Pack ERROR mismatch

Symbadian1: <p>Hi All,</p>
<p>I am new to understanding the packages and how they interconnect!</p>
<p>I am using a MAC M1 ProBook and <strong>THE CODE WORKS FINE</strong> on that OS, the only problem is that. <strong>TRAINING A MODEL takes days and weeks to complete</strong>. The issue is that PyTorch has not released a fix for the MPS GPU training feature for Mac just yet and I’m painfully waiting for the feature.</p>
<p>As a result, <strong>I move to an Ubuntu LINUX GPU super server with the same code</strong> and I am getting some WEIRD Errors for which I’m lacking the knowledge to understand the packages.</p>
<p>The code gets to the VERBOSE stage and MOVE VERY SLOW AND THIS IS A GPU Server…</p>
<p>I TRIED:</p>
<ol>
<li>
<p>research google but still haven’t found a solution to the issues</p>
</li>
<li>
<p>Via the errors received I tried installing but this did nothing<br>
pip3 install "torchvision&gt;=0.8.1</p>
</li>
<li>
<p>I tried changing to a new environment via the below code but that was not useful.</p>
</li>
<li>
<pre><code class="lang-auto"></code></pre>
</li>
</ol>
<p>conda create --name myOps python=3.7.4<br>
conda activate myOps<br>
pip3 install torch<br>
pip3 install tensorflow<br>
pip3 install torchvision<br>
pip3 install tensorboard```</p>
<p>I am faced with these errors here:</p>
<pre><code class="lang-auto">python3 train.py
train: weights=yolov5s.pt, cfg=, data=data/coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=32, batch_size=30, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
default message here: Command 'git fetch origin' timed out after 5 seconds
requirements: torchvision&gt;=0.8.1 not found and is required by YOLOv5, attempting auto-update...
ERROR: Could not find a version that satisfies the requirement torchvision&gt;=0.8.1 (from versions: 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.2.0, 0.2.1, 0.2.2, 0.2.2.post2, 0.2.2.post3)
ERROR: No matching distribution found for torchvision&gt;=0.8.1
requirements: Command 'pip install "torchvision&gt;=0.8.1" ' returned non-zero exit status 1.
requirements: ipython not found and is required by YOLOv5, attempting auto-update...
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: ipython in /opt/anaconda3/lib/python3.7/site-packages (7.8.0)
Requirement already satisfied: prompt-toolkit&lt;2.1.0,&gt;=2.0.0 in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (2.0.10)
Requirement already satisfied: decorator in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (4.4.0)
Requirement already satisfied: traitlets&gt;=4.2 in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (4.3.3)
Requirement already satisfied: pexpect in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (4.7.0)
Requirement already satisfied: backcall in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (0.1.0)
Requirement already satisfied: pickleshare in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (0.7.5)
Requirement already satisfied: jedi&gt;=0.10 in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (0.15.1)
Requirement already satisfied: setuptools&gt;=18.5 in /home/MattCCTV/.local/lib/python3.7/site-packages (from ipython) (65.6.3)
Requirement already satisfied: pygments in /opt/anaconda3/lib/python3.7/site-packages (from ipython) (2.4.2)
Requirement already satisfied: parso&gt;=0.5.0 in /opt/anaconda3/lib/python3.7/site-packages (from jedi&gt;=0.10-&gt;ipython) (0.5.1)
Requirement already satisfied: wcwidth in /opt/anaconda3/lib/python3.7/site-packages (from prompt-toolkit&lt;2.1.0,&gt;=2.0.0-&gt;ipython) (0.1.7)
Requirement already satisfied: six&gt;=1.9.0 in /opt/anaconda3/lib/python3.7/site-packages (from prompt-toolkit&lt;2.1.0,&gt;=2.0.0-&gt;ipython) (1.12.0)
Requirement already satisfied: ipython-genutils in /opt/anaconda3/lib/python3.7/site-packages (from traitlets&gt;=4.2-&gt;ipython) (0.2.0)
Requirement already satisfied: ptyprocess&gt;=0.5 in /opt/anaconda3/lib/python3.7/site-packages (from pexpect-&gt;ipython) (0.6.0)

requirements: psutil not found and is required by YOLOv5, attempting auto-update...
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: psutil in /opt/anaconda3/lib/python3.7/site-packages (5.6.3)

requirements: thop&gt;=0.1.1 not found and is required by YOLOv5, attempting auto-update...
ERROR: Cannot install thop==0.1.1.post2207130030 and thop==0.1.1.post2209072238 because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
requirements: Command 'pip install "thop&gt;=0.1.1" ' returned non-zero exit status 1.
requirements: 2 packages updated per /home/MattCCTV/YOLO9Classes/yolov5/requirements.txt
requirements: ⚠️ Restart runtime or rerun command for updates to take effect```



**THIS IS THE VERBOSE OUTPUT:**
```   Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
       0/31         0G       0.12    0.08108    0.06477        784        640:   2%|▏         | 2/124 [06:36&lt;6:41:17, 197.36s/it]    ```


NOT SURE: how to pass in the GPU command: I tried --device 1,0, --device 0, --device 1, and none worked to initialise the GPU processing!

I am certain that I am doing something wrong!!

Please assist and thanx in advance for acknowledging my digital presence!!</code></pre>

ptrblck: <aside class="quote no-group" data-username="Symbadian1" data-post="1" data-topic="170294">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/symbadian1/48/56423_2.png" class="avatar"> Symbadian1:</div>
<blockquote>
<pre><code class="lang-auto">NOT SURE: how to pass in the GPU command: I tried --device 1,0, --device 0, --device 1, and none worked to initialise the GPU processing!
