print('Not using distributed mode') args.distributed = False return args.distributed = True torch.cuda.set_device(args.gpu) args.dist_backend = 'nccl' print('| distributed init (rank {}): {}'.format( args.rank, args.dist_url), flush=True) torch.distributed.init_process_group(backend=...
Distributed as a small number of .tar.xz files depending on what exact functionality you need - to keep your image's number of layers small. A whole set of utilities included ins6ands6-portable-utils. They include handy and composable utilities which make our lives much, much easier. ...
_instance[cls] = cls(*args, **kargs) return _instance[cls] return _singleton class ExtInit: """ Init event for ext """ def __init__(self): self.funs = [] def reg_event(self, evevt_id, funs): self.funs.append([evevt_id, funs]) UPDATE_LOGGER.print_log( ...
This class should not be used, rather StaticCaptureTraining and StaticCaptureEvaluateshould be used instead for training and evaluation functions."""# Grad scaler and checkpoint class variables use for checkpoint saving and loading# Since an instance of Static capture does not exist for checkpoint fu...
When using Sentry in ESM mode, you can now use Sentry without manually calling init like this:SENTRY_DSN=https://examplePublicKey@o0.ingest.sentry.io/0 node --import=@sentry/node/init app.mjs When using CommonJS, you can do:SENTRY_DSN=https://examplePublicKey@o0.ingest.sentry.io/0 ...
return torch_parse_to(*args, **kwargs) def apply_module_patch(): torch.nn.Module.npu = npu torch.nn.Module.to = to torch.nn.Module.cast_weight = cast_weight torch.nn.LayerNorm.forward = layernorm_forward torch.nn.parallel.distributed._DDPJoinHook.__init__ = DDPJoinHook__...
Args: config (MindFormerConfig): The configuration object containing the run mode and other relevant settings. Returns: MindFormerConfig: A new configuration object with the applied template. """ run_mode = config.run_mode if run_mode not in cls._run_modes: raise ValueEr...
def __init__(self, dataset, batch_size, num_workers=0, distributed=False, test_only=False, timeout=1000): self.test_only = test_only # now decide on a sampler #base_sampler = torch.utils.data.SequentialSampler(self.dataset) base_sampler = torch.utils.data.RandomSampler(dataset) if not...
plugin-webpack:keepdevDependencies,dependencies,optionalDependenciesandpeerDependenciesin the distributed package.json (#3007) (0f75ce08) template-webpack:use new plugin syntax (#2990) (14721498) template-typescript-webpack:use new plugin syntax (#2989) (4f222f48) ...
+ * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See + * the GNU General Public License for more details. ...