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RedisAI Commands

RedisAI is a Redis module, and as such it implements several data types and the respective commands to use them.

All of RedisAI's commands begin with the AI. prefix. The following sections describe these commands.

Syntax Conventions

The following conventions are used for describing the RedisAI Redis API:

  • COMMAND : a command or an argument name
  • <mandatory> : a mandatory argument
  • [optional] : an optional argument
  • " : a literal double quote character
  • | : an exclusive logical or operator
  • ... : more of the same as before

AI.TENSORSET

The AI.TENSORSET command stores a tensor as the value of a key.

Redis API

AI.TENSORSET <key> <type>
   <shape> [shape ...] [BLOB <data> | VALUES <val> [val ...]]

Arguments

  • key : the tensor's key name
  • type : the tensor's data type can be one of: FLOAT , DOUBLE , INT8 , INT16 , INT32 , INT64 , UINT8 or UINT16
  • shape : one or more dimensions, or the number of elements per axis, for the tensor
  • BLOB : indicates that data is in binary format and is provided via the subsequent data argument
  • VALUES : indicates that data is numeric and is provided by one or more subsequent val arguments

Return

A simple 'OK' string or an error.

Examples

Given the following: \begin{equation*} A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \\ \end{bmatrix} \end{equation*}

This will set the key 'mytensor' to the 2x2 RedisAI tensor:

redis> AI.TENSORSET mytensor FLOAT 2 2 VALUES 1 2 3 4
OK

Uninitialized Tensor Values

As both BLOB and VALUES are optional arguments, it is possible to use the AI.TENSORSET to create an uninitialized tensor.

Using BLOB is preferable to VALUES

While it is possible to set the tensor using binary data or numerical values, it is recommended that you use the BLOB option. It requires fewer resources and performs better compared to specifying the values discretely.

AI.TENSORGET

The AI.TENSORGET command returns a tensor stored as key's value.

Redis API

AI.TENSORGET <key> [META] [format]

Arguments

  • key : the tensor's key name
  • META : returns the tensor's metadata
  • format : the tensor's reply format can be one of the following:
    • BLOB : returns the binary representation of the tensor's data
    • VALUES : returns the numerical representation of the tensor's data

Return

Depending on the specified reply format:

  • META : Array containing the tensor's metadata exclusively. The returned array consists of the following elements:
    1. The tensor's data type as a String
    2. The tensor's shape as an Array consisting of an item per dimension
  • BLOB : the tensor's binary data as a String. If used together with the META option, the binary data string will put after the metadata in the array reply.
  • VALUES : Array containing the numerical representation of the tensor's data. If used together with the META option, the binary data string will put after the metadata in the array reply.
  • Default: META and BLOB are returned by default, in case that non of the arguments above is specified.

Examples

Given a tensor value stored at the 'mytensor' key:

redis> AI.TENSORSET mytensor FLOAT 2 2 VALUES 1 2 3 4
OK

The following shows how to retrieve the tensor's metadata:

redis> AI.TENSORGET mytensor META
1) "dtype"
2) "FLOAT"
3) "shape"
4) 1) (integer) 2
   2) (integer) 2

The following shows how to retrieve the tensor's values as an Array:

redis> AI.TENSORGET mytensor VALUES
1) "1"
2) "2"
3) "3"
4) "4"

The following shows how to retrieve the tensor's binary data as a String:

redis> AI.TENSORGET mytensor BLOB
"\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"

The following shows how the combine the retrieval of the tensor's metadata, and the tensor's values as an Array:

redis> AI.TENSORGET mytensor META VALUES
1) "dtype"
2) "FLOAT"
3) "shape"
4) 1) (integer) 2
   2) (integer) 2
5) "values"
6) 1) "1"
   2) "2"
   3) "3"
   4) "4"

The following shows how the combine the retrieval of the tensor's metadata, and binary data as a String:

redis> AI.TENSORGET mytensor META BLOB
1) "dtype"
2) "FLOAT"
3) "shape"
4) 1) (integer) 2
   2) (integer) 2
5) "blob"
6) "\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"

Using BLOB is preferable to VALUES

While it is possible to get the tensor as binary data or numerical values, it is recommended that you use the BLOB option. It requires fewer resources and performs better compared to returning the values discretely.

AI.MODELSTORE

The AI.MODELSTORE command stores a model as the value of a key.

Redis API

AI.MODELSTORE <key> <backend> <device>
    [TAG tag] [BATCHSIZE n [MINBATCHSIZE m [MINBATCHTIMEOUT t]]]
    [INPUTS <input_count> <name> ...] [OUTPUTS <output_count> <name> ...] BLOB <model>