</code></pre>
</blockquote>
</aside>
<p>This seems to be a script issue, so check how the script decides to use the GPU and make sure to pass the expected arguments.<br>
Generally, you would need to move the model as well as the data to the GPU explicitly as described in <a href="https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#training-on-gpu">this tutorial</a>.</p>

Symbadian: <p>Hi <a class="mention" href="/u/ptrblck">@ptrblck</a> and thank you so much for acknowledging my digital presence, I really appreciate this!</p>
<p>I think one of the challenges is to solve the package mismatch before I get to the GPU bits?</p>
<p>No matter how I tried installing the torch/ pytorch etc… Nothing is working, I am constantly getting this is error below and the processing is critically slow…</p>
<p>In order to amend the GPU bits, I some how feel that I need to solve the torch library issues. Am I right? Or rather, can you help to understand where I am going wrong, please? see errors below:</p>
<p>I tried: pip3 pip install torchvision&gt;=0.8.1 and pip install torchvision&gt;=0.8.1! This was futile!</p>
<pre><code class="lang-auto">yolov5$ python3 train.py --data coco128.yaml --weights yolov5n.pt --img 640 --cache 
train: weights=yolov5n.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=32, batch_size=30, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
default message here: Command 'git fetch origin' timed out after 5 seconds
requirements: torchvision&gt;=0.8.1 not found and is required by YOLOv5, attempting auto-update...
ERROR: Could not find a version that satisfies the requirement torchvision&gt;=0.8.1 (from versions: 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.2.0, 0.2.1, 0.2.2, 0.2.2.post2, 0.2.2.post3)
ERROR: No matching distribution found for torchvision&gt;=0.8.1
requirements: Command 'pip install "torchvision&gt;=0.8.1" ' returned non-zero exit status 1.
YOLOv5 🚀 v6.2-61-gffbce385 Python-3.10.8 torch-1.12.1 CPU```</code></pre>

Symbadian: <p>Hi <a class="mention" href="/u/ptrblck">@ptrblck</a> This is my code as I search for the alterations, I may need you guidance here, Please?<br>
Never had to alter these major bits and I am a bit cautious as I’m not sure of the process just yet. Other words, I haven’t acquired the confidence to sort it, I’m taking baby steps to learn this the right way. Please bear with me…</p>
<pre><code class="lang-auto">LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))


def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictionary
    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
        opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
    callbacks.run('on_pretrain_routine_start')

    # Directories
    w = save_dir / 'weights'  # weights dir
    (w.parent if evolve else w).mkdir(parents=True, exist_ok=True)  # make dir
    last, best = w / 'last.pt', w / 'best.pt'

    # Hyperparameters
    if isinstance(hyp, str):
        with open(hyp, errors='ignore') as f:
            hyp = yaml.safe_load(f)  # load hyps dict
    LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
    opt.hyp = hyp.copy()  # for saving hyps to checkpoints

    # Save run settings
    if not evolve:
        yaml_save(save_dir / 'hyp.yaml', hyp)
        yaml_save(save_dir / 'opt.yaml', vars(opt))

    # Loggers
    data_dict = None
    if RANK in {-1, 0}:
        loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
        if loggers.clearml:
            data_dict = loggers.clearml.data_dict  # None if no ClearML dataset or filled in by ClearML
        if loggers.wandb:
            data_dict = loggers.wandb.data_dict
            if resume:
                weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size

        # Register actions
        for k in methods(loggers):
            callbacks.register_action(k, callback=getattr(loggers, k))

    # Config
    plots = not evolve and not opt.noplots  # create plots
    cuda = device.type != 'cpu'
    init_seeds(opt.seed + 1 + RANK, deterministic=True)
    with torch_distributed_zero_first(LOCAL_RANK):
        data_dict = data_dict or check_dataset(data)  # check if None
    train_path, val_path = data_dict['train'], data_dict['val']
    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
    names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
    is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt')  # COCO dataset

    # Model
    check_suffix(weights, '.pt')  # check weights
    pretrained = weights.endswith('.pt')
    if pretrained:
        with torch_distributed_zero_first(LOCAL_RANK):
            weights = attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location='cpu')  # load checkpoint to CPU to avoid CUDA memory leak
        model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
        csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
        csd = intersect_dicts(csd, model.state_dict(), exclude=exclude)  # intersect
        model.load_state_dict(csd, strict=False)  # load
        LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}')  # report
    else:
        model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
    amp = check_amp(model)  # check AMP

    # Freeze
    freeze = [f'model.{x}.' for x in (freeze if len(freeze) &gt; 1 else range(freeze[0]))]  # layers to freeze
    for k, v in model.named_parameters():
        v.requires_grad = True  # train all layers
        # v.register_hook(lambda x: torch.nan_to_num(x))  # NaN to 0 (commented for erratic training results)
        if any(x in k for x in freeze):
            LOGGER.info(f'freezing {k}')
            v.requires_grad = False

    # Image size
    gs = max(int(model.stride.max()), 32)  # grid size (max stride)
    imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2)  # verify imgsz is gs-multiple

    # Batch size
    if RANK == -1 and batch_size == -1:  # single-GPU only, estimate best batch size
        batch_size = check_train_batch_size(model, imgsz, amp)
        loggers.on_params_update({"batch_size": batch_size})

    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
    optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])

    # Scheduler
    if opt.cos_lr:
        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1-&gt;hyp['lrf']
    else:
        lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)  # plot_lr_scheduler(optimizer, scheduler, epochs)

    # EMA
    ema = ModelEMA(model) if RANK in {-1, 0} else None

    # Resume
    best_fitness, start_epoch = 0.0, 0
    if pretrained:
        if resume:
            best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
