Monitor with tpu-info CLI
The tpu-info
CLI is a tool for detecting Cloud TPU devices and reading
runtime metrics from the libtpu
library, including memory usage and duty
cycle. It supports static, one-time snapshots, and live streaming to monitor
metrics continuously.
Installation
Install the latest release using pip
:
pip
install
tpu
-
info
Alternatively, install tpu-info
from source:
pip
install
git+https://github.com/google/cloud-accelerator-diagnostics/#subdirectory =
tpu_info
If you have already installed a version of tpu-info
, make sure it is
compatible with your environment and is not missing any metrics and features.
For more information, see Missing features or metrics
.
Access standard LibTPU metrics using the CLI
Use the following command to view the default tpu-info
metrics with the CLI:
tpu-info
The output is similar to the following:
TPU
Chips
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃
Chip
┃
Type
┃
Devices
┃
PID
┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╕━━━━━━━━━╕━━━━━━━━┩
│
/dev/vfio/0
│
TPU
v6e
chip
│
1
│
1052
│
│
/dev/vfio/1
│
TPU
v6e
chip
│
1
│
1052
│
│
/dev/vfio/2
│
TPU
v6e
chip
│
1
│
1052
│
│
/dev/vfio/3
│
TPU
v6e
chip
│
1
│
1052
│
└──────────────┴──────────────┴─────────┴────────┘
TPU
Runtime
Utilization
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃
Chip
┃
HBM
usage
┃
Duty
cycle
┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╕━━━━━━━━━━━━┩
│
8
│
18
.45
GiB
/
31
.25
GiB
│
100
.00%
│
│
9
│
10
.40
GiB
/
31
.25
GiB
│
100
.00%
│
│
12
│
10
.40
GiB
/
31
.25
GiB
│
100
.00%
│
│
13
│
10
.40
GiB
/
31
.25
GiB
│
100
.00%
│
└────────┴──────────────────────────┴────────────┘
TensorCore
Utilization
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃
Core
ID
┃
TensorCore
Utilization
┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│
0
│
13
.60%│
│
1
│
14
.81%│
│
2
│
14
.36%│
│
3
│
13
.60%│
└─────────┴────────────────────────┘
TPU
Buffer
Transfer
Latency
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃
Buffer
Size
┃
P50
┃
P90
┃
P95
┃
P999
┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━┩
│
8MB+
│
108978
.82
us
│
164849
.81
us
│
177366
.42
us
│
212419
.07
us
│
│
4MB+
│
21739
.38
us
│
38126
.84
us
│
42110
.12
us
│
55474
.21
us
│
└──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘
TPU
gRPC
TCP
Minimum
RTT
┏━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃
P50
┃
P90
┃
P95
┃
P999
┃
┡━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│
35
.99
us
│
52
.15
us
│
53
.83
us
│
55
.51
us
│
└──────────┴──────────┴──────────┴──────────┘
TPU
gRPC
TCP
Delivery
Rate
┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃
P50
┃
P90
┃
P95
┃
P999
┃
┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│
12305
.96
Mbps
│
18367
.10
Mbps
│
24872
.11
Mbps
│
44841
.55
Mbps
│
└───────────────┴───────────────┴───────────────┴───────────────┘
Usage
To view current TPU utilization data, tpu-info
requires a running TPU workload
with a supported ML framework, such as JAX or PyTorch/XLA. You can run the tpu-info
command in your terminal with the following flags.
Process
Use the --process
or -p
flag to display information about the processes
running on the TPU.
$ tpu-info --process
The output should look similar to the following:
TPU Process Info
┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ Chip ┃ PID ┃ Process Name ┃
┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━┩
│ /dev/vfio/0 │ 799657 │ python3 │
│ /dev/vfio/1 │ 799657 │ python3 │
│ /dev/vfio/2 │ 799657 │ python3 │
│ /dev/vfio/3 │ 799657 │ python3 │
│ /dev/vfio/4 │ 799657 │ python3 │
│ /dev/vfio/5 │ 799657 │ python3 │
│ /dev/vfio/6 │ 799657 │ python3 │
│ /dev/vfio/7 │ 799657 │ python3 │
└─────────────┴────────┴──────────────┘
Metric
Use the --metric
flag to display specific metrics. You can specify multiple
metrics separated by spaces. Some common supported metrics are:
-
hbm_usage -
duty_cycle_percent -
tensorcore_utilization -
buffer_transfer_latency -
host_to_device_transfer_latency -
device_to_host_transfer_latency -
collective_e2e_latency
$ tpu-info --metric duty_cycle_percent hbm_usage
The output should look similar to the following:
TPU Duty Cycle
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Core ID ┃ Duty Cycle (%) ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ 0 │ 100.00% │
│ 1 │ 100.00% │
│ 2 │ 100.00% │
│ 3 │ 100.00% │
│ 4 │ 100.00% │
│ 5 │ 100.00% │
│ 6 │ 100.00% │
│ 7 │ 100.00% │
└─────────┴────────────────┘
TPU HBM Usage
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Chip ┃ HBM Usage (GiB) ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ 29.50 GiB / 31.25 GiB │
│ 1 │ 21.50 GiB / 31.25 GiB │
│ 2 │ 21.50 GiB / 31.25 GiB │
│ 3 │ 21.50 GiB / 31.25 GiB │
│ 4 │ 21.50 GiB / 31.25 GiB │
│ 5 │ 21.50 GiB / 31.25 GiB │
│ 6 │ 21.50 GiB / 31.25 GiB │
│ 7 │ 21.50 GiB / 31.25 GiB │
└────────┴───────────────────────┘
List metrics
Use the --list_metrics
flag to display all supported metrics that can be
requested with the --metric
flag.
