message: Update README.md, fixes
  Remove references to `gather` command, clarify documentation on `train`.
diff: @@ -239,8 +239,6 @@ models and games referenced above.
  The commands are
  - bootstrap: initializes a random model
  - selfplay: plays games with the latest model, producing data used for training
  - - gather: groups games played with the same model into larger files of
  - tfexamples.
  - train: trains a new model with the selfplay results from the most recent N
  generations.
  @@ -283,51 +281,23 @@ BOARD_SIZE=19 python3 main.py selfplay gs://$BUCKET_NAME/models/$MODEL_NAME \
  --output-sgf=gs://$BUCKET_NAME/sgf/$MODEL_NAME/local_worker
  ```
  -Gather
  -------
  -
  -```
  -python3 main.py gather
  -```
  -
  -This command takes multiple tfrecord.zz files (which will probably be KBs in size)
  -and shuffles them into tfrecord.zz files that are ~100 MB in size.
  -
  -Gathering is done according to model numbers, so that games generated by
  -one model stay together. By default, [rl_loop.py](rl_loop.py) will use directories
  -specified by the environment variable `BUCKET_NAME`, set at the top of
  -[rl_loop.py](rl_loop.py).
  -
  -```
  -gs://$BUCKET_NAME/data/training_chunks/$MODEL_NAME-{chunk_number}.tfrecord.zz
  -```
  -
  -The file `gs://$BUCKET_NAME/data/training_chunks/meta.txt`is used to keep track of
  -which games have been processed so far. (more about this needed)
  -
  -```bash
  -python3 main.py gather \
  - --input-directory=gs://$BUCKET_NAME/data/selfplay \
  - --output-directory=gs://$BUCKET_NAME/data/training_chunks
  -```
  -
  Training
  --------
  -This command finds the most recent 50 models' training chunks and trains a new
  -model, starting from the latest model weights.
  +This command takes a directory of tfexample files from selfplay and trains a new
  +model, starting from the latest model weights in the `estimator_working_dir` parameter.
  Run the training job:
  ```
  -BOARD_SIZE=19 python3 main.py train\
  +BOARD_SIZE=19 python3 main.py train-dir \
  estimator_working_dir
  gs://$BUCKET_NAME/data/training_chunks \
  gs://$BUCKET_NAME/models/000001-somename \
  --generation-num=1 \
  ```
  -At the end of training, the latest checkpoint will be exported to the named
  -directory. Additionally, you can follow along with the training progress with
  +At the end of training, the latest checkpoint will be exported to the directory with the given name.
  +Additionally, you can follow along with the training progress with
  TensorBoard - if you point TensorBoard at the estimator working dir, it will
  find the training log files and display them.
  