        del ckpt, csd

    # DP mode
    if cuda and RANK == -1 and torch.cuda.device_count() &gt; 1:
        LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
                       'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
        model = torch.nn.DataParallel(model)

    # SyncBatchNorm
    if opt.sync_bn and cuda and RANK != -1:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
        LOGGER.info('Using SyncBatchNorm()')

    # Trainloader
    train_loader, dataset = create_dataloader(train_path,
                                              imgsz,
                                              batch_size // WORLD_SIZE,
                                              gs,
                                              single_cls,
                                              hyp=hyp,
                                              augment=True,
                                              cache=None if opt.cache == 'val' else opt.cache,
                                              rect=opt.rect,
                                              rank=LOCAL_RANK,
                                              workers=workers,
                                              image_weights=opt.image_weights,
                                              quad=opt.quad,
                                              prefix=colorstr('train: '),
                                              shuffle=True)
    labels = np.concatenate(dataset.labels, 0)
    mlc = int(labels[:, 0].max())  # max label class
    assert mlc &lt; nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'

    # Process 0
    if RANK in {-1, 0}:
        val_loader = create_dataloader(val_path,
                                       imgsz,
                                       batch_size // WORLD_SIZE * 2,
                                       gs,
                                       single_cls,
                                       hyp=hyp,
                                       cache=None if noval else opt.cache,
                                       rect=True,
                                       rank=-1,
                                       workers=workers * 2,
                                       pad=0.5,
                                       prefix=colorstr('val: '))[0]

        if not resume:
            if not opt.noautoanchor:
                check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)  # run AutoAnchor
            model.half().float()  # pre-reduce anchor precision

        callbacks.run('on_pretrain_routine_end', labels, names)

    # DDP mode
    if cuda and RANK != -1:
        model = smart_DDP(model)

    # Model attributes
    nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)
    hyp['box'] *= 3 / nl  # scale to layers
    hyp['cls'] *= nc / 80 * 3 / nl  # scale to classes and layers
    hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
    hyp['label_smoothing'] = opt.label_smoothing
    model.nc = nc  # attach number of classes to model
    model.hyp = hyp  # attach hyperparameters to model
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
    model.names = names

    # Start training
    t0 = time.time()
    nb = len(train_loader)  # number of batches
    nw = max(round(hyp['warmup_epochs'] * nb), 100)  # number of warmup iterations, max(3 epochs, 100 iterations)
    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to &lt; 1/2 of training
    last_opt_step = -1
    maps = np.zeros(nc)  # mAP per class
    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
    scheduler.last_epoch = start_epoch - 1  # do not move
    scaler = torch.cuda.amp.GradScaler(enabled=amp)
    stopper, stop = EarlyStopping(patience=opt.patience), False
    compute_loss = ComputeLoss(model)  # init loss class
    callbacks.run('on_train_start')
    LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
                f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
                f"Logging results to {colorstr('bold', save_dir)}\n"
                f'Starting training for {epochs} epochs...')
    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
        callbacks.run('on_train_epoch_start')
        model.train()

        # Update image weights (optional, single-GPU only)
        if opt.image_weights:
            cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
            iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
            dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx

        # Update mosaic border (optional)
        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders

        mloss = torch.zeros(3, device=device)  # mean losses
        if RANK != -1:
            train_loader.sampler.set_epoch(epoch)
        pbar = enumerate(train_loader)
        LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
        if RANK in {-1, 0}:
            pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')  # progress bar
        optimizer.zero_grad()
        for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
            callbacks.run('on_train_batch_start')
            ni = i + nb * epoch  # number integrated batches (since train start)
            imgs = imgs.to(device, non_blocking=True).float() / 255  # uint8 to float32, 0-255 to 0.0-1.0

            # Warmup
            if ni &lt;= nw:
                xi = [0, nw]  # x interp
                # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
                accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
                for j, x in enumerate(optimizer.param_groups):
                    # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                    x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)])
                    if 'momentum' in x:
                        x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])

            # Multi-scale
            if opt.multi_scale:
                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
                sf = sz / max(imgs.shape[2:])  # scale factor
                if sf != 1:
                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
                    imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)

            # Forward
            with torch.cuda.amp.autocast(amp):
                pred = model(imgs)  # forward
                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
                if RANK != -1:
                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
                if opt.quad:
                    loss *= 4.

