PyTorch 2.2 中文官方教程（十五）

# Imports
import copy
import matplotlib.pyplot as plt
import numpy as np
import os
import time

plt.ion() 

pip  install  numpy
pip  install  --pre  torch  torchvision  -f  https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
# For CUDA support use https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html 

import torch
from torchvision import transforms, datasets

# Data augmentation and normalization for training
# Just normalization for validation
data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(224),
        transforms.RandomCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(224),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16,
                                              shuffle=True, num_workers=8)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 

import torchvision

def imshow(inp, title=None, ax=None, figsize=(5, 5)):
  """Imshow for Tensor."""
  inp = inp.numpy().transpose((1, 2, 0))
  mean = np.array([0.485, 0.456, 0.406])
  std = np.array([0.229, 0.224, 0.225])
  inp = std * inp + mean
  inp = np.clip(inp, 0, 1)
  if ax is None:
    fig, ax = plt.subplots(1, figsize=figsize)
  ax.imshow(inp)
  ax.set_xticks([])
  ax.set_yticks([])
  if title is not None:
    ax.set_title(title)

# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))

# Make a grid from batch
out = torchvision.utils.make_grid(inputs, nrow=4)

fig, ax = plt.subplots(1, figsize=(10, 10))
imshow(out, title=[class_names[x] for x in classes], ax=ax) 

def train_model(model, criterion, optimizer, scheduler, num_epochs=25, device='cpu'):
  """
 Support function for model training.

 Args:
 model: Model to be trained
 criterion: Optimization criterion (loss)
 optimizer: Optimizer to use for training
 scheduler: Instance of ``torch.optim.lr_scheduler``
 num_epochs: Number of epochs
 device: Device to run the training on. Must be 'cpu' or 'cuda'
 """
  since = time.time()

  best_model_wts = copy.deepcopy(model.state_dict())
  best_acc = 0.0

  for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs - 1))
    print('-' * 10)

    # Each epoch has a training and validation phase
    for phase in ['train', 'val']:
      if phase == 'train':
        model.train()  # Set model to training mode
      else:
        model.eval()   # Set model to evaluate mode

      running_loss = 0.0
      running_corrects = 0

      # Iterate over data.
      for inputs, labels in dataloaders[phase]:
        inputs = inputs.to(device)
        labels = labels.to(device)

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward
        # track history if only in train
        with torch.set_grad_enabled(phase == 'train'):
          outputs = model(inputs)
          _, preds = torch.max(outputs, 1)
          loss = criterion(outputs, labels)

          # backward + optimize only if in training phase
          if phase == 'train':
            loss.backward()
            optimizer.step()

        # statistics
        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == labels.data)
      if phase == 'train':
        scheduler.step()

      epoch_loss = running_loss / dataset_sizes[phase]
      epoch_acc = running_corrects.double() / dataset_sizes[phase]

      print('{} Loss: {:.4f} Acc: {:.4f}'.format(
        phase, epoch_loss, epoch_acc))

      # deep copy the model
      if phase == 'val' and epoch_acc > best_acc:
        best_acc = epoch_acc
        best_model_wts = copy.deepcopy(model.state_dict())

    print()

  time_elapsed = time.time() - since
  print('Training complete in {:.0f}m {:.0f}s'.format(
    time_elapsed // 60, time_elapsed % 60))
  print('Best val Acc: {:4f}'.format(best_acc))

  # load best model weights
  model.load_state_dict(best_model_wts)
  return model 

def visualize_model(model, rows=3, cols=3):
  was_training = model.training
  model.eval()
  current_row = current_col = 0
  fig, ax = plt.subplots(rows, cols, figsize=(cols*2, rows*2))

  with torch.no_grad():
    for idx, (imgs, lbls) in enumerate(dataloaders['val']):
      imgs = imgs.cpu()
      lbls = lbls.cpu()

      outputs = model(imgs)
      _, preds = torch.max(outputs, 1)

      for jdx in range(imgs.size()[0]):
        imshow(imgs.data[jdx], ax=ax[current_row, current_col])
        ax[current_row, current_col].axis('off')
        ax[current_row, current_col].set_title('predicted: {}'.format(class_names[preds[jdx]]))

        current_col += 1
        if current_col >= cols:
          current_row += 1
          current_col = 0
        if current_row >= rows:
          model.train(mode=was_training)
          return
    model.train(mode=was_training) 

import torchvision.models.quantization as models

# You will need the number of filters in the `fc` for future use.
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_fe = models.resnet18(pretrained=True, progress=True, quantize=True)
num_ftrs = model_fe.fc.in_features 

from torch import nn

def create_combined_model(model_fe):
  # Step 1\. Isolate the feature extractor.
  model_fe_features = nn.Sequential(
    model_fe.quant,  # Quantize the input
    model_fe.conv1,
    model_fe.bn1,
    model_fe.relu,
    model_fe.maxpool,
    model_fe.layer1,
    model_fe.layer2,
    model_fe.layer3,
    model_fe.layer4,
    model_fe.avgpool,
    model_fe.dequant,  # Dequantize the output
  )