Arguments

  • key : the model's key name
  • backend : the backend for the model can be one of:
    • TF : a TensorFlow backend
    • TFLITE : The TensorFlow Lite backend
    • TORCH : a PyTorch backend
    • ONNX : a ONNX backend
  • device : the device that will execute the model can be of:
    • CPU : a CPU device
    • GPU : a GPU device
    • GPU:0 , ..., GPU:n : a specific GPU device on a multi-GPU system
  • TAG : an optional string for tagging the model such as a version number or any arbitrary identifier
  • BATCHSIZE : when provided with an n that is greater than 0, the engine will batch incoming requests from multiple clients that use the model with input tensors of the same shape. When AI.MODELEXECUTE (or AI.MODELRUN ) is called the requests queue is visited and input tensors from compatible requests are concatenated along the 0th (batch) dimension until n is exceeded. The model is then run for the entire batch and the results are unpacked back to the individual requests unblocking their respective clients. If the batch size of the inputs to of first request in the queue exceeds BATCHSIZE , the request is served immediately (default value: 0).
  • MINBATCHSIZE : when provided with an m that is greater than 0, the engine will postpone calls to AI.MODELEXECUTE until the batch's size had reached m . In this case, note that requests for which m is not reached will hang indefinitely (default value: 0), unless MINBATCHTIMEOUT is provided.
  • MINBATCHTIMEOUT : when provided with a t (expressed in milliseconds) that is greater than 0, the engine will trigger a run even though MINBATCHSIZE has not been reached after t milliseconds from the time a MODELEXECUTE (or the enclosing DAGRUN ) is enqueued. This only applies to cases where both BATCHSIZE and MINBATCHSIZE are greater than 0.
  • INPUTS : denotes that one or more names of the model's input nodes are following, applicable only for TensorFlow models (specifying INPUTS for other backends will cause an error)
  • input_count : a positive number that indicates the number of following input nodes (also applicable only for TensorFlow)
  • OUTPUTS : denotes that one or more names of the model's output nodes are following, applicable only for TensorFlow models (specifying OUTPUTS for other backends will cause an error)
  • output_count : a positive number that indicates the number of following input nodes (also applicable only for TensorFlow)
  • model : the Protobuf-serialized model. Since Redis supports strings up to 512MB, blobs for very large models need to be chunked, e.g. BLOB chunk1 chunk2 ... .

Return

A simple 'OK' string or an error.

Examples

This example shows to set a model 'mymodel' key using the contents of a local file with redis-cli . Refer to the Clients Page for additional client choices that are native to your programming language:

$ cat resnet50.pb | redis-cli -x AI.MODELSTORE mymodel TF CPU TAG imagenet:5.0 INPUTS 1 images OUTPUTS 1 output BLOB
OK

AI.MODELSET

This command is deprecated and will not be available in future versions. consider using AI.MODELSTORE command instead. The AI.MODELSET command stores a model as the value of a key. The command's arguments and effect are both exactly the same as AI.MODELEXECUTE command, except that and arguments should not be specified for TF backend.

Redis API

AI.MODELSET <key> <backend> <device>
    [TAG tag] [BATCHSIZE n [MINBATCHSIZE m [MNBATCHTIMEOUT t]]]
    [INPUTS <name> ...] [OUTPUTS name ...] BLOB <model>

AI.MODELGET

The AI.MODELGET command returns a model's metadata and blob stored as a key's value.

Redis API

AI.MODELGET <key> [META] [BLOB]

_Arguments

  • key : the model's key name
  • META : will return only the model's meta information on backend, device, tag and batching parameters
  • BLOB : will return only the model's blob containing the serialized model

Return

An array of alternating key-value pairs as follows:

  1. BACKEND : the backend used by the model as a String
  2. DEVICE : the device used to execute the model as a String
  3. TAG : the model's tag as a String
  4. BATCHSIZE : The maximum size of any batch of incoming requests. If BATCHSIZE is equal to 0 each incoming request is served immediately. When BATCHSIZE is greater than 0, the engine will batch incoming requests from multiple clients that use the model with input tensors of the same shape.
  5. MINBATCHSIZE : The minimum size of any batch of incoming requests.
  6. INPUTS : array reply with one or more names of the model's input nodes (applicable only for TensorFlow models)
  7. OUTPUTS : array reply with one or more names of the model's output nodes (applicable only for TensorFlow models)
  8. MINBATCHTIMEOUT : The time in milliseconds for which the engine will wait before executing a request to run the model, when the number of incoming requests is lower than MINBATCHSIZE . When MINBATCHTIMEOUT is 0, the engine will not run the model before it receives at least MINBATCHSIZE requests.
  9. BLOB : a blob containing the serialized model as a String. If the size of the serialized model exceeds MODEL_CHUNK_SIZE (see AI.CONFIG command), then an array of chunks is returned. The full serialized model can be obtained by concatenating the chunks.

Examples

Assuming that your model is stored under the 'mymodel' key, you can obtain its metadata with:

redis> AI.MODELGET mymodel META
 1) "backend"
 2) "TF"
 3) "device"
 4) "CPU"
 5) "tag"
 6) "imagenet:5.0"
 7) "batchsize"
 8) (integer) 0
 9) "minbatchsize"
10) (integer) 0
11) "inputs"
12) 1) "a"
    2) "b"
13) "outputs"
14) 1) "c"
15) "minbatchtimeout"
16) (integer) 0

You can also save it to the local file 'model.ext' with redis-cli like so:

$ redis-cli --raw AI.MODELGET mymodel BLOB > model.ext

AI.MODELDEL

The AI.MODELDEL deletes a model stored as a key's value.

Redis API

AI.MODELDEL <key>

Arguments

  • key : the model's key name

Return

A simple 'OK' string or an error.

