how to convert/port gensim word2vec to tensorflow projector board.

gensim2projector_tf.py

# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

# loading your gensim
model = Word2Vec.load("YOUR-MODEL")

# project part of vocab, 10K of 300 dimension
w2v_10K = np.zeros((10000,300))
with open("./projector/prefix_metadata.tsv", 'w+') as file_metadata:
    for i,word in enumerate(model.wv.index2word[:10000]):
        w2v_10K[i] = model[word]
        file_metadata.write(word.encode('utf-8') + '\n')
        

# define the model without training
sess = tf.InteractiveSession()

with tf.device("/cpu:0"):
    embedding = tf.Variable(w2v_10K, trainable=False, name='prefix_embedding')

tf.global_variables_initializer().run()

saver = tf.train.Saver()
writer = tf.summary.FileWriter('./projector', sess.graph)

# adding into projector
config = projector.ProjectorConfig()
embed= config.embeddings.add()
embed.tensor_name = 'fs_embedding:0'
embed.metadata_path = './projector/prefix_metadata.tsv'

# Specify the width and height of a single thumbnail.
projector.visualize_embeddings(writer, config)

saver.save(sess, './projector/prefix_model.ckpt', global_step=10000)

# open tensorboard with logdir, check localhost:6006 for viewing your embedding.
# tensorboard --logdir="./projector/"

The cause of my "Point: Loading... | Dimensions: Loading..." error was related to the fact that my laptop doesn't support WebGl...Dunno if it can be someone else's reason of failure though.
Also be aware that if you're running tensorboard in the Virtualbox lots of functionalities will not work.