  # Step 2\. Create a new "head"
  new_head = nn.Sequential(
    nn.Dropout(p=0.5),
    nn.Linear(num_ftrs, 2),
  )

  # Step 3\. Combine, and don't forget the quant stubs.
  new_model = nn.Sequential(
    model_fe_features,
    nn.Flatten(1),
    new_head,
  )
  return new_model 

import torch.optim as optim
new_model = create_combined_model(model_fe)
new_model = new_model.to('cpu')

criterion = nn.CrossEntropyLoss()

# Note that we are only training the head.
optimizer_ft = optim.SGD(new_model.parameters(), lr=0.01, momentum=0.9)

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) 

new_model = train_model(new_model, criterion, optimizer_ft, exp_lr_scheduler,
                        num_epochs=25, device='cpu')

visualize_model(new_model)
plt.tight_layout() 

# notice `quantize=False`
model = models.resnet18(pretrained=True, progress=True, quantize=False)
num_ftrs = model.fc.in_features

# Step 1
model.train()
model.fuse_model()
# Step 2
model_ft = create_combined_model(model)
model_ft[0].qconfig = torch.quantization.default_qat_qconfig  # Use default QAT configuration
# Step 3
model_ft = torch.quantization.prepare_qat(model_ft, inplace=True) 

for param in model_ft.parameters():
  param.requires_grad = True

model_ft.to(device)  # We can fine-tune on GPU if available

criterion = nn.CrossEntropyLoss()

# Note that we are training everything, so the learning rate is lower
# Notice the smaller learning rate
optimizer_ft = optim.SGD(model_ft.parameters(), lr=1e-3, momentum=0.9, weight_decay=0.1)

# Decay LR by a factor of 0.3 every several epochs
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.3)

model_ft_tuned = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                             num_epochs=25, device=device) 

from torch.quantization import convert
model_ft_tuned.cpu()

model_quantized_and_trained = convert(model_ft_tuned, inplace=False) 

visualize_model(model_quantized_and_trained)

plt.ioff()
plt.tight_layout()
plt.show() 

import os
import sys
import time
import numpy as np

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

import torchvision
from torchvision import datasets
import torchvision.transforms as transforms

# Set up warnings
import warnings
warnings.filterwarnings(
    action='ignore',
    category=DeprecationWarning,
    module=r'.*'
)
warnings.filterwarnings(
    action='default',
    module=r'torch.ao.quantization'
)

# Specify random seed for repeatable results
torch.manual_seed(191009) 

from torch.ao.quantization import QuantStub, DeQuantStub

def _make_divisible(v, divisor, min_value=None):
  """
 This function is taken from the original tf repo.
 It ensures that all layers have a channel number that is divisible by 8
 It can be seen here:
 https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
 :param v:
 :param divisor:
 :param min_value:
 :return:
 """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v

class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_planes, momentum=0.1),
            # Replace with ReLU
            nn.ReLU(inplace=False)
        )

class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            # dw
            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup, momentum=0.1),
        ])
        self.conv = nn.Sequential(*layers)
        # Replace torch.add with floatfunctional
        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, x):
        if self.use_res_connect:
            return self.skip_add.add(x, self.conv(x))
        else:
            return self.conv(x)

class MobileNetV2(nn.Module):
    def __init__(self, num_classes=1000, width_mult=1.0, inverted_residual_setting=None, round_nearest=8):
  """
 MobileNet V2 main class
 Args:
 num_classes (int): Number of classes
 width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
 inverted_residual_setting: Network structure
 round_nearest (int): Round the number of channels in each layer to be a multiple of this number
 Set to 1 to turn off rounding
 """
        super(MobileNetV2, self).__init__()
        block = InvertedResidual
        input_channel = 32
        last_channel = 1280

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 2],
                [6, 32, 3, 2],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
            raise ValueError("inverted_residual_setting should be non-empty "
                             "or a 4-element list, got {}".format(inverted_residual_setting))

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
        features = [ConvBNReLU(3, input_channel, stride=2)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
                input_channel = output_channel
        # building last several layers
        features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
        # make it nn.Sequential
        self.features = nn.Sequential(*features)
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, num_classes),
        )

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    def forward(self, x):
        x = self.quant(x)
        x = self.features(x)
        x = x.mean([2, 3])
        x = self.classifier(x)
        x = self.dequant(x)
        return x

    # Fuse Conv+BN and Conv+BN+Relu modules prior to quantization
    # This operation does not change the numerics
    def fuse_model(self, is_qat=False):
        fuse_modules = torch.ao.quantization.fuse_modules_qat if is_qat else torch.ao.quantization.fuse_modules
        for m in self.modules():
            if type(m) == ConvBNReLU:
                fuse_modules(m, ['0', '1', '2'], inplace=True)
            if type(m) == InvertedResidual:
                for idx in range(len(m.conv)):
                    if type(m.conv[idx]) == nn.Conv2d:
                        fuse_modules(m.conv, [str(idx), str(idx + 1)], inplace=True) 

class AverageMeter(object):
  """Computes and stores the average and current value"""
    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count

    def __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)

def accuracy(output, target, topk=(1,)):
  """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res