            # Backward
            scaler.scale(loss).backward()

            # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
            if ni - last_opt_step &gt;= accumulate:
                scaler.unscale_(optimizer)  # unscale gradients
                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)  # clip gradients
                scaler.step(optimizer)  # optimizer.step
                scaler.update()
                optimizer.zero_grad()
                if ema:
                    ema.update(model)
                last_opt_step = ni

            # Log
            if RANK in {-1, 0}:
                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G'  # (GB)
                pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
                                     (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
                callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths)
                if callbacks.stop_training:
                    return
            # end batch ------------------------------------------------------------------------------------------------

        # Scheduler
        lr = [x['lr'] for x in optimizer.param_groups]  # for loggers
        scheduler.step()

        if RANK in {-1, 0}:
            # mAP
            callbacks.run('on_train_epoch_end', epoch=epoch)
            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
            if not noval or final_epoch:  # Calculate mAP
                results, maps, _ = validate.run(data_dict,
                                                batch_size=batch_size // WORLD_SIZE * 2,
                                                imgsz=imgsz,
                                                half=amp,
                                                model=ema.ema,
                                                single_cls=single_cls,
                                                dataloader=val_loader,
                                                save_dir=save_dir,
                                                plots=False,
                                                callbacks=callbacks,
                                                compute_loss=compute_loss)

            # Update best mAP
            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            stop = stopper(epoch=epoch, fitness=fi)  # early stop check
            if fi &gt; best_fitness:
                best_fitness = fi
            log_vals = list(mloss) + list(results) + lr
            callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)

            # Save model
            if (not nosave) or (final_epoch and not evolve):  # if save
                ckpt = {
                    'epoch': epoch,
                    'best_fitness': best_fitness,
                    'model': deepcopy(de_parallel(model)).half(),
                    'ema': deepcopy(ema.ema).half(),
                    'updates': ema.updates,
                    'optimizer': optimizer.state_dict(),
                    'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
                    'opt': vars(opt),
                    'date': datetime.now().isoformat()}

                # Save last, best and delete
                torch.save(ckpt, last)
                if best_fitness == fi:
                    torch.save(ckpt, best)
                if opt.save_period &gt; 0 and epoch % opt.save_period == 0:
                    torch.save(ckpt, w / f'epoch{epoch}.pt')
                del ckpt
                callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)

        # EarlyStopping
        if RANK != -1:  # if DDP training
            broadcast_list = [stop if RANK == 0 else None]
            dist.broadcast_object_list(broadcast_list, 0)  # broadcast 'stop' to all ranks
            if RANK != 0:
                stop = broadcast_list[0]
        if stop:
            break  # must break all DDP ranks

        # end epoch ----------------------------------------------------------------------------------------------------
    # end training -----------------------------------------------------------------------------------------------------
    if RANK in {-1, 0}:
        LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
        for f in last, best:
            if f.exists():
                strip_optimizer(f)  # strip optimizers
                if f is best:
                    LOGGER.info(f'\nValidating {f}...')
                    results, _, _ = validate.run(
                        data_dict,
                        batch_size=batch_size // WORLD_SIZE * 2,
                        imgsz=imgsz,
                        model=attempt_load(f, device).half(),
                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
                        single_cls=single_cls,
                        dataloader=val_loader,
                        save_dir=save_dir,
                        save_json=is_coco,
                        verbose=True,
                        plots=plots,
                        callbacks=callbacks,
                        compute_loss=compute_loss)  # val best model with plots
                    if is_coco:
                        callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)

        callbacks.run('on_train_end', last, best, epoch, results)

    torch.cuda.empty_cache()
    return results


def parse_opt(known=False):
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
    parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
    parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
    parser.add_argument('--epochs', type=int, default=32, help='total training epochs')
    parser.add_argument('--batch-size', type=int, default=30, help='total batch size for all GPUs, -1 for autobatch')
    parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
    parser.add_argument('--rect', action='store_true', help='rectangular training')
    parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
    parser.add_argument('--noval', action='store_true', help='only validate final epoch')
    parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
    parser.add_argument('--noplots', action='store_true', help='save no plot files')
    parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
    parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
    parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
    parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
    parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
    parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--quad', action='store_true', help='quad dataloader')
    parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
    parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
    parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
    parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
    parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if &lt; 1)')
    parser.add_argument('--seed', type=int, default=0, help='Global training seed')
    parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')

    # Weights &amp; Biases arguments
    parser.add_argument('--entity', default=None, help='W&amp;B: Entity')
    parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&amp;B: Upload data, "val" option')
    parser.add_argument('--bbox_interval', type=int, default=-1, help='W&amp;B: Set bounding-box image logging interval')
    parser.add_argument('--artifact_alias', type=str, default='latest', help='W&amp;B: Version of dataset artifact to use')

    return parser.parse_known_args()[0] if known else parser.parse_args()


def main(opt, callbacks=Callbacks()):
    # Checks
    if RANK in {-1, 0}:
        print_args(vars(opt))
        check_git_status()
        check_requirements()

    # Resume
