Return `resume` support for CheckpointCallback and `.train`, `predict`

🚀 Feature Request

Return the resume support for the CheckpointCallback and Runner.train, Runner.predict.

Motivation

That would be a great user-friendly feature – resume is quite common task during deep learning development.

Proposal

1. Uncommen the CheckpointCallback code and check its correctness… from my perspective, it should be refactored a bit.
2. Add its support to Runner.
3. Uncomment the cli interface and update the ConfigRunner and HydraRunner.

Proposed use case:

import os
from torch import nn, optim
from torch.utils.data import DataLoader
from catalyst import dl, utils
from catalyst.data.transforms import ToTensor
from catalyst.contrib.datasets import MNIST

model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.02)

loaders = {
    "train": DataLoader(
        MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
    ),
    "valid": DataLoader(
        MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
    ),
}

runner = dl.SupervisedRunner(
    input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.AccuracyCallback(input_key="logits", target_key="targets", topk_args=(1, 3, 5)),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=10
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
        # catalyst[ml] required ``pip install catalyst[ml]``
        # dl.ConfusionMatrixCallback(input_key="logits", target_key="targets", num_classes=10),
    ],
    logdir="./logs",
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
    load_best_on_end=True,
)

# here is the trick
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    loaders=loaders,
    num_epochs=1,
    callbacks=[
        dl.AccuracyCallback(input_key="logits", target_key="targets", topk_args=(1, 3, 5)),
        dl.PrecisionRecallF1SupportCallback(
            input_key="logits", target_key="targets", num_classes=10
        ),
        dl.AUCCallback(input_key="logits", target_key="targets"),
        # catalyst[ml] required ``pip install catalyst[ml]``
        # dl.ConfusionMatrixCallback(input_key="logits", target_key="targets", num_classes=10),
    ],
    # ----
    logdir="./logs2",
    resume="./logs/checkpoints/train.1.pth",
    # ----
    valid_loader="valid",
    valid_metric="loss",
    minimize_valid_metric=True,
    verbose=True,
    load_best_on_end=True,
)

Alternatives

Additional context

Checklist

• feature proposal description
• motivation
• extra proposal context / proposal alternatives review

FAQ

Please review the FAQ before submitting an issue:

• I have read the documentation and FAQ
• I have reviewed the minimal examples section
• I have checked the changelog for main framework updates
• I have read the contribution guide
• I have joined Catalyst slack (#__questions channel) for issue discussion

[y-ksenia]

We can close it, I guess 🥳

[y-ksenia]

Yes, just started