def evaluate(model, criterion, data_loader, neval_batches):
    model.eval()
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    cnt = 0
    with torch.no_grad():
        for image, target in data_loader:
            output = model(image)
            loss = criterion(output, target)
            cnt += 1
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            print('.', end = '')
            top1.update(acc1[0], image.size(0))
            top5.update(acc5[0], image.size(0))
            if cnt >= neval_batches:
                 return top1, top5

    return top1, top5

def load_model(model_file):
    model = MobileNetV2()
    state_dict = torch.load(model_file)
    model.load_state_dict(state_dict)
    model.to('cpu')
    return model

def print_size_of_model(model):
    torch.save(model.state_dict(), "temp.p")
    print('Size (MB):', os.path.getsize("temp.p")/1e6)
    os.remove('temp.p') 

def prepare_data_loaders(data_path):
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    dataset = torchvision.datasets.ImageNet(
        data_path, split="train", transform=transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))
    dataset_test = torchvision.datasets.ImageNet(
        data_path, split="val", transform=transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ]))

    train_sampler = torch.utils.data.RandomSampler(dataset)
    test_sampler = torch.utils.data.SequentialSampler(dataset_test)

    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=train_batch_size,
        sampler=train_sampler)

    data_loader_test = torch.utils.data.DataLoader(
        dataset_test, batch_size=eval_batch_size,
        sampler=test_sampler)

    return data_loader, data_loader_test 

data_path = '~/.data/imagenet'
saved_model_dir = 'data/'
float_model_file = 'mobilenet_pretrained_float.pth'
scripted_float_model_file = 'mobilenet_quantization_scripted.pth'
scripted_quantized_model_file = 'mobilenet_quantization_scripted_quantized.pth'

train_batch_size = 30
eval_batch_size = 50

data_loader, data_loader_test = prepare_data_loaders(data_path)
criterion = nn.CrossEntropyLoss()
float_model = load_model(saved_model_dir + float_model_file).to('cpu')

# Next, we'll "fuse modules"; this can both make the model faster by saving on memory access
# while also improving numerical accuracy. While this can be used with any model, this is
# especially common with quantized models.

print('\n Inverted Residual Block: Before fusion \n\n', float_model.features[1].conv)
float_model.eval()

# Fuses modules
float_model.fuse_model()

# Note fusion of Conv+BN+Relu and Conv+Relu
print('\n Inverted Residual Block: After fusion\n\n',float_model.features[1].conv) 

num_eval_batches = 1000

print("Size of baseline model")
print_size_of_model(float_model)

top1, top5 = evaluate(float_model, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg))
torch.jit.save(torch.jit.script(float_model), saved_model_dir + scripted_float_model_file) 

num_calibration_batches = 32

myModel = load_model(saved_model_dir + float_model_file).to('cpu')
myModel.eval()

# Fuse Conv, bn and relu
myModel.fuse_model()

# Specify quantization configuration
# Start with simple min/max range estimation and per-tensor quantization of weights
myModel.qconfig = torch.ao.quantization.default_qconfig
print(myModel.qconfig)
torch.ao.quantization.prepare(myModel, inplace=True)

# Calibrate first
print('Post Training Quantization Prepare: Inserting Observers')
print('\n Inverted Residual Block:After observer insertion \n\n', myModel.features[1].conv)

# Calibrate with the training set
evaluate(myModel, criterion, data_loader, neval_batches=num_calibration_batches)
print('Post Training Quantization: Calibration done')

# Convert to quantized model
torch.ao.quantization.convert(myModel, inplace=True)
# You may see a user warning about needing to calibrate the model. This warning can be safely ignored.
# This warning occurs because not all modules are run in each model runs, so some
# modules may not be calibrated.
print('Post Training Quantization: Convert done')
print('\n Inverted Residual Block: After fusion and quantization, note fused modules: \n\n',myModel.features[1].conv)

print("Size of model after quantization")
print_size_of_model(myModel)

top1, top5 = evaluate(myModel, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg)) 

per_channel_quantized_model = load_model(saved_model_dir + float_model_file)
per_channel_quantized_model.eval()
per_channel_quantized_model.fuse_model()
# The old 'fbgemm' is still available but 'x86' is the recommended default.
per_channel_quantized_model.qconfig = torch.ao.quantization.get_default_qconfig('x86')
print(per_channel_quantized_model.qconfig)

torch.ao.quantization.prepare(per_channel_quantized_model, inplace=True)
evaluate(per_channel_quantized_model,criterion, data_loader, num_calibration_batches)
torch.ao.quantization.convert(per_channel_quantized_model, inplace=True)
top1, top5 = evaluate(per_channel_quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches)
print('Evaluation accuracy on %d images, %2.2f'%(num_eval_batches * eval_batch_size, top1.avg))
torch.jit.save(torch.jit.script(per_channel_quantized_model), saved_model_dir + scripted_quantized_model_file) 

def train_one_epoch(model, criterion, optimizer, data_loader, device, ntrain_batches):
    model.train()
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    avgloss = AverageMeter('Loss', '1.5f')

    cnt = 0
    for image, target in data_loader:
        start_time = time.time()
        print('.', end = '')
        cnt += 1
        image, target = image.to(device), target.to(device)
        output = model(image)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        top1.update(acc1[0], image.size(0))
        top5.update(acc5[0], image.size(0))
        avgloss.update(loss, image.size(0))
        if cnt >= ntrain_batches:
            print('Loss', avgloss.avg)