    if opt.resume and not (check_wandb_resume(opt) or opt.evolve):  # resume from specified or most recent last.pt
        last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
        opt_yaml = last.parent.parent / 'opt.yaml'  # train options yaml
        opt_data = opt.data  # original dataset
        if opt_yaml.is_file():
            with open(opt_yaml, errors='ignore') as f:
                d = yaml.safe_load(f)
        else:
            d = torch.load(last, map_location='cpu')['opt']
        opt = argparse.Namespace(**d)  # replace
        opt.cfg, opt.weights, opt.resume = '', str(last), True  # reinstate
        if is_url(opt_data):
            opt.data = check_file(opt_data)  # avoid HUB resume auth timeout
    else:
        opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
            check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project)  # checks
        assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
        if opt.evolve:
            if opt.project == str(ROOT / 'runs/train'):  # if default project name, rename to runs/evolve
                opt.project = str(ROOT / 'runs/evolve')
            opt.exist_ok, opt.resume = opt.resume, False  # pass resume to exist_ok and disable resume
        if opt.name == 'cfg':
            opt.name = Path(opt.cfg).stem  # use model.yaml as name
        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))

    # DDP mode
    device = select_device(opt.device, batch_size=opt.batch_size)
    if LOCAL_RANK != -1:
        msg = 'is not compatible with YOLOv5 Multi-GPU DDP training'
        assert not opt.image_weights, f'--image-weights {msg}'
        assert not opt.evolve, f'--evolve {msg}'
        assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
        assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
        assert torch.cuda.device_count() &gt; LOCAL_RANK, 'insufficient CUDA devices for DDP command'
        torch.cuda.set_device(LOCAL_RANK)
        device = torch.device('cuda', LOCAL_RANK)
        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")

    # Train
    if not opt.evolve:
        train(opt.hyp, opt, device, callbacks)

    # Evolve hyperparameters (optional)
    else:
        # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
        meta = {
            'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
            'lrf': (1, 0.01, 1.0),  # final OneCycleLR learning rate (lr0 * lrf)
            'momentum': (0.3, 0.6, 0.98),  # SGD momentum/Adam beta1
            'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
            'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
            'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
            'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
            'box': (1, 0.02, 0.2),  # box loss gain
            'cls': (1, 0.2, 4.0),  # cls loss gain
            'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
            'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)
            'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
            'iou_t': (0, 0.1, 0.7),  # IoU training threshold
            'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
            'anchors': (2, 2.0, 10.0),  # anchors per output grid (0 to ignore)
            'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
            'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
            'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)
            'hsv_v': (1, 0.0, 0.9),  # image HSV-Value augmentation (fraction)
            'degrees': (1, 0.0, 45.0),  # image rotation (+/- deg)
            'translate': (1, 0.0, 0.9),  # image translation (+/- fraction)
            'scale': (1, 0.0, 0.9),  # image scale (+/- gain)
            'shear': (1, 0.0, 10.0),  # image shear (+/- deg)
            'perspective': (0, 0.0, 0.001),  # image perspective (+/- fraction), range 0-0.001
            'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
            'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
            'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
            'mixup': (1, 0.0, 1.0),  # image mixup (probability)
            'copy_paste': (1, 0.0, 1.0)}  # segment copy-paste (probability)

        with open(opt.hyp, errors='ignore') as f:
            hyp = yaml.safe_load(f)  # load hyps dict
            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
                hyp['anchors'] = 3
        if opt.noautoanchor:
            del hyp['anchors'], meta['anchors']
        opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir)  # only val/save final epoch
        # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
        evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
        if opt.bucket:
            os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}')  # download evolve.csv if exists

        for _ in range(opt.evolve):  # generations to evolve
            if evolve_csv.exists():  # if evolve.csv exists: select best hyps and mutate
                # Select parent(s)
                parent = 'single'  # parent selection method: 'single' or 'weighted'
                x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1)
                n = min(5, len(x))  # number of previous results to consider
                x = x[np.argsort(-fitness(x))][:n]  # top n mutations
                w = fitness(x) - fitness(x).min() + 1E-6  # weights (sum &gt; 0)
                if parent == 'single' or len(x) == 1:
                    # x = x[random.randint(0, n - 1)]  # random selection
                    x = x[random.choices(range(n), weights=w)[0]]  # weighted selection
                elif parent == 'weighted':
                    x = (x * w.reshape(n, 1)).sum(0) / w.sum()  # weighted combination

                # Mutate
                mp, s = 0.8, 0.2  # mutation probability, sigma
                npr = np.random
                npr.seed(int(time.time()))
                g = np.array([meta[k][0] for k in hyp.keys()])  # gains 0-1
                ng = len(meta)
                v = np.ones(ng)
                while all(v == 1):  # mutate until a change occurs (prevent duplicates)
                    v = (g * (npr.random(ng) &lt; mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
                for i, k in enumerate(hyp.keys()):  # plt.hist(v.ravel(), 300)
                    hyp[k] = float(x[i + 7] * v[i])  # mutate

            # Constrain to limits
            for k, v in meta.items():
                hyp[k] = max(hyp[k], v[1])  # lower limit
                hyp[k] = min(hyp[k], v[2])  # upper limit
                hyp[k] = round(hyp[k], 5)  # significant digits

            # Train mutation
            results = train(hyp.copy(), opt, device, callbacks)
            callbacks = Callbacks()
            # Write mutation results
            print_mutation(results, hyp.copy(), save_dir, opt.bucket)

        # Plot results
        plot_evolve(evolve_csv)
        LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
                    f"Results saved to {colorstr('bold', save_dir)}\n"
                    f'Usage example: $ python train.py --hyp {evolve_yaml}')


def run(**kwargs):
    # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
    opt = parse_opt(True)
    for k, v in kwargs.items():
        setattr(opt, k, v)
    main(opt)
    return opt


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)```</code></pre>

ptrblck: <p>Yes, you would need to fix the installation first as it seems you are using the CPU-only binaries:</p>
<pre><code class="lang-auto">Python-3.10.8 torch-1.12.1 CPU
</code></pre>
<p>if I understand this output correctly.<br>
If you have trouble installing the binaries in the current environment I would recommend to create a new vitual environment and reinstall all needed binaries there using the install commands provided <a href="https://pytorch.org/get-started/locally/">here</a>.</p>

