google colab will not install the package.
seems to be a SSH issue on the github side.

@support

100% (119812 of 119812) |################| Elapsed Time: 0:00:00 Time: 0:00:00
Output cleaning...
fix uniq
ffill if the current price is None...
Check liquidity...
Ok.
Check missed dates...
Ok.
Normalization...
Output cleaning is complete.
Write output: /root/fractions.nc.gz
WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses while saving (showing 4 of 4). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: ram://fbf49b7a-6c3a-490c-94cb-b27fb3e70aff/assets
INFO:tensorflow:Assets written to: ram://fbf49b7a-6c3a-490c-94cb-b27fb3e70aff/assets
WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f651d7123d0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with tf.keras.models.load_model. If renaming is not possible, pass the object in the custom_objects parameter of the load function.
WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f651d716c10> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with tf.keras.models.load_model. If renaming is not possible, pass the object in the custom_objects parameter of the load function.
State saved.

Run First Iteration...
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ValueError Traceback (most recent call last)
<ipython-input-13-1448c3f28c90> in <module>
11 start_date='2014-01-01', # backtest start date
12 analyze =True,
---> 13 build_plots= True # do you need the chart?
14 )

~/book/qnt/backtester.py in backtest_ml(train, predict, train_period, retrain_interval, predict_each_day, retrain_interval_after_submit, competition_type, load_data, lookback_period, test_period, start_date, end_date, window, analyze, build_plots, collect_all_states)
143
144 train_data_slice = copy_window(data, data_ts[-1], train_period)
--> 145 model = train(train_data_slice)
146
147 test_data_slice = copy_window(data, data_ts[-1], lookback_period)

<ipython-input-11-b02ff8641cb5> in train_model(data)
22
23 current_size = current_asset.shape[0]
---> 24 current_asset = sc.fit_transform(current_asset.values.reshape(-1,1) )
25
26

/usr/local/lib/python3.7/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
697 if y is None:
698 # fit method of arity 1 (unsupervised transformation)
--> 699 return self.fit(X, **fit_params).transform(X)
700 else:
701 # fit method of arity 2 (supervised transformation)

/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/_data.py in fit(self, X, y)
361 # Reset internal state before fitting
362 self._reset()
--> 363 return self.partial_fit(X, y)
364
365 def partial_fit(self, X, y=None):

/usr/local/lib/python3.7/site-packages/sklearn/preprocessing/_data.py in partial_fit(self, X, y)
396 X = self._validate_data(X, reset=first_pass,
397 estimator=self, dtype=FLOAT_DTYPES,
--> 398 force_all_finite="allow-nan")
399
400 data_min = np.nanmin(X, axis=0)

/usr/local/lib/python3.7/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
419 out = X
420 elif isinstance(y, str) and y == 'no_validation':
--> 421 X = check_array(X, **check_params)
422 out = X
423 else:

/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0

/usr/local/lib/python3.7/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
727 " minimum of %d is required%s."
728 % (n_samples, array.shape, ensure_min_samples,
--> 729 context))
730
731 if ensure_min_features > 0 and array.ndim == 2:

ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by MinMaxScaler.

@support
My original post might be somewhat misleading.
First: I built the function with Keras 2.8 and TensorFlow 2.8 using their API
and not Sequential (),
Second: The error occurs on the Server and Colab using backtest_ml.
It seems that it appears after going thru my code but occurs at some point in the analytic stage.
Third: No error occurred when I ran the "Train_Model " and "Predict" function independently on either the server or on Colab
IE:
Train_Model( Crypto_daily_Data) this returns a Dict.[ history] of separate models for each asset, all with the same number of "Features.

predict(history,Crypto_Daily_Data)
this returns weights a grid of assets containing either [-1,1,0] for each asset.
The toolbox version used in colab is what your website refers to [ " using HTTPS ]. I was unable to determine the actual
version numbers. Also, I jupyter site you are currently providing.

Created an LSTM model for crossover when testing IE train and predict separately on the collab.
No errors.
when running quantic server, I get the following error [last line]

Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by MinMaxScaler.

I know this has been answered before, but I have some questions?

Using Keras 2.8 I have trained a dictionary of trained models. I'm designing it intending to use backtest_ml.

I called each of the functions separately to test. The dictionary of the trained model took about an hour to train; this is probably too long for submission purposes at this point.
I saved the model to my area as a CSV by converting it into a data frame [Pandas].
When I shut down and want to recall it, will it function? I examined the data, and it seems like it should. But I have not tried.
I edited the init.ipynb file but each time I have to run it, no problem. would not be convenient.
Do I have access to GPU's or TPU's

Thanks in advance
Alfred

@support Thanks seems fine

google colab will not install the package.
seems to be a SSH issue on the github side.