            print('Training: * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
                  .format(top1=top1, top5=top5))
            return

    print('Full imagenet train set:  * Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}'
          .format(top1=top1, top5=top5))
    return 

qat_model = load_model(saved_model_dir + float_model_file)
qat_model.fuse_model(is_qat=True)

optimizer = torch.optim.SGD(qat_model.parameters(), lr = 0.0001)
# The old 'fbgemm' is still available but 'x86' is the recommended default.
qat_model.qconfig = torch.ao.quantization.get_default_qat_qconfig('x86') 

torch.ao.quantization.prepare_qat(qat_model, inplace=True)
print('Inverted Residual Block: After preparation for QAT, note fake-quantization modules \n',qat_model.features[1].conv) 

num_train_batches = 20

# QAT takes time and one needs to train over a few epochs.
# Train and check accuracy after each epoch
for nepoch in range(8):
    train_one_epoch(qat_model, criterion, optimizer, data_loader, torch.device('cpu'), num_train_batches)
    if nepoch > 3:
        # Freeze quantizer parameters
        qat_model.apply(torch.ao.quantization.disable_observer)
    if nepoch > 2:
        # Freeze batch norm mean and variance estimates
        qat_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)

    # Check the accuracy after each epoch
    quantized_model = torch.ao.quantization.convert(qat_model.eval(), inplace=False)
    quantized_model.eval()
    top1, top5 = evaluate(quantized_model,criterion, data_loader_test, neval_batches=num_eval_batches)
    print('Epoch %d :Evaluation accuracy on %d images, %2.2f'%(nepoch, num_eval_batches * eval_batch_size, top1.avg)) 

def run_benchmark(model_file, img_loader):
    elapsed = 0
    model = torch.jit.load(model_file)
    model.eval()
    num_batches = 5
    # Run the scripted model on a few batches of images
    for i, (images, target) in enumerate(img_loader):
        if i < num_batches:
            start = time.time()
            output = model(images)
            end = time.time()
            elapsed = elapsed + (end-start)
        else:
            break
    num_images = images.size()[0] * num_batches

    print('Elapsed time: %3.0f ms' % (elapsed/num_images*1000))
    return elapsed

run_benchmark(saved_model_dir + scripted_float_model_file, data_loader_test)

run_benchmark(saved_model_dir + scripted_quantized_model_file, data_loader_test) 

import torch
import torchvision.models as models
import time

model = models.resnet50(pretrained=False)
model.eval()
data = torch.rand(1, 3, 224, 224)

# warm up
for _ in range(100):
    model(data)

start = time.time()
for _ in range(100):
    model(data)
end = time.time()
print('Inference took {:.2f} ms in average'.format((end-start)/100*1000)) 

torch.set_num_threads(num_physical_cores/num_workers) 

import torch
import torchvision.models as models
import time

model = models.resnet50(pretrained=False)
model.eval()
batch_size = 32
data = torch.rand(batch_size, 3, 224, 224)

# warm up
for _ in range(100):
    model(data)

# measure
# Intel® VTune Profiler's ITT context manager
with torch.autograd.profiler.emit_itt():
    start = time.time()
    for i in range(100):
   # Intel® VTune Profiler's ITT to annotate each step
        torch.profiler.itt.range_push('step_{}'.format(i))
        model(data)
        torch.profiler.itt.range_pop()
    end = time.time()

print('Inference took {:.2f} ms in average'.format((end-start)/100*1000)) 

cpu_launcher_enable=true
cpu_launcher_args=--node_id 0 --use_default_allocator 

cpu_launcher_enable=true
cpu_launcher_args=--node_id 0 --enable_jemalloc 

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv = torch.nn.Conv2d(16, 33, 3, stride=2)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x

model = Model()
model.eval()
data = torch.rand(20, 16, 50, 100)

#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################

print(model) 

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv = torch.nn.Conv2d(16, 33, 3, stride=2)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x

model = Model()
model.eval()
data = torch.rand(20, 16, 50, 100)

#################### code changes ####################
import intel_extension_for_pytorch as ipex
model = ipex.optimize(model)
######################################################

# torchscript
with torch.no_grad():
    model = torch.jit.trace(model, data)
    model = torch.jit.freeze(model) 

import torch

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv = torch.nn.Conv2d(16, 33, 3, stride=2)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x

model = Model()
model.eval()
data = torch.rand(20, 16, 50, 100)

import intel_extension_for_pytorch as ipex
############################### code changes ###############################
ipex.disable_auto_channels_last() # omit this line for channels_last (default)
############################################################################
model = ipex.optimize(model)

with torch.no_grad():
    model = torch.jit.trace(model, data)
    model = torch.jit.freeze(model) 

cpu_launcher_enable=true
cpu_launcher_args=--node_id 0 

from pathlib import Path

import torchx

from torchx import specs
from torchx.components import utils

def trainer(
    log_path: str,
    hidden_size_1: int,
    hidden_size_2: int,
    learning_rate: float,
    epochs: int,
    dropout: float,
    batch_size: int,
    trial_idx: int = -1,
) -> specs.AppDef:

