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Lint reference_algorithms/
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‎reference_algorithms/development_algorithms/cifar/cifar_jax/submission.py‎

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import functools
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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from flax import jax_utils
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import jax
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from jax import lax
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import jax.numpy as jnp
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import optax
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from flax import jax_utils
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from jax import lax
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from algoperf import spec
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‎reference_algorithms/development_algorithms/cifar/cifar_pytorch/submission.py‎

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from typing import Any, Dict, Iterator, List, Optional, Tuple
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import torch
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from torch.optim.lr_scheduler import LinearLR
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from torch.optim.lr_scheduler import SequentialLR
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from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
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from algoperf import spec
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‎reference_algorithms/development_algorithms/cifar/tuning_search_space.json‎

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{
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"learning_rate": {"feasible_points": [0.1]},
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"warmup_epochs": {"feasible_points": [5]},
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"num_epochs": {"feasible_points": [200]},
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"l2": {"feasible_points": [5e-4]},
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"momentum": {"feasible_points": [0.9]}
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"learning_rate": { "feasible_points": [0.1] },
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"warmup_epochs": { "feasible_points": [5] },
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"num_epochs": { "feasible_points": [200] },
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"l2": { "feasible_points": [5e-4] },
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"momentum": { "feasible_points": [0.9] }
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}

‎reference_algorithms/development_algorithms/mnist/discrete_space.json‎

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[
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{
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"learning_rate": 1e-3,
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"one_minus_beta_1": 0.999,
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"epsilon": 0.9
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},
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{
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"learning_rate": 1e-2,
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"one_minus_beta_1": 0.99,
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"epsilon": 0.99
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},
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{
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"learning_rate": 1e-1,
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"one_minus_beta_1": 0.9,
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"epsilon": 0.999
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}
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]
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{
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"learning_rate": 1e-3,
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"one_minus_beta_1": 0.999,
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"epsilon": 0.9
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},
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{
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"learning_rate": 1e-2,
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"one_minus_beta_1": 0.99,
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"epsilon": 0.99
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},
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{
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"learning_rate": 1e-1,
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"one_minus_beta_1": 0.9,
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"epsilon": 0.999
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}
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]

‎reference_algorithms/development_algorithms/mnist/mnist_jax/submission.py‎

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import functools
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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from flax import jax_utils
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import jax
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from jax import lax
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import jax.numpy as jnp
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import optax
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from flax import jax_utils
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from jax import lax
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from algoperf import spec
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‎reference_algorithms/development_algorithms/mnist/tuning_search_space.json‎

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{
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"learning_rate": {"min": 1e-4, "max": 1e-2, "scaling": "log"},
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"one_minus_beta_1": {"min": 0.9, "max": 0.999, "scaling": "log"},
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"epsilon": {"feasible_points": [1e-8, 1e-5, 1e-3]}
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"learning_rate": { "min": 1e-4, "max": 1e-2, "scaling": "log" },
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"one_minus_beta_1": { "min": 0.9, "max": 0.999, "scaling": "log" },
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"epsilon": { "feasible_points": [1e-8, 1e-5, 1e-3] }
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}

‎reference_algorithms/paper_baselines/adafactor/jax/sharded_adafactor.py‎

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from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
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import jax
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from jax import numpy as jnp
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import optax
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from jax import numpy as jnp
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JTensor = Any
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NestedJTensor = Any

‎reference_algorithms/paper_baselines/adafactor/jax/submission.py‎

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import functools
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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from flax import jax_utils
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import jax
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from jax import lax
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import jax.numpy as jnp
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import optax
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from flax import jax_utils
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from jax import lax
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from algoperf import spec
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from reference_algorithms.paper_baselines.adafactor.jax.sharded_adafactor import (

‎reference_algorithms/paper_baselines/adafactor/pytorch/submission.py‎

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from functools import partial
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from typing import Any, Dict, Iterator, List, Optional, Tuple
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from absl import logging
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import torch
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import torch.distributed.nn as dist_nn
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from torch.optim.lr_scheduler import LinearLR
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from torch.optim.lr_scheduler import SequentialLR
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from absl import logging
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from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR
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from algoperf import spec
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from algoperf.pytorch_utils import pytorch_setup

‎reference_algorithms/paper_baselines/adafactor/tuning_search_space.json‎

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{
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"learning_rate": {
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"min": 1e-4, "max": 1e-2, "scaling": "log"
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},
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"one_minus_beta1": {
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"min": 1e-2, "max": 0.45, "scaling": "log"
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},
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"warmup_factor": {
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"feasible_points": [0.05]
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},
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"weight_decay": {
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"min": 1e-3, "max": 1.0, "scaling": "log"
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},
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"label_smoothing": {
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"feasible_points": [0.1, 0.2]
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},
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"dropout_rate": {
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"feasible_points": [0.0, 0.1]
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}
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"learning_rate": {
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"min": 1e-4,
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"max": 1e-2,
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"scaling": "log"
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},
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"one_minus_beta1": {
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"min": 1e-2,
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"max": 0.45,
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"scaling": "log"
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},
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"warmup_factor": {
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"feasible_points": [0.05]
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},
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"weight_decay": {
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"min": 1e-3,
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"max": 1.0,
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"scaling": "log"
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},
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"label_smoothing": {
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"feasible_points": [0.1, 0.2]
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},
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"dropout_rate": {
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"feasible_points": [0.0, 0.1]
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}
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}

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