from accelerate import DistributedType if accelerator.distributed_type == DistributedType.TPU: # 执行静态形状的操作 else: # 可以灵活使用 2.2.2 notebook_launcher 使用TPU时,notebook_launcher默认启用8个进程,低资源环境下(如Kaggle内核或者Colab)需要注意避免重复声明模型耗尽内存。 从Jupyter笔记本启动代码时...
# distributed_type: MULTI_GPU deepspeed_config: deepspeed_multinode_launcher: standard gradient_accumulation_steps: 2 offload_optimizer_device: none offload_param_device: none zero3_init_flag: false zero3_save_16bit_model: false zero_stage: 2 distributed_type: DEEPSPEED downcast_bf16: 'fp16' gp...
compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU downcast_bf16:'no' machine_rank: 0 main_training_function: main mixed_precision: fp16 gpu_ids: 1,3 num_machines: 1 num_processes: 2 rdzv_backend:static same_network:true tpu_env: [] tpu_use_cluster:false tpu_use_sudo:false...
distributed_type: MULTI_GPU fp16: false machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main num_machines: 1 num_processes: 2 第三,配置第二个运行配置文件second_config.yaml compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU fp16: false machine...
11 distributed_type: DEEPSPEED 12 downcast_bf16: 'no' 13 dynamo_config: {} 17 main_training_function: main 18 mixed_precision: fp16 19 num_machines: 2 20 num_processes: 8 21 tpu_env: [] 22 tpu_use_cluster: false 23 tpu_use_sudo: false ...
- distributed_type: MULTI_GPU - mixed_precision: fp16 - use_cpu: False - num_processes: 7 - machine_rank: 0 - num_machines: 1 - gpu_ids: 0,1,2,3,4,5,6 - rdzv_backend: static - same_network: True - main_training_function: main ...
个人理解，FairScale 是torchFSDP 的前身，torchFSDP的功能和DeepSpeed的zero-3是等价的。accelerate 是一...
This machineWhich type of machine are you using? Multi-GPUHow many different machines will you use (use more than l for multi node training)? [1]: 1Should distributed operatlons be checked while running for errors? This can avoid timeout issues but will be slover. fves/Nol:Do you wish...
在运行的时候,DistributedDataParallel会往你的程序中加入一个参数local_rank,所以要现在你的代码中解析这个参数,如: parser.add_argument("--local_rank", type=int, default=1, help="number of cpu threads to use during batch generation") 1.
After you associate an endpoint group with a listener, network traffic is distributed to the optimal endpoint in the endpoint group. In the Configure listener step, set the required parameters, and click Next. The following table describes only the parameters that are relevant to this topic. ...