    # define the log path so we can pass it to the TorchX ``AppDef``
    if trial_idx >= 0:
        log_path = Path(log_path).joinpath(str(trial_idx)).absolute().as_posix()

    return utils.python(
        # command line arguments to the training script
        "--log_path",
        log_path,
        "--hidden_size_1",
        str(hidden_size_1),
        "--hidden_size_2",
        str(hidden_size_2),
        "--learning_rate",
        str(learning_rate),
        "--epochs",
        str(epochs),
        "--dropout",
        str(dropout),
        "--batch_size",
        str(batch_size),
        # other config options
        name="trainer",
        script="mnist_train_nas.py",
        image=torchx.version.TORCHX_IMAGE,
    ) 

import tempfile
from ax.runners.torchx import TorchXRunner

# Make a temporary dir to log our results into
log_dir = tempfile.mkdtemp()

ax_runner = TorchXRunner(
    tracker_base="/tmp/",
    component=trainer,
    # NOTE: To launch this job on a cluster instead of locally you can
    # specify a different scheduler and adjust arguments appropriately.
    scheduler="local_cwd",
    component_const_params={"log_path": log_dir},
    cfg={},
) 

from ax.core import (
    ChoiceParameter,
    ParameterType,
    RangeParameter,
    SearchSpace,
)

parameters = [
    # NOTE: In a real-world setting, hidden_size_1 and hidden_size_2
    # should probably be powers of 2, but in our simple example this
    # would mean that ``num_params`` can't take on that many values, which
    # in turn makes the Pareto frontier look pretty weird.
    RangeParameter(
        name="hidden_size_1",
        lower=16,
        upper=128,
        parameter_type=ParameterType.INT,
        log_scale=True,
    ),
    RangeParameter(
        name="hidden_size_2",
        lower=16,
        upper=128,
        parameter_type=ParameterType.INT,
        log_scale=True,
    ),
    RangeParameter(
        name="learning_rate",
        lower=1e-4,
        upper=1e-2,
        parameter_type=ParameterType.FLOAT,
        log_scale=True,
    ),
    RangeParameter(
        name="epochs",
        lower=1,
        upper=4,
        parameter_type=ParameterType.INT,
    ),
    RangeParameter(
        name="dropout",
        lower=0.0,
        upper=0.5,
        parameter_type=ParameterType.FLOAT,
    ),
    ChoiceParameter(  # NOTE: ``ChoiceParameters`` don't require log-scale
        name="batch_size",
        values=[32, 64, 128, 256],
        parameter_type=ParameterType.INT,
        is_ordered=True,
        sort_values=True,
    ),
]

search_space = SearchSpace(
    parameters=parameters,
    # NOTE: In practice, it may make sense to add a constraint
    # hidden_size_2 <= hidden_size_1
    parameter_constraints=[],
) 

from ax.metrics.tensorboard import TensorboardCurveMetric

class MyTensorboardMetric(TensorboardCurveMetric):

    # NOTE: We need to tell the new TensorBoard metric how to get the id /
    # file handle for the TensorBoard logs from a trial. In this case
    # our convention is to just save a separate file per trial in
    # the prespecified log dir.
    @classmethod
    def get_ids_from_trials(cls, trials):
        return {
            trial.index: Path(log_dir).joinpath(str(trial.index)).as_posix()
            for trial in trials
        }

    # This indicates whether the metric is queryable while the trial is
    # still running. We don't use this in the current tutorial, but Ax
    # utilizes this to implement trial-level early-stopping functionality.
    @classmethod
    def is_available_while_running(cls):
        return False 

val_acc = MyTensorboardMetric(
    name="val_acc",
    curve_name="val_acc",
    lower_is_better=False,
)
model_num_params = MyTensorboardMetric(
    name="num_params",
    curve_name="num_params",
    lower_is_better=True,
) 

from ax.core import MultiObjective, Objective, ObjectiveThreshold
from ax.core.optimization_config import MultiObjectiveOptimizationConfig

opt_config = MultiObjectiveOptimizationConfig(
    objective=MultiObjective(
        objectives=[
            Objective(metric=val_acc, minimize=False),
            Objective(metric=model_num_params, minimize=True),
        ],
    ),
    objective_thresholds=[
        ObjectiveThreshold(metric=val_acc, bound=0.94, relative=False),
        ObjectiveThreshold(metric=model_num_params, bound=80_000, relative=False),
    ],
) 

from ax.core import Experiment

experiment = Experiment(
    name="torchx_mnist",
    search_space=search_space,
    optimization_config=opt_config,
    runner=ax_runner,
) 

total_trials = 48  # total evaluation budget

from ax.modelbridge.dispatch_utils import choose_generation_strategy

gs = choose_generation_strategy(
    search_space=experiment.search_space,
    optimization_config=experiment.optimization_config,
    num_trials=total_trials,
  ) 

[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there are more ordered parameters than there are categories for the unordered categorical parameters.
[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=48 use_batch_trials=False
[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=9
[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=9
[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.
[INFO 02-03 05:14:14] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 9 trials, BoTorch for subsequent trials]). Iterations after 9 will take longer to generate due to model-fitting. 