Examples

Assuming that your model is stored under the 'mymodel' key, you can delete it like this:

redis> AI.MODELDEL mymodel
OK

The AI.MODELDEL vis a vis the DEL command

The AI.MODELDEL is equivalent to the Redis DEL command and should be used in its stead. This ensures compatibility with all deployment options (i.e., stand-alone vs. cluster, OSS vs. Enterprise).

AI.MODELEXECUTE

The AI.MODELEXECUTE command runs a model stored as a key's value using its specified backend and device. It accepts one or more input tensors and store output tensors.

The run request is put in a queue and is executed asynchronously by a worker thread. The client that had issued the run request is blocked until the model run is completed. When needed, tensors data is automatically copied to the device prior to execution.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case).

Intermediate memory overhead

The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

Redis API

AI.MODELEXECUTE <key> INPUTS <input_count> <input> [input ...] OUTPUTS <output_count> <output> [output ...] [TIMEOUT t]

Arguments

  • key : the model's key name
  • INPUTS : denotes the beginning of the input tensors keys' list, followed by the number of inputs and one or more key names
  • input_count : a positive number that indicates the number of following input keys.
  • OUTPUTS : denotes the beginning of the output tensors keys' list, followed by the number of outputs one or more key names
  • output_count : a positive number that indicates the number of output keys to follow.
  • TIMEOUT : the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned

Return

A simple 'OK' string, a simple TIMEDOUT string, or an error.

Examples

Assuming that running the model that's stored at 'mymodel' with the tensor 'mytensor' as input outputs two tensors - 'classes' and 'predictions', the following command does that:

redis> AI.MODELEXECUTE mymodel INPUTS 1 mytensor OUTPUTS 2 classes predictions
OK

AI.MODELRUN

This command is deprecated and will not be available in future versions. consider using AI.MODELEXECUTE command instead.

The AI.MODELRUN command runs a model stored as a key's value using its specified backend and device. It accepts one or more input tensors and store output tensors.

The run request is put in a queue and is executed asynchronously by a worker thread. The client that had issued the run request is blocked until the model run is completed. When needed, tensors data is automatically copied to the device prior to execution.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case).

Intermediate memory overhead

The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

Redis API

AI.MODELRUN <key> [TIMEOUT t] INPUTS <input> [input ...] OUTPUTS <output> [output ...]

Arguments

  • key : the model's key name
  • TIMEOUT : the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned
  • INPUTS : denotes the beginning of the input tensors keys' list, followed by one or more key names
  • OUTPUTS : denotes the beginning of the output tensors keys' list, followed by one or more key names

Return

A simple 'OK' string, a simple TIMEDOUT string, or an error.

Examples

Assuming that running the model that's stored at 'mymodel' with the tensor 'mytensor' as input outputs two tensors - 'classes' and 'predictions', the following command does that:

redis> AI.MODELRUN mymodel INPUTS mytensor OUTPUTS classes predictions
OK

AI._MODELSCAN

The AI._MODELSCAN command returns all the models in the database. When using Redis open source cluster, the command shall return all the models that are stored in the local shard.

Experimental API

AI._MODELSCAN is an EXPERIMENTAL command that may be removed in future versions.

Redis API

AI._MODELSCAN

Arguments

None.

Return

An array with an entry per model. Each entry is an array with two entries:

  1. The model's key name as a String
  2. The model's tag as a String

Examples

redis> > AI._MODELSCAN
1) 1) "mymodel"
   2) imagenet:5.0

AI.SCRIPTSTORE

The AI.SCRIPTSTORE command stores a TorchScript as the value of a key.

Redis API

AI.SCRIPTSTORE <key> <device> [TAG tag] ENTRY_POINTS <entry_points_count> <entry_point> [<entry_point>...] SOURCE "<script>"

Arguments

  • key : the script's key name
  • TAG : an optional string for tagging the script such as a version number or any arbitrary identifier
  • device : the device that will execute the model can be of:
    • CPU : a CPU device
    • GPU : a GPU device
    • GPU:0 , ..., GPU:n : a specific GPU device on a multi-GPU system
  • ENTRY_POINTS A list of function names in the script to be used as entry points upon execution. Each entry point should have the signature of def entry_point(tensors: List[Tensor], keys: List[str], args: List[str]) . The purpose of each list is as follows:
  • tensors : A list holding the input tensors to the function.
  • keys : A list of keys that the torch script is about to preform read/write operations on.
  • args : A list of additional arguments to the function. If the desired argument is not from type string, it is up to the caller to cast it to the right type, within the script.
  • script : a string containing TorchScript source code

Return

A simple 'OK' string or an error.

Examples

Given the following contents of the file 'addtwo.py':

def addtwo(tensors: List[Tensor], keys: List[str], args: List[str]):
    a = tensors[0]
    b = tensors[1]
    return a + b

It can be stored as a RedisAI script using the CPU device with redis-cli as follows:

$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU TAG myscript:v0.1 ENTRY_POINTS 1 addtwo SOURCE
OK

AI.SCRIPTSET

This command is deprecated and will not be available in future versions. consider using AI.SCRIPTSTORE command instead. The AI.SCRIPTSET command stores a TorchScript as the value of a key.