$ tpu-info --list_metrics
The output should look similar to the following:
╭─ Supported Metrics ─────────────────────────────────────────────────────────────────────────────╮
│ grpc_tcp_min_rtt │
│ host_to_device_transfer_latency │
│ grpc_tcp_delivery_rate │
│ buffer_transfer_latency │
│ collective_e2e_latency │
│ device_to_host_transfer_latency │
│ hbm_usage │
│ duty_cycle_percent │
│ tensorcore_utilization │
╰─────────────────────────────────────────────────────────────────────────────────────────────────╯
Stream metrics
Streaming mode periodically refreshes and displays up-to-date utilization
statistics. To stream the LibTPU metrics, add the --streaming
flag to the tpu-info
command. Use the --rate
flag to control the cadence of streaming in
seconds.
Use the following command to stream the default tpu-info
metrics with the CLI:
# Refresh metrics every 2 seconds
tpu-info
--streaming
--rate
2
The output is similar to the following:
Refresh rate: 0.1s
Last update: 2025-07-24 11:00:59 UTC
Libtpu version: 0.0.19.dev20250721+nightly
Accelerator type: v6e
TPU Chips
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃ Chip ┃ Type ┃ Devices ┃ PID ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╢━━━━━━━━━╢━━━━━━━━┪
│ /dev/vfio/0 │ TPU v6e chip │ 1 │ 1022 │
│ /dev/vfio/1 │ TPU v6e chip │ 1 │ 1022 │
│ /dev/vfio/2 │ TPU v6e chip │ 1 │ 1022 │
│ /dev/vfio/3 │ TPU v6e chip │ 1 │ 1022 │
└──────────────┴──────────────┴─────────┴────────┘
TPU Runtime Utilization
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Chip ┃ HBM usage ┃ Duty cycle ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━╕━━━━━━━━━━━━┩
│ 8 │ 17.26 GiB / 31.25 GiB │ 100.00% │
│ 9 │ 9.26 GiB / 31.25 GiB │ 100.00% │
│ 12 │ 9.26 GiB / 31.25 GiB │ 100.00% │
│ 13 │ 9.26 GiB / 31.25 GiB │ 100.00% │
└────────┴──────────────────────────┴────────────┘
TensorCore Utilization
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Core ID ┃ TensorCore Utilization ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ 15.17%│
│ 1 │ 14.62%│
│ 2 │ 14.68%│
│ 3 │ 15.14%│
└─────────┴────────────────────────┘
TPU Buffer Transfer Latency
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ Buffer Size ┃ P50 ┃ P90 ┃ P95 ┃ P999 ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━╕━━━━━━━━━━━━━━┩
│ 8MB+ │ 18264.03 us │ 33263.06 us │ 35990.98 us │ 53997.32 us │
└──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘
TPU gRPC TCP Minimum RTT
┏━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ P50 ┃ P90 ┃ P95 ┃ P999 ┃
┡━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ 35.99 us │ 52.15 us │ 53.83 us │ 55.51 us │
└──────────┴──────────┴──────────┴──────────┘
TPU gRPC TCP Delivery Rate
┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ P50 ┃ P90 ┃ P95 ┃ P999 ┃
┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ 12305.96 Mbps │ 18367.10 Mbps │ 24872.11 Mbps │ 44841.55 Mbps │
└───────────────┴───────────────┴───────────────┴───────────────┘
TPU-Z metrics
TPU-Z is a telemetry and debugging facility for TPUs. It provides detailed
runtime status information for all TPU cores attached to a host. The
functionality is provided through the tpuz
module, which is part of the libtpu.sdk
module in the libtpu
Python SDK. The module provides a snapshot
of each core's state.
