Amaraa0404/AI-First
Question Answering • Updated
repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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TiKick | TiKick-main/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The TARTRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 1,788 | 35.510204 | 74 | py |
TiKick | TiKick-main/tmarl/networks/policy_network.py |
import torch
import torch.nn as nn
from tmarl.networks.utils.util import init, check
from tmarl.networks.utils.mlp import MLPBase, MLPLayer
from tmarl.networks.utils.rnn import RNNLayer
from tmarl.networks.utils.act import ACTLayer
from tmarl.networks.utils.popart import PopArt
from tmarl.utils.util import get_shape_... | 5,558 | 41.113636 | 181 | py |
TiKick | TiKick-main/tmarl/networks/utils/distributions.py | import torch
import torch.nn as nn
from .util import init
"""
Modify standard PyTorch distributions so they are compatible with this code.
"""
#
# Standardize distribution interfaces
#
# Categorical
class FixedCategorical(torch.distributions.Categorical):
def sample(self):
return super().sample().unsque... | 3,466 | 27.891667 | 86 | py |
TiKick | TiKick-main/tmarl/networks/utils/mlp.py |
import torch.nn as nn
from .util import init, get_clones
class MLPLayer(nn.Module):
def __init__(self, input_dim, hidden_size, layer_N, use_orthogonal, activation_id):
super(MLPLayer, self).__init__()
self._layer_N = layer_N
active_func = [nn.Tanh(), nn.ReLU(), nn.LeakyReLU(), nn.ELU()]... | 2,116 | 32.603175 | 98 | py |
TiKick | TiKick-main/tmarl/networks/utils/popart.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PopArt(torch.nn.Module):
def __init__(self, input_shape, output_shape, norm_axes=1, beta=0.99999, epsilon=1e-5, device=torch.device("cpu")):
super(PopArt, self).__init__()
self.bet... | 3,796 | 38.968421 | 119 | py |
TiKick | TiKick-main/tmarl/networks/utils/util.py |
import copy
import numpy as np
import torch
import torch.nn as nn
def init(module, weight_init, bias_init, gain=1):
weight_init(module.weight.data, gain=gain)
bias_init(module.bias.data)
return module
def get_clones(module, N):
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
def che... | 426 | 21.473684 | 76 | py |
TiKick | TiKick-main/tmarl/networks/utils/act.py |
from .distributions import Bernoulli, Categorical, DiagGaussian
import torch
import torch.nn as nn
class ACTLayer(nn.Module):
def __init__(self, action_space, inputs_dim, use_orthogonal, gain):
super(ACTLayer, self).__init__()
self.multidiscrete_action = False
self.continuous_action = Fal... | 7,195 | 46.342105 | 121 | py |
TiKick | TiKick-main/tmarl/networks/utils/rnn.py |
import torch
import torch.nn as nn
class RNNLayer(nn.Module):
def __init__(self, inputs_dim, outputs_dim, recurrent_N, use_orthogonal):
super(RNNLayer, self).__init__()
self._recurrent_N = recurrent_N
self._use_orthogonal = use_orthogonal
self.rnn = nn.GRU(inputs_dim, outputs_dim... | 2,816 | 34.2125 | 132 | py |
TiKick | TiKick-main/tmarl/drivers/shared_distributed/base_driver.py | import numpy as np
import torch
def _t2n(x):
return x.detach().cpu().numpy()
class Driver(object):
def __init__(self, config, client=None):
self.all_args = config['all_args']
self.envs = config['envs']
self.eval_envs = config['eval_envs']
self.device = config['device']
... | 4,244 | 39.04717 | 126 | py |
TiKick | TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_algorithm.py | import torch
from tmarl.utils.valuenorm import ValueNorm
# implement the loss of the MAPPO here
class MAPPOAlgorithm():
def __init__(self,
args,
init_module,
device=torch.device("cpu")):
self.device = device
self.tpdv = dict(dtype=torch.float32, ... | 2,234 | 38.210526 | 147 | py |
TiKick | TiKick-main/tmarl/algorithms/r_mappo_distributed/mappo_module.py | import torch
from tmarl.networks.policy_network import PolicyNetwork
class MAPPOModule:
def __init__(self, args, obs_space, share_obs_space, act_space, device=torch.device("cpu")):
self.device = device
self.lr = args.lr
self.critic_lr = args.critic_lr
self.opti_eps = args.... | 1,050 | 41.04 | 135 | py |
TiKick | TiKick-main/tmarl/replay_buffers/normal/shared_buffer.py | import torch
import numpy as np
from collections import defaultdict
from tmarl.utils.util import check,get_shape_from_obs_space, get_shape_from_act_space
def _flatten(T, N, x):
return x.reshape(T * N, *x.shape[2:])
def _cast(x):
return x.transpose(1, 2, 0, 3).reshape(-1, *x.shape[3:])
class SharedReplayBuff... | 28,769 | 52.081181 | 231 | py |
TiKick | TiKick-main/tmarl/configs/config.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The TARTRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 10,665 | 55.734043 | 146 | py |
TiKick | TiKick-main/tmarl/runners/base_evaluator.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The TARTRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 3,402 | 28.08547 | 97 | py |
