Inference TOML

Inference TOML#

Inference process configurations are defined through a TOML-formatted file, where each key systematically governs specific aspects of the computational workflow.

infer.toml:

# ----------------------------- SYSTEM -----------------------------
[system]
note = "Enjoy DeepH-pack! ;-)"
device = "gpu*8:0"
float_type = "fp32"
random_seed = 137
log_level = "info"
jax_memory_preallocate = true


# ------------------------------ DATA -------------------------------
[data]
inputs_dir = "./user/should/set/this/inputs"
outputs_dir = "./user/should/set/this/outputs"

[data.dft]
data_dir_depth = 0

[data.graph]
dataset_name = "INFER-DEMO"
graph_type = "S"
storage_type = "memory"
parallel_num = -1
only_save_graph = false


# ----------------------------- MODEL -------------------------------
[model]
model_dir = "./user/should/set/this"
load_model_type = "best"
load_model_epoch = -1


# ---------------------------- PROCESS ------------------------------
[process.infer]
output_type = "h5"
output_into = "to_output"
target_symmetrize = true
multi_way_jit_num = 1

[process.infer.dataloader]
batch_size = 1

Next, we will go through the semantics of these parameters in the TOML file in detail.

System#

[system.note]
Description and behavior are the same as in the training configuration.

[system.device]
Description and behavior are the same as in the training configuration.

[system.float_type]
Description and behavior are the same as in the training configuration.

[system.random_seed]
Description and behavior are the same as in the training configuration.

[system.log_level]
Description and behavior are the same as in the training configuration.

[system.jax_memory_preallocate]
Description and behavior are the same as in the training configuration.

Data#

[data.inputs_dir]
Description and behavior are the same as in the training configuration.

[data.outputs_dir]
Description and behavior are the same as in the training configuration.

[data.dft.data_dir_depth]
Description and behavior are the same as in the training configuration.

[data.graph.dataset_name]
Description and behavior are the same as in the training configuration.

[data.graph.graph_type]

  • Description: The physical quantities needed for inference. DeepH will build a corresponding graph file. One can choose from Sap and S. The S for overlap, Sap for overlap but do not calculate mask using overlap values.

  • Default: "S"

  • Type: ["Sap", "S"]

[data.graph.storage_type]
Description and behavior are the same as in the training configuration.

[data.graph.parallel_num]
Description and behavior are the same as in the training configuration.

[data.graph.only_save_graph]
Description and behavior are the same as in the training configuration.

Model#

[model.model_dir]

  • Description: The directory storing the trained model, usually with the format of <time_stamp>/model.

  • Default: <Invalid-Input>

  • Type: <STRING>

[model.load_model_type]

  • Description: Infer with best or latest trained model.

  • Default: "best"

  • Type: ["best", "latest"]

[model.load_model_epoch]

  • Description: For load_model_type = "latest" only. Specify a number for a particular epoch saved in the latest model folder. Use -1 for the most recent epoch.

  • Default: -1

  • Type: <INT>

Process: Infer#

[process.infer.output_type]

  • Description: The output file format.

  • Default: "h5"

  • Type: ["h5", "petsc"]

[process.infer.output_into]

  • Description: Location for storing the predicted data. One can choose from a new folder under the output path (<time_stamp>/dft) or the original data folder (<inputs>/dft). The output Hamiltonians are named as hamiltonian_pred.h5.

  • Default: "to_output"

  • Type: ["to_output", "to_input"]

[process.infer.target_symmetrize]

  • Description: Whether to symmetrize the predicted target (e.g., to hermitianize the Hamiltonian).

  • Default: true

  • Type: <BOOL>

[process.infer.multi_way_jit_num]
Description and behavior are the same as the training parameter process.train.multi_way_jit_num.

Process: Infer: Dataloader#

[process.infer.dataloader.batch_size]

  • Description: Batch size for inference. Can be significantly larger than the training batch size.

  • Default: 1

  • Type: <INT>

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