The primary use case for TPU-Z is diagnosing hangs or deadlocks in distributed TPU workloads. You can query the TPU-Z service on hosts to capture the state of every core, comparing the Program Counters, HLO locations, and Run IDs across all cores to identify anomalies.
Use the following command to view TPU-Z metrics using the CLI:
tpu-info
--metric
core_state
tpu-info
--metric
sequencer_state
tpu-info
--metric
sequencer_state_detailed
tpu-info
--metric
queued_program
The output should include the core_state
, sequencer_state
, sequencer_state_detailed
, and queued_programs
tables.
Core State Information
The Core State Information ( core_state
) table provides information on the
cores of a given chip. TPUs have either one or two cores per chip, depending on
the generation.
| Field | Description | Example values |
|---|---|---|
|
Chip ID
|
The ID of the chip that the core belongs to. | 0
|
|
Global Core ID
|
The unique ID of the core within the entire TPU system. | 1
|
|
Core Type
|
The type of the TPU core. | "TPU_CORE_TYPE_TENSOR_CORE"
"TPU_CORE_TYPE_SPARSE_CORE"
|
|
xdb Server Running
|
Indicates whether the Accelerator Debugger (XDB) server is running on a specific TPU core. | True
|
The output should look similar to the following table:
Core Information
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Chip ID ┃ Global Core ID┃ Core Type ┃ xdb Server ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ 0 │ 0 │ TPU_CORE_TYPE_TENSOR_CORE │ True │
│ 0 │ 1 │ TPU_CORE_TYPE_SPARSE_CORE │ True │
│ 1 │ 2 │ TPU_CORE_TYPE_SPARSE_CORE │ False │
│ 1 │ 3 │ TPU_CORE_TYPE_SPARSE_CORE │ False │
│ 2 │ 4 │ TPU_CORE_TYPE_SPARSE_CORE │ True │
│ 2 │ 5 │ TPU_CORE_TYPE_SPARSE_CORE │ True │
└──────────────┴───────────────┴─────────────────────────────┴───────────────┘
Sequencer State Information
The Sequencer State Information ( sequencer_state
) table provides information
about a sequencer state on a core. A sequencer is a control unit within a TPU
core responsible for fetching, decoding, and orchestrating the execution of
instructions. There can be multiple sequencers for a single core.
| Metric | Description | Example values |
|---|---|---|
|
Chip ID
|
The ID of the chip that the core belongs to. | 0
|
|
Global Core ID
|
The unique ID of the core within the entire TPU system. | 1
|
|
Program Counter
|
The memory address of the instruction to be executed by the sequencer. | 15390
|
|
Tracemark
|
The launch ID of the current or most recent program. This field is absent if not applicable. | 2147483647
|
|
Program ID
|
The ID associated with a specific instance of a program being launched for execution on a TPU core. | 3230481660274331500
|
|
Run ID
|
The run ID associated with the program. | 1150
|
|
Sequence Type
|
The type of sequencer. | "TPU_SEQUENCER_TYPE_SPARSE_CORE_SEQUENCER"
"TPU_SEQUENCER_TYPE_SPARSE_CORE_TILE_EXECUTE_CORE_SEQUENCER"
|
The output should look similar to the following table:
Sequencer Info
┏━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Chip ┃ Global┃ Program ┃ Tracemark ┃ Program ID ┃ Run ┃ Sequence Type ┃
┃ ID ┃ Core ┃ Counter:Tag ┃ ┃ ┃ ID ┃ ┃
┡━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ 0 │ 760:1 │ 2147483647 │ -5.75e17 │ 1150 │ TPU_SEQ_SPARSE_CORE_SEQUENCER │
│ 0 │ 1 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 0 │ 1 │ 0:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 1 │ 2 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 1 │ 3 │ 0:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 1 │ 3 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 1 │ 3 │ 0:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 2 │ 4 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 2 │ 4 │ 0:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 2 │ 4 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 2 │ 5 │ 9:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
│ 2 │ 5 │ 0:0 │ 0 │ -1 │ -1 │ TPU_SEQ_SPARSE_TILE_EXECUTE │
└━━━━━━┴━━━━━━━┴━━━━━━━━━━━━━━━┴━━━━━━━━━━━━━━━┴━━━━━━━━━━━━━━━┴━━━━━━━┴────────────────────────────────┘
Sequencer State Information (detailed)
The Sequencer State Information (detailed) ( sequencer_state_detailed
) table
provides all the information from the Sequencer State Information