Symbadian1: <p>Ok ok ok <a class="mention" href="/u/ptrblck">@ptrblck</a> , I understand that bit, but this is the error I am getting FOR THE MAC!!</p>
<p><code>NotImplementedError: The operator 'aten::remainder.Tensor_out' is not currently implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on https://github.com/pytorch/pytorch/issues/77764. As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.</code></p>
<p>HENCE, I moved to the supercomputer on campus as this took weeks to process the training operations when I set the fallback command to 1</p>

Symbadian1: <p>Can this work for the LINUX SERVER as well if i created this environment therein?</p>

ptrblck: <aside class="quote no-group" data-username="Symbadian1" data-post="6" data-topic="170294">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/symbadian1/48/56423_2.png" class="avatar"> Symbadian1:</div>
<blockquote>
<p>I understand that bit, but this is the error I am getting FOR THE MAC!!</p>
</blockquote>
</aside>
<p>My response was not targeting the Mac issue, but your workaround of using the GPU server, which you claimed yields:</p>
<aside class="quote no-group" data-username="Symbadian1" data-post="1" data-topic="170294">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/symbadian1/48/56423_2.png" class="avatar"> Symbadian1:</div>
<blockquote>
<p>some WEIRD Errors for which I’m lacking the knowledge to understand the packages.</p>
</blockquote>
</aside>

Symbadian: <p>Hi Pal, I managed the work around and understood your guidance!</p>
<p>I got a new ERROR and that shows some signs of progress!</p>
<p>Can you guide me?</p>
<ol>
<li>in terms of if the command I am using to initiate the GPU</li>
<li>How to analyse this new error! (I am not seeing a missing module/library, see error below, plz)</li>
</ol>
<pre><code class="lang-auto">$ python3 -m torch.distributed.run --nproc_per_node 4 --master_port 1040 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
WARNING:__main__:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
*****************************************
Traceback (most recent call last):
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 634, in &lt;module&gt;
    main(opt)
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 514, in main
    device = select_device(opt.device, batch_size=opt.batch_size)
  File "/home/MattCCTV/YOLO9Classes/yolov5/utils/torch_utils.py", line 118, in select_device
    assert torch.cuda.is_available() and torch.cuda.device_count() &gt;= len(device.replace(',', '')), \
AssertionError: Invalid CUDA '--device 0,1,2,3' requested, use '--device cpu' or pass valid CUDA device(s)
Traceback (most recent call last):
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 634, in &lt;module&gt;
    main(opt)
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 514, in main
    device = select_device(opt.device, batch_size=opt.batch_size)
  File "/home/MattCCTV/YOLO9Classes/yolov5/utils/torch_utils.py", line 118, in select_device
    assert torch.cuda.is_available() and torch.cuda.device_count() &gt;= len(device.replace(',', '')), \
AssertionError: Invalid CUDA '--device 0,1,2,3' requested, use '--device cpu' or pass valid CUDA device(s)
Traceback (most recent call last):
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 634, in &lt;module&gt;
    main(opt)
  File "/home/MattCCTV/YOLO9Classes/yolov5/train.py", line 514, in main
    device = select_device(opt.device, batch_size=opt.batch_size)
  File "/home/MattCCTV/YOLO9Classes/yolov5/utils/torch_utils.py", line 118, in select_device
    assert torch.cuda.is_available() and torch.cuda.device_count() &gt;= len(device.replace(',', '')), \
AssertionError: Invalid CUDA '--device 0,1,2,3' requested, use '--device cpu' or pass valid CUDA device(s)
train: weights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0,1,2,3, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 48089 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 1 (pid: 48090) of binary: /home/MattCCTV/.conda/envs/YOLO9C/bin/python3
Traceback (most recent call last):
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/run.py", line 765, in &lt;module&gt;
    main()
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 345, in wrapper
    return f(*args, **kwargs)
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/run.py", line 761, in main
    run(args)
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/run.py", line 752, in run
    elastic_launch(
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/MattCCTV/.conda/envs/YOLO9C/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2023-01-18_08:45:09
  host      : copperhead.buckingham.ac.uk
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 48092)
  error_file: &lt;N/A&gt;
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2023-01-18_08:45:09
  host      : copperhead.buckingham.ac.uk
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 48093)
  error_file: &lt;N/A&gt;
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2023-01-18_08:45:09
  host      : copperhead.buckingham.ac.uk
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 48090)
  error_file: &lt;N/A&gt;
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================```


Thank yous in advance and I am ever grateful for your guidance...
I know it says invalid CUDA but the device is there!!



I also tried this command

```CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py```

got this error

```**requirements:** YOLOv5 requirements "thop&gt;=0.1.1" "torchvision&gt;=0.8.1" not found, attempting AutoUpdate...```</code></pre>

ptrblck: <p>Are you able to use the specified 4 GPUs in a pure PyTorch script e.g. just allocating any tensors?</p>
<pre><code class="lang-python">a = torch.randn(1).to("cuda:0")
b = torch.randn(1).to("cuda:1")
c = torch.randn(1).to("cuda:2")
d = torch.randn(1).to("cuda:3")
</code></pre>
<p>Based on the error message the <code>torch_utils.py</code> script from the repository claims the specified devices are invalid.</p>

Symbadian: <p>This is really confusing me pal, should I change the bits in the torch_utils.py?</p>
<p>I’ve been searching therein but didn’t see anything that represent your notes!</p>
<p>so I ran this, below and all of the devices are OFF and I am not certain how to turn them ON!!!</p>
<pre><code class="lang-auto"> nvidia-smi
Sun Jan 15 10:40:44 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01    Driver Version: 440.33.01    CUDA Version: 10.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla P100-SXM2...  Off  | 00000002:01:00.0 Off |                    0 |
| N/A   28C    P0    40W / 300W |      0MiB / 16280MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla P100-SXM2...  Off  | 00000003:01:00.0 Off |                    0 |
| N/A   26C    P0    40W / 300W |      0MiB / 16280MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  Tesla P100-SXM2...  Off  | 0000000A:01:00.0 Off |                    0 |
| N/A   29C    P0    39W / 300W |      0MiB / 16280MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  Tesla P100-SXM2...  Off  | 0000000B:01:00.0 Off |                    0 |
| N/A   30C    P0    30W / 300W |      0MiB / 16280MiB |      4%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found       ```


Your guidance here would be greatly appreciated, please?</code></pre>

Symbadian1: <p>Hi <a class="mention" href="/u/ptrblck">@ptrblck</a>, This is the torch_utils.py file! I am not seeing what you’re referring to! more guidance requested, please…</p>
<pre><code class="lang-auto"># YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
PyTorch utils
"""

import math
import os
import platform
import subprocess
import time
import warnings
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP

