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To avoid repeated loading, the platform allows the model package to be loaded from the local storage space of the node in the resource pool and keeps the loaded files valid even when the service is stopped or restarted (using the hash value to ensure data consistency).
Server Model Only Ascend Snt9b and Ascend Snt9b23 are supported. Type You can select Single node or Integrated rack, or search for a specific node by keyword. Diagnosis Item You can select Parameter Plane Network Diagnosis, Ascend Device Diagnosis, or both.
Server Model Only Ascend Snt9b and Ascend Snt9b23 are supported. Type You can select Single node or Integrated rack, or search for a specific node by keyword. Test Case You can select any of the following pressure test cases.
Server Model Only Ascend Snt9b and Ascend Snt9b23 are supported. Type You can select Single node or Integrated rack, or search by keyword. Select the target node to be upgraded in the node list (batch selection supported) and click OK.
Why Can I Leave the IP Address of the Master Node Blank for DDP? The init method parameter in parser.add_argument('--init_method', default=None, help='tcp_port') contains the IP address and port number of the master node, which are automatically input by the platform.
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.
Synchronization for Existing Nodes (labels and taints) and Synchronization for Existing Nodes (labels) can be modified synchronously for existing nodes (by selecting the check boxes). The updated resource tag information in the node pool is synchronized to its nodes.
Table 3 Parameters for resource configurations Parameter Description Server Server name, which can contain 1 to 64 characters, including letters, digits, hyphens (–), and underscores (_). CAUTION: The server name in the order will not be changed.
Boot Command: /home/ma-user/miniconda3/bin/python ${MA_JOB_DIR}/demo-code/pytorch-verification.py. demo-code (customizable) is the last-level directory of the OBS path.
3 Preparing an Image Server Obtain a Linux x86_64 server running Ubuntu 18.04.
Software Versions Required by Different Models A resource pool for elastic clusters can use either Elastic Bare Metal Servers (BMSs) or Elastic Cloud Servers (ECSs) as nodes. Each node model has its own operating system (OS) and compatible CCE cluster versions.
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 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.
) on the media (optical) side of channel 0 in the NPU optical module dB N/A Natural number instance_id, npu telescope: 2.7.5.9 or later 81 npu_opt_media_snr_lane1 NPU Optical Module Channel 1 Optical SNR The signal-to-noise ratio (SNR) on the media (optical) side of channel 1 in the
Changing or Resetting the Lite Server OS Scenario You can change or reset the Lite Server node OS if a BMS is used. Change the OS in any of the following ways: (Recommended) Change or reset the OS on the server page of the ModelArts console. Change the OS on the BMS console.
In the navigation pane, choose Model Training > Training Jobs. In the job list, click Export to export training job details in a certain time range as an Excel file. A maximum of 200 rows of data can be exported.
Figure 2 Creating a custom image for a model (scenario 2) Scenario 3: The preset does not meet the software environment requirements. You need to import a model package. The new image is larger than 35 GB and needs to be created on a server such as ECS.
Model parallelism uses AllReduce communication, while MoE expert parallelism uses all-to-all communication. Both require high network bandwidth between processing units (PUs).
For the 300IDuo model, set is_300_iduo to True.
): Multiple GPUs work together on one server to speed up training using data parallelism.