( sequencer_state
) table, along with the following additional metrics:
| Metric | Description | Example values |
|---|---|---|
|
HLO Details
|
Detailed HLO information, if available. | []
|
|
Queued Program Run ID
|
The Run ID for this queued program. | 81
|
|
Queued Program Launch ID
|
The Launch ID for this queued program. | 1394130914
|
|
Core Error
|
Contains any error messages for this core. This field is absent if there are no errors. | "Failed to parse launch id: 0xdcf36153"
|
|
HLO Location
|
High-level Optimizer (HLO) location information. | "no HLO mapping"
"HLO: fusion.11; HLO computation: main.126_spmd"
|
The output should look similar to the following table:
Sequencer States (Detailed)
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ Chip ID ┃ Global Core ID ┃ Program Counter ┃ Tracemark ┃ Program ID ┃ Run ID ┃ Sequence Type ┃ Core Error ┃ HLO Location ┃ HLO Details ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ 0 │ 0 │ 760 │ 2147483647 │ -5752110712385440928 │ 114 │ TPU_SEQUENCER_TYPE_TENSOR_CORE_SEQUENCER │ Failed to parse launch id: 0xdcf36109 │ no HLO mapping │ [] │
│ 0 │ 1 │ 9 │ 0 │ -1 │ -1 │ TPU_SEQUENCER_TYPE_SPARSE_CORE_SEQUENCER │ Compiler metadata or executable │ None │ None │
│ │ │ │ │ │ │ │ fingerprint not found. │ │ │
│ 0 │ 1 │ 0 │ 0 │ -1 │ -1 │ TPU_SEQUENCER_TYPE_SPARSE_CORE_TILE_EXE… │ Compiler metadata or executable │ None │ None │
│ │ │ │ │ │ │ │ fingerprint not found. │ │ │
│ 0 │ 1 │ 0 │ 0 │ -1 │ -1 │ TPU_SEQUENCER_TYPE_SPARSE_CORE_TILE_EXE… │ Compiler metadata or executable │ None │ None │
│ ... │ ... │ │ ... │ ... │ ... │ ... │ ... │... │ ... │
└─────────┴────────────────┴─────────────────┴────────────┴──────────────────────┴────────┴──────────────────────────────────────────┴──────────────────────────────────────────┴────────────────┴─────────────┘
Queued programs
The Queued programs ( queued_programs
) table provides the list of programs
queued for execution.
| Metric | Description | Example values |
|---|---|---|
|
Chip ID
|
The ID of the chip that the core belongs to. | 0
|
|
Global Core
|
The unique ID of the core within the entire TPU system. | 1
|
|
Program Counter:Tag
|
The memory address of the instruction to be executed by the sequencer. | 15390
|
|
Tracemark
|
The launch ID of the current or most recent program. This field is absent if not applicable. | 2147483647
|
|
Program ID
|
The ID associated with a specific instance of a program being launched for execution on a TPU core. | 3230481660274331500
|
|
Run ID
|
The run ID associated with the program. | 1150
|
|
Sequence Type
|
The type of sequencer. | "\ufffdU\ufffd4j\u7c6e\ufffd\ufffd{\u0017\ufffd\ufffdHHV\ufffdD\ufffde\uff"
|
Queued Programs
┏━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Chip ┃ Global┃ Program ┃ Tracemark ┃ Program ID ┃ Run ┃ Sequence Type ┃
┃ ID ┃ Core ┃ Counter:Tag ┃ ┃ ┃ ID ┃ ┃
┡━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ 0 │ 0 │ 10712385440928│ 1220 │ -5.75e17 │ 1220 │ \ufffdU\...ufffd{\u0017\...\ufffde |
│ 0 │ 1 │ 31435440272417│ 1530 │ -1 │ 1530 │ \ufff4j\...\ufffd{\u0017\...\ufffde|
│ 0 │ 1 │ 10230672051156│ 1410 │ -1 │ 1410 │ \ufffde\...\ufffd{\u0017\...\ufffde|
│ ... │ ... │ ... │ ... │ ... │ ... │ ... │
└━━━━━━┴━━━━━━━┴━━━━━━━━━━━━━━━┴━━━━━━━━━━━┴━━━━━━━━━━━━━┴━━━━━━━┴────────────────────────────────────┘
Missing features or metrics
If you are unable to view some features or metrics, the most common cause is an
outdated libtpu
version. The features and metrics within tpu-info
are
included in the libtpu
releases, and outdated versions might be missing new
features and metrics.
To check that the version of tpu-info
is compatible with your environment, use
the --version
or -v
flag:
$ tpu-info --version
The following output shows an example of a compatible environment:
-
tpu
-
info
version
:
0.5.1
-
libtpu
version
:
0.0.18
-
accelerator
type
:
v6e
The following output shows an example of an incompatible environment:
-
tpu
-
info
version
:
0.5.1
-
libtpu
version
:
N
/
A
(
incompatible
environment
)
-
accelerator
type
:
N
/
A
(
incompatible
environment
)
If you are using an outdated version, update to the latest version of libtpu
:
pip
install
--upgrade
libtpu