from utils.general import LOGGER, check_version, colorstr, file_date, git_describe

LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))

try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None

# Suppress PyTorch warnings
warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
warnings.filterwarnings('ignore', category=UserWarning)


def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
    # Applies torch.inference_mode() decorator if torch&gt;=1.9.0 else torch.no_grad() decorator
    def decorate(fn):
        return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)

    return decorate


def smartCrossEntropyLoss(label_smoothing=0.0):
    # Returns nn.CrossEntropyLoss with label smoothing enabled for torch&gt;=1.10.0
    if check_version(torch.__version__, '1.10.0'):
        return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
    if label_smoothing &gt; 0:
        LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch&gt;=1.10.0')
    return nn.CrossEntropyLoss()


def smart_DDP(model):
    # Model DDP creation with checks
    assert not check_version(torch.__version__, '1.12.0', pinned=True), \
        'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
        'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
    if check_version(torch.__version__, '1.11.0'):
        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
    else:
        return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)


def reshape_classifier_output(model, n=1000):
    # Update a TorchVision classification model to class count 'n' if required
    from models.common import Classify
    name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1]  # last module
    if isinstance(m, Classify):  # YOLOv5 Classify() head
        if m.linear.out_features != n:
            m.linear = nn.Linear(m.linear.in_features, n)
    elif isinstance(m, nn.Linear):  # ResNet, EfficientNet
        if m.out_features != n:
            setattr(model, name, nn.Linear(m.in_features, n))
    elif isinstance(m, nn.Sequential):
        types = [type(x) for x in m]
        if nn.Linear in types:
            i = types.index(nn.Linear)  # nn.Linear index
            if m[i].out_features != n:
                m[i] = nn.Linear(m[i].in_features, n)
        elif nn.Conv2d in types:
            i = types.index(nn.Conv2d)  # nn.Conv2d index
            if m[i].out_channels != n:
                m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)


@contextmanager
def torch_distributed_zero_first(local_rank: int):
    # Decorator to make all processes in distributed training wait for each local_master to do something
    if local_rank not in [-1, 0]:
        dist.barrier(device_ids=[local_rank])
    yield
    if local_rank == 0:
        dist.barrier(device_ids=[0])


def device_count():
    # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows
    assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'
    try:
        cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""'  # Windows
        return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
    except Exception:
        return 0


def select_device(device='', batch_size=0, newline=True):
    # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
    s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
    device = str(device).strip().lower().replace('cuda:', '').replace('none', '')  # to string, 'cuda:0' to '0'
    cpu = device == 'cpu'
    mps = device == 'mps'  # Apple Metal Performance Shaders (MPS)
    if cpu or mps:
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable - must be before assert is_available()
        assert torch.cuda.is_available() and torch.cuda.device_count() &gt;= len(device.replace(',', '')), \
            f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"

    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
        devices = device.split(',') if device else '0'  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
        n = len(devices)  # device count
        if n &gt; 1 and batch_size &gt; 0:  # check batch_size is divisible by device_count
            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
        space = ' ' * (len(s) + 1)
        for i, d in enumerate(devices):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 &lt;&lt; 20):.0f}MiB)\n"  # bytes to MB
        arg = 'cuda:0'
    elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available():  # prefer MPS if available
        s += 'MPS\n'
        arg = 'mps'
    else:  # revert to CPU
        s += 'CPU\n'
        arg = 'cpu'

    if not newline:
        s = s.rstrip()
    LOGGER.info(s)
    return torch.device(arg)


def time_sync():
    # PyTorch-accurate time
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()


def profile(input, ops, n=10, device=None):
    """ YOLOv5 speed/memory/FLOPs profiler
    Usage:
        input = torch.randn(16, 3, 640, 640)
        m1 = lambda x: x * torch.sigmoid(x)
        m2 = nn.SiLU()
        profile(input, [m1, m2], n=100)  # profile over 100 iterations
    """
    results = []
    if not isinstance(device, torch.device):
        device = select_device(device)
    print(f"{'Params':&gt;12s}{'GFLOPs':&gt;12s}{'GPU_mem (GB)':&gt;14s}{'forward (ms)':&gt;14s}{'backward (ms)':&gt;14s}"
          f"{'input':&gt;24s}{'output':&gt;24s}")

    for x in input if isinstance(input, list) else [input]:
        x = x.to(device)
        x.requires_grad = True
        for m in ops if isinstance(ops, list) else [ops]:
            m = m.to(device) if hasattr(m, 'to') else m  # device
            m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
            try:
                flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
            except Exception:
                flops = 0

            try:
                for _ in range(n):
                    t[0] = time_sync()
                    y = m(x)
                    t[1] = time_sync()
                    try:
                        _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
                        t[2] = time_sync()
                    except Exception:  # no backward method
                        # print(e)  # for debug
                        t[2] = float('nan')
                    tf += (t[1] - t[0]) * 1000 / n  # ms per op forward
                    tb += (t[2] - t[1]) * 1000 / n  # ms per op backward
                mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0  # (GB)
                s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y))  # shapes
                p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0  # parameters
                print(f'{p:12}{flops:12.4g}{mem:&gt;14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):&gt;24s}{str(s_out):&gt;24s}')
                results.append([p, flops, mem, tf, tb, s_in, s_out])
            except Exception as e:
                print(e)
                results.append(None)
            torch.cuda.empty_cache()
    return results


def is_parallel(model):
    # Returns True if model is of type DP or DDP
    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)


def de_parallel(model):
    # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
    return model.module if is_parallel(model) else model


def initialize_weights(model):
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
            m.inplace = True


def find_modules(model, mclass=nn.Conv2d):
    # Finds layer indices matching module class 'mclass'
    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]


def sparsity(model):
    # Return global model sparsity
    a, b = 0, 0
    for p in model.parameters():
        a += p.numel()
        b += (p == 0).sum()
    return b / a


def prune(model, amount=0.3):
    # Prune model to requested global sparsity
    import torch.nn.utils.prune as prune