from ax.service.scheduler import Scheduler, SchedulerOptions

scheduler = Scheduler(
    experiment=experiment,
    generation_strategy=gs,
    options=SchedulerOptions(
        total_trials=total_trials, max_pending_trials=4
    ),
) 

[INFO 02-03 05:14:15] Scheduler: `Scheduler` requires experiment to have immutable search space and optimization config. Setting property immutable_search_space_and_opt_config to `True` on experiment. 

scheduler.run_all_trials() 

[INFO 02-03 05:14:15] Scheduler: Running trials [0]...
[INFO 02-03 05:14:16] Scheduler: Running trials [1]...
[INFO 02-03 05:14:17] Scheduler: Running trials [2]...
[INFO 02-03 05:14:18] Scheduler: Running trials [3]...
[INFO 02-03 05:14:19] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:14:20] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:14:21] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:14:23] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 02-03 05:14:27] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 02-03 05:14:32] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
[INFO 02-03 05:14:39] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 4).
[INFO 02-03 05:14:51] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 4).
[INFO 02-03 05:15:08] Scheduler: Retrieved COMPLETED trials: [1, 3].
[INFO 02-03 05:15:08] Scheduler: Fetching data for trials: [1, 3].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:08] Scheduler: Running trials [4]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:09] Scheduler: Running trials [5]...
[INFO 02-03 05:15:10] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:15:11] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:15:13] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:15:15] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 02-03 05:15:18] Scheduler: Retrieved COMPLETED trials: [2].
[INFO 02-03 05:15:18] Scheduler: Fetching data for trials: [2].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:18] Scheduler: Running trials [6]...
[INFO 02-03 05:15:19] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:15:20] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:15:22] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:15:24] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 02-03 05:15:27] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 02-03 05:15:33] Scheduler: Retrieved COMPLETED trials: [0].
[INFO 02-03 05:15:33] Scheduler: Fetching data for trials: [0].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:33] Scheduler: Running trials [7]...
[INFO 02-03 05:15:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:15:35] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:15:36] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:15:38] Scheduler: Retrieved COMPLETED trials: [4].
[INFO 02-03 05:15:38] Scheduler: Fetching data for trials: [4].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:38] Scheduler: Running trials [8]...
[INFO 02-03 05:15:39] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:15:40] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:15:42] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:15:44] Scheduler: Retrieved COMPLETED trials: [5].
[INFO 02-03 05:15:44] Scheduler: Fetching data for trials: [5].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:46] Scheduler: Running trials [9]...
[INFO 02-03 05:15:47] Scheduler: Retrieved COMPLETED trials: [6].
[INFO 02-03 05:15:47] Scheduler: Fetching data for trials: [6].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:15:50] Scheduler: Running trials [10]...
[INFO 02-03 05:15:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:15:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:15:54] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:15:56] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 02-03 05:15:59] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 02-03 05:16:05] Scheduler: Retrieved COMPLETED trials: [8].
[INFO 02-03 05:16:05] Scheduler: Fetching data for trials: [8].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:06] Scheduler: Running trials [11]...
[INFO 02-03 05:16:07] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 4).
[INFO 02-03 05:16:08] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 4).
[INFO 02-03 05:16:10] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 4).
[INFO 02-03 05:16:12] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 4).
[INFO 02-03 05:16:16] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 4).
[INFO 02-03 05:16:21] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 4).
[INFO 02-03 05:16:28] Scheduler: Retrieved COMPLETED trials: [7].
[INFO 02-03 05:16:28] Scheduler: Fetching data for trials: [7].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:28] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:16:29] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:16:31] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:16:33] Scheduler: Retrieved COMPLETED trials: [9].
[INFO 02-03 05:16:33] Scheduler: Fetching data for trials: [9].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:35] Scheduler: Running trials [12]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:36] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:16:36] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:16:36] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:16:37] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:16:39] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:16:41] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:16:45] Scheduler: Retrieved COMPLETED trials: [10].
[INFO 02-03 05:16:45] Scheduler: Fetching data for trials: [10].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:48] Scheduler: Running trials [13]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:48] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:16:48] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:16:48] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:16:49] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:16:50] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:16:52] Scheduler: Retrieved COMPLETED trials: [11].
[INFO 02-03 05:16:52] Scheduler: Fetching data for trials: [11].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:55] Scheduler: Running trials [14]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:16:56] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:16:56] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:16:56] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:16:57] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:16:58] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:17:00] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:17:04] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:17:09] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:17:17] Scheduler: Retrieved COMPLETED trials: [12].
[INFO 02-03 05:17:17] Scheduler: Fetching data for trials: [12].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:20] Scheduler: Running trials [15]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:17:21] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:17:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:17:22] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:17:23] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:17:25] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:17:29] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:17:34] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:17:41] Scheduler: Retrieved COMPLETED trials: [14].
[INFO 02-03 05:17:41] Scheduler: Fetching data for trials: [14].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:44] Scheduler: Running trials [16]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:45] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:17:45] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:17:45] Scheduler: Retrieved COMPLETED trials: [13].
[INFO 02-03 05:17:45] Scheduler: Fetching data for trials: [13].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:48] Scheduler: Running trials [17]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:17:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:17:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:17:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:17:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:17:52] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:17:54] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:17:58] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:18:03] Scheduler: Retrieved COMPLETED trials: [15].
