# Error in Colab: ImplicitFunc is very large and grpc\_status”:8

**URL:** <https://discuss.ray.io/t/error-in-colab-implicitfunc-is-very-large-and-grpc-status-8/4818>\
**Category:** Ray Tune\
**Created:** [January 27, 2022, 6:30pm UTC](https://discuss.ray.io/t/error-in-colab-implicitfunc-is-very-large-and-grpc-status-8/4818 "2022-01-27T18:30:02Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ko7129](https://avatars.discourse-cdn.com/v4/letter/k/2bfe46/32.png) [@ko7129](https://discuss.ray.io/u/ko7129)\
**Post date:** [January 27, 2022, 6:30pm UTC](https://discuss.ray.io/t/error-in-colab-implicitfunc-is-very-large-and-grpc-status-8/4818/1 "2022-01-27T18:30:02Z")

</div>

Greetings to the community!!

I am trying to grid search some parameters of my training function using ray tune.  
The input data to train\_cifar() used for training and testing are 2 lists of dimensions  
400x13000 and 40x13000, respectively.

Due to size I cannot produce a reproducible example, but below I show three different  
ways I have tried to ray tune my model.

In each case I receive the following error:

The actor ImplicitFunc is very large (95 MiB). Check that its definition is not implicitly  
capturing a large array or other object in scope. Tip: use ray.put() to put large objects  
in the Ray object store.

or this one:

debug\_error\_string = “{“created”:”@1643300850.335447653",“description”:  
“Error received from peer ipv4:172.28.0.2:45437”, “file”:“src/core/lib/surface/call.cc”, “file\_line”:1074, “grpc\_message”: “Received message larger than max (137418486 vs. 104857600)”,“grpc\_status”:8}"

I don’t understand what the limit of 95 MiB is since my lists are really small.

Any ideas of what am I doing wrong?

I am running the following codes to google’s Colab.

Kostas

**CODE I**

```auto
def train_cifar(config, data = None, checkpoint_dir=None):
  X_scaled_train_tmp = config["data1"]
  X_scaled_train2 = ray.get(X_scaled_train_tmp)

  X_scaled_test_tmp = config["data2"]
  X_scaled_test2 = ray.get(X_scaled_test_tmp)

def tunerTrain():
      config = {
        "data1" : X_scaled_train1,
        "data2" : X_scaled_test1,        
      }          
      scheduler = ASHAScheduler(
              ...
          )
      reporter = CLIReporter(
              ...
          )
      result = tune.run(
              partial(train_cifar, data_dir=data_dir),  
              ...
          )

tunerTrain()

```

**CODE II**

```auto
X_scaled_train = ...
X_scaled_test = ...

ray.init()
X_scaled_train1 = ray.put(X_scaled_train)
X_scaled_test1 = ray.put(X_scaled_test)

def train_cifar(config, data = None, checkpoint_dir=None):
  
  X_scaled_train2 = ray.get(data[0])
  X_scaled_test2 = ray.get(data[2])

def tunerTrain():
      config = {
              ...        
      }          
      scheduler = ASHAScheduler(
              ...
          )
      reporter = CLIReporter(
              ...
          )
      result = tune.run(
               tune.with_parameters(train_cifar, data=[X_scaled_train1, X_scaled_train_trait, 
                                                       X_scaled_test1, X_scaled_test_trait]),
              ...
          )

tunerTrain()

```

**CODE III**

```auto
X_scaled_train = ...
X_scaled_test = ...

def train_cifar(config, data = None, checkpoint_dir=None):
  
  X_scaled_train2 = data[0]
  X_scaled_test2 = data[2]

def tunerTrain():
      config = {
              ...        
      }          
      scheduler = ASHAScheduler(
              ...
          )
      reporter = CLIReporter(
              ...
          )
      result = tune.run(
               tune.with_parameters(train_cifar, data=[X_scaled_train, X_scaled_train_trait, 
                                                       X_scaled_test, X_scaled_test_trait]),
              ...
          )

tunerTrain()

```

---

<div class="post-metadata">

**Author:** ![rliaw](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/rliaw/32/24_2.png) [@rliaw](https://discuss.ray.io/u/rliaw)\
**Post date:** [February 8, 2022, 9:15am UTC](https://discuss.ray.io/t/error-in-colab-implicitfunc-is-very-large-and-grpc-status-8/4818/2 "2022-02-08T09:15:24Z")

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Hi there! I suspect that there’s probably a leak in your code implementation somewhere. Can you try making sure that wherever you create or put the data, you do it in `tunerTrain()`?

I suspect that you’re referencing X\_scaled\_train directly somewhere in train\_cifar.