Redis API

AI.SCRIPTSET <key> <device> [TAG tag] SOURCE "<script>"

Arguments

  • key : the script's key name
  • TAG : an optional string for tagging the script such as a version number or any arbitrary identifier
  • device : the device that will execute the model can be of:
    • CPU : a CPU device
    • GPU : a GPU device
    • GPU:0 , ..., GPU:n : a specific GPU device on a multi-GPU system
  • script : a string containing TorchScript source code

Return

A simple 'OK' string or an error.

Examples

Given the following contents of the file 'addtwo.py':

def addtwo(a, b):
    return a + b

It can be stored as a RedisAI script using the CPU device with redis-cli as follows:

$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU TAG myscript:v0.1 SOURCE
OK

AI.SCRIPTGET

The AI.SCRIPTGET command returns the TorchScript stored as a key's value.

Redis API

Get script metadata and source.

AI.SCRIPTGET <key> [META] [SOURCE]

Arguments

  • key : the script's key name
  • META : will return only the script's meta information on device, tag and entry points.
  • SOURCE : will return only the string containing TorchScript source code

Return

An array with alternating entries that represent the following key-value pairs: !!!!The command returns a list of key-value strings, namely DEVICE device TAG tag ENTRY_POINTS [entry_point ...] SOURCE source .

  1. DEVICE : the script's device as a String
  2. TAG : the scripts's tag as a String
  3. SOURCE : the script's source code as a String
  4. ENTRY_POINTS will return an array containing the script entry point functions

Examples

The following shows how to read the script stored at the 'myscript' key:

redis> AI.SCRIPTGET myscript
1) "device"
2) CPU
3) "tag"
4) "myscript:v0.1"
5) "source"
6) def addtwo(a, b):
    return a + b
7) "Entry Points"
8) 1) addtwo

AI.SCRIPTDEL

The AI.SCRIPTDEL deletes a script stored as a key's value.

Redis API

AI.SCRIPTDEL <key>

Arguments

  • key : the script's key name

Return

A simple 'OK' string or an error.

Examples

redis> AI.SCRIPTDEL myscript
OK

The AI.SCRIPTDEL vis a vis the DEL command

The AI.SCRIPTDEL is equivalent to the Redis DEL command and should be used in its stead. This ensures compatibility with all deployment options (i.e., stand-alone vs. cluster, OSS vs. Enterprise).

AI.SCRIPTEXECUTE

The AI.SCRIPTEXECUTE command runs a script stored as a key's value on its specified device. It receives a list of Redis keys, a list of input tensors and an additional list of arguments to be used in the script.

The run request is put in a queue and is executed asynchronously by a worker thread. The client that had issued the run request is blocked until the script run is completed. When needed, tensors data is automatically copied to the device prior to execution.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case).

Intermediate memory overhead

The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the TORCH backend (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

Redis API

AI.SCRIPTEXECUTE <key> <function> 
[KEYS <keys_count> <key> [keys...]]
[INPUTS <input_count> <input> [input ...]]
[ARGS <args_count> <arg> [arg...]]
[OUTPUTS <outputs_count> <output> [output ...]]
[TIMEOUT t]

Arguments

  • key : the script's key name.
  • function : the name of the entry point function to run.
  • KEYS : Denotes the beginning of a list of Redis key names that the script will access to during its execution, for both read and/or write operations.
  • INPUTS : Denotes the beginning of the input tensors list, followed by its length and one or more input tensors.
  • ARGS : Denotes the beginning of a list of additional arguments that a user can send to the script. All args are sent as strings, but can be casted to other types supported by torch script, such as int , or float .
  • OUTPUTS : denotes the beginning of the output tensors keys' list, followed by its length and one or more key names.
  • TIMEOUT : the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned

Note: Either KEYS or INPUTS scopes should be provided this command (one or both scopes are acceptable). Those scopes indicate keyspace access and such, the right shard to execute the command at. Redis will verify that all potential key accesses are done to the right shard.

Return

A simple 'OK' string, a simple TIMEDOUT string, or an error.

Examples

The following is an example of running the previously-created 'myscript' on two input tensors:

redis> AI.TENSORSET mytensor1{tag} FLOAT 1 VALUES 40
OK
redis> AI.TENSORSET mytensor2{tag} FLOAT 1 VALUES 2
OK
redis> AI.SCRIPTEXECUTE myscript{tag} addtwo INPUTS 2 mytensor1{tag} mytensor2{tag} OUTPUTS 1 result{tag}
OK
redis> AI.TENSORGET result{tag} VALUES
1) FLOAT
2) 1) (integer) 1
3) 1) "42"

An example that supports List[Tensor] arguments:

def addn(tensors: List[Tensor], keys: List[str], args: List[str]):
    return torch.stack(tensors).sum()

redis> AI.TENSORSET mytensor1{tag} FLOAT 1 VALUES 40
OK
redis> AI.TENSORSET mytensor2{tag} FLOAT 1 VALUES 1
OK
redis> AI.TENSORSET mytensor3{tag} FLOAT 1 VALUES 1
OK
redis> AI.SCRIPTEXECUTE myscript{tag} addn INPUTS 3 mytensor1{tag} mytensor2{tag} mytensor3{tag} OUTPUTS 1 result{tag}
OK
redis> AI.TENSORGET result{tag} VALUES
1) FLOAT
2) 1) (integer) 1
3) 1) "42"

Note: for the time being, as AI.SCRIPTSET is still available to use, AI.SCRIPTEXECUTE still supports running functions that are part of scripts stored with AI.SCRIPTSET or imported from old RDB/AOF files. Meaning calling AI.SCRIPTEXECUTE over a function without the dedicated signature of (tensors: List[Tensor], keys: List[str], args: List[str] will yield a "best effort" execution to match the deprecated API AI.SCRIPTRUN function execution. This will map INPUTS tensors only, to their counterpart input arguments in the function, according to the order which they appear.