[INFO 02-03 05:18:03] Scheduler: Fetching data for trials: [15].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:08] Scheduler: Running trials [18]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:09] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:18:09] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:18:09] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:18:10] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:18:12] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:18:14] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:18:18] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:18:23] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:18:30] Scheduler: Retrieved COMPLETED trials: [17].
[INFO 02-03 05:18:30] Scheduler: Fetching data for trials: [17].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:34] Scheduler: Running trials [19]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:35] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:18:35] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:18:35] Scheduler: Retrieved COMPLETED trials: [16].
[INFO 02-03 05:18:35] Scheduler: Fetching data for trials: [16].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:39] Scheduler: Running trials [20]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:40] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:18:40] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:18:40] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:18:41] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:18:43] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:18:45] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:18:48] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:18:54] Scheduler: Retrieved COMPLETED trials: [18].
[INFO 02-03 05:18:54] Scheduler: Fetching data for trials: [18].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:57] Scheduler: Running trials [21]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:18:58] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:18:58] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:18:58] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:18:59] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:19:00] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:19:02] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:19:06] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:19:11] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:19:19] Scheduler: Retrieved COMPLETED trials: [19].
[INFO 02-03 05:19:19] Scheduler: Fetching data for trials: [19].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:19:22] Scheduler: Running trials [22]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:19:23] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:19:23] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:19:23] Scheduler: Retrieved COMPLETED trials: [20].
[INFO 02-03 05:19:23] Scheduler: Fetching data for trials: [20].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:19:26] Scheduler: Running trials [23]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:19:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:19:27] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:19:27] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:19:28] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:19:30] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:19:32] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:19:36] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:19:41] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:19:48] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:20:00] Scheduler: Retrieved COMPLETED trials: [21, 23].
[INFO 02-03 05:20:00] Scheduler: Fetching data for trials: [21, 23].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:20:03] Scheduler: Running trials [24]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:20:04] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:20:07] Scheduler: Running trials [25]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:20:08] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:20:08] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:20:08] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:20:09] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:20:11] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:20:13] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:20:17] Scheduler: Retrieved COMPLETED trials: [22].
[INFO 02-03 05:20:17] Scheduler: Fetching data for trials: [22].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:20:20] Scheduler: Running trials [26]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:20:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:20:21] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:20:21] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:20:22] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:20:24] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:20:26] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:20:29] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:20:34] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:20:42] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:20:53] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 02-03 05:21:11] Scheduler: Retrieved COMPLETED trials: 24 - 25.
[INFO 02-03 05:21:11] Scheduler: Fetching data for trials: 24 - 25.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:21:15] Scheduler: Running trials [27]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:21:16] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:21:19] Scheduler: Running trials [28]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:21:20] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:21:20] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:21:20] Scheduler: Retrieved COMPLETED trials: [26].
[INFO 02-03 05:21:20] Scheduler: Fetching data for trials: [26].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:21:25] Scheduler: Running trials [29]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:21:26] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:21:26] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:21:26] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:21:27] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:21:29] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:21:31] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:21:34] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:21:39] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:21:47] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:21:58] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 02-03 05:22:16] Scheduler: Retrieved COMPLETED trials: 27 - 29.
[INFO 02-03 05:22:16] Scheduler: Fetching data for trials: 27 - 29.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:22:20] Scheduler: Running trials [30]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:22:21] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:22:24] Scheduler: Running trials [31]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:22:25] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:22:30] Scheduler: Running trials [32]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:22:31] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:22:31] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:22:31] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:22:32] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:22:34] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:22:36] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:22:40] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:22:45] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:22:52] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:23:04] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 02-03 05:23:21] Scheduler: Retrieved COMPLETED trials: 30 - 32.
[INFO 02-03 05:23:21] Scheduler: Fetching data for trials: 30 - 32.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:23:26] Scheduler: Running trials [33]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:23:27] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:23:32] Scheduler: Running trials [34]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:23:33] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:23:38] Scheduler: Running trials [35]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:23:38] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:23:38] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:23:38] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:23:39] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:23:41] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:23:43] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:23:47] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:23:52] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:23:59] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:24:11] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 02-03 05:24:28] Scheduler: Retrieved COMPLETED trials: 33 - 34.
