"""Sets the torch random seed to the given value.Args: seed: The seed to use or None for no seeding. """ if seed is not None and torch: torch.manual_seed(seed) # See https://github.com/pytorch/pytorch/issues/47672. cuda_version = torch.version.cuda ...
Everything is packaged by pkg and turned in an executable (thus 35M in size per platform). ## Compilation `npm run build` will build the index.html , and windows , mac and linux files ## Usage The application responds with JSON in stdout: ### Get the public ID from a seed `qubic...
- Make systemd args backwards compatible (bz 1210751) - Stop scribbling on stderr (bz 1211008) * Tue May 12 2015 Colin Walters <walters@redhat.com> - 1:1.3.2-7 - Add patch to fix initial start on OSTree managed systems (bz 1219871) *...
# 需要导入模块: from hrlproject.misc import HRLutils [as 别名]# 或者: from hrlproject.misc.HRLutils importset_seed[as 别名]defrun_gridworld(args, seed=None):ifseedisnotNone: HRLutils.set_seed(seed) seed = HRLutils.SEED net = nef.Network("run_gridworld") stateN =400stateD =2actions =...
**dict_union(states, glimpses_needed))ifkwargs:raiseValueError("extra args to take_glimpses: {}".format(kwargs))returnresult 开发者ID:bartvm,项目名称:blocks-fork,代码行数:30,代码来源:attention.py 示例4: compute_states ▲点赞 1▼
def seed_everything(seed: int): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True def transform_points(x, mat): shape = ...
A high-throughput and memory-efficient inference and serving engine for LLMs - vllm/vllm/utils.py at main · cg123/vllm
st1.write(tempfile, format="MSEED")# read everything back (int16 gets converted into int32)st2 = read(tempfile)fordtypein["i4","i4","f4","f8","S1"]:forencin["<",">","="]: tr = st2.pop(0).data self.assertEqual(tr.dtype.kind + ...
seed=13, )ifoscillation: make_traces = gen_sinusoidal_dataelse: make_traces = gen_data traces, _, spikes = map(np.squeeze, make_traces(**gen_params)) time = np.arange(0, traces.shape[1]/sampling_rate,1/sampling_rate) traces = pd.DataFrame(traces.T, index=time, columns=neuron_ids...
Note: this assumes that the description is one word descrip_shift = 0 if use_output_descrip: descrip_shift = 1 if intent_first: # Everthing after the intent slot_chunk_pred = [x.split()[(1+2*descrip_shift):] for x in mod_out] else: # Everything until the intent slot_chunk_pred...