Redis Commands support.

In RedisAI TorchScript now supports simple (non-blocking) Redis commands via the redis.execute API. The following script gets a key name ( x{1} ), and an int value (3). First, the script SET s the value in the key. Next, the script GET s the value back from the key, and sets it in a tensor which is eventually stored under the key 'y{1}'. Note that the inputs are str and int . The script sets and gets the value and set it into a tensor.

def redis_int_to_tensor(redis_value: int):
    return torch.tensor(redis_value)

def int_set_get(tensors: List[Tensor], keys: List[str], args: List[str]):
    key = keys[0]
    value = args[0]
    redis.execute("SET", key, value)
    res = redis.execute("GET", key)
    return redis_string_int_to_tensor(res)
redis> AI.SCRIPTEXECUTE redis_scripts{1} int_set_get KEYS 1 x{1} ARGS 1 3 OUTPUTS 1 y{1}
OK
redis> AI.TENSORGET y{1} VALUES
1) (integer) 3

RedisAI model execution support.

RedisAI TorchScript also supports executing models which are stored in RedisAI by calling redisAI.model_execute command. The command receives 3 inputs: 1. model name (string) 2. model inputs (List of torch.Tensor) 3. number of model outputs (int) Return value - the model execution output tensors (List of torch.Tensor) The following script creates two tensors, and executes the (tensorflow) model which is stored under the name 'tf_mul{1}' with these two tensors as inputs.

def test_model_execute(tensors: List[Tensor], keys: List[str], args: List[str]):
    a = torch.tensor([[2.0, 3.0], [2.0, 3.0]])
    b = torch.tensor([[2.0, 3.0], [2.0, 3.0]])
    return redisAI.model_execute(keys[0], [a, b], 1) # assume keys[0] is the model name stored in RedisAI.
redis> AI.SCRIPTEXECUTE redis_scripts{1} test_model_execute KEYS 1 tf_mul{1} OUTPUTS 1 y{1}
OK
redis> AI.TENSORGET y{1} VALUES
1) (float) 4
2) (float) 9
3) (float) 4
4) (float) 9

Intermediate memory overhead

The execution of scripts may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

AI.SCRIPTRUN

This command is deprecated and will not be available in future versions. consider using AI.MODELEXECUTE command instead.

The AI.SCRIPTRUN command runs a script stored as a key's value on its specified device. It accepts one or more input tensors and store output tensors.

The run request is put in a queue and is executed asynchronously by a worker thread. The client that had issued the run request is blocked until the script run is completed. When needed, tensors data is automatically copied to the device prior to execution.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case).

Intermediate memory overhead

The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the TORCH backend (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

Redis API

AI.SCRIPTRUN <key> <function> [TIMEOUT t] INPUTS <input> [input ...] [$ input ...] OUTPUTS <output> [output ...]

Arguments

  • key : the script's key name
  • function : the name of the function to run
  • TIMEOUT : the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned
  • INPUTS : denotes the beginning of the input tensors keys' list, followed by one or more key names; variadic arguments are supported by prepending the list with $ , in this case the script is expected an argument of type List[Tensor] as its last argument
  • OUTPUTS : denotes the beginning of the output tensors keys' list, followed by one or more key names

Return

A simple 'OK' string, a simple TIMEDOUT string, or an error.

Examples

The following is an example of running the previously-created 'myscript' on two input tensors:

redis> AI.TENSORSET mytensor1 FLOAT 1 VALUES 40
OK
redis> AI.TENSORSET mytensor2 FLOAT 1 VALUES 2
OK
redis> AI.SCRIPTRUN myscript addtwo INPUTS mytensor1 mytensor2 OUTPUTS result
OK
redis> AI.TENSORGET result VALUES
1) FLOAT
2) 1) (integer) 1
3) 1) "42"

If 'myscript' supports variadic arguments:

def addn(a, args : List[Tensor]):
    return a + torch.stack(args).sum()

then one can provide an arbitrary number of inputs after the $ sign:

redis> AI.TENSORSET mytensor1 FLOAT 1 VALUES 40
OK
redis> AI.TENSORSET mytensor2 FLOAT 1 VALUES 1
OK
redis> AI.TENSORSET mytensor3 FLOAT 1 VALUES 1
OK
redis> AI.SCRIPTRUN myscript addn INPUTS mytensor1 $ mytensor2 mytensor3 OUTPUTS result
OK
redis> AI.TENSORGET result VALUES
1) FLOAT
2) 1) (integer) 1
3) 1) "42"

Intermediate memory overhead

The execution of scripts may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

AI._SCRIPTSCAN

The AI._SCRIPTSCAN command returns all the scripts in the database. When using Redis open source cluster, the command shall return all the scripts that are stored in the local shard.

