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Boot Command: /home/ma-user/miniconda3/bin/python ${MA_JOB_DIR}/demo-code/pytorch-verification.py. demo-code (customizable) is the last-level directory of the OBS path.
--env.MASTER_ADDR=<master_addr>: IP address of the active master node. Generally, rank 0 is selected as the active master node. --env.NNODES=<nnodes>: total number of training nodes. --env.NODE_RANK=<rank>: node ID, starting from 0.
Figure 14 RoCE test result (receive end) Figure 15 RoCE test result (server) If the RoCE bandwidth test has been started for a NIC, the following error message is displayed when the task is started again.
torch.cuda.set_device(hvd.local_rank()) cudnn.benchmark = True # Set up standard model. model = getattr(models, args.model)() # By default, Adasum doesn't need scaling up learning rate. lr_scaler = hvd.size() if not args.use_adasum else 1 if args.cuda: # Move model to GPU.
Model parallelism uses AllReduce communication, while MoE expert parallelism uses all-to-all communication. Both require high network bandwidth between processing units (PUs).
3 Preparing an Image Server Obtain a Linux x86_64 server running Ubuntu 18.04.
Users cannot add pay-per-use nodes (including AutoScaler scenarios) in a yearly/monthly resource pool.
The value can be t2v (text-to-video), i2v (image-to-video), or t2i (text-to-image). The default value is i2v. i2v_image_path: path of the image used for video generation. For other parameters, use the same settings as those of infer_wan_14b_t2v_480p.sh.
For details, see (Optional) Selecting a Training Mode. Add tags if you want to manage training jobs by group. For details, see (Optional) Adding Tags. Perform follow-up procedure. For details, see Follow-Up Operations.
NOTE: The notebook instances with remote SSH enabled have VS Code plug-ins (such as Python and Jupyter) and the VS Code server package pre-installed, which occupy about 1 GB persistent storage space. Key Pair Set a key pair after remote SSH is enabled.
VPC_CIDR="7.150.0.0/16" VPC_PREFIX=$(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 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
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 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"])
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).
Tokens Per Minute (TPM): The number of tokens (input + output) processed per minute. Requests Per Minute (RPM): The number of requests processed per minute. If the model service has an RPM of 300, it means that up to 10 requests can be processed per second (300/30 = 10).
Using PyTorch to Create a Training Job (New-Version Training) This section describes how to train a model by calling ModelArts APIs.
) 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.
