Thanks for the help.
I tried both of your answers, but neither works:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokenizer.add_tokens([  'committed'  ])
tokenizer.tokenize('hellocommitted')
>> ['hello', '##com', '##mit', '##ted']


tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokenizer.add_special_tokens({'unk_token': 'committed' })
tokenizer.tokenize('hellocommitted')
>> ['hello', '##com', '##mit', '##ted']

(Note: add_special_tokens failed with an assertion error, so i used unk_token as key)
Any other ideas?
Edit: What does work though is if I tokenize two new words, as follows:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
num = tokenizer.add_tokens(['helloxxx', 'committed'])
tokenizer.tokenize('helloxxxcommitted')
>> ['helloxxx', 'committed']

So I guess I could delete every word in the vocab and re-add it to achieve my goal, but I imagine there must be a better solution?