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--env.MASTER_ADDR=<master_addr>: IP address of the active master node. Generally, rank 0 is selected as the active master node. --env.NNODES=<nnodes>: total number of training nodes. --env.NODE_RANK=<rank>: node ID, starting from 0.
FLAGS = tf.flags.FLAGS import moxing as mox TMP_CACHE_PATH = '/cache/data' mox.file.copy_parallel('FLAGS.data_url', TMP_CACHE_PATH) mnist = input_data.read_data_sets(TMP_CACHE_PATH, one_hot=True) Parent topic: ModelArts Standard Model Training
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.
Server Model Snt9b nodes and Snt9b23 supernodes are supported. Select Node Click Select Node. In the node list displayed on the right, select the nodes where the driver and firmware need to be upgraded. You can select nodes in batches or search for nodes by keyword and click OK.
Model parallelism uses AllReduce communication, while MoE expert parallelism uses all-to-all communication. Both require high network bandwidth between processing units (PUs).
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.
Server Model Snt9b nodes and Snt9b23 supernodes are supported. Select Node Click Select Node. In the node list displayed on the right, select the nodes where Cloud Eye Agent needs to be upgraded. You can select nodes in batches or search for nodes by keyword. Click OK.
ECS An Elastic Cloud Server (ECS) is a basic computing unit that consists of vCPUs, memory, OS, and Elastic Volume Service (EVS) disks. After creating an ECS, you can use it like your local PC or physical server. Lite Server supports multiple server types, including ECSs.
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.
Table 1 Mappings between ModelArts Lite Servers and OS versions Server Model Image Status Released On Image EOS Date Snt3 CentOS 7.6 64bit for Kai1s(40GiB) EOS June 2023 June 2024 Ubuntu 18.04 server 64bit for Kai1s(40GiB) In commercial use June 2025 June 2026 Snt3PD Huawei-Cloud-EulerOS
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.
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.
For details about the image path {image_url}, see Table 4. docker pull {image_url} Step 3: Creating a Training Image Go to the folder (see key training files in the AscendCloud-LLM code package in Software Package Structure) containing the Dockerfile in the decompressed code directory
The calculation example is as follows: If the weights for saving the optimizer state is 200 GB and the recommended storage duration is 20 minutes, the required bandwidth is: (200 GB x 1,024 x 8)/1,200s = 1,365 MB/s Parent topic: Training Preparations
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.
NOTE: 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.
VPC_CIDR="7.150.0.0/16" VPC_PREFIX=$(echo "$VPC_CIDR" | cut -d'/' -f1 | cut -d'.' -f1-2) POD_INET_IP=$(ifconfig | grep -oP "(?<=inet\s)$VPC_PREFIX\.\d+\.
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.
The following is an example of using the offline mode: from vllm import LLM, SamplingParams from vllm.sampling_params import GuidedDecodingParams MODEL_NAME = ${MODEL_NAME} llm = LLM(model=MODEL_NAME) guided_decoding_params = GuidedDecodingParams(choice=["Positive", "Negative"])