TiKick | TiKick-main/tmarl/runners/base_runner.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 The TARTRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 1,079 | 22.478261 | 74 | py |
TiKick | TiKick-main/tmarl/utils/valuenorm.py |
import numpy as np
import torch
import torch.nn as nn
class ValueNorm(nn.Module):
""" Normalize a vector of observations - across the first norm_axes dimensions"""
def __init__(self, input_shape, norm_axes=1, beta=0.99999, per_element_update=False, epsilon=1e-5, device=torch.device("cpu")):
super(V... | 3,110 | 37.8875 | 131 | py |
TiKick | TiKick-main/tmarl/utils/util.py |
import copy
import numpy as np
import math
import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from torch.autograd import Variable
from gym.spaces import Box, Discrete, Tuple
def check(input):
if type(input) == np.ndarray:
return torch.from_numpy... | 13,893 | 31.846336 | 122 | py |
TiKick | TiKick-main/tmarl/utils/gpu_mem_track.py | # code from https://github.com/Oldpan/Pytorch-Memory-Utils
import gc
import datetime
import inspect
import torch
import numpy as np
dtype_memory_size_dict = {
torch.float64: 64/8,
torch.double: 64/8,
torch.float32: 32/8,
torch.float: 32/8,
torch.float16: 16/8,
torch.half: 16/8,
torch.int6... | 4,432 | 36.888889 | 129 | py |
TiKick | TiKick-main/tmarl/utils/modelsize_estimate.py | # code from https://github.com/Oldpan/Pytorch-Memory-Utils
import torch.nn as nn
import numpy as np
def modelsize(model, input, type_size=4):
para = sum([np.prod(list(p.size())) for p in model.parameters()])
# print('Model {} : Number of params: {}'.format(model._get_name(), para))
print('Model {} : para... | 1,428 | 34.725 | 116 | py |
RobDanns | RobDanns-main/deep_learning/tools/corruptions-inference-tinyimagenet.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the original graph2nn github repo.
# File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal
# LICENSE file in the root directory ... | 25,928 | 41.092532 | 139 | py |
RobDanns | RobDanns-main/deep_learning/tools/train_resnet18_on_tinyimagenet200.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the original graph2nn github repo.
# File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal
# LICENSE file in the root directory ... | 21,617 | 37.741935 | 129 | py |
RobDanns | RobDanns-main/deep_learning/tools/adversarial-inference-tinyimagenet200.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the original graph2nn github repo.
# File modifications and additions by Rowan AI Lab, licensed under the Creative Commons Zero v1.0 Universal
# LICENSE file in the root directory ... | 23,184 | 38.768439 | 147 | py |
End of preview. Expand in Data Studio
Dataset Card for "AlgorithmicResearchGroup/arxiv_python_research_code"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code
Dataset Summary
AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code contains over 1.49B of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
How to use it
from datasets import load_dataset
# full dataset (1.49GB of data)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", split="train")
# dataset streaming (will only download the data as needed)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", streaming=True, split="train")
for sample in iter(ds): print(sample["code"])
Dataset Structure
Data Instances
Each data instance corresponds to one file. The content of the file is in the code feature, and other features (repo, file, etc.) provide some metadata.
Data Fields
repo(string): code repository name.file(string): file path in the repository.code(string): code within the file.file_length: (integer): number of characters in the file.avg_line_length: (float): the average line-length of the file.max_line_length: (integer): the maximum line-length of the file.extension_type: (string): file extension.
Data Splits
The dataset has no splits and all data is loaded as train split by default.
Dataset Creation
Source Data
Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from ArXiv papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.
These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.
Who are the source language producers?
The source (code) language producers are users of GitHub that created unique repository
Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub.
Additional Information
Dataset Curators
Matthew Kenney, AlgorithmicResearchGroup, matt@algorithmicresearchgroup.com
Citation Information
@misc{arxiv_deep_learning_python_research_code,
title={arxiv_deep_learning_python_research_code},
author={Matthew Kenney},
year={2023}
}
- Downloads last month
- 198
Size of downloaded dataset files:
1.49 GB
Size of the auto-converted Parquet files:
1.49 GB
Number of rows:
391,496
Models trained or fine-tuned on AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code