# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    parser = argparse.ArgumentParser()
    parser.add_argument("--user", type=str, default="ubuntu")
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--key_path", type=str, default=None)
    parser.add_argument("--instance", type=str, default="V100:1")
    parser.add_argument("--provider", type=str, default="cheapest")
    parser.add_argument("--use_spot", type=bool, default=False)
    parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
    args, unknown = parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError("Cannot specify both BYO and on-demand cluster args")
        cluster = rh.cluster(
            name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
        )
    else:
        cluster = rh.cluster(
            name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    example_dir = args.example.rsplit("/", 1)[0]

    # Set up remote environment
    cluster.install_packages(["pip:./"])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
    cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    parser = argparse.ArgumentParser()
    parser.add_argument("--user", type=str, default="ubuntu")
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--key_path", type=str, default=None)
    parser.add_argument("--instance", type=str, default="V100:1")
    parser.add_argument("--provider", type=str, default="cheapest")
    parser.add_argument("--use_spot", type=bool, default=False)
    parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
    args, unknown = parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError("Cannot specify both BYO and on-demand cluster args")
        cluster = rh.cluster(
            name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
        )
    else:
        cluster = rh.cluster(
            name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    example_dir = args.example.rsplit("/", 1)[0]

    # Set up remote environment
    cluster.install_packages(["pip:./"])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([f"pip install -r transformers/examples/{example_dir}/requirements.txt"])
    cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)