[INFO 02-03 05:24:28] Scheduler: Fetching data for trials: 33 - 34.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:24:34] Scheduler: Running trials [36]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:24:35] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:24:41] Scheduler: Running trials [37]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:24:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:24:42] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:24:42] Scheduler: Retrieved COMPLETED trials: [35].
[INFO 02-03 05:24:42] Scheduler: Fetching data for trials: [35].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:24:48] Scheduler: Running trials [38]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:24:49] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:24:49] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:24:49] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:24:50] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:24:51] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:24:53] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:24:57] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:25:02] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:25:10] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:25:21] Scheduler: Waiting for completed trials (for 17 sec, currently running trials: 3).
[INFO 02-03 05:25:38] Scheduler: Retrieved COMPLETED trials: 36 - 38.
[INFO 02-03 05:25:38] Scheduler: Fetching data for trials: 36 - 38.
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:25:42] Scheduler: Running trials [39]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:25:42] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:25:47] Scheduler: Running trials [40]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:25:47] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:25:54] Scheduler: Running trials [41]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:25:55] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:25:55] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:25:55] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:25:56] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:25:58] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:26:00] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:26:03] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:26:09] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:26:16] Scheduler: Waiting for completed trials (for 11 sec, currently running trials: 3).
[INFO 02-03 05:26:28] Scheduler: Retrieved COMPLETED trials: [39].
[INFO 02-03 05:26:28] Scheduler: Fetching data for trials: [39].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:33] Scheduler: Running trials [42]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:34] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:26:34] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:26:34] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:26:35] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:26:37] Scheduler: Retrieved COMPLETED trials: [40].
[INFO 02-03 05:26:37] Scheduler: Fetching data for trials: [40].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:44] Scheduler: Running trials [43]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:45] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:26:45] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:26:45] Scheduler: Retrieved COMPLETED trials: [41].
[INFO 02-03 05:26:45] Scheduler: Fetching data for trials: [41].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:50] Scheduler: Running trials [44]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:26:51] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:26:51] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:26:51] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:26:52] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:26:54] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:26:56] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:27:00] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:27:05] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:27:12] Scheduler: Retrieved COMPLETED trials: [42].
[INFO 02-03 05:27:12] Scheduler: Fetching data for trials: [42].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:27:18] Scheduler: Running trials [45]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:27:19] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:27:19] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:27:19] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:27:20] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:27:21] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:27:23] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:27:27] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:27:32] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:27:40] Scheduler: Retrieved COMPLETED trials: [43].
[INFO 02-03 05:27:40] Scheduler: Fetching data for trials: [43].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:27:45] Scheduler: Running trials [46]...
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:27:46] ax.modelbridge.torch: The observations are identical to the last set of observations used to fit the model. Skipping model fitting.
[INFO 02-03 05:27:46] Scheduler: Generated all trials that can be generated currently. Max parallelism currently reached.
[INFO 02-03 05:27:46] Scheduler: Retrieved COMPLETED trials: [44].
[INFO 02-03 05:27:46] Scheduler: Fetching data for trials: [44].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:27:52] Scheduler: Running trials [47]...
[INFO 02-03 05:27:53] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 3).
[INFO 02-03 05:27:54] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 3).
[INFO 02-03 05:27:55] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 3).
[INFO 02-03 05:27:58] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 3).
[INFO 02-03 05:28:01] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 3).
[INFO 02-03 05:28:06] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 3).
[INFO 02-03 05:28:14] Scheduler: Retrieved COMPLETED trials: [45].
[INFO 02-03 05:28:14] Scheduler: Fetching data for trials: [45].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:28:14] Scheduler: Done submitting trials, waiting for remaining 2 running trials...
[INFO 02-03 05:28:14] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 2).
[INFO 02-03 05:28:15] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 2).
[INFO 02-03 05:28:16] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 2).
[INFO 02-03 05:28:19] Scheduler: Waiting for completed trials (for 3 sec, currently running trials: 2).
[INFO 02-03 05:28:22] Scheduler: Waiting for completed trials (for 5 sec, currently running trials: 2).
[INFO 02-03 05:28:27] Scheduler: Waiting for completed trials (for 7 sec, currently running trials: 2).
[INFO 02-03 05:28:35] Scheduler: Retrieved COMPLETED trials: [46].
[INFO 02-03 05:28:35] Scheduler: Fetching data for trials: [46].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[INFO 02-03 05:28:35] Scheduler: Waiting for completed trials (for 1 sec, currently running trials: 1).
[INFO 02-03 05:28:36] Scheduler: Waiting for completed trials (for 1.5 sec, currently running trials: 1).
[INFO 02-03 05:28:37] Scheduler: Waiting for completed trials (for 2 sec, currently running trials: 1).
[INFO 02-03 05:28:40] Scheduler: Retrieved COMPLETED trials: [47].
[INFO 02-03 05:28:40] Scheduler: Fetching data for trials: [47].
/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

OptimizationResult() 

from ax.service.utils.report_utils import exp_to_df

df = exp_to_df(experiment)
df.head(10) 

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[WARNING 02-03 05:28:40] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column. 

from ax.service.utils.report_utils import _pareto_frontier_scatter_2d_plotly

_pareto_frontier_scatter_2d_plotly(experiment) 

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/ax/core/map_data.py:188: FutureWarning:

The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.

[WARNING 02-03 05:28:40] ax.service.utils.report_utils: Column reason missing for all trials. Not appending column. 

from ax.modelbridge.cross_validation import compute_diagnostics, cross_validate
from ax.plot.diagnostic import interact_cross_validation_plotly
from ax.utils.notebook.plotting import init_notebook_plotting, render

cv = cross_validate(model=gs.model)  # The surrogate model is stored on the ``GenerationStrategy``
compute_diagnostics(cv)

interact_cross_validation_plotly(cv) 

from ax.plot.contour import interact_contour_plotly

interact_contour_plotly(model=gs.model, metric_name="val_acc") 

interact_contour_plotly(model=gs.model, metric_name="num_params")