    for name, m in model.named_modules():
        if isinstance(m, nn.Conv2d):
            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
            prune.remove(m, 'weight')  # make permanent
    LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')


def fuse_conv_and_bn(conv, bn):
    # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
    fusedconv = nn.Conv2d(conv.in_channels,
                          conv.out_channels,
                          kernel_size=conv.kernel_size,
                          stride=conv.stride,
                          padding=conv.padding,
                          dilation=conv.dilation,
                          groups=conv.groups,
                          bias=True).requires_grad_(False).to(conv.weight.device)

    # Prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))

    # Prepare spatial bias
    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv


def model_info(model, verbose=False, imgsz=640):
    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
    n_p = sum(x.numel() for x in model.parameters())  # number parameters
    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
    if verbose:
        print(f"{'layer':&gt;5} {'name':&gt;40} {'gradient':&gt;9} {'parameters':&gt;12} {'shape':&gt;20} {'mu':&gt;10} {'sigma':&gt;10}")
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace('module_list.', '')
            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))

    try:  # FLOPs
        p = next(model.parameters())
        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
        im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
        flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2  # stride GFLOPs
        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
        fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs'  # 640x640 GFLOPs
    except Exception:
        fs = ''

    name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'
    LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")


def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
    # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
    if ratio == 1.0:
        return img
    h, w = img.shape[2:]
    s = (int(h * ratio), int(w * ratio))  # new size
    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
    if not same_shape:  # pad/crop img
        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean


def copy_attr(a, b, include=(), exclude=()):
    # Copy attributes from b to a, options to only include [...] and to exclude [...]
    for k, v in b.__dict__.items():
        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
            continue
        else:
            setattr(a, k, v)


def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
    # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
    g = [], [], []  # optimizer parameter groups
    bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
    for v in model.modules():
        for p_name, p in v.named_parameters(recurse=0):
            if p_name == 'bias':  # bias (no decay)
                g[2].append(p)
            elif p_name == 'weight' and isinstance(v, bn):  # weight (no decay)
                g[1].append(p)
            else:
                g[0].append(p)  # weight (with decay)

    if name == 'Adam':
        optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum
    elif name == 'AdamW':
        optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
    elif name == 'RMSProp':
        optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
    elif name == 'SGD':
        optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
    else:
        raise NotImplementedError(f'Optimizer {name} not implemented.')

    optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
    optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
                f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
    return optimizer


def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
    # YOLOv5 torch.hub.load() wrapper with smart error/issue handling
    if check_version(torch.__version__, '1.9.1'):
        kwargs['skip_validation'] = True  # validation causes GitHub API rate limit errors
    if check_version(torch.__version__, '1.12.0'):
        kwargs['trust_repo'] = True  # argument required starting in torch 0.12
    try:
        return torch.hub.load(repo, model, **kwargs)
    except Exception:
        return torch.hub.load(repo, model, force_reload=True, **kwargs)


def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
    # Resume training from a partially trained checkpoint
    best_fitness = 0.0
    start_epoch = ckpt['epoch'] + 1
    if ckpt['optimizer'] is not None:
        optimizer.load_state_dict(ckpt['optimizer'])  # optimizer
        best_fitness = ckpt['best_fitness']
    if ema and ckpt.get('ema'):
        ema.ema.load_state_dict(ckpt['ema'].float().state_dict())  # EMA
        ema.updates = ckpt['updates']
    if resume:
        assert start_epoch &gt; 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
                                f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
        LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')
    if epochs &lt; start_epoch:
        LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
        epochs += ckpt['epoch']  # finetune additional epochs
    return best_fitness, start_epoch, epochs


class EarlyStopping:
    # YOLOv5 simple early stopper
    def __init__(self, patience=30):
        self.best_fitness = 0.0  # i.e. mAP
        self.best_epoch = 0
        self.patience = patience or float('inf')  # epochs to wait after fitness stops improving to stop
        self.possible_stop = False  # possible stop may occur next epoch

    def __call__(self, epoch, fitness):
        if fitness &gt;= self.best_fitness:  # &gt;= 0 to allow for early zero-fitness stage of training
            self.best_epoch = epoch
            self.best_fitness = fitness
        delta = epoch - self.best_epoch  # epochs without improvement
        self.possible_stop = delta &gt;= (self.patience - 1)  # possible stop may occur next epoch
        stop = delta &gt;= self.patience  # stop training if patience exceeded
        if stop:
            LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
                        f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
                        f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
                        f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')
        return stop


class ModelEMA:
    """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
    Keeps a moving average of everything in the model state_dict (parameters and buffers)
    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
    """

    def __init__(self, model, decay=0.9999, tau=2000, updates=0):
        # Create EMA
        self.ema = deepcopy(de_parallel(model)).eval()  # FP32 EMA
        self.updates = updates  # number of EMA updates
        self.decay = lambda x: decay * (1 - math.exp(-x / tau))  # decay exponential ramp (to help early epochs)
        for p in self.ema.parameters():
            p.requires_grad_(False)

    def update(self, model):
        # Update EMA parameters
        self.updates += 1
        d = self.decay(self.updates)

        msd = de_parallel(model).state_dict()  # model state_dict
        for k, v in self.ema.state_dict().items():
            if v.dtype.is_floating_point:  # true for FP16 and FP32
                v *= d
                v += (1 - d) * msd[k].detach()
        # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'

    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
        # Update EMA attributes
        copy_attr(self.ema, model, include, exclude)
</code></pre>