Experimental API

AI._SCRIPTSCAN is an EXPERIMENTAL command that may be removed in future versions.

Redis API

AI._SCRIPTSCAN

Arguments

None.

Return

An array with an entry per script. Each entry is an array with two entries:

  1. The script's key name as a String
  2. The script's tag as a String

Examples

redis> > AI._SCRIPTSCAN
1) 1) "myscript"
   2) "myscript:v0.1"

AI.DAGEXECUTE

The AI.DAGEXECUTE command specifies a direct acyclic graph of operations to run within RedisAI.

It accepts one or more operations, split by the pipe-forward operator ( |> ).

By default, the DAG execution context is local, meaning that tensor keys appearing in the DAG only live in the scope of the command. That is, setting a tensor with TENSORSET will store it local memory and not set it to an actual database key. One can refer to that key in subsequent commands within the DAG, but that key won't be visible outside the DAG or to other clients - no keys are open at the database level.

Loading and persisting tensors from/to keyspace should be done explicitly. The user should specify which key tensors to load from keyspace using the LOAD keyword, and which command outputs to persist to the keyspace using the PERSIST keyspace. The user can also specify a tag or key which will assist for the routing of the DAG execution on the right shard in Redis that are going to be accessed for read/write operations (for example, from within AI.SCRIPTEXECUTE command), by using the keyword ROUTING .

As an example, if command 1 sets a tensor, it can be referenced by any further command on the chaining.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case). Note that individual MODELEXECUTE or SCRIPTEXECUTE commands within the DAG do not support TIMEOUT . TIMEOUT only applies to the DAGEXECUTE request as a whole.

Redis API

AI.DAGEXECUTE [LOAD <n> <key-1> <key-2> ... <key-n>]
          [PERSIST <n> <key-1> <key-2> ... <key-n>]
          [ROUTING <routing_tag>]
          [TIMEOUT t]
          |> <command> [|>  command ...]

Arguments

  • LOAD : denotes the beginning of the input tensors keys' list, followed by the number of keys, and one or more key names
  • PERSIST : denotes the beginning of the output tensors keys' list, followed by the number of keys, and one or more key names
  • ROUTING : denotes a key to be used in the DAG or a tag that will assist in routing the dag execution command to the right shard. Redis will verify that all potential key accesses are done to within the target shard.

While each of the LOAD, PERSIST and ROUTING sections are optional (and may appear at most once in the command), the command must contain at least one of these 3 keywords. * TIMEOUT : an optional argument, denotes the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned * |> command : the chaining operator, that denotes the beginning of a RedisAI command, followed by one of RedisAI's commands. Command splitting is done by the presence of another |> . The supported commands are: * AI.TENSORSET * AI.TENSORGET * AI.MODELEXECUTE * AI.SCRIPTEXECUTE

AI.MODELEXECUTE and AI.SCRIPTEXECUTE commands can run on models or scripts that were set on different devices. RedisAI will analyze the DAG and execute commands in parallel if they are located on different devices and their inputs are available. Note that KEYS should not be specified in AI.SCRIPTEXECUTE commands of the DAG.

Return

An array with an entry per command's reply. Each entry format respects the specified command reply. In case the DAGEXEUTE request times out, a TIMEDOUT simple string is returned.

Examples

Assuming that running the model that's stored at 'mymodel', we define a temporary tensor 'mytensor' and use it as input, and persist only one of the two outputs - discarding 'classes' and persisting 'predictions'. In the same command return the tensor value of 'predictions'. The following command does that:

redis> AI.DAGEXECUTE PERSIST 1 predictions{tag} |>
          AI.TENSORSET mytensor FLOAT 1 2 VALUES 5 10 |>
          AI.MODELEXECUTE mymodel{tag} INPUTS 1 mytensor OUTPUTS 2 classes predictions{tag} |>
          AI.TENSORGET predictions{tag} VALUES
1) OK
2) OK
3) 1) FLOAT
   1) 1) (integer) 2
      1) (integer) 2
   2) "\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"

A common pattern is enqueuing multiple SCRIPTEXECUTE and MODELEXECUTE commands within a DAG. The following example uses ResNet-50,to classify images into 1000 object categories. Given that our input tensor contains each color represented as a 8-bit integer and that neural networks usually work with floating-point tensors as their input we need to cast a tensor to floating-point and normalize the values of the pixels - for that we will use pre_process_3ch function.

To optimize the classification process we can use a post process script to return only the category position with the maximum classification - for that we will use post_process script. Using the DAG capabilities we've removed the necessity of storing the intermediate tensors in the keyspace. You can even run the entire process without storing the output tensor, as follows:

redis> AI.DAGEXECUTE ROUTING {tag} |> 
            AI.TENSORSET image UINT8 224 224 3 BLOB b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00....' |> 
            AI.SCRIPTEXECUTE imagenet_script{tag} pre_process_3ch INPUTS 1 image OUTPUTS 1 temp_key1 |> 
            AI.MODELEXECUTE imagenet_model{tag} INPUTS 1 temp_key1 OUTPUTS 1 temp_key2 |> 
            AI.SCRIPTEXECUTE imagenet_script{tag} post_process INPUTS 1 temp_key2 OUTPUTS 1 output |> 
            AI.TENSORGET output VALUES
1) OK
2) OK
3) OK
4) OK
5) 1) 1) (integer) 111

As visible on the array reply, the label position with higher classification was 111.

