to organize training jobs into experiments for better management.
For a single-node job (running on only one node), ModelArts starts a training container that exclusively uses the resources on the node. For a distributed job (running on more than one node), ModelArts starts a parameter server (PS) and a worker on the same node.
A model is deployed as a web service on an edge node through Intelligent EdgeFabric (IEF). that provides a real-time test UI and monitoring capabilities. The service keeps running.
Letters, digits, and hyphens (-) are allowed.
Letters, digits, and hyphens (-) are allowed.
Contain at least three of the following types: uppercase letters, lowercase letters, digits, and special characters (!@%-_=+[{}]:,./?). Cannot be the username or the username spelled backwards. Cannot contain root, administrator, or their reverse.