By combining DAG with multiple SCRIPTEXECUTE and MODELEXECUTE commands we've substantially removed the overall required bandwith and network RX ( we're now returning a tensor with 1000 times less elements per classification ).

Intermediate memory overhead

The execution of models and scripts within the DAG may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

AI.DAGEXECUTE_RO

The AI.DAGEXEUTE_RO command is a read-only variant of AI.DAGEXECUTE . AI.DAGEXECUTE is flagged as a 'write' command in the Redis command table (as it provides the PERSIST option, for example). Hence, read-only cluster replicas will refuse to run the command and it will be redirected to the master even if the connection is using read-only mode.

AI.DAGEXECUTE_RO behaves exactly like the original command, excluding the PERSIST option and AI.SCRIPTEXECUTE commands. It is a read-only command that can safely be with read-only replicas.

Further reference

Refer to the Redis READONLY command for further information about read-only cluster replicas.

AI.DAGRUN

This command is deprecated and will not be available in future versions. consider using AI.DAGEXECUTE command instead. The AI.DAGRUN command specifies a direct acyclic graph of operations to run within RedisAI.

It accepts one or more operations, split by the pipe-forward operator ( |> ).

By default, the DAG execution context is local, meaning that tensor keys appearing in the DAG only live in the scope of the command. That is, setting a tensor with TENSORSET will store it local memory and not set it to an actual database key. One can refer to that key in subsequent commands within the DAG, but that key won't be visible outside the DAG or to other clients - no keys are open at the database level.

Loading and persisting tensors from/to keyspace should be done explicitly. The user should specify which key tensors to load from keyspace using the LOAD keyword, and which command outputs to persist to the keyspace using the PERSIST keyspace.

As an example, if command 1 sets a tensor, it can be referenced by any further command on the chaining.

A TIMEOUT t argument can be specified to cause a request to be removed from the queue after it sits there t milliseconds, meaning that the client won't be interested in the result being computed after that time ( TIMEDOUT is returned in that case). Note that individual MODELRUN or SCRIPTRUN commands within the DAG do not support TIMEOUT . TIMEOUT only applies to the DAGRUN request as a whole.

Redis API

AI.DAGRUN [LOAD <n> <key-1> <key-2> ... <key-n>]
          [PERSIST <n> <key-1> <key-2> ... <key-n>]
          [TIMEOUT t]
          |> <command> [|>  command ...]

Arguments

  • LOAD : an optional argument, that denotes the beginning of the input tensors keys' list, followed by the number of keys, and one or more key names
  • PERSIST : an optional argument, that denotes the beginning of the output tensors keys' list, followed by the number of keys, and one or more key names
  • TIMEOUT : the time (in ms) after which the client is unblocked and a TIMEDOUT string is returned
  • |> command : the chaining operator, that denotes the beginning of a RedisAI command, followed by one of RedisAI's commands. Command splitting is done by the presence of another |> . The supported commands are:
    • AI.TENSORSET
    • AI.TENSORGET
    • AI.MODELRUN
    • AI.SCRIPTRUN

AI.MODELRUN and AI.SCRIPTRUN commands can run on models or scripts that were set on different devices. RedisAI will analyze the DAG and execute commands in parallel if they are located on different devices and their inputs are available.

Return

An array with an entry per command's reply. Each entry format respects the specified command reply. In case the DAGRUN request times out, a TIMEDOUT simple string is returned.

Examples

Assuming that running the model that's stored at 'mymodel', we define a temporary tensor 'mytensor' and use it as input, and persist only one of the two outputs - discarding 'classes' and persisting 'predictions'. In the same command return the tensor value of 'predictions'. The following command does that:

redis> AI.DAGRUN PERSIST 1 predictions |>
          AI.TENSORSET mytensor FLOAT 1 2 VALUES 5 10 |>
          AI.MODELRUN mymodel INPUTS mytensor OUTPUTS classes predictions |>
          AI.TENSORGET predictions VALUES
1) OK
2) OK
3) 1) FLOAT
   2) 1) (integer) 2
      2) (integer) 2
   3) "\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"

A common pattern is enqueuing multiple SCRIPTRUN and MODELRUN commands within a DAG. The following example uses ResNet-50,to classify images into 1000 object categories. Given that our input tensor contains each color represented as a 8-bit integer and that neural networks usually work with floating-point tensors as their input we need to cast a tensor to floating-point and normalize the values of the pixels - for that we will use pre_process_3ch function.

To optimize the classification process we can use a post process script to return only the category position with the maximum classification - for that we will use post_process script. Using the DAG capabilities we've removed the necessity of storing the intermediate tensors in the keyspace. You can even run the entire process without storing the output tensor, as follows:

redis> AI.DAGRUN_RO |> 
            AI.TENSORSET image UINT8 224 224 3 BLOB b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00....' |> 
            AI.SCRIPTRUN imagenet_script pre_process_3ch INPUTS image OUTPUTS temp_key1 |> 
            AI.MODELRUN imagenet_model INPUTS temp_key1 OUTPUTS temp_key2 |> 
            AI.SCRIPTRUN imagenet_script post_process INPUTS temp_key2 OUTPUTS output |> 
            AI.TENSORGET output VALUES
1) OK
2) OK
3) OK
4) OK
5) 1) 1) (integer) 111

As visible on the array reply, the label position with higher classification was 111.

By combining DAG with multiple SCRIPTRUN and MODELRUN commands we've substantially removed the overall required bandwith and network RX ( we're now returning a tensor with 1000 times less elements per classification ).

Intermediate memory overhead

The execution of models and scripts within the DAG may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory configuration settings of Redis.

AI.DAGRUN_RO

This command is deprecated and will not be available in future versions. consider using AI.DAGEXECUTE_RO command instead. The AI.DAGRUN_RO command is a read-only variant of AI.DAGRUN .

Because AI.DAGRUN provides the PERSIST option it is flagged as a 'write' command in the Redis command table. However, even when PERSIST isn't used, read-only cluster replicas will refuse to run the command and it will be redirected to the master even if the connection is using read-only mode.

AI.DAGRUN_RO behaves exactly like the original command, excluding the PERSIST option. It is a read-only command that can safely be with read-only replicas.

Further reference

Refer to the Redis READONLY command for further information about read-only cluster replicas.

AI.INFO

The AI.INFO command returns information about the execution of a model or a script.

Runtime information is collected each time that AI.MODELEXECUTE or AI.SCRIPTEXECUTE is called. The information is stored locally by the executing RedisAI engine, so when deployed in a cluster each shard stores its own runtime information.

Redis API

AI.INFO <key> [RESETSTAT]

Arguments

  • key : the key name of a model or script
  • RESETSTAT : resets all statistics associated with the key

Return

An array with alternating entries that represent the following key-value pairs:

  • KEY : a String of the name of the key storing the model or script value
  • TYPE : a String of the type of value (i.e. 'MODEL' or 'SCRIPT')
  • BACKEND : a String of the type of backend (always 'TORCH' for 'SCRIPT' value type)
  • DEVICE : a String of the device where execution took place
  • DURATION : the cumulative duration of executions in microseconds
  • SAMPLES : the cumulative number of samples obtained from the 0th (batch) dimension (only applicable for RedisAI models)
  • CALLS : the total number of executions
  • ERRORS : the total number of errors generated by executions (excluding any errors generated during parsing commands)

When called with the RESETSTAT argument, the command returns a simple 'OK' string.

Examples

The following example obtains the previously-run 'myscript' script's runtime statistics:

redis> AI.INFO myscript
 1) key
 2) "myscript"
 3) type
 4) SCRIPT
 5) backend
 6) TORCH
 7) device
 8) CPU
 9) duration
10) (integer) 11391
11) samples
12) (integer) -1
13) calls
14) (integer) 1
15) errors
16) (integer) 0

The runtime statistics for that script can be reset like so:

redis> AI.INFO myscript RESETSTAT
OK

AI.CONFIG

The AI.CONFIG command sets the value of configuration directives at run-time, and allows loading DL/ML backends dynamically.

Loading DL/ML Backends at Bootstrap

Instead of loading your backends dynamically, you can have RedisAI load them during bootstrap. See the Configuration page for more information.

Redis API

AI.CONFIG <BACKENDSPATH <path>> | <LOADBACKEND <backend> <path>>

Arguments

  • BACKENDSPATH : Specifies the default base backends path to path . The backends path is used when dynamically loading a backend (default: '{module_path}/backends', where module_path is the module's path).
  • LOADBACKEND : Loads the DL/ML backend specified by the backend identifier from path . If path is relative, it is resolved by prefixing the BACKENDSPATH to it. If path is absolute then it is used as is. The backend can be one of:
    • TF : the TensorFlow backend
    • TFLITE : The TensorFlow Lite backend
    • TORCH : The PyTorch backend
    • ONNX : ONNXRuntime backend
  • MODEL_CHUNK_SIZE : Sets the size of chunks (in bytes) in which model payloads are split for serialization, replication and MODELGET . Default is 511 * 1024 * 1024 .

Return

A simple 'OK' string or an error.

Examples

The following sets the default backends path to '/usr/lib/redis/modules/redisai/backends':

redis> AI.CONFIG BACKENDSPATH /usr/lib/redis/modules/redisai/backends
OK

This loads the PyTorch backend with a path relative to BACKENDSPATH :

redis> AI.CONFIG LOADBACKEND TORCH redisai_torch/redisai_torch.so
OK

This loads the PyTorch backend with a full path:

redis> AI.CONFIG LOADBACKEND TORCH /usr/lib/redis/modules/redisai/backends/redisai_torch/redisai_torch.so
OK

This sets model chunk size to one megabyte (not recommended):

redis> AI.CONFIG MODEL_CHUNK_SIZE 1048576
OK